Gut instinct about what your shopper really wants during her in-store customer journey should no longer be driving your business decisions – instead, data science should be the key driver of retail success in the coming decades.
Yes, all the technology is here, and based on the deep-level accuracy that retail intelligence tools can provide, retailers are on the cusp of delighting customers with incredible in-store experiences and customer service. The trick is converting data into that all-important retail stardust: insight. Once you have it, you’ll know what will drive conversions and what won’t.
For instance, forward-thinking retailers not only analyze shopper traffic trends and additional data at a macro-level in the head office, but also use it to equip store managers, allowing them to plan sales activity, schedule workloads, and assign tasks that are based on anticipated visits.
Insights into customer shopping patterns around the store can inform the layout of merchandise displays, marketing activity and inventory flow. Cross-channel customer behavior data can shape store culture and provide a baseline for implementing sales performance competitions across the company.
Fishing for insight in a sea of data
However, the process is complicated. The challenge is to navigate the ocean of data flowing from multiple customer touchpoints, and shape operations, based on the actionable insights that arise through the use of in-store analytics.
Currently, the majority of retailers are best described as ‘Data Rich, Insight Poor’ (DRIP), because they’re not effectively converting data into insight. They have data from in-store traffic tracking, loyalty schemes, social media mentions and reviews, operations data such as seasonal merchandising trends and fulfilment history, and, of course, transactional data across a host of digital and physical sales channels – there’s even ‘textual data’, in the form of customer feedback and word-of-mouth reviews.
The difficulty lies in knowing where to start dredging for insight – but failing to do so is not an option, as shopper expectations are rising like a spring tide.
Leveraging the connected in-store customer journey
According to a recent Deloitte study, digital channels will influence almost 48% of in-store sales in 2017. Customers log onto laptops and smartphones to research the best deals for the wine, trainers or sunglasses on their shopping list. Product details and reviews are devoured, and peer recommendations are shared instantly on social media.
Naturally, these connected consumers would appreciate similar gratification from the physical stores they stroll into. A survey earlier this year from Daymon Worldwide found that 40% of shoppers welcome the idea of personalized messages in store, and 60% said they’re more likely to buy if they’ve seen a product demonstration on the shop floor. There’s a growing appetite for human engagement, quality information, real-time deals and personalization in the physical store – and, of course, for 100% availability.
The advantages of analytics maturity
Retailers aiming to improve customer engagement have never been more reliant on store associates who are on hand to make the crucial connections that turn an experience into a sale. With knowledge of customer behavior – including traffic flow, product preferences, shopping history and buying habits – store associates can offer a personalized service and build stronger, longer-term relationships with high-value customers.
Best of all, new experiential ideas aimed at impressing specific customer groups can be tried and tested in store, with clear scientific results informing the long-term strategy of the brand. Very few retailers are there yet, but by achieving analytics maturity they can begin to align business decisions with customers’ actual requirements – making gut instinct a thing of the past.
To find out more about the importance of analytics maturity, download our latest report on understanding in-store customer journey, developed in association with EKN.
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