Sainsbury's Self-Checkout
Interventions
The challange
The business was increasing the number of self-checkouts across all stores while reducing manned checkouts to support a 'Cost-to-Serve' goal. However, increasing self-serve traffic also increased *interventions at self-checkout, which were negatively impacting CSAT, labour efficiency and loss (theft - intentional or other).
We needed to identify, remove or reduce the most impactful interventions to improve customer experience and support business strategy.
Definitions:
*Intervention: Any action a colleague has to take to enable a customer to complete their checkout.
My role(s)
Lead CX & Researcher
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Identifying interventions through field research
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Collaborate with data analytics teams & senior stakeholders
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Synthesise and playback findings to align teams
Service designer
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Using systems thinking to understand all components of interventions
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Defining the metrics and design leavers for solutions
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Vision mapping
Implementation and experiment consultation
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Working with various teams to bring experiments to life
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Keeping the customer and business needs front and centre
1. Defining the project and stakeholder alignment
Collaborating with the key work sponsors (Transformation & Change) we devised a work program that spanned across functions to ensure the work was informed and supported by Tech, Labour, Supplier, CX/UX and policy team via a working group.
The end goal was to create various solution concepts across the key journey moments that we could distribute with various teams for testing and implementation.
2. Understanding the current performance and opportunity
Working with in-house data analytics & insights team (ADA) I began to gather information to allow us to define the extent of the problem. We chose a 12 week window across all stores to bring together our key insights (below)
Below: To drive high-level communication of the works intent across the business, I created a one-pager which included the problem, action areas, strategic alignment and a modelled positive outcome. This was at the forefront of all communications moving forward. This also supported finding our key metrics to measure and test by.
3. Identifying ALL interventions.
Before this work was started, there was no collective visibility of how many interventions we had or why they were happening. I worked with store managers and colleagues to start to map the interventions across the journey phases: Scanning, Packing, Paying, Post-Payment and SmartShop only.
The problem with data.
Unfortunately, the manner in which the data was set-up in Self-Checkouts (SCO) meant we didn't know how often a specific intervention was happening, how long it took to resolve, or if colleagues were choosing the correct 'reason codes' for the intervention. As the data was never previously measured at a granular level, we didn't gain the depth of insight we hoped.
Our workaround: We chose to utilise card sorting, customer and colleague impact/frequency surveys as well as SME weighting in an effort to get an insight into the frequency and impact of interventions.
Below: 1 of 5 working maps and source of truths - our intervention Rosetta Stone.
4. The big reveal
Presenting back to senior stakeholders our identification and categorisation of interventions was a truly powerful moment. We had never had the numbers or known the extent of interventions, but as we continued to introduce more self-checkouts to stores, we also increased the problem.
Key Takeaway: Bringing clarity and categorisation to interventions supported more purposeful actions and conversations. There was a keen movement to focus on the 'Process Driven' interventions with many being down to tunnel maintenance or self-checkout UX.
Below: To help support future solution workshops, we modelled the data (where we could) to illustrate the impact of changing the parameters of the intervention.
5. Workshop: Ideating with key stakeholders
We had already mapped the interventions across a now, near and future framework, as well as highlighting any existing or impending enablers (Headsets in stores, facial recognition, SmartShop rescan algorithm change), so we were now ready to ideate on interventions we thought we could impact in the now and near term.
We had decided on an all-day in person workshop (eventually spilling into 2 rooms due to lack of wall space!) which brought together the key stakeholders (Formats, Policy, Tech, Labour, CX/UX/Product, Store and Shrink).
Below: Workshop inputs included persona cards to represent the teams and their principles and goals. These were distributed to mixed teams to increase empathy and understanding of other stakeholders view point.
Below: Workshop outputs included +40 solutions to our HMW's. We also created +15 Concept cards which identified owners and collaborators and kick started experiements and which were then roadmapped into TheyDo. (Customer Journey Management software).
6. Future vision and concept experimentation.
I worked with Product and CX research to create a Future Vision of the self-checkouts map. This was reviewed and iterated with all teams and provided a tool to communicate and unite us towards a common goal.
Below: Part of our Future Vision for Self Checkouts map based on workshop outputs, 'Hassel Inclusion' accessibility research as well as tech and CX trends in self-checkouts.
Below: From concept to reality. With sponsorship from various teams, design and testing is underway across multiple stores and touch points to test and learn on our concept solutions for self-checkouts.
Below: From concept to reality. With sponsorship from various teams, design and testing is underway across multiple stores and touch points to test and learn on our concept solutions for self-checkouts.
Outcomes
8
Interventions removed or minimised
22
Interventions under exploration or roadmapped for testing
+10
Interventions to be removed or minimised by the new OS
8
Categories created to support better future data monitoring
+40
Concepts collaboratively created in workshops
1st
My favourite end-to-end project at Sainsbury's.
6. Final summary.
This was a great project to lead, it had everything from UX heuristic reviews of digital flows, policy exploration, ethnographic research and the right mix of in store colleagues and internal function owners.
The biggest win for me personally was the level of collaboration across teams and the empathy and understanding people showed to others. Doing meaningful work that actually drove change was a really rewarding experience.
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