Retailers across the globe are witnessing the undercurrents of change as their customer’s shopping habits continue to rapidly evolve. With the exponential growth of online retail, the landscape has changed forever with high levels of personalization being the new norm. And in the context of India, the incumbent retailers are now realizing that they are sitting on large portfolios of brick-and-mortar stores that have diminishing customer retention rates. To survive the retail evolution, they need to adopt a data-driven culture and start sweating these assets with the help of AI.
Creating Business Intelligence with Video Analytics
Traditional retailers are trying to recoup the customers they lost during the pandemic. The prerogative of being technology first, agile, omnichannel, and future-proof is simply non-negotiable. To remain relevant, retailer needs to radically transform their shopping experiences. To start this journey of transformation, retailers will need to gain additional data points.
Looking at point of sale (POS) data in isolation is not useful as it gives no insights into why a customer visited their store and didn’t complete a purchase. They need to clearly understand the variables in each of their stores that prevent their shoppers from attaining a frictionless shopping experience.
To efficiently manage this data-driven approach at scale, one needs to leverage the latest developments in artificial intelligence. A subsect of AI called computer vision presents immense potential to help retailers understand the in-store customer conversion funnel. This technology commonly referred to as video analytics leverages the available video feeds from the existing CCTV cameras in stores and delivers insights through object detection and gesture-based algorithms.
Analyzing how shoppers behave in each of your stores is essential for retailers to get a direct line of sight to what is, and what isn’t working in one’s retail space. Once a retailer understands the bottlenecks in the shopper’s journey, they can begin to implement a store-level strategy to micro-optimize the shopping experiences that they are delivering. Let’s jump into some use cases on how stakeholders such as merchandising, marketing, and operations can use these data-driven insights to transform the way they run their stores.
Transforming Your Merchandizing Strategy
Multi-label fashion retailers have a plethora of brands in their portfolios that remain relevant to a wide range of customers. To stay relevant to their customers across their large footprints, they must match their brand mix to the customer demographics that frequent their respective stores. Video analytics solutions unlock visibility into the shopper’s age and gender. The retailers can then analyze the behavior of the different customer cohorts to help store managers dynamically A/B test their merchandising strategy to ensure they are displaying the brands and labels that result in maximum customer conversion.
In addition, in-store video analytics platforms can provide insights into product engagement, shopper-dominant paths, and heatmaps. Once a merchandising manager understands how shoppers are moving across different brands and zones, they can use this data to optimize the store layout to maximize product discovery, engagement, and dwell time in each store. With the help of these customer behavioral analytics, large format locations can ensure that they sweat every square footage of shelf space that they have.
Assessing the Impact of Marketing Promotions
When it comes to acquiring customers online, e-commerce operators have a very granular understanding of the customer acquisition costs for each of their marketing channels as they can track the customer journey pixel by pixel. In contrast, brick-and-mortar operators struggle to attribute their offline marketing campaigns to the performance of their stores as they have limited visibility.
With the help of intelligent video analytics, retailers can fill this gap and create a rich mine of data that can help them benchmark the baseline footfall in their stores against periods where they are running promotional campaigns or offline advertisements such as billboards, radio, or television campaigns. They can then compare the variance of the footfall to get a more tangible understanding of the incremental footfalls from each promotion they run. In addition, store managers can overlay the footfall data with demographic data to understand the conversion rates of the customer segments each promotional campaign is attracting. This assists marketing managers in better assessing the return on investment of each offline campaign.
Optimizing In-Store Operations
- After the cost of real estate, the expenditure on labor tends to be the second biggest line item for most stores. The two main categories that these staffs tend to fall into are store associates and cashiers as they directly co-relate to higher customer conversion rates. However, in today’s inflationary environment retailers need to be extremely cost-conscious and ensure they are not overstaffing their stores. The era of fixed 8-hour worker shifts is no longer applicable as retailers have begun to optimize their labor hours to match the customer traffic coming into the stores.
- A smooth checkout experience is quintessential to ensure a higher customer satisfaction index. Video analytics data can be used to predict line length and wait times during different times of the day. Using predictive analytics teams can keep track of the queue abandonment rates and ensure that they have an adequate number of cashiers and tills open to optimize the throughput of traffic at each hour of the day. Furthermore, they can get visibility of the peak occupancy and dwell times at each zone in the store to help operators plan when and where they need to place their sales associates.
Maintaining A Unified Data Layer
Retailers need to mature from testing enterprise-level software to adopting it in a meaningful manner. To effectively compete with the online shopping experience, operators need to move away from utilizing siloed data structures to establishing a unified data layer across their brands. The go-forward strategy for retailers should be to create a digital avatar of their customers by augmenting the POS, CRM, inventory, labor, and marketing data with in-store video analytics. Understanding the interplay of each of these variables on a micro-store level is an efficient strategy that will radically transform the shopping experience while improving customer retention across brick-and-mortar stores.