The intelligent way to manage your brand in stores using AI

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The intelligent way to manage your brand in stores using AI

The store shelves are where the action is at and customers make key buying decisions here. CPG companies that want to stay ahead of the competition are using artificial intelligence (AI) to gather key information at this crucial point.

AI applications, which reflect human-like characteristics such as problem solving and planning, can help field reps optimize the order of store visits, spend less time on product placement and distribution in favour of revenue-generating tasks, and use gathered data to devise smart new initiatives.  

CPG companies have always faced fierce competition, and that’s still the case. What’s changed recently is that some are using new technology on retail shelves to significantly grow their market share.

In the past, they used technology to gather vital information about consumer trends, brand performance, customer experiences, inventory and marketing initiatives to understand new developments and make merchandizing decisions. But these always had a blind spot – what’s happening at the store level, which is where many customers make their buying decisions.

Enter AI, which provides both CPG and retail companies crucial data — down to category, brand and item levels — about availability, pricing, position and promotion. When tracked and analyzed, this data offers the potential to execute in-store execution strategies more effectively, thereby growing share of shelf and revenue.

New developments in AI that are smartening up retail strategies

AI uses computer vision and deep learning to achieve these results. Computer vision incorporates neural networks that help computers detect objects within shelf images and classify them accurately.

Deep learning is a new area that is moving machine learning closer to one of its original goals: AI. In deep learning, a computer system is fed a lot of data, which it then observes and analyzes to recognize emergent patterns and trends, ultimately generating appropriate recommendations and predictions.

Deep learning applications, especially in the field of computer vision, are more than ready for prime time. Recently, a neural network architecture won the ImageNet challenge by classifying a wide variety of objects – like birds, ships, and cats – into the correct categories more accurately than humans did.

Back at the store shelf, even subtle differences between objects, poor lighting, reflections and background clutter can be overcome with these powerful AI applications.

From data to insights: Enabling significant efficiency improvements

Integrating AI applications into field operations enables end-to-end efficiency improvements that can unlock revenue opportunities and significantly increase share of shelf.

Before sales reps even reach the stores on their route, AI applications can determine the optimal order for store visits based on the best time to visit each one, available personnel, in-store promotions and specific store needs.

Within the store, digital image recognition can be used to streamline store audits by capturing information about SKUs, tasks that previously depended on time-consuming and error-prone manual entry. Less time spent on data gathering and product placement and distribution leaves more time for what reps are actually meant to do – active selling.

For instance, using Trax Retail Execution, a global FMCG leader with more than 8,000 brands across multiple markets was able to reduce the time its reps spent in store audits by a whopping 50 percent, thus allowing them to spend 40 percent more time on in-store execution activities.

Further, the gathered information can be quickly analyzed to evaluate out-of-stock items, facings, prices and share of shelf, and even find buried patterns that can point to sensible next steps. In another successful implementation, Coca-Cola Hellenic Bottling Company used Trax Retail Execution and drastically reduced out-of-stocks by 63 percent while increasing execution scores by more than 10 percent.

The uses briefly mentioned here represent just a fraction of the potential of AI on a company’s sales operations. Given that the discipline is still new, it’s likely that further advancements in AI will continue to help CPG and retail companies improve processes, and increase revenue and market share.

Read our second blog in this series to truly understand the  power of AI in retail, demonstrated through real-life case studies.

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