28 Jan Point of sale mindset: How analytics can increase in-store sales
By Nagendra Sastry
Head of Analytics, IQR Consulting
Your company may offer a customer loyalty program or perhaps even a store credit card, both of which are important tools for driving sales. Of course, they also are great avenues for collecting customer-specific data to help your marketers target audiences more precisely. These offers may encourage a customer to buy more of a particular item or drive them toward an entirely new item that is relevant to his or her lifestyle â€” and that helps increase sales in the process.
Retailers are constantly conducting experiments to determine which offers lead to the most profitable sales most quickly, such as bundling offers or emphasizing a deadline to drive urgency. Needless to say, with the proliferation of online purchases and social media, the ability to gather specific data about customer behaviors, purchases and preferences is only growing.
But much of the data that is collected, analyzed and acted upon is based on customer behaviors that have already happened. What if you could use near real-time analytics to entice your customers currently in the store? The use of in-store data as a way to increase sales is one of the most cutting-edge fields in analytics.
Using in-store analytics
In-store analytics are primarily used to understand the consumer’s behavior within the retail store: The order in which they buy products, how much time they spend in the store, their shopping path and other behaviors. This approach has also been used effectively in the online space to design websites and determine ad placement.
Retailers may use in-store video to understand the customer’s shopping path, such as the order and process of how consumers fill their baskets during a shopping trip. This provides important intelligence about product placement and store layout that optimizes convenience for the customer and drives sales for the retailer. In-store cameras have also been used to determine how to optimize staff allocation and to understand consumer behavior in the check-out lanes, including which items are bought near the counter or how often a customer changes lanes if lines are long.
Some European retailers are using the signals emitted by customers’ smart phones to better understand shopping behavior and send out real-time offers. Smart phones send out signals at very frequent intervals to search for any nearby Wi-Fi signal. Once in the store, the retailer can continuously track the mobile device and utilize that information to optimize the store layout and make customer-friendly offers. If a customer has accessed a retailer’s app on his or her phone, or signed up for an offer using a mobile device, the retailer can recognize that customer by his or her phone once it has entered the store, allowing the retailer to send offers better-suited to the customer’s lifestyle.
By tracking customers via a mobile device, even more information can be collected about the customer’s in-store behavior. After several visits, this data can be compared with the demographic information and other details already known by the retailer via the customer’s loyalty card. Upon future visits to the store when the retailer recognizes that a particular customer has arrived, the analysis of this information can be used to send him or her very specific, targeted offers.
For example, a retailer may know that Customer A frequently buys fruits and vegetables at the same time, but rarely purchases dairy items. On the customer’s next visit, the retailer may send him or her a mobile coupon â€” good for that day only â€” for a dairy product when it recognizes that the customer is near the dairy section. This encourages Customer A to stop in a new area of the store and communicates a sense of urgency about taking advantage of the offer. Ultimately, of course, this act is meant to expose the customer to new items and increase store loyalty.
What’s in store: Integrating data
The act of using in-store data analytics to drive sales will be most successful when data from many systems can be shared, integrated and compared. Just as our personal phones, tablets and desktop computers can “talk” to each other, we are not far from in-store cameras, loyalty programs and visit-specific data collected from a customer’s mobile device having the ability to share many different types of information with each system. Of course, those of us in the analytics industry cannot ignore that the increase in responsible data collection will likely give rise to privacy concerns among consumers, and so retailers will have to find a balance between using technology to better serve consumers and ensuring that privacy requests are honored.
We are just at the beginning of maximizing in-store analytics to drive sales and enhance customer loyalty. It is an exciting time that will give companies a more detailed picture of their customer and allow them to react more timely and accurately to behaviors, trends and lifestyles.