Computer vision based AI models can help to improve the shopping experience for customers whilst reducing costs for retailers. AI applications can monitor and analyse how people move around a store, understand how customers react to certain products and promotions, and even identify individual shoppers.
Machine learning developers are continually expanding the capacity of this technology with the help of training data annotation. However, it can often be difficult to collect image and video data that fully corresponds with the needs of a given retail AI project. In these cases it is necessary to create data.
This blog will show how data creation services, such as Keymakr, can help AI innovators to construct maximally effective training datasets. These services empower a wide range of use cases that are transforming the in-store shopping experience.
Data creation services
The majority of training material for computer vision models is assembled from open sources. These online repositories are generally sufficient for most machine learning projects. However, there are often specific training data requirements that mean that data collection is not enough. The publicly available data may be of poor quality, not varied enough, or may not conform to legal and privacy standards. Data creation, using production facilities like cameras and sets, allows developers to get exactly the images and videos that they need.
AI powered cameras have the capacity to track customers around stores and produce detailed assessments of their movements and actions. Mapping the progress of individuals in a shop allows retailers to place promotions in optimal positions, increasingly the likelihood of a sale. This information also allows retailers to calculate the rate of pass-by-traffic and assess which products or promotions capture the most engagement.
For in-store traffic AI systems data creation can add variety to training materials. This might mean creating videos with customers from different ethnic groups, or simulating shopping conditions specific to a particular region or country.
Knowing how customers are responding emotionally to products and promotions can be a powerful tool for retailers seeking to maximise profits. Facial expressions can be analyzed by machine learning models and assigned values based on the emotions revealed. Real-time, detailed analytics of this kind can help store managers to respond quickly to remove low performing products or promotions.
When collecting data from open sources for this kind of technology quality can often be a significant challenge. Publicly available images and video may be low definition, making them unsuitable for detailed sentiment analysis. Data creation services can guarantee high definition training material that supports model development.
Facial recognition technology is gradually being introduced to a variety of public spaces. In the retail sector it promises to improve the shopping experience whilst making stores safer. Facial recognition allows frequent customers to be recognized and rewarded through loyalty programs. It also means that customers purchasing histories and preferences can be stored, streaming the shopping experience for them.
Finally, facial recognition can ensure that potential shoplifters are spotted and contacted by security staff. This combination of ease and security make this technology a promising avenue for future development and innovation.
Security is essential when facial recognition software is being developed. Data creation guarantees that training data conforms to the strictest privacy and data protection standards. Keymakr, for example, employs a range of security processes, including encryption, data expiration, and vpns, to ensure that data and information is kept private.
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