7-Eleven · 7NOW App
Slurpee Spark: A Collaborative Evaluation Tool That Activates Personas for Product Decisions
A real-time web application that helps 7NOW product and design teams evaluate initiatives against shared user personas and business criteria — turning static research into actionable, aligned decisions.
- My Role
- Business Strategy, Retail & eCommerce Research, Product Scoping
- Team
- Jenny Lian (Product Management), Nam Nguyen (IT / Engineering)
- Context
- Design Studio for Business — SMU MADI Program
- Stack & Methods
- User interviews, co-creation workshops, iterative prototyping, AI-assisted synthesis
Designed for 7NOW teams to move personas out of storage folders and into everyday product decisions.
At a glance
The case in under 30 seconds
- Role
- Business strategy, research and product scoping
- Problem
- Five research personas existed, but they were not shaping everyday product decisions.
- Intervention
- Designed a five-factor collaborative evaluation flow with AI-assisted synthesis.
- Result
- Prototype validated with 10 cross-functional participants and five standardized factors.
- Reading time
- 7 min
Evidence note: Participant, interview and framework counts come from project research and prototype test records.
How might we
How might we activate persona research findings to impact internal design and product decisions for the 7NOW app?
01 — The Challenge
7NOW had invested in developing five official user personas through interviews with 20+ users and a cross-team co-creation workshop. But after the presentation, the personas were stored in a Teams folder and never revisited. Product, design, and research teams were misaligned — research findings were not tied to business KPIs, and there was no standardized way to integrate personas into the product development lifecycle.
- Personas were descriptive but not actionable — teams could not connect them to initiative decisions.
- Research, design, and product operated in silos, causing delays and missed opportunities.
- There was no shared framework for evaluating initiatives against user needs and business goals.
- By the time user feedback reached product teams, features had already launched.
02 — The Strategy
We designed a shared decision-making framework where user personas are directly connected to measurable business outcomes. The tool needed to feel lightweight for facilitators while creating enough structure to produce consistent, reusable outputs.
Step 1
Research-first: understand the real pain points
Conducted 6 interviews with 7NOW managers, designers, researchers, and product managers to identify why personas were not being used and what would make them actionable.
Step 2
Define a standardized 5-factor evaluation framework
Co-created with the 7NOW team five key evaluation dimensions: User Personas, Business Impact, User Experience, Team, and Resources. Each factor includes reflection prompts to guide discussion.
Step 3
Prototype iteratively with real users
Built three prototypes in succession — a static scorecard, a digital version, and finally a real-time collaborative session tool — testing each with 10 cross-functional participants and refining based on feedback.
Step 4
Integrate AI-assisted synthesis
Added Gemini-powered insights that summarize session data, surface alignment gaps, and generate discussion questions — turning individual scores into team conversation starters.
03 — The Product
Slurpee Spark is a real-time web application where teams create evaluation sessions, select relevant personas, score initiatives across five factors, and receive AI-generated insights. The interface is designed for facilitation — the host controls the flow while participants join with a 6-digit code.
04 — The Results
The final prototype — Slurpee Spark — was tested with 7NOW product, design, and research teams and validated as a practical tool for aligning initiative evaluation.
Business Impact
Slurpee Spark transforms how 7NOW teams use persona research — from static documents to active decision-making inputs.
- →Replaced ad-hoc workshop debates with a repeatable, factor-by-factor evaluation process.
- →Created a shared language between research, design, and product through standardized criteria.
- →Reduced misalignment by surfacing where teams agree and disagree before committing to initiatives.
- →Produced reusable session outputs (scores, sticky notes, AI insights) for documentation and retrospectives.
05 — Key Learnings
Listen to users and iterate rapidly
The first prototype — a static scorecard — failed because testers did not see how it connected to personas. We pivoted to a real-time collaborative tool after just one round of feedback.
Structure creates alignment, but flexibility preserves collaboration
Teams needed fixed evaluation factors to keep sessions comparable, but they also needed open-ended sticky notes and reflection prompts to capture nuance.
AI insights work best when grounded in explicit data
Gemini-generated recommendations became useful only after we tied them to structured factor scores and session context — not when they were generic.
Cross-functional teams need conversation, not just scores
The most valued feature was not the final score — it was the composite view that sparked group discussion and surfaced hidden assumptions.
Need a collaborative evaluation framework for your product team?
I design research-to-action systems that turn user insights into aligned, measurable product decisions.