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In-store Analytics

Everything about In-store Analytics

     

What is in-store analytics?

In-store analytics is basically the process of analyzing and pulling meaningful insights from customers’ behavioral data. The analysis focuses on various customer behaviors, which can be measured when the customer is visiting the store. In-store analytics is therefore focused on optimizing store performance and is widely used by store owners to enhance both customer experience and drive sales.

What are the benefits of in-store analytics?

In-store analytics gives retailers a real-time look at what customers do when they visit a store. This allows for a deeper understanding of the customer, helping retail managers make informed business decisions. With this in mind, there are a number of benefits to applying in-store analytics. Some of the most common advantages include:

Understanding customer needs
The most obvious benefit to applying in-store analytics is getting a better understanding of customer needs. Naturally, customer needs can be met much better once there is an understanding of in-store footfall patterns and the aspects driving in-store customer behavior. Even small improvements in retail conversions make a big monetary difference. Insights gathered from customer data can reveal tips for the following:

  • Better product replacement
  • Improving store design to guide footfall to desired areas or products
  • Improving the street capture rate from people who pass the store

 

Marketing attribution
Once a baseline for footfall patterns has been established, retailers can move on to marketing attribution. With in-store analytics software, retailers can measure the marketing attribution with the following KPI’s:

  • Number of visits/time of day
  • Number of new vs. known visitors
  • Different advertisement methods and their effect on visits
  • Different ad placements or locations and their effect on visits
  • Advertising effect on hit rate and average purchases

 

Personalized in-store experiences
Providing personalized in-store experiences requires combining existing customer data with the in-store location data to create unique omnichannel experiences. In-store analytics can be used to identify the shopper as they enter the store. This event data enables automated workflows to trigger and the in-store experience to be personalized. With this data, retailers can for example:

  • Send a personalized coupon on entrance
  • Suggest retrieving an abandoned shopping cart from e-commerce sessions
  • Personalize digital signage displays with last browsed products
  • Personalize in-store personnel service gestures

Why in-store analytics software is important

The need for instore analytics has become even more apparent considering the growing complexity of the industry. In addition to this, the amount of available information about any given product or company, and the many alternatives for consumers, means that retail businesses face increased competition and pressure at all times.

Retail is about making sure that the right products are available to the right people, at the right time, in their preferred shopping environment. Therefore, the more data that retailers collect about their customers, products, and shopping behavior, the better they can optimize for a shopping experience that customers will love.

Regarding product development, by understanding why certain products sell best, using in-store analytics software, it is easier to drill down into what about them is selling best and if that trend is likely to continue into the future.

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