Much like e-commerce, walk-in customers are the fulcrum for a business to accelerate retail growth with people counting and in-store customer analytics. Most brick-and-mortar stores such as shopping malls, fashion outlets, high-value convenience stores, and events rely completely on walk-in customers to run their business. Traditional retailers have a quest to know more about their customers and analyze their behavior to glean valuable insights on in-store displays, inventories, and advanced technologies. If a customer walks out of your store with empty shopping bags, chances are your store is inefficiently designed or the product positioning is not well planned. There is also a high chance that you are unable to help your customers with the right offers based on their preferences. A sound knowledge on customer behavior and preferences is paramount for a successful brick-and-mortar store.
Although the millennial shoppers are leaning more towards e-commerce of late, the extinction of physical retail stores is being exaggerated. A report suggests that even by 2023, only 21% of total retail sales is forecasted to be accounted for e-commerce. As e-commerce giants like Amazon and Flipkart develop their own brick-and-mortar network, it is evident that the future of retail belongs to retail stores that promises a better omnichannel experience.
83% customers say they want a personalized shopping experience and a research suggests that effective personalization can increase store revenues by 20-30%.
Retailers are trying hard to comply with the changing preferences of customers at various geographical locations based on demographic criteria such as ethnicity, age, and social status. In order to match the customer preferences with the store layout, retailers should look beyond manual layout segmenting by deploying emerging technologies that leverage rich, granular data to enhance the in-store experience. Cognitive Robotic Process Automation (CRPA) brings the luxury of applying predictive analytics to create store-specific product disposal and eventually aligning with what customers demand most.
Function of In-Store Analytics
In-store analytics allow retail professionals to gather customer behavioral data and transform them into essential insights. Retailers are bound to use smart carts, install button cameras on the shelves, and the Wi-Fi network to monitor customers entering the store and track their movements to know which bays they visited. Cognitive automation will connect these dots by gathering data and predicting buyer decisions.
Factors Influencing In-Store Analytics
Unlike e-commerce platforms, retail stores need much more information about their customers so that they can recommend bestsellers and understand what could be causing losses to the store.
a) Traffic Counting and Conversion Rates
Cognitive automation will be able to track the number of customers entering the store while providing room for conversion rates and make smarter decisions about product placement strategies. The data gathered from traffic counting accounts for effective staff scheduling and measuring the returns from a marketing campaign.
b) Workforce Productivity
One of the best ways for a retail store to cut costs is through optimizing the workforce to effectively manage the store. Leveraging traffic data allows stores to reduce the workload on task associates and yet meet the demands without any hassles. These changes have a direct impact on operational costs and yet retailers ensure service meets customer demand. The relationship between customer conversion and workforce gets healthy with the help of in-store analytics.
c) Customer Behavior Insights
Although customer behavior changes frequently, retailers must make sure customers have a personalized experience whenever they step into the retail store. Cognitive automation can understand the customers’ natural behaviors and offers startling insights to provide assistance in terms of store layout, design, and navigation along with staff interaction.
Sneak Peek into a Personalized Convenience Store
Due to lack of a fool-proof strategy to measure the impact of merchandising decisions, a leading convenience store was unable to offer a personalized experience for their customers. But with the implementation of cognitive automation, comprising features such as Artificial Intelligence and machine learning techniques, a new shopping format was introduced to the store whereby they found new ways to analyze customer’s in-store behavior and assess merchandising efforts.
Let’s look at a scenario at this convenience store post the implementation of in-store analytics with the help of cognitive automation.
Once a customer enters the convenience store, the store recognizes the customer and provides full access as his smartphone is connected to the store’s Wi-Fi or through a facial recognition technology. As the customer logs in to the network, the store accesses his regular shopping list he has created on his phone by scanning them. As he walks through the store, the smart shelf on the aisles light up to show the location of those products while also recommending complementary items and any other product he forgot to make it to the list.
As he walks through the aisles, he is also tempted by a new personalized promotion that appears on his smartphone screen, he scans the product to check for a particular ingredient that he likes the most. Once he verifies the same, he adds the product to his cart as well. With his cart full, the customer leaves the store without even checking out. The new intelligent convenience store has verified every item he packed along with the details of his credit card and the right amount is debited the moment he leaves the store.
Automating in-store analytics with cognitive automation will help traditional as well as modern retail operations to decipher customer demands, improve staff optimization, drive sales growth, and better appeal to customers. The time is ripe for every retail provider to implement automation technologies to counter the varying customer demands especially in the post-pandemic world.