AI is expanding in every industry, especially in retail, where experts estimate that spending on AI will be $12 billion by 2023. They projected the retail industry to grow from 23.6 trillion U.S. dollars in 2018 to around 26.7 trillion U.S. dollars by 2022. The enormous growth has led to major competition in the industry. To sustain the competition retailers, need to gear up for better customer engagement and satisfaction. Customers expect personalization and with millennials being an enormous chunk of consumers spending nearly $600 billion every year on retail alone, the industry needs to innovate.
Retailers are using AI worldwide to provide customers with a customized, unique, seamless, and memorable in-store experience. AI in retail has empowered businesses with information that is leveraged to improve retail operations. AI also helps in providing high-level customer service and improving business opportunities. They estimate that $40 billion of additional revenue was driven by AI in retail in 3 years.
We expect AI spending in retail to grow over 200% between 2019 and 2023. It talks a lot about how AI is affecting the retail industry. Adopting and implementing AI in various functions of the retail business has significant benefits. But it requires plenty of data annotation work to train the machine learning algorithms to function properly. Data annotation is an important AI adaptation in retail. In this article, we talk about the benefits of adopting AI in retail and the role of data annotation.
Importance of data annotation in retail’s AI adoption
AI/ML project requires to be fed with a huge amount of data for significant results. The raw data needs to be transformed into structured data for the machine to understand the input data and come up with the correct output. And that is where data annotation and labeling in, which requires time; in fact, data labeling represents over 25% of time consumed in most AI/ML projects.
Accurate data annotation is imperative for the success of AI and ML projects. Data annotation helps the retail business in several ways.
Customer sentiment analysis
- Customer sentiment towards the products on the shelves is understood by analysing their facial expressions and dilating pupils.
In-store traffic analysis
- Shoppers‘ path around the store are mapped to optimize the placement of products and promotions.
- Capture rate of pass-by traffic.
- Measure which promotions captured engagement.
Real-time in-store performance monitoring
- Alerts on missing products on the shelf or empty spaces.
- Inventory management.
- Distinguish between individual items through instance segmentation annotation.
Facial expression recognition
- Key point annotation identifies facial features in AI training images.
- Regular and loyal customers are identified and rewarded.
Checkout monitoring for theft
- Smart checkouts minimize theft by monitoring checkouts in real-time.
- Retailers monitor each item passing through the checkout with help of pixel-perfect image annotation.
AI application areas in retail where data annotation becomes inevitable
AI in retail is offering customers a high level of convenience and helps streamline processes. Product traceability is faster, checkout processes are smooth and expedited. Customized offers are made based on preferences.
Facial expressions are captured to find the customer’s reaction towards a product or promotion, and much more.
However, none of these can be possible without data annotation so that the machine learning algorithms operate accurately.
Some of the AI application areas in retail that require data annotation include the following:
Self-service checkouts
Self-service checkouts optimize the checkout process, attract more customers, save time, improve in-store productivity, and most important customers love them. The system is gradually replacing humans and retailers are investing in technology to make it more user-friendly. The self-checkout market is expected to topple $5 billion by 2024. Retailers are turning automatic; you walk into the store, pick up stuff off the shelf, and walk out.
With the help of facial recognition technology, the AI system matches the customer’s face with details in the file and bills the credit card. However, this would require plenty of facial annotations so that the computer can recognize the customer from any angle and in every image.
Automated warehouses
AI is replacing humans in warehouse management with the help of AI-powered robots. This helps companies save time, resources and improve efficiency. Gone are the days when a person would physically walk around the store with pen and paper and ensure all supplies were in place or pick up items needed. Retailers are even using robots to fulfil orders where robots walk around the store, pick up items, and pack.
These robots use LiDAR technology to recognize their surroundings which also requires data annotation. The images of all items in the store need to be annotated by tagging, 2D/3D bounding boxes, or semantic segmentation to ensure accurate delivery. And since item packaging, features, etc. change it is better to go for fresh annotation instead of depending on historical data.
Virtual fitting rooms
A virtual fitting room allows shoppers to try clothes, watches, belts, beauty products, and other accessories virtually without physically touching the item. With the help of AI technology, every item is placed over live imaging of the customer letting them check the size, style, and fit of the product. The global virtual fitting room market is predicted to grow from $3 million in 2019 to $6.5 million by 2025.
To support the AI technology, image annotation for training datasets is required. Accurate labeling of thousands of items like clothes, watches, frames, etc. requires labeling techniques like polygon annotation to capture complex images. Granular details from certain images can be captured by using annotation techniques like segmentation. Certain items like jewellery require smart image and video annotation to capture the design intricacies.
Shopping assistants
Virtual shopping assistants use machine learning and natural language processing technology that enables text-based interactions with online visitors. Chatbots use AI to communicate with customers through text or voice. It performs multiple tasks like answering queries, recommending products, sending abandoned cart reminders, etc. that works as ready assistance to customers 24/7.
With technology advancing virtual shopping assistants are getting effective; they can answer up to 80% of routine questions and 34% of online shoppers prefer chatbots over human support agents. Chatbots recognize words and answer queries faster which satisfies improving the customer support experience. But developing an AI-based chatbot needs language-based data to train the model.
Large amounts of conversation data sets containing the relevant conversations between customers and human-based customer support services are needed to prepare training data for the chatbot. The data is labeled by experts through NLP and a bot developed that communicates like humans.
Customer journey mapping
Customer journey mapping is very important for the success of the retail business as it relates to the customer experience while interacting with your store or brand. The journey starts from the first exposure of the customer with your product and continues till the point of sale and post-purchase experience. However, this is not simple because different customers react differently to different products, and to get complete picture the diverse customer experiences need to be analyzed.
Capturing data on customer behaviour is simple but the challenge remains in translating the data into a customer journey map so that you understand your brand performance vis-a-vis your customers. And this is where data annotation comes in; with the help of sentiment analysis, one can capture sentiments, moods, customer reactions, preferences, etc. in the customer mapping journey. It helps the business understand the social sentiment of their brand or services while monitoring online conversations. Intent analysis, contextual semantic search, etc. helps derive actionable insights on customer reaction to your brand.
Trend analysis
Retail business is tricky and requires strategic planning and trend analysis. The business needs to capture sales trends, customer buying habits, product knowledge, customer preferences and these can be done through retail trend analysis. This helps evaluate past trends, current scenarios, and position brands accordingly.
It also helps discover the problem areas, lack of services, what issues need attention, etc. so that retailers can strategize and plan accordingly. Knowing trending product categories can help the business grow and boost the retailer’s margin. All these can be done through image recognition technology that provides information on fabric texture, colors, prints, etc. that spark consumer demand. Social media images are scanned to predict fashion trends.
Conclusion
Artificial intelligence (AI) is rediscovering the retail business. Retailers are using AI to connect to customers, offering customized promotions in real-time, automating warehouses, mapping customer journeys, trend analysis, and much more. All these help in waste reduction and smooth operations. It is all a game of data and collecting data is also not a challenge. But businesses struggle to draw insights from that data which requires serious intelligence; AI-enabled solutions are the key to these intelligent insights.
However, all ML solutions require data annotation. Therefore, data annotation becomes an important adaptation for all AI projects. To stay in competition retail industry needs to focus on AI/ML-enabled solutions and for the success of these projects, there is a huge requirement of data annotation so that data can be trained effectively. This challenge can be worked in-house or outsourced to annotation experts.
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