Is Your Front-End Merchandising Strategy Working?
In-queue merchandising (merchandise for sale in your checkout line) is proven to increase impulse sales. But how do you maximize your efforts? And how can you prove that the combination of your queuing approach and your merchandising mix is really working?
Merchandising analytics may have the answers.
In-queue merchandising analytics takes key queuing-related data, such as how long customers spend waiting in your checkout line, and combines it with product sales in the queue to give you a clear view into your entire front-end approach.
Here we present three key ways to use merchandising analytics to understand and optimize your front-end merchandising strategy.
1. Link SKU sales data to queue data
When sales data from SKUs merchandised in the queue is tied to queue data such as impressions, dwell times, and attrition rates, powerful insights emerge. Time of day, day of week, and other time intervals can be monitored and analyzed in relation to sales and wait time data to maximize sales and optimize the merchandise mix.
2. Test wait time and the effect on in-queue sales
Even in the busiest of times it may not be ideal to have every cash register open to keep the line moving as quickly as possible. Having customers linger in a queue longer will give them more time to pick up an impulse product. That said, finding the right balance is key. Sales data tied to queue data can help determine optimal wait times for maximizing merchandising sales in the queue while keeping the customer experience within target levels.
3. Split-test by zone
Merchandising placement within the queue can also have an effect on your sales. By splitting the queue into zones, you can see how each zone performs. Split-testing works by collecting data on customers as they enter and exit each zone and make their purchases. After a time interval you change merchandise locations and collect more data. Once your testing is complete, the data is totaled and averaged, and by-zone dwell time and impression counts are married to SKU sales data for each time interval. All of this data can be combined to show you the optimal merchandise mix for maximizing sales in the queue.
Gone are the days of guessing and blindly experimenting. With merchandising analytics and intelligent queue management testing and optimizing your front-end merchandising plan has never been easier.