Real-time retail: using data in motion to shape the in-store experience.

Three Business Blog Team
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On: 16 May 2019
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Real-Time Retail: Using Data in Motion to Shape the In-Store Experience

In this week’s blog Stuart Pearson, Product Director of data innovation company CK Delta outlines the value for Irish retailers in using data sensors to gather real-time analytics about the shop floor.  

Knowing your customers is essential to the longevity of any business, and this is especially true for retailers. While online shops might revel in rich transaction history data and “save for later” wish lists that even hint at what the shopper might buy next time, high street retailers with real longevity are those who understand the shoppers coming through the door and know instinctively how factors like seasonality and fashion should shape the retail products and environment.

There is a real opportunity for offline retailers to supplement instinct with data, and there are a number of opportunities to do so by leveraging real-time data analytics about the shop floor, which can deliver surprising insights even for the most experienced shopkeepers.

Who goes there?

In our work with major European department stores, we’ve seen real interest in the basic data that fuels business decisions: including footfall counts both in and around shop locations. The data available includes information on how many shoppers pass by the door during certain times of the day, and what proportion of those passers-by come inside.

It might seem that an individual standing in the doorway with a clipboard could do the same job. Sensor data, however, provide a much more comprehensive and scientific count that doesn’t just tally overall traffic, but that also determines which parts of the store shoppers visit and how long they dwell.

The technology used here is the shop’s Wi-Fi Access Points, which passively scan and detect the mobile phones carried by members of the public. It’s important to note that no personal information is collected, and the person doesn’t need to log onto the store’s Wi-Fi in order to be detected.

Customising shopfront displays.

When gathered and analysed, this data presents insights like heat maps, allowing the retailer to detect which parts of the shop are receiving little or no traffic.

For example, are evening rush-hour shoppers making a beeline for certain product lines that could be promoted in a well-timed doorway display designed to bring them in store? How do these footfall patterns differ from morning rush-hour traffic, or lunch-hour shoppers? One of our department store customers is already using the insights provided from its analysis to customise the “daily basket,” a doorway display with items designed to draw shoppers in.

In-store layouts redesigned for better engagement.

The real-time data that we collect often gives our high street customers their first comprehensive look at data that indicates the health of their business, like the number and type of people coming into the store, their dwell times, how long they queue, and (when linked in with EPOS data) the proportion of shoppers who purchase something.

This information provides an important baseline for retailers who want to take a data-driven approach to improving performance across their stores. That might include adjustments to in-store layout, for example: prestige and luxury brand displays could be positioned at the back of the store to encourage the customer to walk through. Alternatively, these could be kept towards the front, to deliver instant impact and to get customers thinking, “this store has what I want.”

Importantly, if the retailer decides to try each strategy, the resulting data provides hard evidence of success: and the same goes for adjusting major features such as window displays.

While A/B testing is better known in the advertising and email marketing world, rich real-time data available from real-world sensor data allows management to apply the same, data-based rigour to store management, which can be especially valuable when looking for successful models to pilot before roll-out across networks of branch locations.

Positioning and scheduling of sales assistants.

A killer feature of off-line retail continues to be the fact that shoppers can see and touch what they’re planning to buy, and ideally speak to someone knowledgeable in-store who can offer advice. Hotspots of shop-floor traffic can allow store management to position a greater number of trained staff in the busiest spots, potentially to offer added value, such as a product demonstrations or free samples.

Data on peak traffic times means it’s also easier to schedule staff rotas, to keep queue times down and ensure excellent customer service when it’s needed most.

This is the kind of insight that’s particularly useful for fast-growing retail operations. As operations managers become responsible for a larger number of sites and staff, sensor data can provide invaluable support for the decision process, allowing even a rapidly expanding store network to be managed with the insight and knowledge that’s usually only possible from being physically present in a location, day after day.

Anonymised demographics for next-level customer engagement.

Real-time retail data continues to develop as a technology discipline, and it’s increasingly feasible to gain rich, anonymised demographic insight about the shoppers who come in store, even if the customers don’t sign on to the in-store Wi-Fi or check in with Facebook – two traditional ways that shops gleaned information such as age or gender.

Using microcell technology, retailers can draw down aggregate information about shoppers who visit, including gender, age range, affluence, and even catchment area. This last metric lets retailers know which parts of the city or region shoppers are coming from, or even whether shoppers are coming from other countries: an important insight for retailers into the impact and measurable financial value of tourism.

The next era of real-time retail data may be even more detailed, if shoppers can be incentivised to trade additional personal information in exchange for targeted offers and discounts. But from what we’re seeing in the market now, the real revolution is this initial phase of retailers getting their first hard data about footfall, and putting that to work in their forward strategies. Retailers who adopt a culture of continuous learning and refinement, based on hard data, are those who will be best equipped to compete in the years to come.


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