How big data is being used to boost profits in brick-and-mortar retail

 

Mick Moore, Group Managing Director, Scalene

---

Online retailers have long understood the benefits of big data and AI. Whether to improve customer experience, optimise product selection or target marketing initiatives, there are few areas in which online retailers are not enhancing their operations with well-leveraged data.

 

Brick and mortar retailers, however, have been slower to capitalise on the many advantages developments in the fields of data science and AI can yield.

 

One business helping to change this, however, is Scalene Group, which services a global clientele of leading retailers from its offices in Australia and the UK.

 

Scalene Group’s Managing Director Mick Moore says brick-and-mortar retailers typically have a wealth of data at their disposal, but that the challenge is not in gathering or storing the data but in applying it to day-to-day decision making.

 

One of the most effective ways in which brick-and-mortar retailers can translate data-led decision making into sales growth, he says, is through localising the offer at each store in a network.

 

He points out that traditional retail models often rely on centralised decision making scaled out across a national store network. And while this one-size-fits-all approach creates efficiencies within the business, it often fails to consider local differences in customer preferences.

 

Providing a localised offer at each store allows retailers to offer customers more of what they want and tailor a shopping experience geared toward the preferences of customers at a particular location, he adds.

 

 “In our experience, stores geared to a local market see higher growth in customer numbers and these customers become more loyal and visit more frequently. In addition, when they shop, they tend to buy more and often trade up to higher-value items. The combination of all these benefits can ladder up to sales increases of 5 to 8 per cent, per store.”

 

As well as understanding how customer preferences and shopping patterns differ between geographical areas, store data analysis also allows retailers to understand how these preferences and patterns change over time. Moore says understanding these temporal changes can make stores more profitable and also more resilient.

 

“Throughout the course of the global pandemic, we’ve seen shifting customer preferences and shopping patterns impact retailers. What we’ve learned from this is that those who can most quickly and effectively respond to these changes can not only better survive, but actually drive sales and margin growth throughout periods of change,” he says.

 

 “What has also become apparent over the past two years is that what worked before may not work in the future. But by leveraging the rich data retailers can gather from their store networks, they can continually learn and deploy better ways to engage customers and convert sales. 

 

Moore says that throughout the pandemic Scalene has been working with a leading UK food retailer to optimise category space allocations in each of its more than 1,000 stores to maximise sales and margins and minimise the cost of food waste.

 

“To achieve this, we developed a tailored space optimisation model configured to the needs and strategic goals of the retailer. Throughout the pandemic, we have been refreshing this model in response to changing customer purchase patterns in the space allocation for each store. As customers have been cooking more at home, our approach has supported rapid, store-specific increases in space for areas such as produce, meat and baking, while simultaneously pulling back on space for certain convenience categories.”

 

While Moore is optimistic about the future of brick-and-mortar retail, he does caution retailers who might be looking to cut expenditure on their service offer to reduce operational costs.

 

It is a common pitfall for retailers faced with growth and cost pressures to heavily cut investment in in-store labour, which is one of the few material variable costs they can influence. The problem with this, he adds, is that store service is a compelling reason why customers continue to shop in Bricks & Mortar stores. The focus should rather be on fine-tuning service models and labour planning to provide the right service team at the right times in each store, he says.

 

“These retailers can also look to optimise their service models so that, rather than them being a national service model, they can be more nuanced to the needs of local customer environments or local shopping patterns. That could mean moving investment in store labour to different hours of the day or different days of the week, based on the way customers shop at a particular store location as opposed to how they shop on average across the course of a week at a national level. This can all enable a more efficient use of store labour and save money, but still provide or even enhance the service model for certain stores.”

---

For more on how brick-and-mortar retailers are boosting store profits with big data and AI, click here.