Menu
In recent assessments, we've observed a decline in the efficacy of one of our primary software solutions, Mobee, in capturing and converting data to the desired extent. We've encountered challenges in addressing the diverse data requirements of our corporate users, particularly concerning specific data types.
It also had become evident that Mobee is exhibiting signs of obsolescence in regards to the information a user needs to make decisions to convert, this hurried us to find the urgent patches and fixes needed to solve this, particularly in critical junctures where user interactions significantly impact conversion rates. This paradigm shift in user engagement underscores the imperative for swift and comprehensive updates to ensure seamless functionality and optimize conversion outcomes.
In the operational structure of the retail intelligence division that I worked with within the company, multiple applications and components comprise the framework I needed to devise solutions for.
Each solution implemented had ripple effects across all facets of the ecosystem. To provide clarity, here is a diagram illustrating the interconnections within the ecosystem:
My initial strategy involved convening key team leaders representing different segments of the ecosystem and doing a discovery workshop. This collaborative approach facilitated comprehensive analysis of issues from diverse perspectives, enabling us to pinpoint root causes and implement collective solutions.
The following is Workshop plan for our first Kickoff Discovery meeting
One of the first thing I noticed while trying to understand "What is coverage?" was the divergence in definitions across various departments. While each interpretation was valid within its respective context, they lacked a holistic perspective encompassing the entirety of the business operations. Thus, one of my primary objective for the workshop was to achieve alignment among all stakeholders regarding the definition of "coverage" tailored to their specific business domain and inclusive of its comprehensive implications.
With a consensus reached regarding the the definition of "coverage", our focus shifted towards identifying the underlying root causes and peripheral factors contributing to our coverage challenges.
To facilitate this process, we conducted a structured card sorting exercise. Initially, participants were tasked with individually documenting the specific issues they were currently encountering that impeded coverage in varying capacities. Following this, collaborative discussions ensued to categorize these issues into distinct thematic "buckets," based on similarities in expression across team members or shared causal factors, thereby allowing for the identification of interconnected challenges that could potentially be addressed collectively.
Additionally, we spent some time deliberating on the impact of each identified "bucket" in relation to the coverage issues, as well as their frequency of occurrence within the workflow. This exercise aimed to provide insights into effective prioritization strategies, enabling us to allocate resources efficiently and address the most critical issues impacting coverage.
The outcome of this exercise manifested in the following structured framework akin to the following:
Click on image and zoom if you want to read the cards
After achieving consensus on the nature of the coverage problem, including its underlying causes and their respective impact on the ecosystem, we conducted a prioritization exercise. Each participant was allotted three votes to allocate to the top three issues deemed most urgent for immediate attention. The outcome of the voting process was as follows:
Mission Complexity: Missions which are long, confusing, complicated or difficult (63%) have a higher rate of abandons, rejections and suppressions.
Missions Depth: Missions are not gathering all the data points required by customers.
Coverage Speed: We can't achieve coverage quickly enough for our customers.
Achievements: The achievements functionality is not built correctly to incentivize mission completion.
Low Bee Density:When customers request results from stores which are in low-population areas we struggle to fill that demand quickly or thoroughly enough.
Notifications: Bees are not notified of critical mission information.
Ops & Bees are not communicating properly: The Ops team, the one that verify and approve/reject all missions done by Bees, are not properly communicating on why rejections are happening.
Following a comprehensive phase of discovery, discussion, and meticulous prioritization, the moment has arrived to initiate the development of solutions for the seven primary challenges we have meticulously identified and analyzed. Each solution entails its own dedicated research and procedural framework. Below, I will provide only the summaries of the outcomes for each solution, but you can click on each and see the full version of the use case.
Problem:
Missions which are long, confusing, complicated or difficult (63%) have a higher rate of abandons, rejections and suppressions.
Research Results:
Solution:
Success: