In the competitive retail market, in-store analytics helps retailers to better monitor and understand their customers while providing valuable information on the actual point-of-sale performance.
This type of analytics has become an indispensable part of the operational strategy of brick & mortar retail stores. Its benefits can also be reflected in other areas, such as marketing, merchandising, and fraud prevention.
How does it work?
In-store analytics is the process of finding meaningful information from customer behavioral data. Retailers use spatial data and footfall analytics, to see how many consumers visited the store, how they moved through the store, and what key areas they visited. This process can even provide basic demographic data, such as gender, age group, and relative wealth index status, connecting the dots between the consumer, the retail store, and the shopper’s decisions.
In order to compete with online stores, retailers need as much information as possible about their customers, and we’re not just talking about their preferences, but their behavior patterns at different times of the day, which can range from hours to years, and thereby understand and reduce the phenomenon of showrooming, i.e. when someone enters the store to see something they want to buy elsewhere or by another means.
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Who is a typical shopper or consumer, who left without buying anything and why, what was their purchase path?
In-store analytics answers these questions by being able to show retailers how customers behave, allowing them to improve the shopping experience and optimize store design, turning shoppers into brand-loyal customers.
What’s missing from inventory?
Retailers know what customers buy in their store. But they also need to know what customers don’t buy and why; these analytics provide detailed information about the effectiveness of store advertising, employee actions, and other factors that can be used to influence buying decisions.
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Moving from data to information and into action.
Intra-store analytics measure in real-time to improve decision making, even in on-the-go situations. The technology behind these analytics can turn complex data sets into actionable choices.
For merchandise loss prevention
These analytics identify shoplifting behaviors and help determine which areas of the store are especially prone to shoplifting.
In conclusion, from merchandising and marketing to loss prevention and operations, the benefits of in-store analytics extend across several vertices. Marketing is often the biggest beneficiary in these cases while merchandising and inventory management also receive a tangible boost.
At PREDIK Data-Driven, we help our clients generate in-store analytics to understand customer behavior in detail, improve marketing ROI, optimize costs and operations, prevent merchandise theft and increase customer loyalty.