The problem is that a freelance consultant and an enterprise project manager might both use your collaboration tool, but they have completely different goals, workflows, and definitions of success.

We recently worked with a leading enterprise SaaS company facing exactly this challenge. Their product served everyone from solo creators to enterprise teams, but the experience treated everyone the same.

Users logging in to track a single client project got bombarded with team collaboration features. Power users managing complex workflows couldn’t find advanced capabilities buried in generic menus.

Users felt overwhelmed by irrelevant features, engagement plateaued, and retention suffered.

The solution wasn’t another traditional segmentation model. It was intent-based segmentation; a framework for grouping users by what they’re actually trying to accomplish, then personalizing the experience to match those specific goals.

Understanding behavioral segmentation and where intent fits

Before diving into how to build intent-based clusters, it helps to understand where this approach fits within the broader landscape of user segmentation.

Most SaaS companies are familiar with demographic segmentation (company size, industry, role) and firmographic segmentation (ARR, team size, geographic location). These approaches tell you who your users are, but they don’t tell you what they’re trying to do, which is why they often fail to predict engagement or retention.

Behavioral segmentation takes a different approach. Instead of looking at static attributes, it focuses on how users actually interact with your product.

Behavioral segmentation divides users based on engagement. This includes actions like feature usage patterns, open frequency, purchase behavior, and time-to-value metrics.

While not the only way to do things, behavioral segmentation is widely regarded as more effective than demographic segmentation alone, with plenty of research backing it up.

Within behavioral segmentation, there are several approaches:

• Usage-based segmentation looks at frequency and intensity of use.
• Lifecycle segmentation tracks where users are in their journey.
• Benefit-sought segmentation groups users by the outcomes they want to achieve.

Intent-based segmentation sits at the intersection of all three.

It identifies clusters of users who share similar goals and workflows, then maps those patterns to create a more personalized experience.

Intent-based clusters answer the question: “What is this user trying to accomplish right now?”

In a recent client engagement that inspired this article, this distinction mattered. They had mountains of usage data showing which features people clicked, but no framework for understanding why certain feature combinations existed or what job users were trying to complete.

They knew “Business Professionals” used their tool, but that category was so broad it offered no actionable insights. A marketing manager building campaign timelines has completely different needs than a legal team tracking contract approvals, even though both might be classified as “business professionals.”

Intent-based clustering gave them that missing layer of insight.

Case study: How to spot the need for intent-based segmentation

Let’s talk more about the client engagement I mentioned. This is a great case study for when to use intent-based segmentation.

We work on a quarterly retainer for these clients with our on-demand growth research services. So, when they mentioned struggling with how to personalize experiences and improve retention, we opened up a research project that same day.

The team could see that certain users logged in daily and used five or more features. Great, right? Not really. When we dug deeper, we discovered something critical. Heavy feature usage didn’t predict retention. Some power users churned while casual users stuck around for years.

The issue wasn’t the quantity of features used; it was whether those features aligned with what users were trying to accomplish. A user coming in weekly to update a single client dashboard showed higher retention than someone exploring ten features that didn’t match their core workflow.

The symptoms: What teams told us was broken

During our stakeholder interviews, we heard the same frustrations across departments:

From product: “We know the top five features everyone uses, but that doesn’t help us understand why they’re using them or what to build next. Two users might both use our template feature, but one is building client proposals while the other is standardizing internal processes. They need completely different things from that feature.”

From marketing: “Our segments are too broad to be useful. ‘Business Professional’ could mean anyone from a solo consultant to an enterprise VP. When we send educational content, we can’t make it relevant because we don’t know what problem they’re trying to solve.”

From customer success: “We can see when someone is at risk of churning because their usage drops off, but we can’t predict it before it happens. By the time we notice, they’ve already decided the product isn’t right for them. We need to understand intent earlier so we can intervene proactively.”

From UX research: “Users think in terms of tasks, not features. They don’t say ‘I want to use the dependency mapping tool.’ They say, ‘I need to make sure the design team finishes before development starts.’ But our product talks about features, not outcomes.”

The underlying problem: Missing the ‘why’

What became clear was that the organization had plenty of data about behavior but no framework for understanding intent. They could answer questions like:

• How many people use feature X?
• What’s the average session duration?
• Which users log in most frequently?

But they couldn’t answer the questions that actually mattered:

• What are users trying to accomplish when they use feature X?
• Why do some users stick around while others churn?
• What combination of goals and workflows predicts long-term retention?

This may sound familiar. They have data about behavior but lack context about intent. Without understanding the users’ different definitions of success, they use generic personalization that recommends “similar features” and misses the mark entirely.

The four-phase framework for creating intent-based user clusters

Based on our work with the enterprise SaaS client, we developed a systematic framework for building intent-based clusters from scratch.

The process has four distinct phases, each building on the previous one.

Think of this as a directional guide rather than a rigid formula. You can adapt the scope based on your resources and organizational complexity.

Phase 1: Capture institutional knowledge and identify gaps

Most organizations have more customer insight than they realize; it’s just scattered across teams, buried in old reports, and locked in individual team members’ heads. The first phase consolidates that knowledge and identifies what’s missing.

1. Conduct cross-functional interviews

Start by interviewing stakeholders who interact with users regularly: product managers, customer success, sales, support, and marketing. For our client, we conducted interviews with seven team members from UX research, growth, engagement, marketing, and analytics.

The goal isn’t consensus. You want to uncover patterns and contradictions instead. Focus your conversations on these questions:

• How do you currently describe different user types?
• What patterns have you noticed in how different users engage with the product?
• What data exists about user behavior that isn’t being used to inform decisions?
• Where do personalization efforts break down today?
• What questions about users keep you up at night?

These conversations surface institutional knowledge that never makes it into documentation.

Capture everything. The contradictions are especially valuable—they reveal where teams operate with different mental models of the same users.

2. Audit existing research and reports

Next, gather relevant user research, data analysis, and customer insight. For our client, we analyzed eight existing reports, including retention data, cancellation surveys, user studies, engagement patterns, and conversion analysis.

Look for:

• Marketing segmentation models (usually demographic-heavy)
• User research studies (often small sample, rich insights)
• Behavioral analytics reports (feature usage patterns, cohort analysis)
• Customer journey maps (theoretical vs. actual paths)
• Support ticket analysis (pain points and use cases)
• Cancellation surveys (why users leave)

Pay attention to gaps between how different teams think about users. Marketing might segment by company size, while product segments by feature usage. Neither is wrong, but the lack of a unified framework means teams optimize for different definitions of success.

3. Define hypotheses about intent-based variables

Based on interviews and research, develop hypotheses about what variables might define user intent. This is where you move from “who uses our product” to “what are they trying to accomplish.”

For our client, we identified several dimensions that seemed to correlate with intent:

• Primary workflow type: Are users managing team projects, client deliverables, or personal tasks?
• Collaboration patterns: Solo work, small team coordination, or cross-functional orchestration?
• Usage frequency: Daily operational tool or periodic project management?
• Success metrics: Speed (quick task completion) vs. thoroughness (detailed planning and tracking)?
• Document complexity: Simple task lists or multi-layered project hierarchies?

The goal is to create testable hypotheses that can be validated in Phase 2.

Phase 2: Validate clusters through user research and behavioral analysis

Once you have initial hypotheses, Phase 2 tests them against real user behavior and feedback. This is where hunches become data-backed insights.

1. Develop provisional cluster groups

Transform your hypotheses into provisional clusters. For our client, we identified six distinct intent-based clusters. Let me illustrate with a fictional example of how this might work for a project management SaaS tool:

Sprint Executors: Users focused on rapid task completion and daily standup workflows. They need speed, simple task updates, and quick team coordination. Think startup teams moving fast with lightweight processes.

Client Project Coordinators: Users managing multiple client engagements simultaneously with strict deliverable timelines. They need client visibility controls, progress tracking, and professional reporting. Think agencies and consultancies.

Cross-Functional Orchestrators: Users coordinating complex projects across departments with dependencies and approval workflows. They need Gantt views, resource allocation, and stakeholder communication tools. Think enterprise program managers.

Personal Productivity Optimizers: Users treat the tool as their second brain for personal task management and goal tracking. They need customization, recurring tasks, and minimal collaboration features. Think solopreneurs and executives.

Seasonal Campaign Managers: Users with predictable high-intensity periods followed by dormancy. They need templates, bulk operations, and the ability to archive/reactivate projects easily. Think retail operations teams or event planners.

Mobile-First Coordinators: Users who primarily access the tool from mobile devices for field work or on-the-go updates. They need streamlined mobile experiences and offline sync. Think field service teams or traveling consultants.

Each cluster gets a descriptive name that captures the user’s primary intent, not just their behavior. “Sprint Executor” tells you more about what someone is trying to do than “high-frequency user.”

2. Conduct targeted user research

With provisional clusters defined, recruit users who fit each profile and conduct interviews to understand:

• Their primary use cases and goals when they first adopted the tool
• How they discovered and currently use the product
• Their typical workflows from start to finish
• What defines success in their role
• Pain points and unmet needs
• How they decide which features to explore
• What would make them cancel vs. what keeps them subscribed

For our client, we conducted three to four interviews per cluster, totaling around 24 user conversations. This gave us enough coverage to validate patterns without drowning in data.

The insights were eye-opening. We discovered that one cluster had the fastest time-to-value but the lowest feature adoption. They found what they needed immediately and never explored further. Another cluster showed the highest retention but needed the longest onboarding. They invested time up front because the tool was critical to their workflow.

3. Analyze behavioral data to confirm patterns

User interviews reveal what people say they do. Behavioral data shows what they actually do. Cross-reference your clusters against:

• Feature usage sequences (which tools appear together in sessions)
• Time-to-value metrics by cluster (how quickly do they get their first win)
• Retention and churn patterns (which clusters stick around)
• Upgrade and expansion behavior (which clusters grow their usage)
• Support ticket themes (which clusters need help with what)
• Feature adoption curves (how exploration patterns differ)

For our client, the data revealed critical differences. The “Sprint Executor” equivalent had fast initial adoption but plateaued quickly. They found their core workflow and stopped exploring.

The “Cross-Functional Orchestrator” cluster showed slow initial adoption but deep engagement over time. They needed to learn the tool thoroughly to unlock value.

These patterns weren’t visible in aggregate data. Only by segmenting users by intent could we see that different clusters had fundamentally different paths to retention.

4. Build detailed cluster profiles

For each validated cluster, create a comprehensive profile that becomes the foundation for personalization. For example:

Cluster name: Sprint Executors

Primary intent: Complete daily tasks quickly with minimal friction and maximum team visibility

Most-used features:

• Quick-add task creation
• Board view for visual sprint planning
• Mobile app for on-the-go updates
• Real-time team activity feed

Typical workflow patterns:

• Morning standup with task assignments
• Throughout the day: quick updates and status changes
• End of day: marking tasks complete and planning tomorrow

Behavioral flags that identify this cluster:

• Creates 5+ tasks in first week
• Returns daily within first 14 days
• Uses mobile app within first 7 days
• Rarely uses advanced features like Gantt charts or dependencies

Retention drivers:

• Speed of task completion
• Team visibility and accountability
• Mobile accessibility

Churn risks:

• Tool feels too complex for simple needs
• Feature bloat is making core actions harder to find
• Forced upgrades to access speed-focused features

Personalization opportunities:

• Streamlined onboarding focused on quick task creation
• Mobile-first feature discovery
• Templates for common sprint workflows
• Integrations with communication tools

These profiles become the single source of truth that product, marketing, and customer success can all reference.

Phase 3: Develop indicators and personalization strategies

The final phase connects clusters to action. This is where the framework moves from insight to implementation.

1. Create behavioral flags for cluster identification

Most users won’t self-identify their intent at signup. You need to infer cluster membership from behavioral signals early in their journey. The key is identifying flags that appear within the first 7-14 days. It should be early enough to personalize the experience before users decide if the tool is right for them.

For reference, the “Sprint Executor” cluster in our fictional example:

• Created 5+ tasks in first week
• Logged in on 4+ separate days in first 14 days
• Used mobile app within first 7 days
• Board or list view used more than timeline/Gantt view (80%+ of sessions)
• Invited at least one team member within first 10 days
• Never explored advanced dependency features
• Average session length under 10 minutes

Versus the “Client Project Coordinator” cluster:

• Created 3+ separate projects within first week (indicating multiple clients)
• Used folder or workspace organization features within first 5 days
• Set up client-specific permissions or external sharing settings
• Created custom views or reports within first 14 days
• Longer average session times (20+ minutes per session)
• Uses professional or client-specific terminology in project names
• High usage of export or presentation features

The goal is to find the minimum viable signal set that reliably predicts cluster membership. Start with more flags and refine over time based on which actually correlate with long-term behavior.

One critical finding from our client work: early behavioral flags predicted retention better than demographic data.

A user who exhibited “Client Project Coordinator” behaviors in week one showed 40% higher 90-day retention than the average user, regardless of their company size or job title.

2. Map personalization opportunities to each cluster

With clusters and flags defined, identify specific ways to personalize the experience across the user journey:

Onboarding sequences: Tailor the first-run experience to highlight features that match user intent. Show Sprint Executors how to set up their first sprint board, not the full feature catalog with Gantt charts and resource allocation tools they don’t need.

In-app messaging: Trigger contextual tips based on usage patterns. When a Client Project Coordinator creates their third project with similar structure, suggest project templates to save time.

Feature discovery: Recommend next-step features that align with cluster workflows. For Sprint Executors who’ve mastered basic task management, introduce the mobile app and integrations with their communication tools—not complex dependency mapping.

Content and education: Send targeted educational content that addresses cluster-specific goals. Client Project Coordinators get tips on professional reporting and client permissions. Sprint Executors get productivity hacks and team coordination strategies.

Upgrade paths: Present pricing tiers and feature upgrades that match cluster needs. Don’t push team collaboration features to Personal Productivity Optimizers who work solo and won’t use them.

Support prioritization: Route support tickets differently based on cluster. Client Project Coordinators might get priority support since they’re often managing paying clients. Seasonal Campaign Managers might get proactive check-ins before predicted busy periods.

For our client, this mapping revealed opportunities they’d completely missed. One cluster had been receiving generic “explore more features” emails when what they actually needed was advanced security capabilities for compliance requirements. Another cluster kept churning at the end of trial because onboarding emphasized features they’d never use instead of the speed-focused tools that matched their workflow.

Phase 4: Develop test concepts to validate impact

Turn personalization opportunities into testable hypotheses. Don’t roll everything out at once. Start with high-impact, low-effort tests that prove the value of intent-based segmentation.

For our client, we proposed several test concepts structured to validate clusters quickly and build organizational confidence in the framework. Here are a few examples.

Example Test 1: Intent-Based Onboarding Survey

Background: The organization lacked a way to identify user intent at the critical moment when users were most open to guidance: right after signup, but before they’d formed opinions about product fit.

Hypothesis: By asking users to self-identify their primary goal during their first meaningful session, we can segment them into actionable clusters that enable personalized feature discovery and messaging, resulting in improved 3-month retention rates by 5-10%.

Test design: During the first session (after initial signup but before deep engagement), present a brief survey asking: “What brings you here today?” with options that map directly to identified clusters:

☐ Coordinate my team’s daily work (Sprint Executors)

☐ Manage multiple client projects (Client Project Coordinators)

☐ Organize complex cross-functional initiatives (Cross-Functional Orchestrators)

☐ Track my personal tasks and goals (Personal Productivity Optimizers)

☐ Plan seasonal campaigns or events (Seasonal Campaign Managers)

☐ Update projects while on the go (Mobile-First Coordinators)

☐ Something else (with optional text field)

Then immediately personalize their first experience based on their response: Sprint Executors see a streamlined task creation tutorial, Client Project Coordinators get guidance on setting up client workspaces, etc.

Success metrics:

• Primary: 3-month retention rate by selected cluster (looking for 5-10% lift)
• Secondary: Survey completion rate (target: >80%), feature adoption aligned with cluster (target: 20% lift), time to first value-generating action
• Guardrails: No negative impact on day 2 or day 7 retention

Acceptance criteria for “winning test”:

• Survey completion rate >80%
• 60% of users select a pre-set option (vs. “something else”)
• Statistically significant retention lift in at least one cluster
• No degradation in key engagement metrics

Acceptance criteria for “learning test”:

• 40% of users select “something else” (suggests clusters don’t match user mental models)
• No statistically significant difference in retention (suggests clusters exist, but personalization approach needs refinement)

Audience: New paid subscribers on first day, trial users converting to paid, reactivated users returning after 30+ days dormant. Start with 25% of eligible users to minimize risk.

Timeline: 90 days to measure primary retention metric, but early signals (completion rate, feature adoption) available within 14 days.

Example Test 2: Cluster-Specific Feature Recommendations

Background: Generic in-app messaging had low click-through rates (<5%) and wasn’t driving feature adoption. Users felt bombarded by irrelevant suggestions.

Hypothesis: For users who match behavioral flags within the first 14 days, triggering personalized feature recommendations will increase feature adoption by 20% and engagement depth by 15%.

Test design: Identify users by behavioral flags, then trigger targeted in-app messages at contextually relevant moments:

• Sprint Executors see mobile app download prompt after completing 5 tasks on desktop: “Update tasks on the go: get the mobile app”
• Client Project Coordinators see reporting features after creating third project: “Impress clients with professional progress reports”
• Cross-Functional Orchestrators see dependency mapping after creating complex project: “Map dependencies to keep cross-functional teams aligned”

Success metrics:

• Primary: Feature adoption rate for recommended features (target: 20% lift vs. control)
• Secondary: Overall engagement depth (features used per session), message click-through rate
• Guardrails: No increase in feature abandonment (starting but not completing flows)

Audience: Users who match cluster behavioral flags within first 14 days. Test one cluster at a time to isolate impact.

Timeline: 30 days to measure feature adoption impact.

Example Test 3: Retention Email Campaigns by Cluster

Background: Generic “tips and tricks” email campaigns had 8% open rates and weren’t moving retention metrics. Content felt irrelevant to most recipients.

Hypothesis: Segmenting email campaigns by identified cluster will improve email engagement by 50% and show a measurable correlation with retention.

Test design: Replace generic weekly tips emails with cluster-specific content:

• Sprint Executors: “5 ways to speed up your daily standup” / “Mobile shortcuts that save 2 hours per week”
• Client Project Coordinators: “How to impress clients with professional project reports” / “3 ways to give clients visibility without overwhelming them”
• Personal Productivity Optimizers: “Build your second brain: advanced filtering techniques” / “Automate your recurring tasks in 5 minutes”

Send to users identified through either the onboarding survey or behavioral flags. Track engagement and retention by cluster.

Success metrics:

• Primary: Email open rates (target: 50% lift), click-through rates (target: 100% lift)
• Secondary: Correlation between email engagement and 90-day retention
• Guardrails: Unsubscribe rates remain stable or decrease

Audience: Users identified as belonging to specific clusters either through survey responses or behavioral flags, minimum 14 days after signup.

Timeline: 6 weeks for initial engagement metrics, 90 days for retention correlation.

Post-test analysis framework

For each test, we established a clear decision framework:

If “winning test”:

• Roll out to 100% of eligible users
• Begin development on next phase of personalization for that cluster
• Use learnings to inform tests for other clusters
• Document what worked to build organizational playbook

If “learning test”:

• Analyze all “something else” responses for missing clusters or unclear framing
• Review behavioral data to see if clusters exist, but personalization was wrong
• Iterate on messaging, timing, or format
• Decide whether to retest with refinements or try different approach

If negative impact:

• Immediately roll back to the control experience
• Conduct user interviews to understand what went wrong
• Reassess cluster definitions or personalization approach
• Consider whether the cluster exists but needs a different treatment

The key to successful testing is starting small, measuring rigorously, and being willing to learn from failures. Not every cluster will respond to every type of personalization, and that’s valuable information. The goal isn’t perfect personalization immediately; it’s continuous improvement based on what actually moves metrics.

Intent-based segmentation mistakes and how to avoid them

Based on our experience implementing this framework, here are the mistakes that will derail your efforts:

1. Starting with too many clusters

More isn’t better. Six well-defined clusters are more useful than fifteen overlapping ones. You need enough clusters to capture meaningfully different intents, but few enough that teams can actually remember and act on them. Start with 4-6 clusters and refine over time. If you find yourself creating clusters that differ only slightly, you’ve gone too granular.

2. Confusing demographics with intent

Job title, company size, or industry might correlate with intent, but they don’t define it. We’ve seen solo consultants behave like “Cross-Functional Orchestrators” and enterprise teams behave like “Sprint Executors.” Focus on what users are trying to accomplish, not who they are on paper.

3. Creating overlapping clusters

Each cluster should be distinct in its primary intent and workflow patterns. If you’re struggling to articulate how two clusters differ behaviorally, they’re probably the same cluster with different labels. Test this by asking: “If I saw someone’s usage data, could I confidently assign them to one cluster?”

4. Ignoring edge cases entirely

Some users will span multiple clusters or switch between them based on context. That’s fine. The framework should accommodate primary intent while recognizing that users are complex. A user might primarily be a “Client Project Coordinator” but occasionally use “Personal Productivity Optimizer” features for their own task management. Don’t force rigid categorization.

5. Skipping the validation step

Your initial hypotheses will be wrong in places. User research and behavioral data keep you honest and prevent confirmation bias. We’ve seen teams fall in love with theoretically elegant clusters that don’t actually exist in their user base, or miss entire segments because they didn’t fit the initial hypothesis.

6. Treating clusters as static

User intent evolves. Someone might start as a “Personal Productivity Optimizer” and grow into a “Client Project Coordinator” as their business scales. Review and refine your clusters quarterly based on new data, product changes, and market shifts.

7. Personalizing too aggressively too soon

Start with high-confidence, low-risk personalization (like targeted email content) before you completely diverge user experiences. You want to validate that clusters behave differently before you build entirely separate onboarding flows.

8. Forgetting to measure impact

Intent-based segmentation is valuable only if it improves outcomes. Define success metrics upfront (e.g., retention lifts, engagement depth, upgrade rates, support ticket reduction) and track them by cluster. If personalization isn’t moving these metrics, refine your approach.

Making intent-based segmentation work for your organization

The framework we’ve outlined works across product categories and company sizes, but implementation varies based on your resources and organizational maturity.

If you have limited data: Start with Phase 1 and Phase 2, using qualitative research to define clusters before investing in behavioral infrastructure. You can manually tag users based on interview responses and onboarding surveys, then personalize through targeted emails and customer success outreach. As you grow, build the data systems to automate cluster identification.

If you have rich behavioral data but limited research capabilities: Reverse the order. Start with data patterns and validate through targeted interviews. Look for natural groupings in your analytics that suggest different workflow types, then talk to representative users from each group to understand their intent.

If you’re a small team: Don’t let perfect be the enemy of good. Start with 3-4 obvious clusters based on your highest-level workflow differences. The founder of a 10-person startup probably has a better intuitive understanding of user intent than a 500-person company with siloed data. Write down what you know, test it with a few users, and start personalizing.

If you’re a large enterprise: The challenge is getting organizational alignment, not defining clusters. Use Phase 1 to surface where teams already operate with different mental models, then use data to arbitrate. Create executive sponsorship for the new framework so it becomes the shared language across product, marketing, and CS.

The key is starting somewhere. Most companies know their one-size-fits-all approach isn’t working, but they keep personalizing around the wrong variables that don’t actually predict what users are trying to accomplish.

Intent-based segmentation reorients everything around the question that actually matters: What is this user trying to accomplish, and how can we help them succeed at that specific goal?

Turn insights into retention that drives revenue

Understanding user intent is just the first step. The real value comes from translating those insights into personalized experiences that keep users engaged and drive measurable revenue growth.

At The Good, we’ve spent 16 years helping SaaS companies identify their most valuable user segments and optimize experiences around what actually drives retention. Our systematic approach to user segmentation goes beyond frameworks. We help you implement experimentation strategies that prove which personalization efforts move the needle on the metrics your board cares about.

Plenty of companies struggle to implement segmentation that’s actually actionable. They end up with beautiful personas gathering dust or broad categories that don’t inform product decisions.

Intent-based segmentation is different because it connects directly to behavior you can observe and experiences you can personalize.

If you’re struggling with generic experiences that fail to resonate with different user types, or if you know your segmentation could be better but aren’t sure where to start, let’s talk about how intent-based segmentation could transform your retention strategy and drive revenue growth.

The post The Exact Framework We Used To Build Intent-Based User Clusters That Drive Retention For A Leading SaaS Company appeared first on The Good.

This thinking is not only wrong, but also expensive and potentially harmful to your product strategy.

Today’s SaaS products exist across multiple “surfaces,” not just desktop and mobile apps, but also mobile web, browser extensions, widgets, and even smart TVs. Each surface represents a different way users can interact with your product, and each can serve a distinct purpose.

After working with dozens of scaling SaaS companies and analyzing surface strategies across hundreds of products, we’ve discovered that the most successful companies don’t aim for feature parity. Instead, they make deliberate, strategic decisions about which surfaces serve which purposes in their ecosystem.

Here’s the framework that’s helping product leaders at companies like Adobe, Slack, and emerging SaaS startups rethink their entire multi-surface strategy.

Organic growth spurs feature parity

The pressure to achieve feature parity stems from a fundamental misunderstanding of how users actually interact with different surfaces. Product teams often default to replicating their experience across surfaces without considering the strategic implications.

“Most products start with just one surface,” explains Natalie Thomas, Director of Strategy & UX at The Good. “Adobe started with a desktop app, and YouTube started on the web. Then they often bleed into other surfaces. The family of surfaces is likely to grow over time, and they are of different strategic importance.”

This organic growth pattern creates a dangerous assumption that every surface should eventually do everything the original surface does. But here’s what we’ve learned from analyzing successful SaaS ecosystems: the most strategic approach isn’t about matching features. It’s about defining distinct purposes.

The four strategic surface types every product leader should know

Rather than thinking in terms of feature parity, successful SaaS companies categorize their surfaces based on strategic purpose. This categorization is based on our analysis of high-performing SaaS ecosystems.

1. Replica surfaces

These are true feature-parity experiences where users expect identical functionality across platforms.

Example: Workplace productivity tools where users frequently switch between devices. Slack exemplifies this perfectly. You can upload documents, chat, huddle, and access virtually every feature across web, desktop, and mobile.

For collaboration tools, inconsistent experiences create friction in team workflows. Users expect to pick up exactly where they left off, regardless of device.

2. Utility surfaces

These platforms fundamentally can’t work without each other. One surface serves as a critical utility that supports the primary platform.

Example: TLDV’s Chrome extension functions as a utility for their web-based recording platform. “In this situation, we’re not really looking for feature parity in the Chrome extension because it really does serve as a utility that adds a lot of functionality and depth to what we are able to get out of the web experience,” notes Natalie.

Don’t waste product development resources building standalone functionality in utility surfaces. Their entire value comes from integration with the core platform.

3. Accessory/companion surfaces

These add value to the main platform but can’t function independently.

Example: Figma’s mobile app serves as a companion to their desktop design tool. Users can’t create designs on mobile, but they can preview prototypes and test user flows on actual devices.

You can’t do anything without the main surface, but the accessory/companion adds value. The mobile app enhances the design process without attempting to replicate the full desktop experience.

4. Growth lever surfaces

These exist primarily to acquire new users, not to provide comprehensive functionality.

Example: Adobe’s free web tools, like online PDF converters, serve as growth levers. Users get limited functionality for free, experience the brand value, then convert to paid desktop or mobile experiences.

“A surface, especially one with very, very limited capabilities, can exist solely as a strategic growth lever. It doesn’t have to exist just to get feature parity or to add value to an existing platform. It can exist just to try to get new customers in the door,” explains Natalie.

What it looks like to intentionally limit feature parity

One of the most instructive examples of strategic surface limitation comes from Instagram’s deliberate choice to restrict posting capabilities on desktop. While it can frustrate users, it actually reveals Instagram’s strategic genius. By limiting posting to mobile, they:

• Maintain their mobile-first brand identity
• Prevent the platform from becoming a business publishing tool
• Keep content creation spontaneous and authentic
• Reduce operational complexity

Mobile-first continues to dominate 2025 SaaS trends, with companies prioritizing mobile user experiences over desktop feature replication.

The lesson? Sometimes the features you don’t build are more strategically important than the ones you do.

How to start building a surface strategy that avoids the feature parity trap

So, with all of this in mind, how do you build a great surface strategy? Instead of blindly building features across all surfaces, successful SaaS companies have a few strategies in common to make smarter surface decisions.

1. Let platform economics shape your strategy

Understanding how users discover and purchase your product should directly influence your surface strategy. The path differs dramatically between mobile apps and web/desktop experiences.

Mobile considerations:

• App store optimization becomes critical
• Apple retains approximately 30% of subscription revenue
• Updates require user opt-in and are often batched
• Attribution becomes increasingly difficult

Web/desktop considerations:

• Direct-to-payment journeys possible
• Immediate updates without user intervention
• Better attribution tracking
• More flexible pricing models

These fundamental differences should influence not just your pricing strategy, but also which surfaces you prioritize for different user segments.

2. Build where your users engage

How users engage with surfaces could shape your strategy. For example, mobile users are significantly more likely to opt into push notifications than desktop users.

While working on surface strategy for a leading SaaS company, our client shared, “Opt-in rates for push notifications on desktop are so low that the only avenue to do outreach to those existing dormant customers is through emails.”

In this case, the ideal was to build any push notification functionality into mobile because on desktop it was practically useless. The learning can be applied across the board. Build your retention features on surfaces where users actually engage, not where you think they should engage.

3. Design for authentication, not attribution

Cross-device attribution is getting harder thanks to privacy changes and cookie deprecation. Instead of fighting this trend with complex tracking, design surface experiences that get users logged in quickly.

“Once someone is logged in, all bets are off; we’ve got good information about them. But until then, they are anonymous and we’re generally not able to attribute data,” says Natalie.

This means prioritizing authentication flows over extensive anonymous functionality. In this case, depending on your growth initiatives, your surface strategy may prioritize guiding users toward logged-in states rather than providing comprehensive experiences for guest users.

4. Match your tools to your strategy

Most SaaS companies default to familiar tools like Google Analytics and Hotjar because they’ve historically focused on web experiences. But scaling to multiple surfaces requires different technology approaches.

Web-Focused Tools:

• Google Analytics
• Hotjar
• Traditional A/B testing platforms

App-Optimized Tools:

• Amplitude: Combines analytics and testing specifically for app experiences; allows product managers direct data access
• Pendo: Integrates surveys, heat maps, and onboarding flows for mobile apps
• Adobe Journey Optimizer: Enables in-product testing across surfaces

Choose tools that support your surface strategy rather than forcing your strategy to fit your existing tool stack. Surface strategy is a business decision that should be driven by user needs, revenue models, and competitive positioning, not technical capability.

5. Define success differently for each surface

A growth lever surface shouldn’t be measured the same way as a full-featured replica surface. Define success metrics that align with each surface’s strategic purpose:

• Growth surfaces: Conversion rate to core platform; cost per qualified lead
• Utility surfaces: Integration success rate; core platform usage lift
• Companion surfaces: Feature adoption in main platform; user satisfaction
• Replica surfaces: Cross-device workflow completion; feature usage parity

Stop measuring everything the same way. Different surfaces serve different purposes and should be evaluated accordingly.

6. Start with purpose, not capability

The wrong question: “Can we build this feature on mobile?” The right question: “Should this feature exist on mobile given our strategic purpose for this surface?”

Before building anything new, clearly define what strategic purpose each surface serves:

• Growth lever: Limited functionality to drive awareness and conversion
• Utility: Essential support that makes the core platform more valuable
• Companion: Unique value that leverages platform-specific capabilities
• Replica: Full feature parity for seamless cross-device workflows

Once you’re clear on purpose, feature decisions become much easier to make.

Building everything, everywhere, isn’t the answer. Many product teams default to feature parity because it feels “fair” to users. In reality, this often creates mediocre user experiences across all surfaces instead of excellent user experiences where they matter most.

Getting started with a surface strategy that doesn’t over-emphasize feature parity

The companies winning in the multi-surface SaaS landscape aren’t the ones with the most features across the most platforms. They’re the ones making the smartest strategic decisions about where to focus their development resources.

If you’re struggling with where to start, here are a few ideas:

• Start with one surface audit. Pick your least strategic surface and honestly evaluate whether you’re over-building functionality that doesn’t serve your business goals or user needs in the name of feature parity.
• Question your assumptions about user expectations. Users might actually prefer a focused, excellent user experience over a comprehensive one that is mediocre.
• Align your team around surface strategy. Make sure product, engineering, and growth teams understand the strategic purpose of each surface, not just the feature requirements.

The goal isn’t to build less, it’s to build more strategically.

Ready to optimize your SaaS surface strategy? At The Good, we help scaling SaaS companies make smarter product decisions through data-driven audits and optimization. Our team has guided companies, from Adobe to emerging startups, in creating multi-surface user experiences that actually drive growth, rather than just checking feature boxes.

Schedule a strategic consultation to discover which surfaces are driving growth and which are consuming resources without strategic return. Let’s turn your multi-surface challenge into your competitive advantage.

The post Why Feature Parity Isn’t Always the Goal: A Guide to Cross-device SaaS Strategy appeared first on The Good.

For most SaaS companies, a high feature adoption rate is linked to increased upgrades, retention, and loyalty. When users fully adopt a product, they integrate it into their daily workflow and continually find value.

But alarmingly, a reasonable feature adoption rate in SaaS is between 20% and 30%, and similarly, only 20% of launched features are used. We can all understand that some features won’t hit the mark, but should we really accept that up to 80% of the features we build will go unused?

I don’t think so.

By refining the prioritization process, we can make sure you’re working on the right features that will drive value for users and improve feature adoption rates. And it starts with understanding your user.

Reasons for low feature adoption

As product teams focus on developing innovative capabilities and addressing technical debt, the gap between feature development and feature adoption widens.

Underperforming features are a drain on your company’s time and resources. But what causes low adoption rates in the first place?

• Lack of awareness: The new feature isn’t presented/marketed to users in a compelling way
• Wrong messaging: The marketing message doesn’t resonate with users, and they’re unaware of the benefits
• Bad feature: The feature doesn’t actually address a user’s need or pain point

While these are the three most commonly cited reasons for low feature adoption, we’ve found that these symptoms often stem from underlying issues with how features are prioritized for development and release. Teams let internal assumptions, stakeholder requests, or competitive pressures (rather than genuine user insights) drive priorities. In turn, the wrong features are released, spurring feature bloat, low adoption rates, and more.

Think of that ‘AI-powered suggestion’ feature that no one uses. Was it truly solving a user need, or just a cool tech demo?

We’ve seen firsthand with clients how prioritization directly impacts performance. When companies prioritize effectively, they stay focused on what is proven to deliver results. And when they don’t, the opposite happens.

What is user-centered prioritization?

There are plenty of ways to address low feature adoption, but user-centered prioritization might be the Trojan horse you didn’t see coming.

User-centered prioritization is an approach that places the user at the heart of every decision regarding feature development and enhancement.

It’s a systematic way to ensure that the features you build truly solve your users’ problems, meet their needs, and provide the most value. This contrasts with traditional prioritization methods that might heavily weigh internal opinions, market trends, or ease of development.

With user-centered prioritization, you leverage user research, behavior analytics, and feedback loops to make data-driven development decisions. By understanding not just what users say they want, but how they actually behave, product teams can make more strategic choices about which features to build, when to release them, and how to position them for maximum adoption.

User-centered prioritization is the first step to higher feature adoption rates

We’ll get to some specific strategies in a minute, but for now, I want to provide some additional context on why user-centered prioritization is the first step to higher feature adoption.

It’s more than just a method; it’s a mindset. The core idea is to build products and services that truly solve user problems and provide a positive experience.

When faced with a long list of potential features or improvements, user-centered prioritization helps teams decide what matters most. It can:

• Identify pain points and focus on features that directly address user frustrations or obstacles.
• Hone in on the most frequent and important tasks users want to accomplish, then prioritize content and features that support these tasks.
• Visualize the user journey and break it down into actionable user stories, prioritizing those with the highest potential impact on user satisfaction and business goals.
• Classify and prioritize usability problems based on their impact on user task completion, frequency, and ease of fix.

To make any of this happen, you need a deep understanding of users. Prioritization begins with thorough user research. Use various methods such as interviews, surveys, observational studies, usability testing, and analyzing user data to gather insights. Try to build an understanding of how and where users will interact with the feature.

In essence, user-centered prioritization ensures that product development efforts are aligned with what users truly need and value, leading to ethical and successful products.

Strategies for improving feature adoption rates with user-centered prioritization in the customer journey

So, what does it look like in action when a user-centered approach dictates feature development and deployment? Here are eight strategies.

1. Build the right features

The foundation is to build the right features, and to ensure you don’t do it in a vacuum. As we have covered, you need user research to understand the problems you are trying to solve.

Before moving to development, test concepts and prototypes with real users to ensure the feature addresses a need and has a clear value proposition.

Prioritize features that will deliver the most significant value to users, not just those that are “nice to have” or technically interesting.

2. Be clear about the value proposition of your feature

Users need to understand why a feature is beneficial for them and how it solves a problem, not just what it does. Articulate this clearly in all communications.

Each feature should address a distinct user pain point or enable a new, valuable capability. Ideally, use language that has been tested and proven to clearly convey the value of the feature.

3. Make onboarding frictionless

Segment users (by role, industry, goals, etc.) and tailor onboarding experiences. A marketing professional might need a different introduction than a developer.

Guide users to the core value of the product and its key features as quickly as possible. This is the moment they realize “this product is for me.”

Instead of static tours, interactively prompt users to take the desired action so that you’re teaching them while they accomplish tasks.

4. Create context in the app experience

Use subtle, in-context cues to highlight new features or explain specific UI elements when a user is in a relevant area.

For more significant feature announcements, use in-experience banners or modals that appear at relevant moments. Behavioral triggers can also deliver guidance based on what a user is currently doing or has done.

When a feature’s area is empty, use this space to explain the feature’s purpose and guide the user on how to get started. Make it easy to find the features your user is looking for.

5. Educate and clearly communicate with users

As mentioned above, prioritize in-app methods for immediate context, but be sure to supplement with marketing materials like:

• Targeted emails that announce new features, explain their benefits, and link directly to the feature in the product. Segment these emails to ensure relevance.
• Blogs that add in-depth explanations, use cases, and technical details for those who want them.
• For complex or high-impact features, host live or recorded webinars to demo features and answer questions.
• Social media, including short, engaging content (videos, graphics) to announce features and drive interest.

6. Personalize the feature

Not all features are for every user. To be sure the right features are being shown to the target user, you can hide or highlight features based on a user’s role or permissions.

Allow users to tailor their experience, making the most relevant features easily accessible, and use machine learning to suggest features or workflows based on a user’s past behavior or similar user segments.

7. Gather data and feedback

Instead of relying on just feature adoption rates, gather supplemental data and feedback to understand why users are or aren’t adopting the feature. Use micro-surveys (e.g., after a user interacts with a new feature) to get immediate feedback on usability and value. Monitor overall satisfaction with NPS & CSAT surveys, conduct regular user interviews, and look for recurring issues in support tickets.

Make sure to analyze all this information across different user segments to identify differences and tailor strategies.

8. Iterate on the feature

Don’t just launch and leave a feature; you can continue iterating on the experience and messaging post-launch until you figure out what works. Test different onboarding flows, in-app messages, or feature designs to see what drives higher adoption.

Feature adoption is an ongoing process. Regularly review data, implement changes, and measure their impact. Don’t stop promoting after the announcement.

By adopting these strategies, SaaS companies can move beyond simply launching features to truly integrating them into their users’ workflows, maximizing the value delivered, and ultimately driving sustainable growth.

A good feature adoption rate is always improving

We’ve often touted the uselessness of benchmarks. And while they are meaningless for setting goals, they can help to paint a picture of industry averages and to set expectations. In the case of feature adoption rates, if you’re below that 20% mark, you should strongly consider building a more user-centered prioritization process.

Incorporating user feedback early and often can significantly reduce development time and costs. Instead of building features based on incorrect assumptions, you’ll focus resources where they’ll have the most impact, leading to higher ROI.

The direct link between user-centered prioritization and feature adoption is clear.

The days of simply building features and hoping for the best are over. If you’re ready to take a different approach, our team is available to support.

The post How User-Centered Prioritization Helps Improve Feature Adoption Rates appeared first on The Good.

What is going wrong? The most common assumption is a poor product experience, but something else could be the culprit: SaaS products take too long to deliver value.

If users don’t quickly experience how software makes their life better, they will delete the app or simply abandon their account.

If you find your users in this position, accelerating time-to-value should be at the top of your priority list. Reducing the time it takes a customer to find value in your product can increase customer satisfaction by 10% to 30%, which will have a direct impact on retention.

In this article, we’ll share how strategies like optimizing onboarding, personalizing the user experience, and implementing quick wins can help you improve time-to-value and reduce churn.

What is time-to-value (TTV)?

Time-to-value is the duration of time it takes a user to experience the value of your SaaS product. It measures the time lapsed from when a user starts engaging with your tool to when they have an ‘Aha!’ moment about the positive impact on their life.

Types of time-to-value:

• Time-to-basic value: A metric measuring the time it takes a customer to see any value from your product.
• Time-to-exceed value: A metric measuring the time it takes to exceed a user’s expectations about your product’s value.
• Long time-to-value SaaS: A product or service that takes a longer duration of time to realize value (sometimes weeks or months). Usually, for SaaS, this is true when it takes time to integrate systems or data. In this case, it’s important to demonstrate incremental value during the journey to full value.
• Short time-to-value SaaS: A product or service that meets an immediate need, such as a transactional software product with a one-time use.
• Immediate time-to-value SaaS: A product or service with an instant reward for customer actions, such as a picture resizing or link shortening software.

How to calculate time-to-value

The simple time-to-value formula is:
TTV = Time that value is realized – Time user starts engaging with your product

But while the concept of time to value is pretty straightforward, how you calculate it will vary.

Your unique product, user, and definition of value will help guide the inputs of your TTV formula. Here is a quick overview of how to calculate time to value for your business:

1. Define value: What does finding value mean in the context of your product? Usually, it is how long it takes a user to complete a specific task that showcases the core function of your product.
2. Define the start time: When do you start measuring the users’ path to value? What is the starting point of their journey? Usually, it’s during the registration or onboarding process.
3. Define the end time: What is the moment a customer realizes value for your product? You’ll likely need to conduct research to pinpoint the ‘Aha!’ moment (more on that later). Typically, it takes the form of achieving a specific outcome, uncovering a benefit, or reaching a milestone.
4. Calculate the duration: Measure the time between the start and end points. This is your time to value.

Source

Some examples of how actual SaaS companies measure time-to-value include: how long it takes for a user to upgrade from free to paid, how quickly users start a new project after onboarding, or how long it takes to get the first ROI from the tool.

Why is time-to-value important?

Time-to-value directly impacts product success. It is an early indicator of retention and churn, and can help uncover areas for optimization.

The metric is especially important for companies with freemium or free trial pricing models. When your monetization happens during the product experience, you need to show your value explicitly and efficiently. Time-to-value is a way to measure if you are doing so successfully.

How to improve time-to-value

Demonstrating your value early on takes your SaaS product from ‘nice-to-have’ to ‘can’t live without.’ Here are a few strategies to help you do it.

Identify your product’s ‘Aha!’ moments

To get to the ‘Aha!’ moments faster, you first need to understand what those eye-opening experiences are. What turns casual users into lifelong customers? What is the first product experience that clearly shows your product’s potential impact on a user’s life?

To identify those ‘Aha!’ moments (or the end time in your formula), there is nothing more effective than talking to and studying the users themselves.

With user research methods like session recordings, usability tests, interviews, and surveys, you can uncover the intrinsic motivations of your users and take stock of their goals. What you find in your UX research process will help identify which product experiences will be most valuable to them and help prioritize which to show off early.

For example, if a new user of your project management tool wants to gain transparency across their team, the product should show them how to do that before it does anything else.

It will be tempting to bombard new users with all the amazing features of your tool, but to provide the quickest route to value for each user, you have to understand their objectives and why they are exploring your tool in the first place.

Personalize the user experience

The meaning of value will vary across your user base and their unique needs. For example, a 10-strong team signing up for an analytics tool may find different early value than a one-person team looking to monitor their small business. Or someone who has less tech know-how might need more hand-holding, while someone already comfortable with complex systems might want to see more intricate aspects upfront.

That’s why it’s important to personalize the user experience to accelerate time-to-value.

It doesn’t have to be 1:1 personalization, though. There’s a good chance there will be groups of users who have similar goals. Gain insight into your users, identify patterns, and create experiences for those groups. Start with a few segments and then continue to add on personalization if you see it’s working.

Optimize the registration and onboarding experiences

One significant lever for accelerating time to value is improving early product experiences.

The ROPES framework, designed to help product-first leaders think about, optimize, and improve the end-to-end customer experience, shows how the R (registration) and O (onboarding) experiences might help or hinder TTV.

While the other elements of the customer journey can play a part, the biggest drivers for reducing TTV lie in those first stages of the product experience.

While there is no exact blueprint for optimization, there are some commonalities you can bear in mind for improving registration and onboarding:

• Take it slowly: The last thing you want to do is dump everything onto new signups. Avoid overwhelming by trickling out information and allowing users to go at their own pace.
• Put the user first: The best experiences focus on the needs of each individual user and what they need to know to get started.
• Leverage novel UI mechanisms: Make use of novel mechanisms like tooltips or a modal window to gently guide users through the registration onboarding process.
• Give clear step-by-step instructions: Clearly guide users through your registration and onboarding step-by-step. Make it straightforward to complete, and be sure to highlight your product’s use cases during the process.
• Follow good form design principles: Show clear progress, give feedback as they go, and group common information to make it easily digestible.
• Collect feedback: Track and measure how successful your process is by asking new signups for their feedback.

Your product experience should be fluid. The needs of your user base will change over time, and you must listen to user feedback if you want to continue to improve the experience and showcase your value more efficiently.

Implement quick wins for users

Quick wins are a great tactic for improving time to value. Get your new users to complete small actions quickly so they start to feel comfortable using your tool ASAP.

Quick wins create initial momentum for users, playing into the psychology that when you feel accomplished or successful, you’re inclined to continue succeeding. This is similar to basketball players who gain confidence as they continue scoring points (hot streak) or that feeling when you have a task list of chores to do and you begin crossing them off one by one. Momentum is something that is built and will carry users forward.

As you build their confidence with your tool, you can use those quick wins to show the valuable features or functionality that is relevant to them.

Measure and iterate

No product experience gets it exactly right the first time, and there are always ways to continue improving your time to value.

Just like your users are learning how to use your product, you’re also learning how they use it. The more clarity you get on challenges, use cases, and goals, the easier it’ll be to create a valuable experience early on.

Continuously test different journeys, tweak steps based on user feedback, and don’t forget to track everything you do to see what works and what doesn’t.

Signs your time-to-value is too slow

It’s true that there is always room for TTV improvement, but how do you know you have a big problem?

Here are some red flags to look out for:

• High churn or low retention rates: Customers are leaving your service early in their subscription cycle, usually the first 30, 60, or 90 days.
• Low upgrade rate: Users don’t upgrade on their own, and/or sales teams get resistance to any account expansion or new offering pitches. This means customers are either seeing too much or too little value on their current account level.
• Low engagement with core features: Infrequent logins or low engagement with your core product functions/features means users don’t see the value of your product.
• Poor NPS: The feedback you receive is largely neutral or even negative.
• High customer acquisition costs: When users abandon, and you have to replace lost users, your cost of acquiring new customers will increase.
• Customer success or sales complaints: Your customer success team spends almost all their time resolving issues, or the sales team can’t clearly show the ROI of your product.

If you see one or many of these red flags, it’s time to start a new UX research cycle and dig into where there is room to optimize and show your value earlier on.

To get an expert’s POV on the situation, reach out to The Good. We can help uncover where and why your users are dropping off, and help you fix it.

The post Accelerating Time-to-Value: How SaaS Products Get Users to ‘Aha!’ Moments Faster appeared first on The Good.

Heuristics play a crucial role in how customers navigate and perceive digital experiences. So, we developed a framework called the Heuristics for Digital Experience Optimization to theme common optimization issues and opportunities through the lens of heuristics.

Leveraging the tool keeps users at the center of analyses and, when done correctly, ensures your strategy creates journeys that feel familiar, do what they say, and function intuitively.

One of the six Heuristics for Digital Experience Optimization focuses on incentives. In this article, we’ll explore how SaaS teams can leverage the Incentives Heuristic to build better user experiences and ultimately increase referrals through a well-done SaaS referral program.

How does the Incentives Heuristic work?

The goal of the Incentives Heuristic is to find opportunities for digital experiences to motivate users to take action. For example, sharing promotional offers or guarantees to get a user to register, convert free-to-paid, or make a referral would fall under the Incentives Heuristic.

Instead of relying on discounts, which have historically been the go-to to incentivize an action, digital experiences that adhere to this heuristic typically incentivize with offers that add value for the user and don’t devalue the product, such as bonus features or upgrades.

One great way to leverage the Incentives Heuristic is to build a referral program.

The power of a SaaS referral program

Well-done SaaS referral programs leverage incentives to encourage existing users to promote your tool.

The goal is to harness word-of-mouth referrals and increase product relevance and reach.

SaaS referral programs work well because they systemize the referral process and make it easy for users to share products with their network. And with 86% of B2B buyers saying word-of-mouth is the most influential factor in making purchase decisions, it’s an incredibly important part of the sales cycle.

Referral programs are effective for SaaS companies because of the social nature of tools. Professional peers frequently share their favorite tools and discuss new options in their communities. Incentivizing these conversations makes them mutually beneficial.

It’s a win-win. Your customer receives valuable incentives and trusted recommendations while you generate an organic growth cycle and harness positive network effects.

Viral growth loops

A growth loop is a compounding referral motion that leverages existing users to grow your user base through referrals. Referrals help keep marketing expenses low and increase the potential value of every new user.

Unlike traditional funnel frameworks, growth loops turn each interaction into a chance to draw in new users. When they start spinning on their own, they lead to lower customer acquisition costs and increased loyalty.

Positive network effects

SaaS referral programs that center on user benefits and incentives create positive network effects.

A positive network effect is when the value of a SaaS tool increases as the user base grows. For many companies, this larger user base leads to increased retention, bigger advertising budgets, unique user-generated content, improved user trust, and more.

How the best SaaS referral programs incentivize users

As mentioned, a straight discount or percentage off isn’t always the right way to incentivize referrals. Here are a few ways SaaS tools creatively incentivize users AND show the value of their product.

Service upgrades

Companies can incentivize referrals by offering upgraded account status. For example, Trello rewards referrers with one month of their “Premium” level service for each signup, up to 12 months. This gives users access to a new service level with a variety of new features and member benefits.

Dropbox pioneered the dual-sided incentive model, rewarding both the referrer and the new user with additional storage space. Referrers earn 500 MB for each successful referral, while new users receive 500 MB upon signing up, creating a viral loop that significantly boosts their user base.

Premium features

Some companies offer access to specific features as part of their referral program to add value for users while showing off the capabilities of their products. Evernote uses a points system where users can redeem points to unlock premium features. This tiered reward system encourages multiple referrals and gamifies the process.

Value-based offers

Another way to incentivize users is to offer a product that aligns with your tool’s value proposition. GetResponse combines financial rewards with educational incentives by offering the referrer and the new user a $30 reward alongside a digital marketing certification valued at $200 after three successful referrals. This approach aligns with the professional development goals of many users.

Credits

While we don’t necessarily recommend cash rewards or discounts, credits can be a valuable way to drive referrals. Airtable provides a $10 account credit for each referral, and DigitalOcean offers a $200 credit for new users to explore their services for 60 days. Referrers earn a $25 credit once the referred user spends their first $25, making it attractive for both parties.

Best practices for a SaaS referral program that leverages the incentives heuristic

Once you have the idea to build a SaaS referral program or optimize an existing one, these are a few best practices to keep in mind.

Stay user-centered

The key to building a successful SaaS referral program is to prioritize the user. Conduct research to understand what they want and tailor the program to their needs.

Your rewards structure should be based on what motivates them, and then the UX should be seamless to your digital platform. For example, provide easy-to-share unique referral links and offer in-app dashboards for users to track their referrals and rewards.

Also, make the act of referring intuitive, reminding users when it is natural to the actions they’re already taking. By integrating these incentives directly into their digital experience, SaaS companies make referral programs an engaging and rewarding part of using their products.

Gamify the rewards system

Make your referral process fun, easy, and satisfying to refer users. One way to do this is to gamify the experience. Gamification is the use of game mechanics in nongame contexts, and many SaaS companies do this extremely well in their actual product experience but not necessarily in their referral experience.

Leveraging gamification to find ways to make tasks more engaging and to make the referral process entertaining encourages users to track their success, refer more customers, and unlock rewards.

Align incentives with your value proposition

Incentives work best when they’re aligned with what makes your product compelling, so your first step is to clarify your offer and/or value proposition.

Ask yourself: What core action do users find most valuable? What helps them realize the most value from your product?

Then, incorporate incentives that encourage users to make referrals. These don’t have to be financial rewards. Sometimes, the product’s value is plenty, like in the case of Dropbox where users get more storage (the product’s primary value) for sharing. Closer alignment to the value will encourage more sharing.

Strategically place incentives throughout the user journey

Users love incentives like upgrades, guarantees, and other promises, but can fail to see them when they are in the thick of purchase indecision, comparing a number of other variables.

Placing key incentives near CTAs can increase the visibility of the offer and increase conversion readiness.

Incorporate optimization

There is no one-size-fits-all referral program or incentivization strategy. To find what works for you, test and validate ways to make sharing a natural part of your product experience.

Some tools you can leverage to iterate on the experience include rapid experimentation, user testing, A/B testing, or simply talking to your customers or customer service team to identify pain points.

And if you aren’t sure how to do this on your own, hire an expert for support.

Incentivize referrals to spur SaaS growth

So, now you have a starting point for building a strong referral program with heuristics in mind. But, the Incentives Heuristic is only one of the six Heuristics for Digital Experience Optimization. The full list includes:

• Priming & Expectation Setting
• Trust & Authority
• Ease
• Benefits & Unique Selling Points
• Directional Guidance
• Incentives

Each heuristic can help you identify and theme optimization issues or opportunities. These are the building blocks for a theme-based roadmap and are indispensable for any data-backed product team.

At The Good, we leverage the heuristics and work with your team to implement optimizations and build viral loops that help you scale faster. Learn more about how we increase referrals and get in touch here.

The post Building Viral Growth: Leveraging Incentives in SaaS Referral Programs & Strategies appeared first on The Good.

But, as more companies adopt the methodology for their tools, PLG strategies become table stakes rather than competitive differentiators. The best practices that might have helped you stand out a few years ago are now run-of-the-mill.

So, how do you make sure your product-led growth efforts stand out, improve the user experience, and go beyond the typical best practices you see day-to-day?

What are the product-led growth best practices?

Freemium and free-trial pricing models have spurred the movement toward product-led growth.

Because the purchase happens later in the customer lifecycle, the user evaluation period is longer and more thorough. Users can evaluate the product for what it offers, how it solves problems, and its ease of use. Instead of relying on marketing messages and sales calls to make a purchase decision, the user evaluates and engages with the product before converting.

This simplifies the recipe for success. If a SaaS tool provides more value than it costs, the user will convert.

There is a lot of literature out there on foundational strategies for PLG. Generally, the industry’s product-led growth best practices include specific tactics related to the following:

• Develop a product-first company culture so that the whole organization is focused on delivering the best product experience.
• Emphasize free trial or free accounts in marketing and sales to increase registrations.
• Minimize friction during sign-up with a clear, personalized, and engaging onboarding experience.
• Make it clear to freemium or free trial users what they are missing out on and what they will get by converting to a paid account.
• Prioritize account expansion over net new users with plan upgrades and customer marketing strategies.
• Gather customer feedback, review user behavior, and conduct testing to measure and improve the product experience.

While all of these are true and valuable, they will only get you so far, and it’s hard to know how to actually make them happen.

Best practices are for beginners

One of our favorite mottos at The Good is that “best practices are for beginners.” Yes, it is important to stick to foundational truths in SaaS optimization work: stay user-centered, establish consistent research practices, iterate your way to success, etc. But, to scale your SaaS organization, you need to go further.

There are many reasons for this, including:

• Best practices are tethered to the past, but your tool is not
• What works for your competition won’t necessarily work for you
• Sticking religiously to best practices holds you back from making data-backed improvements
• Prescribing solutions without diagnosing challenges sets you up for failure

Best practices can be a good starting point for companies looking to dip their toes into product-led growth or optimization, but they’re like training wheels. Once you’ve mastered them, they quickly cap how much you can scale. True growth demands a more tailored approach.

In his book, Opting In To Optimization, Jon MacDonald notes, “Above-average businesses—the ones converting their target customers in droves—are learning in real-time from every click and movement of their current users.”

As established, the ultimate goal of product-led growth is to leverage the product itself to improve acquisition, conversion, and retention metrics. So, how do you actually make that happen once you already have the foundational elements in place?

How to leverage PLG to go from product-market fit to scale

For companies that have outgrown best practices and are ready to scale, here are a few ways to take your product-led growth strategies to the next level.

1. Review the ROPES framework and identify levers you haven’t pulled

First, start by looking at the big picture with the ROPES framework.

The ROPES framework was designed by our team to help product-first SaaS leaders think about, optimize, and improve the end-to-end customer experience. It goes deeper than simple best practices and keeps the product at the center of everything you do by covering the user journey from registration to cancellation. Ultimately, it helps product-led companies:

• Define key stages in the customer journey
• Identify important metrics to measure each stage
• Understand the elements, forces, and factors that help or hinder engagement

Looking at your organization through the lens of the ROPES framework provides context to what makes a great product experience, what levers you can pull at each stage of the customer journey to improve acquisition, conversion, and retention metrics, as well as who on a SaaS product team should be leading the phase.

Once you have the foundational elements of product-led growth in place and are hitting a plateau, review the ROPES framework and identify which areas you aren’t fully leveraging to encourage engagement.

2. Optimize your team

Next, take a look at your team structure and ensure that the right people are leading the right stages of optimization. For example, the registration phase should be driven by the marketing team in collaboration with UX designers, while the product stage should be owned by the product team.

But don’t let this hold you back from letting cross-team collaboration happen. It’s important as a product leader to:

• Bridge the gaps and translate messages across teams
• Stay open-minded and ready for the unexpected
• Bring in people who might not always be part of the ideation phase but can offer a lot of valuable input

That’s because creativity doesn’t just come from the top.

Emma Leyden, product leader from IDEO, Title Nine, and more, says, “I have a deep belief that everyone is creative. I think that engineers are some of the most creative people in any organization. When I say that, CEOs look at me shocked, but engineers are closest to the work and want to ship products that will actually be used, so they have a good idea of what should be built.”

Leveraging your entire team to bring creative new approaches to PLG and allowing the right teams to drive their stages of the product forward allows for the proper balance of collaboration and ownership.

3. Deepen your feature moat

If you’ve reached a plateau in your PLG strategies, it might be time to dig into your feature moat.

A feature moat is when a product offers such unique and superior product features that the competition can’t quickly replicate them. There’s literally a gap—a moat—that your competitors will be scrambling to cross.

Think of it like this: If your product is a great solution, it will change the lives and work of your users. Their needs and preferences change. They develop new problems that you’re positioned to solve. Each solved problem represents a widening moat between you and your competitors.

How do you create this advantage? By continuing to drill deep into user needs and pain points even after you’ve achieved product-market fit.

Don’t rest, satisfied that you’ve learned enough about your users. Continue to leverage generative and evaluative research to uncover new insights into their behavior and needs. Ultimately, this is key to developing a customer experience that evolves with the user.

4. Transition from “launch and learn” to “test and learn”

When you are just starting to implement PLG practices, you may rely on hunches or best guesses. But as you grow, experimentation should happen pre-launch.

You are transitioning from “launch and learn” to “test and learn.”

Even in scenarios where you need to launch quickly, you should at least perform what Emma Leyden calls a “gut check.”

“Your ‘gut check’ can be done in low-effort ways. It won’t give you the most confident answer, but something as simple as showing a design to friends and family before you launch can teach you a lot.”

As a good rule of thumb, Emma encourages having some kind of user research scheduled every week, even if it’s as simple as letting someone see or use the prototype of a product and voicing their thoughts aloud.

While product intuition is important, it’s important to keep in mind we all have our biases. Sometimes, it’s hard to see our products from different perspectives, which is why testing or validating your ideas is essential.

5. Ditch generic benchmarks

Benchmarks are like best practices. They are a great starting point for companies looking to set goals, but for most SaaS companies, they are practically meaningless. We discuss the problems with benchmarking in our article “Why Industry Benchmarks are Bullshit,” but it comes down to this:

• Competitor data can be unreliable, inaccurate, or simply made up.
• Even niched-down industry data still contains too much noise.
• Your products, market conditions, pricing strategy, channel mix, and/or customer groups are just too different to control against even a true competitor.
• Goal-setting, testing, and learning are better alternatives to industry benchmarks.

Essentially, benchmarks are too simplistic to be useful, especially if you’re looking at only one metric, like conversion rate. And even if you match your competitor’s metric, it’s not like you’re going to stop optimizing your experience. You always want that number to improve.

So what’s the alternative? Instead of benchmarking against competitors, we recommend the recipe that works for top companies: setting strong data foundations, checking your assumptions about your audience and their behavior, and building a research practice.

Ditch generic benchmarks. Measure yourself against top optimization teams and identify high-impact areas for improvement instead.

6. Circulate your research across the organization

SaaS leaders with PLG foundations can improve how and when they share customer research to move from product-market fit to scale. Even if you have already established an ongoing research practice, to take this to the next level, improve how customer data and insights are circulated across teams.

When you skip this step, the disconnect between great research and doing something about the insights holds you back from building a user-centered culture and slows innovation.

To fully capitalize on customer insights:

• Give your research a home: Organize data into digestible, prioritized recommendations for teams across your company rather than overwhelming them with raw data.
• Identify patterns and form insights: Regularly circulate customer research to key stakeholders through internal newsletters, reports, or collaborative tools to align, identify areas for improvement, and uncover insights.
• Generate potential improvement ideas to address insights: Use shared insights as a foundation for brainstorming and decision-making across marketing, product, sales, and support teams.

By creating clear channels for sharing and proactively acting on research, SaaS leaders drive growth beyond what PLG best practices alone can achieve.

7. Try new free-to-paid conversion strategies

Converting free trial users to paid users is about demonstrating your product’s value. You can do this by strategically placing messaging throughout your site and/or app.

Keep in mind that your free trial signups already know the product is good. That’s why they signed up in the first place. Your job is to convince them that the value they’ll get from the product is worth the price.

You need them to conduct a cost-benefit analysis of your product and decide that it comes out on top. Highlighting benefits, offering social proof, giving product tours, and boosting user engagement are just some of the techniques to increase activation rates.

You can’t invite this kind of thinking unless you know your customers well. Exceedingly well. Only once you know what triggers them to buy can you build a user experience that entices them to convert.

Here are some strategies to consider for improving free-to-paid conversions:

• Remind users to upgrade early and often
• Drive users to value quickly
• Present gated features near free features
• Make your calls to action clear and consistent
• Be thoughtful about which features are gated
• Make free users aware of their trial time
• Offer a great onboarding experience
• Use paywalls to demonstrate paid features
• Clearly label your paid features

Remember, these are just ideas. Tailor them to your audience based on the issues you’ve identified and your proprietary user research.

8. Extend your capabilities with external support

Whether exploring new features, testing improvements, or mitigating risk, effective product-led growth teams use research at every stage of the product lifecycle.

Yet, research departments are often under-resourced, with typical staffing ratios at one researcher for every 50 developers. This imbalance leads to long research roadmaps that struggle to address the immediate needs of product teams.

In response, SaaS teams rely on external support to supplement their efforts and move beyond best practices to real, sustainable growth. One-off research projects can help, but sophisticated organizations find the most effective partners to work with them long-term.

Heidi Dean, Principal Product-Led Growth Manager at Adobe, says, “When you work with somebody long-term, they learn your products, the organization and your stakeholders. They understand the pain points that you’re dealing with, and then you just develop a shorthand.”

Integrating a specialized firm like The Good to come in and work on projects without much uptime can exponentially increase the user insights you receive and, in turn, the impact you can have on your organization.

Don’t get stuck at best practices

When you don’t push yourself past the comfortable, known best practices, you hold yourself back from scaling your SaaS tool.

If you recognize that you have reached that plateau, hopefully, this article has provided some inspiration for the next steps and areas in which you can focus your energy.

It can be tough to read the label from inside the jar, and if you want to get a fresh perspective to help you scale, reach out to our team. We bring years of experience optimizing SaaS user experiences and providing expert consulting for SaaS product teams.

After a short call to ensure a good mutual fit, we’ll get started supporting your product-led growth efforts with research, strategy, and experimentation.

The post Product-led Growth Best Practices Will Only Get You So Far appeared first on The Good.

But have you thought about how you might leverage your current user base to increase product relevance and reach?

This can be a low-cost, high-impact way to improve product quality, re-engage dormant users, or get in front of some new eyeballs.

It’s hugely beneficial to both your tool and the users by creating positive network effects. The more people that use your product, the more valuable it becomes. It’s a self-propelling mechanism to drive growth.

What is a positive network effect?

A positive network effect is when the value of a SaaS tool increases as the user base grows.

Take LinkedIn, for example. It becomes more valuable for users if their peers, colleagues, and dream employers use the tool. You can make more connections, learn more from the user-generated content, hunt for more jobs, and generally get more out of the tool.

For SaaS companies, larger networks can lead to several competitive advantages, such as:

• Are more trustworthy
• Entice advertisers
• Encourage referrals and word-of-mouth
• Build more unique user-generated content that can’t be copied by competition
• Increase retention

Understanding network effects is only the first step. The real challenge lies in building strategies that activate and sustain these effects. Creating positive network effects typically starts by leveraging your current user base, so let’s explore five proven ways to mobilize your users for growth.

5 ways to mobilize your user base to drive growth

Products like Pinterest, SurveyMonkey, or LinkedIn use network effects to grow. Each new user or engagement encourages more new users or engagements.

Tools target different objectives with their efforts. Some might prioritize user acquisition, while others reinforce retention, monetization, or product quality.

The most effective SaaS companies often find a couple of different ways to leverage current users as part of their growth strategy. Here are some examples to inspire your efforts.

1. Increase reach with shareable features

One way to leverage your current users’ network is to build product features that encourage natural sharing and engagement. To maximize reach, they need to be easy to use, helpful, and solve a real problem for users.

Pinterest allows users to create shared ‘boards’ to collaborate on home decor, event planning, and more. This either re-engages current users or prompts new sign-ups by sharing their board with both current and new users.

Here’s how it works:

1. A current user returns to Pinterest, ready to explore new content.
2. The user creates a ‘board’ to collect commonly themed content in support of a goal (redecorating their home, brainstorming for a trip, planning an event).
3. The user saves, pins, or repins content to their board.
4. The user shares the board with a collaborator (decorator, travel partner, event planner, etc) either directly on Pinterest or via a messaging tool.
5. The collaborator is either re-engaged or prompted to sign up for an account to collaborate on the board.
6. If the board is public, other users can stumble upon a piece of content and pin to their own boards.

Another example is Calendly, which builds natural sharing into the user journey. They incorporate simple scheduling and the speed of using invitation links. It’s also brilliant in that you don’t need an account to add yourself to a user’s schedule.

Here’s how Calendly includes shareable features to increase reach:

1. A Calendly user sends an invitation link to book a meeting. The invitee can select a time without the usual back-and-forth emails.
2. The invitee schedules a meeting without the typical friction of the scheduling process.
3. If an invitee schedules a lot of meetings themselves, they’re likely to sign up for Calendly to streamline their own scheduling.
4. As new users share Calendly links, more people experience the simplicity, driving additional sign-ups.

2. Drive referrals with growth loops

While shareable features focus on increasing reach, growth loops create a self-reinforcing cycle of engagement and referrals.

Think of a growth loop framework like a flywheel: Once it’s moving, it picks up speed and sustains momentum. For example, a user finds your product, interacts meaningfully, and creates content or engages in a way that attracts other users who repeat this cycle.

The goal here is to maximize viral reach without high acquisition costs. For a viral loop to succeed, the incentive needs to resonate with the users and align naturally with the product.

DocuSign’s growth loop leverages the need for digital document signing. Every document sent for a signature serves as an introduction to the platform.

Here’s how it works:

1. A user uploads a document to DocuSign and sends it to recipients for signature.
2. Recipients receive an email with a link to the document. They review and sign without needing an account. This helps them experience the platform’s convenience.
3. Impressed, recipients often sign up for their own accounts to send and manage their own documents, especially if they frequently need to get things signed.
4. As new users send their own documents for signatures, they introduce even more users to the platform. Each document sent by a new user brings in additional recipients.

3. Improve product quality and engagement with user-generated content

In addition to growth loops, user-generated content can amplify engagement and improve product quality by turning users into contributors.

Content engagement relies on user-generated or brand-created content to attract and retain users. This thrives when content shared or created by users on the platform is accessible to non-users. New visitors become intrigued and decide to join or engage.

GitHub, for example, leverages network effects to improve product quality. The collaborative coding and open-source project visibility encourage the use of their tool. Developers join to contribute to existing projects and then end up hosting their own projects.

Here’s how Github’s engagement of their customer network works to improve product quality:

1. Developers upload projects or contribute to open-source repositories.
2. Other developers discover these projects and contribute code, fix bugs, or fork the project for personal use. Each interaction boosts the project’s visibility on GitHub.
3. Developers who were attracted by the collaborative environment sign up to host their own code. This contributes to the platform’s network effect.
4. These new projects become additional attractors that bring in new developers.

4. Drive acquisition with incentives

A way to incentivize current users to aid in acquiring new users is through referral programs. Build shareable moments, incentives, and visibility into the user journey. Each new user not only becomes a customer but also a potential referrer by making sharing a natural part of the user experience.

The best referral programs provide support and education to users, making sharing about the product as simple as possible.

A great example is Airtable, which credits $10 to your account automatically when you invite new users to the tool.

Here’s how it works:

1. Current users invite new users to Airtable or share your unique referral link.
2. When a new user signs up and verifies their email address, $10 is automatically credited to your account.
3. You can see the credits on your account page and apply them to your charges.
4. Users can accumulate the credits, so the incentive to invite new users continues.

5. Re-engage dormant users

Social community features and push/message notifications prompt dormant or inactive users to re-engage with your tool.

For example, Venmo builds social engagement into its payment tool and uses these features to drive engagement. Each transaction re-engages the network of contacts with ‘reminder’ functionality and social engagement features.

Here’s a breakdown of how Venmo re-engages users:

1. A user requests payment from a friend, roommate, or coworker. If they don’t pay promptly, the user can ‘remind’ their contact about the pending payment.
2. Once complete, Venmo posts this transaction to a public or semi-public feed. This visibility serves as social proof.
3. Friends see the transaction and can like or comment on the payment.

Tools can leverage multiple strategies

From shareable features to re-engagement tactics, each strategy leverages your users’ networks in unique ways. By combining them, you can unlock exponential growth.

Let’s use LinkedIn as an example again:

• Referrals: New LinkedIn users are encouraged to invite their friends to the tool. This creates a positive network effect by increasing acquisition immediately with each new user.
• Re-engagement: When users join LinkedIn, they’re encouraged to connect with their contacts. Each connection re-engages current users on the platform. Users post updates, share articles, and comment on others’ posts. This drives users back to the platform frequently, increases time spent, and encourages interactions that deepen the network’s value.
• Registrations: Companies post job listings on LinkedIn and widely share the URL. There is a ‘quick apply’ function for LinkedIn users, which incentivizes applicants to sign up for their own profile.

These are just a few examples of how a tool might incorporate multiple strategies leveraging their customer’s network.

Start growing with support from your user base

Ready to start leveraging your customers’ network to increase product relevance and reach? Start by defining your goal and key metrics. Do you want to improve referral rates? User activation? New registrants?

Monitor these metrics to identify bottlenecks and opportunities to improve. If a specific action isn’t driving the desired results, you may need to adjust the user engagement structure, incentives, or experience.

Other best practices for leveraging your customer’s network to increase product relevance and reach:

• Segment customers
• Leverage social proof
• Make it easy
• Celebrate success and loyalty

Ready to amplify your SaaS growth through positive network effects? Our Digital Experience Optimization Program can help you identify and implement strategies tailored to your user base.

The post Leverage Your Customers’ Network to Increase Product Relevance and Reach appeared first on The Good.

Ultimately, the buyers aren’t cowardly; they are rational and thorough in their decision-making. Forrester reported that “an astonishing 43% of B2B buyers admitted that they make defensive purchase decisions more than 70% of the time,” meaning that less than 30% of B2B buyers are risk-tolerant.

And it makes sense. They are on the hook with their company and colleagues regarding the spending. In many cases, the purchase also has a direct effect on how they do their job day-to-day.

So, this raises the question of how B2B companies, like SaaS tools, can bridge the gap between risk-averse and purchase. The answer is trust.

There is plenty we could go into on the theory and psychology of trust-building, but instead, I’d like to focus on the actionable. Specifically, one great lever SaaS companies can use to build trust with their users is website optimization.

Read on to learn:

• How trust and authority fit into the Heuristics for Digital Experience Optimization
• Strategies for identifying the trust gap in user research
• Specific tactics to build trust via the UX design and content of your website

What is the Trust & Authority heuristic?

It takes longer for B2B leaders to trust vendors, and on top of that, according to PWC’s Trust Survey, it is harder to regain that trust once lost. So, it’s crucial that SaaS companies establish and maintain trust in all their sales avenues, one of the most important being the website.

So, how do you ensure your website not only looks credible but genuinely inspires trust? The key lies in aligning your website with proven trust-building principles, like The Good’s Trust & Authority heuristic, and implementing targeted strategies to address common user hesitations.

Trust & Authority is one of the six Heuristics for Digital Experience Optimization, a tool developed at The Good to theme common optimization issues and opportunities with the user at the center of analyses.

The Trust & Authority heuristic focuses on establishing and maintaining perceived trust, authority, and security throughout the digital experience. Issues like bugs, AI-generated images/quotes, or other elements that violate users’ sense of trust can lead to disengagement. Building trust, as we know, enhances users’ confidence in the website and typically leads to a better conversion rate.

To follow this heuristic and build trust with users, you can try tactics like mitigating bugs, featuring social proof, or adding additional educational “how it works” content for complex products.

But, before you begin to solve trust and authority issues, it’s important to identify where in the funnel users are dropping off because of heuristic violations.

Identifying user trust gaps through research

User behavior often reveals where trust is lacking. Here are a few signs you’ve violated user trust that you can look for in user research.

Bugs: When site elements or pages don’t function as intended or when they produce error messages or glitches.

Attentive/Intentional Reading: When a user slowly scrolls over content on mobile or desktop, their mouse hovers over text, typically line-by-line.

Halted Scrolling: When a user pauses on the site to possibly engage with content/reorient themselves, it could indicate that the user perceives a false bottom.

Dig even deeper by speaking to your customer support teams and conducting data analysis. Try to gather both quantitative and qualitative data that helps identify violations of the Trust & Authority heuristic.

Tactics to build user trust on your SaaS website

A visually cohesive and intuitive design contributes significantly to perceived trust. Users judge credibility in milliseconds based on aesthetics alone. Clean layouts, consistent fonts, and strategic use of white space can make your website feel more authoritative. But beyond visual design, what can you do to build trust with users? Here are tactics we’ve seen work time and time again.

Get creative (and more detailed) with your social proof

When marketing and optimization teams hear they need to build trust with users, minds rightfully jump straight to social proof.

But, to effectively signal authority in today’s digital world, you need to get even more creative and even more human. Here are a few ways to do it.

Try adding social media handles to customer reviews like ActiveCampaign

We all know that featuring expert testimonials can increase trust and confidence and increase conversions in the same way positive reviews can build user confidence to make a purchase decision.

But, it’s table stakes to include reviews on your site. Try to take things a step further and make those reviews more human. ActiveCampaign, for example, uses X handles on featured reviews to increase the credibility of quotes from real users.

Or add “customer since” dates like Dynamic Yield

Alternatively, if your reviews don’t come from social media or you’re featuring a case study as social proof, you can try other added authority indicators. In the case of Dynamic Yield, a label with “customer since” dates shows the loyalty of current users along with the results they achieved with the product.

Build social proof into the user journey, like U-screen’s forms

Humans tend to “reference the behaviors of others to guide their own behavior” (NNG, 2014). To leverage this tendency, you can build different types of proof, such as social proof, testimonials, and proof in numbers, into unique areas of the site. One place that can make or break the experience is form design.

U-screen does this well on their registration page with clear proof in numbers to accompany examples of their products’ output.

To achieve similar trust-inducing outcomes, the numbers, testimonials, and social proof you’re using should be the primary, or at least secondary, text on the form screen to catch the user’s attention.

Build trust with logos and badges

Another way that SaaS companies might think to build trust with users is by featuring client logos on their sites. But again, this is table stakes for most.

To build a stronger bridge between the risk-averse client and your product, try taking a supplemental approach to featuring logos and badges.

Borrow credibility from partners like Zapier

To integrate social proof and demonstrate the value of your product, you can borrow credibility from partners.

Zapier clearly includes logos from their integration partners in the hero section of the homepage, immediately building trust with customers who are familiar with or use any of the tools they partner with.

Show your certifications and badges like Dynamic Yield

Similarly, you can feature privacy certifications or data policy badges on your site, similar to what Dynamic Yield does. And if it is close to the CTA, even better!

Offer (and then stick to) a guarantee like Freshbooks

Guarantees can help prime users to make purchasing decisions and incentivize them to purchase. They give users a feeling that the brand is making a commitment to them. Highlighting guarantees in a quickly scannable way can increase a sense of trust, reduce decision paralysis, and highlight the value of a product.

Highlighting guarantees is great for sites with high-value products and/or companies with trust-reducing user-dependent variables. Freshbooks offers a full refund within 30 days of purchasing their product. It is similar to a free trial but framed differently.

Add a how-it-works model like SignNow

Describing “How it Works” for some business models and/or features can give users the context and confidence that they need to understand competitive differentiators like price and quality.

Doing so for complex products will boost user trust, encourage buy-in to the brand, and instill purchasing confidence.

SignNow describes the steps to enable dual-factor authentication for a PDF while showing a summary of how it works to show users how simple it is to protect a document with their tool.

Improving user trust increases registrations and retention

All of these are proven tactics we’ve seen across clients, but let’s remember one key part of optimization. Not everyone’s users are the same.

Adding an industry license badge to your product page is a great way to build trust. But you shouldn’t simply add the badge and pat yourself on the back. Job well done, right? Not quite. Now, you have to actually measure whether it creates the intended trust. Otherwise, you have no idea if your tactic satisfied the issue.

To track and measure this, we suggest planning with a theme-based roadmap.

With a theme-based roadmap, you can plan, communicate, and track the initiatives and associated metrics. You also have a clear path to conduct testing to make sure changes achieve results.

By aligning your website with The Good’s Trust & Authority heuristic, you not only build confidence but also position your SaaS business for sustained growth. Take the first step toward a more trusted digital experience—and watch how it transforms your registrations and retention.

Ready to optimize your website for trust and authority? Let’s talk.

The post How to Build User Trust on Your SaaS Website appeared first on The Good.

Many leaders think the solution is straightforward: “Just scale up!” they say.

In truth, it’s not that simple. Product-market fit isn’t a single moment in time. It’s a state that you must maintain, even as the market and your customers change. If you’re not careful, you could lose it.

Let’s explore product-market fit and what to do once you’ve found it.

Product-Market Fit Definition

Before we get too deep into the post-product-market fit part of your lifecycle, let’s get on the same page.

Product-market fit is the point where your product satisfies a strong market demand. It happens when your target customers recognize your product as the ideal solution to their problem. This recognition leads to organic growth.

Essentially, it’s the point where you’ve created something people want and are willing to pay for.

There is a misconception that product-market fit happens at the $1M ARR mark, but product-market fit is not a revenue stage. It can happen at any income level.

A trusted way to gauge if you have found product-market fit is the 40% test by Sean Ellis. It is a pretty straightforward assessment using a customer survey question, “How would you feel if you could no longer use [product]?”

If more than 40% of your surveyed customers answer “very disappointed,” then you have found product-market fit. It’s a leading indicator of what portion of users really value your product.

Product-market fit is often considered the milestone that signals your product is ready to scale. It’s not just about acquiring customers, though—it’s about retaining them because your product consistently delivers value.

What Comes After Product-Market Fit?

Once you find your place in the market, the next obvious step is to scale up. But that comes with a caveat: You have to scale without losing product-market fit.

“Product-market fit is a key milestone to reach, but it’s often misinterpreted as being a static moment in time,” say Fareed Mosavat and Casey Winters, product leaders from companies like Eventbrite, Pinterest, Grubhub, Instacart, and Slack.

“The reality is that your customer base is always changing and consumer expectations are always growing. Once you get [an] initial product-market fit, you not only have to keep it, but also expand it.”

If you focus too hard on acquisition and fail to refine your product, there’s a chance you could lose product-market fit. Obviously, that’s disastrous.

Smart leaders who find product-market fit are wise to protect it to avoid losing market share. They continue to iterate on their product so customers always see it as the ideal solution.

Sean Ellis, head of growth at companies like Dropbox, LogMeIn, and Eventbrite, and the guy who coined the term “growth hacker,” says it perfectly:

“The mistake that many marketers make is that they are optimizing for short-term conversions. They think it’s all about maximizing clicks and sign-ups. But if the product isn’t truly great at delivering on the promise, then you will likely lose these people anyway.”

Strategies for After Product-Market Fit

All of this begs the question: How do you scale up without losing product-market fit? It requires a shift in thinking and a bit of strategy.

Step 1: Reassess Your User

In some cases, product-market fit can be fleeting because the users who loved the early version of your product aren’t the same as your long-term users.

We call this the “early adopter problem.” Early adopters love to try new products, especially when those products promise to disrupt existing systems or ways of doing things.

However, those early adopters are also likely to move on to the next big thing. If too many of your customers are these early adopters, your customer base might bleed away until you lose your share of the market.

“The problem is, the early adopters are only ever a small percentage of the overall market,” says Marc Andreessen. “And so a lot of founders, especially technical ones, will convince themselves that the rest of the market behaves like the early adopters, which is to say that the customers will find them. And that’s just not true.”

Early adopters aren’t your only challenge. It’s counterintuitive, but if your product-market fit is good, you tend to grow fast, and your customers raise their expectations.

“Slack had [an] extremely strong product market fit from the early days and ended up growing so fast,” says Fareed Mosavat, VP Programs and Partners at Reforge. “It was extremely difficult to keep up with the rising expectations of our customers over time, and took us a while to launch things like WYSIWYG editing, better ways to launch apps vs. just text commands, and simplified channel discovery that were important for our newer, less-technical users.”

Once you find your place in the market, turn your attention to your users. Continue to conduct research to understand how to meet the needs of customers who aren’t the early adopters.

Step 2: Build a Data-Driven Culture

The need for robust customer research should come as no surprise to any growth-focused company, yet too many leaders take their foot off the metaphorical research gas pedal once they achieve product-market fit.

Maintaining product-market fit and scaling up both require a culture that makes its decisions based on data and research. Scrappy startups can rely on quick, intuition-based movements, but scaling companies can’t ignore their data.

The first step to a data-driven culture is to establish your shared growth KPIs early and ensure your team is moving in the same direction.

Next, dig into your customer research. Conduct interviews, analyze data, and gather feedback to identify pain points, features, and reasons for churn/low engagement. Ideate on improvements to address those challenges.

Finally, rapid test or A/B test changes to your website, marketing, and product to understand if investing resources makes sense.

The key to building a data-driven culture is to make it a habit. You can start small by scheduling a few user/customer interviews each week, as expert human-centered product leader Emma Leyden suggests, and then build on that. With more and better data, you can more easily fold it into your process.

The good news is the value of your data continues to grow as you read this article. A bigger user base and more sessions mean more data points, which makes trends and patterns more apparent and reliable for decision-making.

Step 3: Shift Your Hiring Priorities

With product-market fit, your team and priorities will naturally evolve. You have new scale goals and likely a growing team that needs oversight.

Strong leadership is key here. You’ll likely also struggle at this stage with balancing team support for your growing user base while optimizing your product. Admittedly, that balance isn’t easy to achieve. It requires clear prioritization. Sometimes, newly hired leaders make the mistake of assuming their product is “built” and devest product design and development to work on other initiatives.

It can help to leverage an external pod of product experts to fill in any gaps and help prioritize changes.

The Good is a great option to supplement your product team post-product-market-fit. We can help you simplify decision-making by staying laser-focused on research, data, and goals through our Digital Experience Optimization Program.

Step 4: Start Building a Feature Moat

A feature moat is when a product offers such unique and superior product features that the competition can’t quickly replicate them. There’s literally a gap—a moat—that your competitors will be scrambling to cross.

Think of it like this: If your product is a great solution, it will change the lives and work of your users. Their needs and preferences change. They develop new problems that you’re positioned to solve. Each solved problem represents a widening moat between you and your competitors.

How do you create this advantage? By continuing to drill deep into user needs and pain points even after you’ve achieved product-market fit.

Don’t rest, satisfied that you’ve learned enough about your users. Continue to leverage generative and evaluative research to uncover new insights into their behavior and needs. Ultimately, this is key to developing a customer experience that evolves with the user.

Step 5: Stop Obsessing Over Registration

Registration is just one moment in the user lifecycle. It’s a big moment, for sure, but too much focus on new users can cause you to ignore your existing user base. After you have product-market fit is an ideal time to level-set with your team about initiatives beyond registration.

This is the time to work on your product-led growth strategy. Focus on improving the rest of the customer experience after registration, including onboarding, activation, engagement, expansion, and evangelism. These stages not only increase growth (through new users and retention), but they also give you a roadmap to iterate on your product moving forward.

Use research in the post-product-market fit stage to lower the cost of acquisition by considering that every user who doesn’t churn is an opportunity to spread positive word-of-mouth.

Step 6: Consider Market Expansion

Your product evolves as you introduce new features, achieve more growth, and scale up your platform. But eventually, your current product will reach a saturation point where growth reaches the limits of the market.

In this case, the only way to grow is to expand your product-market fit by expanding into adjacent products or markets to find new potential customers.

Expanding product-market fit doesn’t necessarily mean building new features. It’s a turn from fulfilling your users’ problems to anticipating their next problems. Expanding usually happens in three ways:

• Same product in a new target market, e.g., Instacart expanding into pharmacy delivery.
• Same market with an adjacent product, e.g., Lyft expanding to bikes and scooters.
• New product in a new market, e.g., Amazon launching AWS.

This kind of expansion does not happen incrementally. It typically happens in bursts when you recognize a new market, vertical, or user to serve. And in nearly all cases, it requires taking bigger bets.

Support for Post-Product-Market Fit

When your entire organization is built around finding product-market fit, the switch to a post-product-market fit strategy can be challenging. It requires a new way of thinking for this new stage in your company’s lifecycle.

In these cases, outside perspective is more important than ever. Our Digital Experience Optimization Program brings the pieces you need to build a better digital product. Our team can help you scale up without losing product-market fit. We bring the tools, technique, and expertise that you just can’t find in a single hire.

The post What Comes After Product Market Fit? appeared first on The Good.

Emma Leyden knows. As a senior product manager who specializes in human-centered design, she’s spent years using unique research techniques to create explosive product growth.

Recently, Emma was with Polygence, a high-growth seed-stage startup that connects students with mentors. Before that, she was with IDEO, an award-winning global design firm, where she honed her ability to uncover user insights through user research to make product decisions. And before that, she worked at Title Nine, an ecommerce women’s clothing company.

Emma is committed to designing user-centric and data-driven products that drive engagement. She believes that if you create great experiences for customers, business value will follow.

“I’ve really always been focused on understanding the deep needs,” she tells us. “Once I figure out a user’s need, I can use those insights to inform product decisions and fuel growth.”

Emma brings a robust basket of tools to the table: UX research, design, agile methodologies, and honed skill of marrying user needs and business objectives. We had the pleasure of working with Emma when she was with IDEO, and we caught up with her recently to get her best tips on how today’s product leaders can make a measurable impact on their organizations.

Today, we’re sharing insights from that chat, including:

• What human-centered product management is
• Why Emma can’t live without experimentation
• Her favorite tools for product research
• How to pick an agency partner

A Human-Centered Approach to Product Management

There is something uniquely refreshing about talking to a digital leader who remembers there is a real person on the other side of the screen. For Emma Leyden, a human-centered approach to product management keeps her focused on what really matters: the end user.

“As a product manager, you should feel as if the customer is in the room with you as you’re making product decisions,” Emma says. “You should be able to speak with the voice of the customer because you’ve talked to so many people, but also because you’ve synthesized enough and you understand your audience.”

What Is Human-Centered Product Management?

Human-centered product management is exactly what it sounds like. A style of work that has a person anchoring everything you do. It can show up in the way you talk to customers, experiment, or lead your team.

Talking To Your Customers

In some cases, human-centered product management means literally bringing customers into the office. For example, in one role, Emma’s team saw sports bra sales fall 5% year over year. To uncover why, she brought in five women who consistently shop for sports bras to learn what’s important to them when buying a bra.

After her conversations with real customers, she learned about the vulnerability and body issue challenges of purchasing a bra online. She also learned that the site’s photography wasn’t meeting their needs, and color choices within the same style were important.

“It wasn’t an extensive study. It was just five women, but talking to them helped us change the way we merchandised our products.” Once you learn your customer, you can take a human-centered approach to those decisions.

Data & Experimentation

Though crucial to the success of human-centered product management, physically talking to the customer isn’t the only way to inform your work. Emma incorporates a diverse mix of strategies to ensure she is making the right decisions.

“Everything should be data-driven when you're making a product decision,” Emma says. Every single stakeholder - including developers, designers, and leaders - will ask you for data to justify your decision. So you always have to have data to back it up and also to track if your enhancements improved something.”

Where does the data come from? Experimentation. “Experimentation is a tool to gut-check your decisions,” Emma tells us. It’s an important way to identify possible improvements as well as validate what you think you know. “It might not give you 100% or even 80% confidence, but it can tell you if you’re headed in the right direction or not.”

It embodies the “human-centered” spirit by keeping your personal bias out of the picture.

“I can't live without A/B testing. There have been so many times in my career I found myself too close to the product. I thought I had a handle on things, but an A/B test showed that I wasn’t right. It’s actually fun to be surprised like that.”

Data and experimentation can tell stories about customers if you know how to listen. The right metrics, when considered together, can paint a picture of delight, frustration, and everything in between, keeping leaders focused on the customer experience.

Leading Your Team

Human-centered product management means building products and making decisions based on and for the user. But it also means collaborating with your team and connecting with them where they are.

“Having strong leadership skills is important, but also I think strong collaboration skills are key as well.” Collaborating with different experts, in many cases, means learning to speak their language. “For example, if you have the skill to say to a designer, ‘Here’s where the engineer is coming from’ or ‘Here’s how they’re going to interpret your design,’ you can make a more efficient communication channel, which makes the team work faster and ship better products.”

A good product manager can bridge the gaps and translate messages across teams because of that human-centered approach. It also keeps you open-minded and ready for the unexpected. It's important as a product leader to bring in people who might not always be part of the ideation phase but can offer a lot of valuable input. That’s because creativity doesn't just come from the top.

“I have a deep belief that everyone is creative. I think that engineers are some of the most creative people in any organization. When I say that, CEOs look at me shocked, but engineers are closest to the work and want to ship products that will actually be used, so they have a good idea of what should be built.”

Using Creative Research Methods to Gain Confidence In Product Decisions

The good news for product leaders everywhere is that you don’t need millions of dollars and thousands of customer conversations to take a human-centered approach. Sometimes, you just need a cardboard box.

If you’re going to embrace creativity from unexpected places across your organization, why not get a little “out-of-the-box” in your own techniques and tactics? Emma shared some of her favorite tools for creative product research. Hopefully, these drive home the point that anyone can take a human-centered approach to product management regardless of budget, time, or audience size.

Low-Fidelity Prototyping

User testing with prototypes is one of Emma’s main tools for getting a gut check before taking design and messaging to production.

“The point of a prototype is to communicate the absolute bare minimum of a feature enhancement and see how users react,” she says. “You can throw a prototype together, put it in front of someone, and learn a lot quickly.”

Emma uses two types of prototyping:

• Prototyping for designers: Designers are visual people, so to help them understand what you want from a product, you have to give them something visual. It doesn’t have to be pretty, but a quick mockup or sketch is a powerful way to bridge the communication gap.
• Prototyping for users: These don’t need to be fully developed, but they have to be something users can use. “This does not need to be fancy,” she tells us “I’ve literally made prototypes with cardboard and put them in front of users.” The point is to communicate the absolute bare minimum of a feature.

“One of the beauties of prototyping is that when you take the design elements out of it, you're stripping down the feedback,” Emma says. It prevents users from being influenced by unimportant details or things that can be tested and changed later. It lets you focus on the functionality and usability of a product.

Out-of-the-box User Research

Emma also likes to use creative, outside-the-box UX research techniques to uncover insights to inform design and product decisions. Here are a few of the fun examples she shared that may come in handy for your own efforts.

The Scavenger Hunt Approach for Discoverability Insights

The scavenger hunt approach is useful when you’re trying to validate whether users can find information. In this test, Emma asks a user (or a group of users) to find a piece of information in a website, webpage, or document.

How they search and how long it takes them to find the information helps you understand their mental models and whether the site, page, or document matches their thinking.

"In one case, we knew a specific piece of information was key from a previous test, but we had to validate if users could easily find it," Emma explains. "People were scanning through the document like crazy, and we quickly learned that what we thought was obvious on page six was actually buried too deep."

Hot Dot Voting for Honest Feedback

Hot dot voting is an exercise where Emma gives users access to the digital workspace of some product. Then she asks them to add green dots to portions of the workspace that resonate with them and red dots to portions they find confusing or frustrating.

"The beauty of this method is that it gives people time to think,” Emma tells us. “They’re not being put on the spot to say something they like or dislike at the moment, which can lead to biased answers. Instead, they reflect quietly and provide more thoughtful responses."

Ultimately, this technique produces valuable conversations about the product. The facilitator gets a chance to see the themes people follow when exploring or using the product.

Turning Creative Research & Design Into Business Success

Emma’s human-centered approach has served her well in her career. She has a long history of creating impactful change at every organization she’s been a part of. Sticking to her unique approach has delivered huge results and some key learnings along the way.

Turning User Insights Into A 733% Sales Increase

In one instance, Emma delved into a product that was underperforming at Polygence. It was intended to serve as an add-on to the core product, but customers weren’t buying. After talking with users (students and program mentors), operational staff, and salespeople, she discovered that the product was built to serve two very distinct user needs. Customers found this dichotomy confusing.

The solution was to split the product into two separate products, each serving a different purpose. The new products were given clean messaging and offered to different customer segments alongside the core product.

The results of Emma’s research approach created a 733% sales increase. “This is an example of where good research and strategic thinking can help you make simple choices that make a big impact on business metrics,” she tells us.

Using Experimentation To Increase Clicks By 250%

In another case, Emma learned that what users say they want isn’t always what they really want. At IDEOU, customers requested more price transparency, so Emma’s team displayed course prices throughout the website. Unfortunately, this had a negative impact on sales.

After running A/B tests, she learned that user feedback didn’t match their behavior. When she removed prices, they saw a 250% increase in clicks to the enroll button.

“This was a clear example (that actually happens often) where a user says they want something, but their behavior is actually different,” Emma says. “Experimentation is important because it helps you understand how much to follow what users say.”

Finding an External Partner for Product Success

While Emma has had plenty of success on her own, she’s no stranger to calling in external partners who can make her optimization team stronger.

Hiring an agency is a lot like finding a romantic partner. You can’t grab just anyone. You have to find the one that’s right for you. Emma tries to look deeper into potential relationships with external partners, beyond the initial pitch.

“When you get a slide deck from an agency, they’ll try to show you how they’re going to move the needle and get good results,” she tells us. “But I think you should go beyond that. You should try to understand if they truly understand your business and if they align with your values.”

Furthermore, Emma likes asking hard questions. She wants to know, for instance, what happens if a test has a strong negative result. How the agency responds will tell you a lot. Do they answer honestly or do they sugarcoat their response?

How does she build good agency relationships? With a practiced vetting process.

Emma believes relationships with agencies should be collaborative. Neither side should dictate the relationship, what needs to be done to move the needle or the pace. You and the agency should be on the same team with the same priorities, but each brings different perspectives to the table.

“Once you start a working relationship, everything beyond the kickoff call should feel mutual. It should not feel like they are talking at you for the whole time, and then you get to ask a question at the end.”

Why was Emma attracted to The Good? We value the same kind of partnership that Emma requires in an agency. We both recognize that great product development comes from a collaborative effort between the internal stakeholders who know the customers well and external partners who know optimization.

One Final Piece of Advice? Approach Product Growth With Nuance For The Best Results

There are some folks with a "test everything" mindset, where nothing is launched without testing, but there are other leaders who advocate for almost the opposite. "Founder mode" is about instinct and speed. So, to wrap up our conversation, we asked Emma a question that frequently occurs in the industry: Is there one “right” approach to product growth?

“Your approach depends on the size and status of your company,” Emma says. “If you’re a small seed stage startup, you’re launching and learning and doing your experimentation post-launch. But a more established company has an expectation of a certain experience, so they have to be more thoughtful about what and how often they launch.”

While product intuition is important, it’s important to keep in mind we all have our biases. Sometimes, it’s hard to see our products from different perspectives, which is why testing is important. If you feel the need to launch quickly, you should at least perform what she calls a “gut check.”

“Your ‘gut check’ can be done in low-effort ways. It won’t give you the most confident answer, but something as simple as showing a design to like friends and family before you launch can teach you a lot.”

As a good rule of thumb, Emma encourages having some kind of user research scheduled every week, even if it’s as simple as letting someone see or use the prototype of a product and voicing their thoughts aloud. You can learn a lot about the usability of a product with this kind of approach.

Getting Results As A Product Leader

Emma’s incredible results are a testament to her human-centered approach to product design. We hope more product managers take such a deep interest in their customers to design incredible products and experiences.

Good product leaders like Emma know that staying user-centered and making informed, data-backed decisions is the key to success. Hiring an agency like The Good can help you do just that. Our team can amplify your impact with the tools, technique, and expertise that you just can’t find in a single hire.

Learn more about our Digital Experience Optimization Program. We bring all the pieces you need to complete an optimization puzzle and build a better digital journey.

The post How Emma Leyden’s Approach to Human-Centered Product Management Delivers Results appeared first on The Good.