Dining Rewards Habit-forming Strategy
Project scope:
Product Strategy, Rapid Prototyping, Concept Validation
Date:
February - March 2026
Role:
Design Lead / User Research Support
*Some project specifics have been omitted to protect client privacy.
Background
Following initial discovery to define the baseline MVP, the business recognized a critical strategic need to shift users from passive earners to highly engaged, active platform participants.
To achieve this without interrupting the core engineering timeline, our habit-forming discovery pod was spun up as a standalone, specialized 8-week work stream in parallel with the MVP delivery team. This allowed us to focus exclusively on uncovering long-term behavioral drivers, validating innovative feature concepts, and creating a future-ready product strategy while the MVP was simultaneously being built.
Problem
How might we shift member behavior from passive earning to proactive engagement to drive higher dining frequency and improve merchant attribution?
Most members approached the program as a passive utility: they connected a card once, earned rewards in the background, and rarely returned to the product between transactions. That created two connected business problems:
The client had limited insight into member intent, preferences, and planning behavior.
It was difficult to prove to merchant partners that the program was influencing discovery, visits, and repeat dining (rather than simply rewarding behavior that would have happened anyway).
Solution Summary
Our approach was structured into four key stages within the 8-week timeline, designed to move from broad academic theory to a validated, technical roadmap:
Opportunity Framing & Alignment: We aligned project goals with the client’s vision by conducting secondary market research to analyze best-in-class habit-forming experiences and establishing a foundational motivational framework across 4 strategic Opportunity Areas:
Keep purpose (and goals) in mind at all times
Build momentum towards goals with small wins
Elevate the experience through moments of surprise and delight
Make the experience ‘social’ for contributors and observers.
Concept Development & Round One Testing: I generated 7 distinct feature concepts, including a GenAI "Host" functionality, based on the identified opportunities. We then conducted 10 concept testing sessions with existing members to gather early user sentiment.
Feature Refinement & Round Two Validation: I evolved initial concepts into interactive, high-fidelity prototypes based on initial insights, expanding to 12 additional validation sessions with Members and Non-Members to test acquisition signals.
Strategy, Feasibility & Roadmapping: Following a technical feasibility audit, we mapped validated features into a phased three-release roadmap to transform a transactional utility into a personalized, AI-driven loyalty journey.
Challenges & Results
Poor Data Quality → Strategic Data Collection
Around midway through the project, the MVP team shared with us that they were being blocked by inaccurate restaurant metadata (incorrect hours, poor category tagging, etc.). Given the client’s ambitions to roll our AI-enabled features, this posed a major blocker for effective AI recommendations. Customers told us they would be open to Ai recommendations, only if they were accurate and provided value by helping them discover restaurants they will love.
To address this challenge, our team collaborated to adjust the roadmap we were proposing: account for data quality issues by strategically gathering the data needed to deliver a powerful AI experience via initial habit-forming features.
Business Ambition → Phased Behavior Change
A major challenge was balancing the client’s appetite for bold AI-led experiences with the reality that habit formation depends on trust, relevance, and repetition. Instead of recommending a large leap from passive rewards to a fully AI-driven journey, we framed the work as a behavior-change ladder: start with small, useful reasons to re-engage, then use those interactions to progressively improve personalization.
The resulting roadmap gave product and engineering teams a clear path from MVP to differentiated loyalty experience. It prioritized features that could generate immediate member value while also capturing the signals needed for better attribution, stronger merchant storytelling, and more credible AI recommendations over time.
Research Output → Decision-ready Strategy
By the end of the workstream, the team had a validated set of habit-forming opportunities, a prioritized feature roadmap, and a clearer strategic narrative for how dining rewards could evolve beyond passive earning. The work helped leadership make near-term tradeoffs without losing sight of the larger product vision.
Next Steps & Reflections
This project reinforced that habit formation is not about adding points, streaks, or notifications on top of a product. The most durable engagement loops come from understanding what users are already trying to accomplish and making the next useful action easier, more rewarding, or more memorable.
It also highlighted the importance of sequencing AI experiences responsibly. A personalized dining host can only feel magical if the underlying data is accurate and the user understands why a recommendation is relevant. For this client, the strongest strategy was not to rush toward AI as a feature headline, but to design the engagement loops that would make future AI genuinely useful.
If this work moved forward, my next focus would be measuring which lightweight engagement moments most reliably translate into repeat dining behavior, richer preference signals, and clearer merchant attribution.