Beyond Mockups: Sachin Rekhi Advocates for Functional Prototypes with Analytics in Customer Disco…

In a recent LinkedIn post, Sachin Rekhi discusses a significant shift in customer discovery methodologies, advocating for the use of functional prototypes integrated with analytics over traditional feedback on static mockups. Rekhi posits that this modern approach offers a far more insightful and actionable understanding of user behavior.

Rekhi highlights the core difference: observing actual user actions versus relying on users to predict their future behavior. He states:

“I get to observe actual user behavior instead of asking the user to guess how they might use my product.”

The Pitfalls of Expressed Preferences

To illustrate the limitations of relying solely on what users say they want, Rekhi shares a compelling anecdote from a Sony Walkman user study. In the study, participants favored a yellow Walkman, describing it as “sporty” and “not boring.” However, when given the choice to take a Walkman home, everyone selected the black model. This divergence between expressed preference and actual behavior underscores Rekhi’s central argument.

As Sachin Rekhi points out:

“We learned a lot more from user behavior than we did expressed preferences.”

This classic example, according to Rekhi, demonstrates that observed actions provide a more reliable basis for product development decisions than verbal feedback on designs that haven’t been interacted with in a realistic context.

Rekhi’s Analytics-Driven Prototype Setup

Rekhi outlines a practical workflow for implementing this enhanced customer discovery process. His setup involves leveraging functional prototypes and integrating an analytics platform like PostHog. The steps he recommends are:

1. Create a functional prototype using a preferred tool (mentioning options like Bolt, Lovable, Reforge Build, Magic Patterns, and Claude Code).
2. Integrate PostHog analytics with the prototyping tool.
3. Instrument key user actions within PostHog to track interactions.

Leveraging Behavioral Data for Insights

Once this setup is in place, Rekhi explains the wealth of behavioral data that becomes accessible. This includes observing key metrics that reveal user engagement and retention. As Sachin Rekhi details:

“DAUs WAUs retention curves – I can actually see if people come back and use my prototype instead of taking their word for it.”

Beyond retention, Rekhi emphasizes the value of action metrics dashboards to understand which features or flows users are engaging with and which they are not. He also points to the utility of post-usage surveys, allowing for targeted questions after a user has experienced the prototype. Furthermore, session replays and heatmaps offer granular insights into usability issues and design effectiveness by visualizing exactly how users interact with the prototype.

A Paradigm Shift in Product Development

The implications of this approach are profound, according to Rekhi. By moving beyond the limitations of static mockups and embracing the power of observed behavior through functional prototypes and analytics, product teams can gain a much deeper and more accurate understanding of their users’ needs and behaviors. Rekhi concludes with a strong statement about his commitment to this method:

“I’d never go back to testing with just a mockup after this.”

In Rekhi’s view, this integration represents a fundamental upgrade in the customer discovery process, enabling more data-driven and user-centric product development.