Image recognition technology can help food manufacturers understand the marketplace and react in real-time.
Understanding what and why consumers buy is a tricky science. While purchase patterns can be obtained from cash registers with relative ease, understanding the consumer’s behaviours and preferences and the circumstances behind the purchase is much more complicated but equally important. As much as 80 percent of the consumer’s purchase decisions are made while he or she is in front of the shelf.
In a complex and competitive landscape such as retail, food manufacturers are grappling with audits that are expensive, manual and time-consuming, with shelves and store promotion lagging behind real-time changes and customer demands. To audit one food product category takes approximately 15 minutes and involves physical measurements that are prone to human error, inaccuracies and inconsistencies. The number of food brands and sub-brands, design changes (for example, regular versus “Limited Edition”) and SKUs make auditing a colossal and very costly task. Food and beverage manufacturers can spend as much as US$12 million annually in a single market to employ a sizeable sales force to undertake these audits.
Despite the massive efforts and costs, food manufacturers can only be content with basic statistics and KPIs, which offer little value to the business. Even if reports can be generated from the data collected, they can take weeks or months to produce, rendering them of little or no use to manufacturers. Such limited reporting not only hinders timely and effective responses to tackle the ever-changing taste buds of fussy consumers, it prevents manufacturers from making more intelligent, accurate and profitable business decisions in a highly competitive marketplace.
Businesses around the world are realising that technology is paramount to driving growth and enhancing customer engagement. Having the ability to capture and manage huge amounts of customer or product data and transform them into pieces that can be actioned upon, will give them a leg up over the competition. It is no different for food manufacturers. They are beginning to see the benefits of image recognition technology and are starting to aggressively pursue the technology to allow for greater efficiencies in their audit and execution processes and drive more intelligent and profitable business decisions as a brand.
Reducing auditing time
Image recognition technology—a combination of fine-grained recognition algorithms and contextualisation models—allows manufacturers and retailers to leverage and manage the huge amount of data collected in-store to understand the marketplace and react in real-time.
Food sales representatives simply use a smartphone to take photos of relevant store shelves, which are stored, analysed and reported in real-time. Within minutes, the food sales rep has actionable reports in the store, detailing key metrics such as share of shelf, competitors’ share, shelf standards, planogram compliance, pricing and promotional materials.
The use of the technology can save up to 60 percent of audit time in stores, freeing the reps’ time for other sales activities. Imagine the cost savings food manufacturers such as Nestle, which have as many as 6,000 brands, can achieve in just one grocery store.
Accurate, multi-faceted data in real-time
Not only does image recognition remove human error out of the equation, it also delivers accurate and reliable data on product distribution and availability. Using image recognition can provide at least a 20 percent improvement on the accuracy level achieved by manual auditing and can reach an accuracy level as high as 99 percent.
By leveraging image recognition, a rep can get over 50 different measurements, such as share of shelf (market share), planogram compliance, pricing and competitive insights, just from a few images of the shelf. This is a giant leap for food manufacturers, who have to contend with today’s auditing methods that only consider merely four or five KPIs.
Unearthing more opportunities to sell
Food manufacturers are facing shrinking margins and limited opportunities for horizontal growth. As competition intensifies, the emphasis has turned to vertical growth or new customer segments.
Image recognition technology can support this strategic focus. A rep can identify and process all the category opportunities available for the store in real-time to upsell, cross-sell and provide range extensions in-store. In some markets, companies like Coca Cola have successfully leveraged the data they have gained through the use of image recognition; they have seen three percent gains in market share, better performance of their brands in-store and a positive impact on their bottom-line.
Future of image recognition apps is bright
Newer and more innovative technology such as image recognition on all smartphones, tablets, analytics and wearable electronics are quickly becoming a reality. The technology is also leveraging video capabilities, which is better suited for modern trade channels, i.e. big supermarkets with long aisles, as it does not require reps to take as many images and they can simply scan the shelf.
Although current applications of image recognition are mainly benefitting businesses, it is evolving into the consumer space, enabling them to shop faster in a time-poor world. Mobile applications with real-time information on shoppers’ favourite food and other products in the store are available to help them make smarter and more informed purchase decisions. It allows shoppers to check if their favourite products are in stock and receive targeted promotions and discounts. For consumers, wearable electronics allow them to engage directly with manufacturers at the shelf, make informed purchase decisions and receive targeted sales promotions.
While the retail market is huge, the industry’s ability to grow and compete has been challenged by infrastructure, increased competition, and most importantly, ineffective tracking and analysis tools at the shelves within stores. Image recognition not only resolves these challenges but also provides a better yardstick of how consumers react to brands.
Joel Bar-El is the CEO of Trax Image Recognition.