In-store analytics to aid data driven decisions
In-store analytics
As more retailers turn to use in-store analytics to help understand shoppers and improve their experience, there is a great deal of focus on marketing, store operations and the customer experience. But there is also a lot of use from such analytics for other departments around the store. Here we look at why buyers and planners need in-store analytics.
Understanding in-store analytics
In-store analytics is a series of clever systems that allow physical stores to gain the kind of information that was once limited to online shops. It uses a combination of data from CCTV cameras and Wi-Fi hotspots to paint a picture for retailers of what shoppers do when they enter the store, where they go and even what they look at. All of this is done in a legitimate way that doesn’t collect personal information or risk breaking data protection legislation.
This insight allows businesses to glimpse into the minds of their shoppers and therefore predict better what the customer wants. This can range from something as simple as what displays work in the front window to where the latest promotions should be located or what aisles are the most used within the store.
More than just customer experience
Currently, most stores use in-store analytics to look at metrics such as traffic, conversion, fixture engagement and the full path of the shopper within the store. The focus is very much on the customer experience and this makes perfect sense but there is also the potential for other areas of the business to make use of the system.
For example, data often isn’t shared with buyers and planners yet these are the people responsible for filling the shop, arranging windows and ensuring that the store runs properly. So why can this new data be of use to these departments?
Understand what works and what doesn’t
Every store will have experienced the situation where they invest in a new item enthusiastically and then it crashes – no-one buys it or it gets terrible reviews. This is just a fact of retail life but with the data provided by in-store analytics, it is possible to better understand why this occurs and possibly even make amendments in real time to ease the problem.
For instance, if the new item is located in a low traffic area of the store, it might not be selling because people simply don’t see it. By relocating it to a different, high traffic area of the shop, the results may be better. And if not, then this may indicate a problem with the item itself and the item can be removed from the store entirely, minimalizing losses.
In-store analytics allows more accurate range planning in merchandising PPT and makes the process of range planning retail style much more adaptable. Rather than simply leaving a poor selling item on the shelves, the store can use in-store analytics to make a decision in real time about a range and make adjustments to see what works and what doesn’t.
More data to study
For planners and buyers, their success is often reviewed against sales and gross margin targets. The use of in-store analytics means that these departments can get a better picture of what is actively happening within the store and see how they can make changes to their policies. It also helps the departments create better reports for upper management to demonstrate what is working and what changes need to be made to improve overall results.
Harmony with other departments
In-store analytics can also help for better integration between the different departments of the store to gain a better idea about what is happening and what changes need to be made. Examples include the better use of loyalty schemes – marketing departments create these and control what information is sent to customers. The planners and buyers bring in products that are then marketed via the loyalty schemes and the results collected from in-store analytics. Did customers respond to the marketing? Did they make a purchase?
As the online e-commerce world grew, many thought that the physical stores would eventually die out but this has proven not to be the case. Many customers still prefer to use a physical store to buy food shopping, big ticket items and even just to browse. The use of in-store analytics allows retailers to maximise all areas of their business, from better understanding where products should be located to what has the maximum effect in the store window.
By bringing all the departments within the business together and making decisions based on hard data, businesses can reduce the chance of failed product ranges or at least better understand why these happen. It allows a peep into the minds of their customers to help predict that appeals and what doesn’t. And this insight into what customers want can make all the difference going forward.
