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Prioritizing Features Based on Market Feedback and Testing

Balancing which features to build, improve, or remove requires insight into what capabilities customers truly value most. Research and experiments identify the capabilities that influence purchase decisions.

This guide explores both qualitative and quantitative tactics to rank features by importance. We’ll cover concept and prototype testing, conjoint analysis, A/B testing, and surveying customers directly on preferences.

Let’s help you invest development resources into features your market wants most!

Why Features Should Be Research-Driven

It’s tempting for internal teams to decide which features seem important during planning. But assumptions often miss how customers evaluate utility.

Without evidence, you risk:

  • Building features users don’t need or find worth the additional cost
  • Neglecting small enhancements that would meaningfully improve experience
  • Overcomplicating the product by adding too many bells and whistles
  • Misunderstanding which functionality users are willing to compromise or tradeoff
  • Failing to highlight “must-have” differentiating functionality in marketing
  • Increasing development and training costs on unused features

Robust research instead reveals:

  • Clear rankings of feature utility and value to users
  • Willingness to pay more for certain capabilities
  • Features which, if removed, would lose customers
  • New capabilities or integrations prospects request
  • Must-have and nice-to-have divides to inform launch roadmap
  • Mismatches between your assumptions and actual feedback

Prioritizing features by validated importance ensures you deliver the optimal mix of utility improving customer experience, satisfaction, and stickiness.

Qualitative Feedback on Features

Open-ended qualitative research provides “directional” guidance on feature preferences:

Focus Groups

Discuss feature trade-offs with 6-8 engaged prospects. Observe group dynamics.

In-depth Interviews

Explore emotions and reasoning behind feature opinions through 1-on-1 conversations.

Job Site Observations

Watch users interact with competitive or related products and note workarounds.

Content Analysis

Review user-generated content mentioning must-haves or lacking features.

Advisory Panels

Check in regularly with a council of current users to gather feedback.

Qualitative techniques reveal language, emotion, and nuance behind feature priorities that surveys miss.

Quantitative Research on Features

Quantitative data scales qualitative insights to larger samples with hard metrics:

Discrete Choice Modeling

Ask respondents to select their preferred feature combinations to reveal importance.

Max Diff Analysis

Have prospects rank features from most to least critical from a set. Determine relative weights.

Conjoint Analysis

Similar to discrete choice, determine preferences based on feature trade-offs.

Concept and Prototype Testing

Evaluate feature reactions by showing wireframes, renderings, or demo videos of your concept.

A/B and Multivariate Testing

Build alternate product pages showcasing different features. See which version converts more users.

Willingness to Pay

Directly ask prospects how much more they would pay for certain features. Quantify value.

Customer Satisfaction

Survey users on whether specific existing features met expectations and needs. Identify gaps.

Numbers reveal hard feature preferences to guide prioritization.

Crafting Strategic Feature Roadmaps

With robust data, thoughtfully plan feature rollout:

Rank by Importance

Stack rank features by hard metrics on utility and value. Avoid opinions.

Identify Must-Haves

Call out the table stakes features users expect as bare minimum from competitive parity.

Prioritize Differentiators

Accelerate features you validated as novel differentiators users want that competitors lack.

Schedule Enhancements

Balance major undertakings with quick wins to show iteration based on feedback.

Align to Resources

Weigh development costs, technical complexity, and resources required to set realistic timelines.

Map to Buyer Journey

Attach features to onboarding, adoption, retention and growth phases when they deliver most value.

Leave Room for Innovation

Don’t overplan – leave flexibility for entirely new capabilities you can’t yet predict.

With buyer wisdom guiding roadmaps, features remain aligned to actual needs not internal guesses. But stay nimble.

Monitoring Post-Launch Feedback

After releasing features, keep researching:

Measure Usage Rates

Analyze usage data to confirm popular versus neglected features. Pivot efforts accordingly.

Survey User Sentiment

Talk to users about pros and cons of recently launched capabilities.

Review Support Tickets

Note frequent pain points or feature requests submitted through support channels.

Check Reviews and Forums

Monitor user reviews on app stores and community forums for recurring feedback.

Run A/B Tests

Try enhancing new features vs. legacy versions to see if experience improves.

Listen closely post-launch to validate executed features succeeded. Research never ceases.

Avoiding Common Feature Prioritization Mistakes

While research should guide planning, some common missteps include:

Equating Loudest Feedback with Majority

Beware the most vocal requests don’t necessarily represent the entire user base. Verify importance broadly.

Getting Stuck in Existing Paradigms

Don’t just benchmark competitors. Research breakthrough experiences users don’t realize are possible.

Jumping on Temporary Fads

Distinguish lasting innovations and platform shifts from short-term fads when prioritizing.

False Dichotomies

Avoid framing trade-offs as binary choices. Explore hybrid solutions appealing to multiple needs.

Ignoring Long-Term Vision

Balancing short-term requests with revolutionary functionality for the future growth.

With the right perspective, research guides evolution, not reactive changes.

Key Takeaways for Research-Driven Prioritization

Here are the core principles for optimizing features based on user feedback:

  • Solicit directional feedback on preferences through focus groups, interviews, and observations.
  • Validate findings with larger sample quantitative research through surveys, discrete choice modeling, and conjoint analysis.
  • Map features to user needs identified in research. Don’t rely on internal assumptions.
  • Stack rank features by measured importance and willingness to pay based on data.
  • Focus first on must-have table stakes, then differentiating capabilities.
  • Balance major enhancements with quick iterative fixes to show responsiveness.
  • Continuously monitor usage metrics and user feedback post-launch to confirm successes.
  • Avoid reacting to outlier feedback and temporary fads that may not represent most users.
  • Remain open to breakthrough innovations beyond just common feature requests.

By truly listening to customers, you take the guesswork out of where to invest finite development resources.

So stay nimble, keep researching, and let wisdom guide how to deliver the optimal mix of utility and delight through features users value most.

By Dani Davis

Dani Davis is the pen name of the writer of this blog with more 15 years of constant experience in Content marketing and informatics product, e-commerce niche.

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