TLDR
Despite the diverse product teams within Partnerships, a common challenge was the limited data-driven approach to product development. To bridge this gap, I initiated a cross-functional initiative to optimise product development through data-driven insights and probes
TEAM
Solo designer, with guidance from my manager
ROLE
Leading the project, planning and executing, facilitating workshops, and managing collaborators and stakeholders
TIMELINE
6 months
APPROACH
Define
Problem mapping
Project briefing
Plan
Project plan
Feedback collection
Approach definition
Execute
Framework creation
Collaboration management
Workshop facilitation
Outcome collection
Analyse
Metric extraction and cleanup
Cross-functional collaboration
Outcome sharing and measuring impact
CONTEXT
Partnerships department
Booker experience track
Focusing on consumer facing products
Platforms track
Focusing on the Partner Center and business facing products
Ops track
Focused on internal tooling for commercial and product development
Product teams
Rewards and gating
Branded platforms
Apps
Partner Centre
API
Reporting
Fee admin
Internal tooling
Portal
The diversification of end-users (b2b vs b2c vs internal tooling) and the nature of the products we build in the department make it hard to have a useful success tracking system in place.
No common metrics used across teams and products, even though we feed into one departmental goal and priorities.
Tracked metrics were not necessarily helpful towards understanding how to continue iterating, or simply assess how the product is doing at a behavioral level.
Example
The primary metric tracked for a new launch sheet added to the core app is: number of total bookings.
While it serves the much bigger purpose, what can we learn from this metric? How can our product development and reiteration based itself off of that metric?
1
Share the project plan
It was shared with all stakeholders from each product team in the department, as well as relevant principle developers. Feedback was collected then.
2
Assess engagement and knowledge
Based on the feedback and comments collected, we saw high interest, but low knowledge, even for product managers.
3
Define next steps
We decided to translate the document shared into a digestible workshop aimed at better defining success and failure metrics when launching products and features
DIRECTION
This set the direction the workshop would take. Based on this, I wished to make the workshop practical by asking the different teams to choose one product or feature to be used as a reference/example in the workshop.
EXPECTED IMPACT
This would help tailor each team's outcome to their product and process needs, and it showed the practicality and value of such a mindset using real-life examples.
We combined the outcome into one source of truth: a sheet that recognises metrics of common value across teams as well as individual metrics.
My manager and I then had a few sessions with people representing business and tech, where we normalised the metrics. We looked for overlaps, suggestions and categories for easier usage.
Jackpot
Product, tech, and UX now had the right tools and methods to find the optimal metrics for their products, making product development more efficient
We normalised data points that impact the products within the department and created a base to be used for primary metrics
Observations
A few months have passed since this initiative. I have noticed a shift in the way different product teams within the department handle metrics and data. Designers are more involved, discussions are made around measuring data instead of relying on the assigned developer to list down minimal metrics.
We are now able to make better decisions when building and improving products thanks to an data-informed process.
Next steps
As for next steps, I regularly follow up the developers in charge of implementing the common metrics and check with the designers in my department if their collaboration and processes are better.
The influence of designers on product success
Designers' role and involvement in defining metrics is essential.
Behavioral metrics are key to setting the expectations for a product or funnel's success/failure
No size-fits all approach
I spent a good amount of time researching workshops and frameworks I can use to approach my stakeholders. Sometimes breaking down my goal into smaller probes will guide the exercises and optimal approach
Assigning efficiently as a leader
Not everything needs a meeting, and I can't be the one managing all 9 product teams and their engagement. Knowing when to involve myself or others taught me to take more of a leader role in this project