Post-Launch User Testing: Continuous Improvement Strategies
Launching a product is like sending your child off to their first day of school. You’ve fed them, clothed them, and prepared them as best you can, but how will they actually fare once out in the real world? As any parent knows, children need continuous nurturing, support, and guidance to thrive. The same is true for products after launch.
Continuously testing and improving your product post-launch is critical to its success and growth. Without ongoing optimization, you miss opportunities to identify pain points, bugs, and confusing user flows that frustrate customers and cause them to abandon your product.
Post-launch user testing allows you to optimize conversion funnels, increase customer satisfaction and retention, and ultimately drive more sales and revenue.
In this article, we will provide an overview of different post-launch user testing methods that can help you uncover issues and opportunities in your product. Specifically, we will cover surveys, usability testing, analytics reviews, etc.
Implementing a combination of these methods will provide ongoing feedback from real users so you can iterate and improve the product over time. The goal is to create a cycle of continuous improvement driven by customer data rather than guesses and assumptions.
Let’s get started.
Table of Contents
Post-Launch User Testing Methods
Surveys are a valuable tool for gathering direct feedback from users post-launch. Well-designed and distributed surveys provide ongoing insights to inform your optimization efforts.
When creating effective surveys, follow these best practices:
- Limit the survey to 10 or fewer focused questions. Survey fatigue sets in quickly if surveys are too long or broad. Carefully curate the set of questions that will yield the most impactful data.
Use a mix of question types:
- Multiple choice for quantitative data and easier analysis. For example: “How did you first hear about our product?”
- Rating scales (1-5, strongly disagree to strongly agree) to quantify attitudes and perceptions. For example: “On a scale of 1-5, how easy was the checkout process?”
- Open-ended questions for qualitative feedback. For example: “What aspect of our onboarding process was most confusing?”
Keep surveys concise. More extended surveys see dramatically lower response rates.
Distribute surveys through the channels your users engage with most actively:
- In-app prompts and notifications to gather feedback on specific workflows. Time them to display after users experience key flows.
- Email campaigns and newsletters to reach both active and lapsed users. Send post-purchase and churn surveys.
- Social media polls and posts to leverage your existing audience on these channels.
When writing effective questions:
- Avoid biased or leading wording that influences the response.
- Ask one question at a time to prevent confusing or mixed data.
- Use rating scales to quantify attitudes, issue severity, likelihood to recommend, etc.
- Analyze results by looking at statistical significance but also outliers:
Small sample sizes may still reveal issues if a large percentage of users report problems. Dig into these.
Outliers can uncover problems specific user segments experience. Look beyond basic statistics.
Regular pulse surveys provide an always-on feedback loop to your team.
Continuously measure satisfaction, pain points, feature needs, and more. Then rapidly iterate based on the insights. Surveys should provide ongoing fuel for your post-launch optimization efforts.
See an example of a survey:
1. Low effort to implement –
Using dedicated survey builder tools like Typeform, SurveyMonkey, or Google Forms, surveys can be created, distributed, and analyzed with minimal effort compared to other user research methods. Marketers can quickly build and iterate on surveys as needs arise.
2. Can reach large sample sizes –
Surveys excel at reaching a broad number of respondents efficiently. They can be sent out at scale across your entire user base or targeted to specific segments based on attributes like demographics, behavior, purchase history, etc. This provides feedback from a sizable subset of users.
3. Flexible and adaptable –
The questions themselves can be changed rapidly allowing surveys to be adapted to evolving needs. New questions can be inserted and existing ones tweaked or removed quickly. This makes surveys ideal for taking pulse checks and gathering regular feedback from customers.
4. Scalable for large user bases –
Once built, surveys can be distributed to even thousands of users with minimal incremental effort using email campaigns, social promotions, in-app prompts, and other channels. This scalability allows large volumes of feedback to be captured.
5. Can segment data by user attributes –
Leading survey tools have built-in filtering capabilities that allow marketers to compare responses across user segments. You can analyze satisfaction, feature requests, behavior, etc. across personas, geographies, usage levels, purchase history, and other attributes.
1. Self-reported data not as reliable –
Users are not always fully self-aware or objective when self-reporting. Their portrayed usage may differ from actual observed behavior. Take self-reported data with a grain of salt.
2. Users may be biased or mistaken –
Respondents can inadvertently misremember or mischaracterize their experiences due to limitations of memory and perception. Some may rush through just to enter a prize draw rather than providing thoughtful answers.
3. Can lack rich qualitative feedback –
Pre-defined closed-ended questions make it hard for respondents to explain nuances, provide context, or go in-depth on issues. Surveys are not well-suited for gathering rich qualitative insights compared to open-ended interviews.
4. Low response rates –
Survey invitations are easy to ignore and response rates typically sit below 30%. Engagement falls as more questions are added. Offering incentives and keeping surveys focused can help, but expect a fraction of recipients to respond.
5. Infrequent distribution can miss issues –
Unlike always-on methods like session replays, periodic survey distributions can fail to detect emergent UX issues and changing perceptions in real time between surveys. Problems may go unnoticed.
Usability testing is a valuable post-launch method for observing real users interact with your product. Best practices include:
- Recruit representative users by offering incentives to motivate participation.
- Seek a mix of demographics, personas, usage levels, and other attributes to get feedback from different segments.
- Screen participants to recruit those with behaviours and characteristics you want input from.
Conduct both moderated and unmoderated tests:
- Moderated tests allow you to interview users as they work through tasks and workflows. Ask probing questions, seek feedback, and understand the motivations behind actions.
- Unmoderated tests provide naturalistic data by having users complete tasks independently without interference. This reveals native behaviours and pain points.
Set structured tasks and scenarios that cover critical workflows and user journeys. Record sessions to uncover issues like confusing navigation, unintuitive designs, error messages, and points of friction.
Look for trends in where users get stuck and their satisfaction levels at each step. Identify areas of the product needing iteration.
Usability testing combines the benefits of direct user observation with interview insights into motivations. Schedule ongoing rounds of testing after launch to continuously improve experience and interactions. Leverage both remote moderated and unmoderated methods to increase participant diversity while controlling costs.
The goal is to regularly gather actionable, qualitative insights you can use to remove obstacles and refine workflows in the live product experience. Usability testing provides human-centred feedback no survey can match.
1. Provides observational and qualitative data –
By directly observing users interact with your product, you gain insights into actual behaviors versus stated ones. Moderators can ask follow-up questions on pain points and sources of confusion uncovered during the test. This qualitative data is invaluable.
2. Can uncover usability issues –
Watching users navigate through key workflows will surface usability issues like confusing navigation, unintuitive designs, unclear terminology, and missing help content. You see firsthand where users get stuck.
3. Highly actionable insights –
Session recordings show the exact user interactions to pinpoint areas for optimization. You see body language, hear feedback, and gain context behind behaviors. These actionable insights fuel iterative improvements.
4. Catches bugs early –
Usability tests frequently reveal functional bugs missed during development testing. Real-world use cases expose edge cases. Catching bugs early improves quality and satisfaction.
5. Uncovers emotions and attitudes –
The think-aloud process along with facial expressions provides qualitative data on user mindset, frustration, delight, and more. This level of emotional insight is nearly impossible to extract from surveys.
1. Small sample size –
Most tests involve 3-5 users. While useful feedback can be gleaned from each session, the overall sample size is small. Findings may not generalize across broader populations. Supplement with surveys and analytics.
2. Resource intensive –
End-to-end, the process of recruiting representative participants, conducting 1-on-1 moderated sessions, analyzing recordings to extract insights, and translating findings into prioritized recommendations requires significant time and money. The resource intensity limits testing frequency.
3. Geographic constraints –
For in-person moderated tests, it can be expensive and logistically challenging to conduct sessions with users across different countries and regions. Remote moderated testing helps but lacks physical observation.
4. Risk of observer effect –
Being observed during usability testing can cause some users to think aloud more or behave differently than they would naturally. This threatens data validity. Mitigate via anonymous unmoderated testing.
Analytics should be regularly reviewed post-launch to uncover optimization opportunities. Key practices include:
- Closely analyze page-level analytics across the website and app. Look for spikes in exit rates and bounce rates that may indicate confusion, lack of interest, or other user experience issues on those pages.
- Examine conversion funnel data from entry pages through key workflows like signups, purchases, content downloads, etc. Look for significant fall-off at each step and diagnose technical or design issues causing excessive dropoff.
- Evaluate behavioural metrics like active users, sessions per user, retention by cohort, and churn drivers. Compare across user segments to surface potential issues for personas with poor engagement.
- Conduct user flow and funnel analysis to understand how visitors navigate through the customer journey end-to-end. Look for differences across segments and identify workflow bottlenecks.
- Implement event tracking for interactions, conversions, and trends. Expand event coverage over time to fill analytics blindspots. Events can reveal nuances missed by page and funnel analysis.
- Leverage tools like Google Analytics, Amplitude, and Mixpanel to analyze behaviour across web, mobile, and other platforms. Integrate data for unified insights.
Regular analytics review equips teams with user behaviour data to pair with qualitative insights. Together, these inputs help diagnose underperforming areas and identify what to iteratively test and optimize post-launch. Analytics provide the critical what to complement the why from other methods.
1. Leverages existing data –
Analytics provide a readily available data source for insights without requiring implementation effort like surveys or usability tests. You can start uncovering insights immediately from historical data.
2. Large sample size –
Analytics aggregate data across all users and sessions rather than relying on small samples. This provides a more representative cross-section of customers to draw conclusions from versus other methods based on limited participants.
3. Holistic view of entire customer base –
Analytics data encompasses all users and segments rather than specific sampled subsets. This provides a broader perspective of engagement trends and behavioral shifts across your whole customer base.
4. Identify macro trends and shifts –
By analyzing metrics over longer time periods, analytics can surface evolving behavioral patterns, changes in popular content, shifts in key segments, and other high-level trends across the full user population.
5. Low cost to implement –
Tapping into existing analytics platforms means minimal incremental cost to unlock a rich dataset compared to conducting primary research. Analytics provide highly cost-efficient data access.
1. Data requires interpretation –
On their own, analytics metrics do not explain the underlying motivations and reasons for user behaviours. The data requires analysis and interpretation.
2. Doesn’t explain why users behave in certain way –
Analytics quantify what users do but not why. Qualitative insights are needed to understand the motivations behind actions and unpack the human context.
3. Retroactive data –
Because analytics provide a historical view, they may miss very recent shifts in usage and engagement that are still evolving in the present.
4. Hard to dig into causal factors –
While useful for identifying macro trends, high-level analytics metrics lack the detailed contextual data needed for deeper analysis of nuanced causal factors behind behaviours.
5. Potential data quality issues –
Analytics ultimately depend on the quality of the underlying tracking implementation. Incomplete or incorrect data severely undermines the insights extracted.
Turning Feedback Into Action
1. Prioritization framework for improvements
When prioritizing which findings and recommended improvements to execute on, it is critical to take a structured approach weighing factors like implementation effort, impact on key metrics, breadth of customers affected, and short versus long-term value.
Highly complex changes may provide immense value but require significant engineering resources and time to reach customers, so balancing quick wins that positively impact targeted pain points against longer-term solutions is key.
Defining clear criteria and a weighted scoring model brings consistency and transparency into what gets prioritized on the roadmap, rather than relying on gut feelings. The voice of the customer should anchor prioritization decisions rather than just internal opinions on value.
2. Role of product roadmap and resources
The product roadmap is crucial in defining what post-launch insights can be actioned on and when based on engineering priorities and capacity planned for upcoming sprints and releases.
Proposed changes must be mapped to the roadmap cadence, accounting for the design, development, testing, and release timelines required. Quick fixes and small enhancements may be fast to implement, while more complex and impactful changes likely require scheduling further out.
Trade-offs between technical debt backlog items and net new features also factor in based on their respective customer value. Continually assess if priorities need shifting based on new customer inputs, but avoid constant churn. Communicate changes cross-functionally so all teams have aligned expectations, especially regarding release timing.
3. Communicating changes to users
Keeping customers informed on which suggested improvements are being rolled out and when builds transparency and trust that their voice is valued in evolving the product.
Release notes, in-app messaging, email campaigns, and other channels should be leveraged to share enhancement details and highlight those stemming directly from user research.
When launching major new features based on feedback, build excitement by explaining their value add. For complex changes touching multiple areas, consider a phased communications plan that sets expectations on what is changing and when over a timeline. The goal is to close the feedback loop by showing customers their inputs drive meaningful improvements.
4. Maintaining continuous improvement
A culture focused on continual optimization should be fostered so enhancements are an ongoing priority rather than a point-in-time project.
Regular check-ins with customers via surveys, interviews, analytics analysis, and usability testing ensure the backlog of potential improvements remains populated over time as needs evolve.
Analyzing usage metrics and engagement by cohort identifies areas declining in favour that require attention before they become severe pain points.
Cadence is key – establish a regular release rhythm balancing speed with stability, avoiding massive batched changes. Dedicate personnel and resources explicitly towards iteration initiatives so they do not get deprioritized relative to new capabilities. Sustaining continuous improvement through regular small enhancements demonstrates true user-centricity.
Frequently Asked Questions
1. Q: What is the best post-launch testing method for a B2B SaaS product?
A: For B2B SaaS, a combination of quantitative and qualitative methods is recommended. Surveys, interviews, and usability testing provide qualitative feedback, while A/B tests and analytics analysis uncover usage trends. Blend these approaches.
2. Q: How much should my budget be for post-launch testing activities?
A: Aim for 5-10% of total product budget allocated towards ongoing testing and research. The payoff from optimization typically outweighs the costs.
3. Q: When should I start testing my product after launch – immediately or after some time?
A: Begin post-launch testing as soon as possible. Early feedback identifies critical issues to address before they become major problems.
4.Q: How frequently should post-launch testing be conducted?
A: Conduct some form of testing at least quarterly. For surveys and interviews, once or twice a year is common. Usability testing and A/B tests can be done more frequently.
5. Q: How many users do I need to test to get valid results?
A: 5-8 users per round of usability testing catches most issues. For surveys, at least 100-200 responses. A/B tests depend on traffic.
6. Q: What are some best practices for recruiting test participants?
A: Leverage screening criteria to get a mix of demographics and user types. Offer incentives for participation. Be transparent on goals.
7. Q: How do I prioritize feedback from different post-launch testing methods?
A: Align priorities with a product roadmap focused on customer value versus internal goals. Consider effort, impact, and breadth of users affected.
8. Q: What metrics should I track to measure the impact of changes from post-launch testing?
A: Key metrics include retention, engagement, conversion rates, funnel completion, NPS, task time, and self-reported satisfaction.
9. Q: How can I create an ongoing culture of continuous testing and improvement in my organization?
A: Allocate resources towards iteration. Set regular testing cadences. Empower teams to make data-driven changes. Share results cross-functionally.
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