Many clients, while considering using Photo Recognition to assess the quality of work done by their sales representatives, merchandising companies, or store chain staff, wonder what measurable benefits it will bring.

For years Google was at the forefront of AI research and its application. Their search engine has the largest market share (about 85%!) to this day. But a change in the status quo can be seen on the horizon.

 💡 As the old saying goes, competition never sleeps. Microsoft’s Bing search engine is in a distant 2^nd place, but with the decision to integrate ChatGPT therein, they might be able to turn the whole situation around. ChatGPT, created by OpenAI, is incredible on its own, as we discussed a few weeks back here. This integration could change the way we browse the web. Not only will we get the search results of our query, but also the exact answer to our prompt.

Cost cutting is the most common response to a crisis.

Meanwhile, producers and retailers have to sell their products to survive, and investing in Photo Recognition systems such as PRO.Display allows companies to maximize sales while minimizing losses.

I often wonder how companies that collect data on their in-store presence manually can make any business decisions at all.

🔎 At the 2018 Retail Summit, during our sharing session with speakers from Mars and Sandoz, we shared the following business case with the audience.
One of our clients had decided to measure its distribution performance while launching a new brand. The brand included 10 SKUs.
📝 During the first month of launch, our client measured distribution performance traditionally. By that, we mean using manually entered data from its sales reps.
📸 At the same time, those sales reps took photos during the same store visit, which were analyzed by our AI-supported image recognition system PRO.Display.
🎯 The intention was to deliver a new range of products to 2000 stores, and these stores were divided into 3 formats.
So a pretty simple case we had all encountered in our business lives while launching a new brand. The key here was a rare opportunity to compare manually entered data with automatic, or more accurately, AI-generated data.

📊 Results: the manual data was only 46% accurate. This meant errors in more than 50% of the reports. 97% of these errors came from manually adding more SKUs than were visible in the photos.

🗓 This has not changed over time. In 2022 we performed a similar test with another client; this time, the reports were 29% inaccurate. Slightly better, but still not good enough.

💡 How can you make reliable business decisions or draw conclusions from such highly corrupted data?

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