CHALLENGE
Tesco’s new purpose is
“Serving our customers, communities and planet a little better every day”.
This gave birth to Colleague Shop (CS). An initiative that lets Tesco employees take products that are expiring for free. However, a long-winded process had to be followed to take the products for free. As a result, products never made it to CS and would end up still going to waste.
TEAM
I worked remotely as a Senior User Experience (UX) Designer, alongside the product, engineering, business, transformation and eventually the analytics team.
USER
Our primary users were store managers and store colleagues using the Food Waste (FW) app to add products into CS.
APPROACH
The project kicked off with a meeting with the entire FW team (excluding analytics at this stage). We started with understanding the current domain from various stakeholders and aimed to define problem statements and goals we wanted to achieve. The biggest challenge we had at the start was that the product, business and transformation teams had a very vague understanding of the problem space.
My expected contribution was to help map the as-is journey, design the new app and process through insights from on ground research with colleagues. While the qualitative research was taking place, I convinced the FW team to include the analytics team as well.
While the qualitative research helped understood the painpoints faced by colleagues doing the process in stores, the quantitative research allowed the team to understood colleague behaviour using the app across the Tesco estate over a period of time. This equipped the FW team to define measurable problem statements and goals.
Keeping all the findings and insights in mind, I went to the drawing board and presented two concepts to the FW team. While creating the concepts I ensured they both worked for all store formats. The screen below showcases the two concepts.
At this point, the most important topic of discussion was with the engineering team. We needed to evaluate if the Stock API team would work with us. If yes, then would they be able to differentiate “Best before” and “Use by” products when an item was scanned. This would lead to a significant enhancement as colleagues would not need prior knowledge about products. The new intelligent app would be making those decisions.
After much deliberation and discussions, we got approval to work with the Stock API team and the FW team decided to go with concept 2.
Before starting to go into designs, captured various scenarios and product types. During the wireframe design phase, our analytics revealed that 80% of the products being scanned had only 1-3 items. To enhance efficiency, I incorporated plus and minus buttons for adjusting quantities. This feature was well-received by colleagues, who noted that it significantly expedited the process compared to manually entering numbers. For larger quantities, users still had the option to tap the field and input numbers directly.
Recognizing Tesco's system of color-coded bags for different product categories, we integrated these colors into the app's interface. This enabled colleagues to instantly match products with the correct bags. We ensured the color contrasts met accessibility standards and included text labels for each bag color to accommodate colleagues with color vision impairments. This thoughtful design approach not only streamlined the process but also ensured inclusivity and ease of use for all team members.
Lastly, created a prototype for research. Using research findings iterated designs and handed the wireframes/prototype to the engineering team.
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"This will make our life easier and save us a lot of time"
"A lot easier as not everybody understands Best before, use by, carry forward"
Supported the build by being part of the sprint stand ups, refinement sessions and demos.
1. Helped business create training manual.
2. 200 store trial spread across the Tesco estate for 2 months.
3. The roadmap was to migrate 200 additional stores every week till the full Tesco estate is on the new Food Waste app.
IMPACT AND LEARNINGS
Reduction in cost estimated to be £930,000 per annum.
Faster process.
Reduced cognitive load for colleagues and as a result reduced errors.
Taking advantage of existing API's to make decisions and reduce cognitive load of users.
Conduct retrospectives at the end of projects to improve ways of working.
In the future I would include analytics to be part of the core team.
View more work
Nabihaiahmed@gmail.com
