Introduction
In today’s digital landscape, personalization is key to user satisfaction. As Head of Product, I spearheaded the development of Personalization Launchpoints, a system powered by a segmentation engine designed to deliver tailored experiences. This project transformed how users interacted with our platform, making every touchpoint feel uniquely relevant. My contribution was crafting a detailed product specification that turned this vision into a actionable reality for our team.
Problem Statement
Our platform faced a critical challenge: users wanted content that reflected their individual preferences, but delivering this dynamically at scale was a struggle. The existing system lacked the flexibility to adapt to diverse user behaviors, resulting in generic experiences that failed to engage. We needed a robust solution that could scale efficiently while remaining manageable for our technical and operational teams.
Baseline Methodology & Functioning:
The methodology is to run a model on each segment a customer is eligible for and calculate the overall relevancy of a Kristal to customer in that segment to prfioritize the cards to be shown to the customer.
Below is a example of how segments will work -
| Static segment based on rules | Segment qualification based on customers | Segment qualification based on number of Kristals | |
|---|---|---|---|
| S1 | -- | Y | Y |
| S2 | -- | N (<20 customers) | N(< 10 kristals) |
| S3 | -- | Y | Y |
| S4 | -- | Y | N (< 10 kristals) |
| S5 | -- | N (<20 customers) | N |
| S6 | -- | Y | Y |
| S7 | -- | Y | Y |
3. In the above table we can see that S2, S5 are not qualified on both criteria whereas S4 is not qualified on no. of Kristals criteria. So the segments eligible to the customer are S1, S3, S6 and S7.
4. Check the eligibility of customers to be part of the segments. In this example let’s take two customers. Customer A & B.
| Static segment based on rules | Customer A | Customer B | Customer C |
|---|---|---|---|
| -- | Status of the customer falling in the below segments | Status of the customer falling in the below segments | Status of the customer falling in the below segments |
| S1 | Y | N | N |
| S2 | -- | -- | -- |
| S3 | Y | Y | N |
| S4 | -- | -- | -- |
| S5 | -- | -- | -- |
| S6 | N | Y | N |
| S7 | Y | Y | N |
5. Assuming only two layers are to be shown on the UI, based on the above table, Customer A will be shown a Ribbon aligned with S1 and S3 and for Customer B it will be S3 and S6. Since Customer C, doesn’t fall in any segment so he will not see any of the segments from S1 to S7.
Solutioning Through Mindmapping
Collaboration was key to designing the segmentation engine, and we used a mindmap to brainstorm and structure our approach. This visual tool connected ideas across the team—product, design, engineering, and data science.
The mindmap evolved into the solution architecture, ensuring a holistic, scalable design. It fostered innovation by encouraging cross-functional input.
Structuring the Spec
Opportunity
Every spec begins with the "why." I outlined the rising demand for personalized experiences and tied it to our roadmap goal of deepening user engagement, giving the team a clear sense of purpose.
Target Audience
I identified key user segments—like frequent explorers and high-value users—to focus the solution on those who’d benefit most.
Customer Insights
Drawing from user research, I distilled feedback into themes, such as the need for less overwhelming options, supported by direct quotes to build team empathy.
Competitive Insights
I reviewed competitors’ personalization strategies, pinpointing strengths to leverage and gaps to fill, shaping our unique approach.
Success Metrics
Clear metrics defined success: primary goals (e.g., engagement with personalized content), deprioritized metrics (e.g., session time), and guardrails (e.g., user satisfaction).
Scope with User Stories
User stories made the spec relatable and concrete, translating goals into features. Here are two examples from the project:
These stories guided prioritization and kept the team user-focused.
Experience (UI/UX Focus)
Personalization hinges on presentation as much as content. I worked with designers to embed UI/UX principles into the spec, ensuring the interface felt intuitive and trustworthy.
This section empowered designers to innovate within a user-centric framework.
Implementation Details
I highlighted technical constraints affecting the experience—like latency limits for real-time personalization—without over-specifying the architecture.
Launch Plan
A phased rollout mitigated risk: starting with a 5% beta group, then scaling based on feedback.
Investigative Metrics
Post-launch data—like interaction patterns—would fuel iteration.
Outcome
The ribbons looked as below.
The results spoke for themselves. Post-launch, we recorded a 25% increase in user interactions with recommended content within the first month, surpassing our initial 20% target. Overall platform engagement rose by 15%, with average session times jumping from 8 to 9.2 minutes. User feedback highlighted the ribbons’ relevance, with one user noting, "It feels like the platform knows exactly what I need." These proof points underscore how a well-executed specification can drive tangible impact.
Reflections
This project underscored key takeaways: