Imagine you have a retail brand that sells both online and in-store. As you know the number of potential customers entering your site on your website where you sell online, how many people enter which category, which products they add to their cart and buy or just leave them in the cart; you can see which category and how long your potential customer spends on the product. Accordingly, you can make campaigns on the products on your website, make changes in the website design, and edit the product stocks according to these metrics. But can you do this in your physical store? Let’s answer this question now.
One of the most frequently asked questions at budget meetings is “Why didn’t target goal?” may be a question. Many answers can be given to this. For example, if a store manager is asked this question, he might say “The customer didn’t come”. But did the customer come? We can find the answer to this question with Udentify’s People Counting technology. With the Udentify People Counting technology, we saw enough customers. What about “Why didn’t he hit the target?” Let’s ask the question to the department staff, which interacts more with the products and customers this time. He can also answer this question, “not enough people came to my department”. Right here, using Udentify In-Store Analytics technology, we can see that customers come to the aisle, how long they stay in the aisle, and their transitions between the departments. But how does Udentify’s In-Store Analytics technology give us this information? Let’s say your retail store sells white goods. Let’s say this store has categories, subcategories, and sub-subcategories:
For example, we learned with Udentify People Counting technology that 100 people enter your store one day. With the sales integration, we saw that products were sold to 30 people out of these 100. The conversion rate of this store that day was determined as 30%. But why couldn’t the product be sold to 70 people whose products could not be sold? Which departments did these 70 people visit? What aisles did it go through? How long was he interested in which aisle? Udentify all these questions to you local it presents with Analyze.
Let’s continue our examples according to the representative store aisle lineups above. Local without analysis, we cannot see how many people come to the No-Frost section during the day, but local thanks to analysis, we can access information like this. For example, on the day of the analysis 60 people came to the deep freeze department 1. the average stopping time of these people in this aisle is 3 minutes and 23 seconds. We can see it with analysis. Although these departments are all in the same aisle, the average waiting time of customers in departments 2 and 3 is 1 minute and 3 seconds. In the 4th and 5th aisles, where the refrigerators are located, the average time is 5 minutes and 26 seconds. From these average times, we can understand that the customers visiting the store use the area where the 2nd and 3rd departments are located as a transition area, there is little interest in the products here. In this section, the department layout can be changed. -We share such recommendations with our store owners in the Udentify in-store analytics reports. – Later, we see that there are 2 minutes between the average times of the 1st department and the 5th department in the same aisle. This may be due to insufficient staffing. Did the relevant personnel in that aisle take care of the potential customers coming in that aisle during working hours? The answer to this is Udentify staff we can achieve this with optimization technology. You can find solutions such as changing working hours, shift arrangements, and completing staff shortages by seeing how long your staff is working in which department from the regular staff reports and Udentify panel.
Many data such as in-store department densities, sales density per m2, and transitions between departments. You can reach it with analysis. Later, we can reach the Local Conversion Rate by using the formula of sales made in the department / customer from the department.
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