Analyzing the information that’s gathered from digitized in-store video cameras and other in-store technologies is important for retailers. It can help them gain valuable insights about wait times on checkout lines, abandon rates, and even shopper behaviors around the placement of specific items in strategic store locations.
Retailers can use analytics with in-store information that’s gathered to identify ways to improve the in-store shopper experience while helping to optimize marketing and operations.
For example, digital information that can be gleaned from in-store video cameras can be used by retailers to help shape strategies around the number of checkout lanes to have open during certain store hours. This data can also help them schedule checkout clerks to closely meet shopper demand.
In addition, the information that’s gathered by in-store video and scanning technologies, when used with analytics, can help identify the types of items that shoppers are buying to help guide inventory, marketing, and pricing strategies.
Case in point: the use of in-store technologies and data analysis may lead grocery executives to determine the certain times of day or days of the week when specific produce items (tomatoes, carrots, lettuce) sell most.
Grocers can use this information to help determine the optimal times for having fresh produce delivered and available for shoppers. They can also use these insights to offer sales on certain items, especially those that could potentially accompany produce (croutons, certain brands of salad dressing) that they’re looking to promote.
Customers who know they can count on fresh produce from a particular grocer will be more likely to remain loyal.
Analytics can also help retailers determine the best locations to position merchandise to sell quickly. Retailers can use analytics to analyze traffic patterns and consumer trends in a store and the areas where featured merchandise is most likely to be picked up and purchased.
Retailers can further use analytics to determine promotional pricing that can be applied to merchandise that’s placed in a particular location to help incent customers to purchase.
For instance, an electronics retailer could combine a sales promotion for a line of laptop computers with offers for associated gear such as laptop cooling pads, flash drives, carrying cases, and other accessories and locate the computers and the gear next to each other.
Business leaders for the electronics retailer can then use analytics to determine which customers added specific accessories to their laptop orders and use this information to help create future offers.
Retailers can also use analytics to determine the types of messaging that different customer segments respond to and the channels used to reach them. Retailers can then develop the right content and delivery channels to strengthen engagement and drive higher conversion rates.
For instance, a sporting goods retailer might determine through informal surveys conducted by cashiers that a certain customer segment tends to respond to newspaper ads when certain brands of athletic shoes are placed on sale at specific prices.
Meanwhile, other shoppers who have shared their mobile phone numbers might respond well to SMS alerts that promote certain categories of athletic gear such as golf clubs and running apparel.
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