What are the drivers for the increasing adoption of retail video analytics?
There are three primary factors driving the adoption of retail video analytics.
1. There was a massive shift in retail consumer behavior due to COVID-19. Retailers that had the capability to predict and react to these changes fared better than those that were too slow to respond. Retail video analytics can help retail chains move beyond descriptive analytics and implement predictive analytics to answer questions in real-time such as “What window display unit maximizes footfall conversion for a specific store format?” or “How many checkout counters should be opened between 5 pm to 7 pm on a Friday for a particular location to minimize checkout times?”
2. According to PwC, “Success at the shelf is no longer about the depth and breadth of inventory, but rather creating engaging experiences for customers.” Creating engaging customer experiences calls for real-time access to customer behavior data in the context of the store design or layout, time, and a slew of external contextual factors.
3. Profit margins are tight even as retailers retool their operations to cater to changing customer expectations. The only way retailers can find the right business model is to release products in the shortest time possible, gain tactical insights in real-time, and validate the success or failure based on measurable customer actions. Retail video analytics allows retailers to rapidly identify correlations (For example, “what’s the impact of a new layout on sales?”), validate the hypothesis (For example, does a new layout increase sales at all locations or at all store formats?), and consistently implement changes across all locations (For example, are all locations implementing the new layout as per the specifications?”).