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 be the simple solution to a
big business problem
This article is taken from our January 2022 stores deep-dive, Prioritize your Store Leaders!
No retailer is debating the ever-increasing demand from consumers for personalized retail experiences.
And customer needs drive action, so it is no surprise that 79% of retailers are now investing in customer personalization. For most of them this involves using customer data and analytics to provide specific and relevant information and recommendations to each customer.
Such desire for personalization is rooted in human nature – we all are unique and want to be treated as individuals. Such treatment makes us feel cared about and drives loyalty. 80% of companies that adopt personalization methods see an uplift in performance.
Yet customers are not the only people that retailers interact with every day.
While retail is the highest performing sector for customer engagement, it is the worst-performing sector for employee engagement.
Personalization, or rather a lack of it, is a core cause of this problem. Traditional store management takes a one-size-fits-all approach. Retailers send a uniform hosepipe of data, reports, and recommended tasks to the field. It is little wonder that stores feel like mere cogs in a machine.
We asked our Agile Stores Lab to research this and found three core issues with how information and recommendations are sent to stores that personalization and prioritization would rectify.
Every week Quorso analyzes trillions of retail management information data points to find millions of specific areas where individual stores can drive improvement. Stores then decide whether to act on these opportunities (we call them Missions) and, if they do, we track the impact they have.
The following analysis is based on 22,583 suggested Missions + actions taken + improvement delivered, as well as surveys of our retailer customers to understand the amount of information they are sending stores and what they are asking them to do.
With stores having to shift to become multi-functional, store teams are responsible for a huge array of activities. But piling up things to do on a large list isn’t going to mean they get done well – or even at all.
Primacy – the impact of being the first thing people see – is a well-known concept in marketing. Whole industries have been formed around things like Google search, where the 1st ranked page is four times more likely to be clicked on than the 5th.
Few retail operators would think about a concept like primacy when sending activities to stores. Our Quorso data shows, however, that throwing too many activities at teams is causing a drop in actionability, shown in Chart 1.
For example, following the chart, we can see that the seventh thing you ask someone to do or look at is 33% less likely than the first thing to be actioned, and this worsens with each activity added. In other words: if you’re piling on things for your store teams to do, they’re not all going to get done. Give them manageable bursts of activities, however, and you’ll see them being completed.
The number of things we give to an employee or their priority order has a huge impact on what they achieve. That’s why personalizing and prioritizing each store colleague’s activity list is of such critical importance.
Overly aggregated data has never been useful in retail. For example, telling a store its sales are down isn’t as helpful as pointing out its dog food sales specifically that are down. Even better is to say which particular SKUs are underperforming.
In a well-meaning effort to provide this valuable granularity, central teams have sent all information possible: sales reports, category reports, product reports.
Our research showed that at the level store teams deem relevant (e.g. a subcategory for sales like dog food), when running a detailed exception analysis and then confirming actionability with store teams, 80% of actionable exceptions were surfaced at less than four stores in multithousand store chains.
The broader reality of this is that 96% of information sent is irrelevant, so finding ways to filter and personalize it before sending it to the stores has huge potential value.
As an industry, retail is very focused on Last Year comparables. As an overall measure this is important, companies are assessed based on their annual growth in sales and profitability. The challenge, however, is that this overall view of performance does not translate into actionable improvement at a more granular level. The last couple of years have really highlighted this issue. Changing consumption trends, stock issues, regulation can all have dramatic impacts from year to year, that make traditional ways of measuring pretty irrelevant. An alternative approach we have honed at Quorso is to use statistics to look at how certain items perform in a given period vs their relevant control group. This approach is easy for any retailer with over fifty stores (or comparable departments) to analyze. What we have seen is that the greatest predictor of actionable improvement is not LY comp performance but rather, as Chart 3 shows, the degree to which a store is an outlier vs its relevant control group. In other words, the greatest opportunities for improvement come from detecting where you’re falling short week-to-week, not year-to-year. To be able to do this, central teams need to be far more specific and personalized in their methodology for exception reporting to ensure they give ongoing insights to stores that actually drive impact.
Just like personalizing experiences for retail customers drives greater loyalty and business results, the same is true of personalization for store employees.
Personalized and prioritized activities, data and insights for stores all result in greater engagement, faster onboarding, lower churn, increased sales and boosted labor productivity.
Retailers have always been so focused on customer data, and have invested heavily in using it to enhance the customer experience. But the biggest opportunity – even demand – is now to apply this personalized in-store experience to the other critical people in your business – your employees.
