The Top 10 ways AI can improve Retail Store Operations.
The Top 10 AI use cases that Retail Operators at every level told us would transform their daily work. AI for Retail Operations is...
To drive productivity, Store Ops team initiatives can’t just focus on cost, they need to drive revenue.
Store Ops teams will be pushed to focus on costs, but there are two parts of the productivity equation and sales is just as important, with just as much potential.
Any Store Ops initiative, especially now, needs CFO buy-in. But there’s a secret that everyone in Store Ops who has taken a business case to the CFO knows.
They don’t think Store Ops initiatives can drive sales.* (*beyond further store openings)
Typically this is just included as a “soft factor” in any proposal initiative.
But in a world where productivity growth is essential, Store Ops teams need to look for the low hanging fruit when it comes to driving sales.
In our work with retailers, we’ve found that there are three core areas where taking a more data-informed approach can unlock at least a 100 bps sales uplift for any business.
New promotions, product launches and pricing changes are a fundamental part of driving sales in store. Yet as is well known, executional compliance remains very low. Task completion is neither an accurate signal of actual completion, nor that an initiative has been implemented correctly. Retailers have to rely on highly manual checks to follow up.
Adding a data-informed approach helps by giving better contextual reminders throughout the sales lifecycle. Here’s an example of an approach we took with a national specialty retailer on a new product launch:
Alert stores which have not sold any SKUs in the new range and request realtime feedback. Through this, we discovered 62% of stores had issues in the first week, either through product not being received or third-party teams not setting up the displays, allowing immediate rectification.
After the initial launch, alerts were based on underperformance vs forecast. We were able to pinpoint launches that still weren’t operating as expected and capture learnings on how to fix them, getting compliance up to 90%+ within 3 weeks.
Post-launch, alerts continued to be triggered by underperformance vs comparable stores in this new product range, but only when the opportunity was significantly valuable.
In high-velocity retail stores, something is always going wrong. The question is: are you noticing and fixing it quickly enough?
Drop-offs are items we analyze that have rapidly dropped off from expected sales patterns. Ultimately these will always get noticed and fixed. However, the longer it takes to fix them the greater the degree of lost sales.
By using data-informed alerts, our data shows drop offs typically see a return to trend, (rather than continued underperformance), recovering 25% more sales over the next 4 week period. The Chart below shows visually how this occurs.
The final area a data-informed approach can drive sales is structural opportunities, by identifying areas in the store that are under-indexing vs peer stores. This is especially valuable when stores have the ability to experiment and e.g., a certain presentation change they’ve made may be detrimental but doesn’t get picked up for months.
Data-informed alerts can spot this breakaway by identifying the poor performers much faster, and then sharing contextual best practice from the leading stores (i.e. what training techniques a fashion retailer is using at the fitting room to help staff better attach accessory products).
We’ve typically found that structural opportunities in sales lead to 6.3% of sales uplift when suggested to stores (see Chart below).
The beauty of a more data-informed approach is it builds more scientific learnings into the organization. Crowdsourced knowledge is shared organically about which actions drive impact and which don’t.
So you’re CFO won’t just be excited by the immediate ROI of such an approach, but the ongoing benefits over time as well.