Your retail business has accurate statistics about sales. You can say exactly what has been sold over a given period, the price it has been sold for, the discounts that might have applied, and much more. But how much do you know about the people who walked into the store, browsed through the stock, and left again without buying anything? How many were there? Where did they enter the shop? Where did they exit? When did they visit? Which sections did they visit?
If you had answers to these questions, you would know a lot more about what your prospective customers were really interested in, and how many of them actually became buyers. Fortunately, you don’t have to employ a team of trackers who follow everyone who enters the store and make notes. There is a much more refined solution: an AI/Machine Learning system that counts store traffic. The most sophisticated ones available can give you exactly where your visitors entered, which departments they visited, and at what time of the day.
Conversion ratio. Once you have accurate traffic data for every segment of the store, it’s easy to calculate what percentage of those visitors were turned into buyers. First, calculate the overall conversion ratio by dividing the total number of sales transactions by the total number of visitors. Then also do it for every individual segment. That will immediately show which sections are performing well and which ones get a lot of browsers who are not being converted to buyers. These are the ones that need attention.
Measure the success of advertising and promotions. If you are running an ad campaign in the local newspaper, for example, a traffic counting system will immediately tell you how much foot traffic increased immediately after the start of the campaign. If foot traffic increased by 30 percent but the number of sales transactions only increased by 15 percent, you will have to investigate why these prospective buyers at the last moment decided not to buy. Did you run out of stock? Did the product not live up to buyer expectations?
Refine your store layout. Once you have accurate data about which sections of the store receive the most visitors and which ones have the highest conversion rates, it’s easy to tweak the store layout to help maximize sales per square foot of floor space. You might find that a segment hidden at the back of the store has the highest conversion rate, but it gets very few visitors because of its location. Once you become aware of that, it’s easy to rectify.
Do something to boost traffic during quiet times. With a traffic counting system, you will know exactly when the store gets the lowest (or highest) number of visitors. Let’s say the quietest periods are Monday mornings and during the last week of the month. Why not run a special promotion during these periods?
Prevent shoplifting. Once you know which sections of the store get the most visitors during specific times, it’s easy to make sure there are enough staff members on duty to watch out for shoplifters. These people often choose the busiest periods to strike, when sales staff are helping customers.
Measure the effect of external factors. With accurate data, you will know exactly how external events such as, for example, the weather, affect sales. Let’s say there was a week long rainstorm. Your data shows a drop in foot traffic in some sections, but a sharp increase in others. Knowing that will help you to plan staff allocation, stock levels, and perhaps special offers next time similar weather is predicted.
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