Most retailers seem to ignore the potential of the data from video cameras in the stores. As a marketer working in the IT sphere and a passionate shopper, I find it frustrating. I often lack a more personal approach and more attentive service.
Learning about the possibilities of Computer Vision for retail, marketers would be able to do incredible things relying on the data received from cameras. Gartner predicts that 30% of organizations will increase the implementation of AI after COVID, and it’s not a big surprise.
Being in the phase of “through disillusionment” in the Gartner hype cycle, Computer Vision is already bringing real value to many businesses. Yet this technology is often associated with “big brother,” “mass surveillance,” and “lack of privacy.” Yet, in reality, most retail stores use cameras for security purposes. However, marketers can also use the same video footage.
Just to give you a general impression of Computer Vision, I would say that Computer Vision is responsible for the detection, classification, and tracking of any object on the image or video. Here is just one of the popular applications of video analytics in retail:
All in all, it is simply a tool: it can be used to reach good results, and it can also be used for evil purposes. Learning more about it and understanding the myriad of computer vision applications can help to unleash its incredible positive potential.
I am personally interested in discovering new applications of computer vision, among other technologies, in marketing and sales simply because implementing these technologies will lead to a better customer experience.
Heatmaps of the Stores
It is one of the most accessible solutions for any supermarket, a small brick-and-mortar store selling kitchenware, or a salon with designer furniture.
After all, there is video surveillance in any big supermarket or luxury boutique. So why should a business owner ignore receiving maximum information from it? Creating heatmaps will give a retailer a coherent idea of what is going on with the store.
With computer vision, any marketer would not mind knowing the answers to the following questions:
- Where do customers go first when they enter the store?
- What areas in the stores do they avoid?
- How long are the queues?
- What products attract customers’ attention naturally, and what products are often unnoticed?
Precise Demographic/Behavioral Data
Using Computer Vision, it is possible to create a more accurate portrait of the customer, the real portrait of a buyer persona, so to speak. Most buyers in shopping malls and boutiques come and go, never getting inside the system.
The demographic and behavioral parameters are left unnoticed. Gender, age, behavioral patterns, preferences – all this information can be easily extracted from the video footage with the help of Computer Vision.
Collecting this data daily, weekly, and annually is possible to form a coherent picture. After all, each marketer knows that to segment customers, you need to collect as much information about them as possible.
Emotions Analysis with CV
Computer Vision is pretty good today in the recognition of human emotions. Of course, there are many complicated emotions, deep feelings that can be reflected on human faces. Using simple Paul Ekman’s classification of basic emotions (there are 7 of them, anger, sadness, disgust, surprise, happiness, fear, and contempt), it’s possible to find the weak spots in the marketing strategy of any retail store.
For example, It turns out that people feel sadness when they pay. How about changing it?
Emotion recognition based on Computer Vision answers many questions marketers and sales managers have.
- Why do men after the 50s ignore the store and walk out as soon as they get in there?
- What are the most popular zones in retail stores for kids?
- What emotions do people feel looking at my Christmas shop window?
After all, good marketing is all about positive emotions and experience.
Here are some Computer Vision applications not related to marketing as a bonus to change the perception of this technology as the “big brother.”
- Lab specialists can determine the absence/presence of cholera bacteria in the Petri dish automatically with 99% accuracy;
- Manufacturers can use computer vision software to detect counterfeits;
- Farmers can use drone photos to detect if everything is OK with the crops (if it’s changing color).
Using video analytics software, retailers can get a full picture of what is happening inside their stores regarding customer service.