How Data Labeling for AI is Shaping The Future of E-commerce and traditional Retail
-Written by our team at Enlabeler
The growth of e-commerce and in-store digital experiences has accelerated traditional retailers into the “new retail”. Although BI and data analytics have been a focus point for many retailers for years, Artificial Intelligence is a relatively new topic. AI and it’s underlying algorithms help automate processes and transfer learnings from one functional area to the other (for example from an online shopping platform to an in-store experience). And with new tools in image recognition, object detection and sentiment analysis — retailers and ecommerce players possess a new array of tools to better serve their customers. When utilised in the correct way, these technologies help increase efficiency and save costs when it comes to the contemporary e-commerce shopping experience.
In order to train these algorithms and machine learning models for AI — retailers with the most efficient and targeted data management systems have a competitive advantage. Data is the new gold. And when you know how to apply the right datasets for training the right algorithms, you gain an even bigger edge over your competitors. The implementation of data labeling in new retail has been received as a revolutionary solution and has already been commercialized in some regions. It can save labour costs, improve customer experiences, optimize operational processes and grow consumer insights. New retail is becoming the main model in our society today as it is a seamless reconciliation of the digital and physical world. Below we dive into a few examples:
Data labeling & classification for Retail & E-commerce
Object detection and classification models for unmanned stores help automate the end to end shopping experience. For example, machine learning models for automatic product recognition identify which products a shopper has in their cart and facilitate a virtual check-out. These models first need to be fed with thousands of labeled images to understand what are the products on each image, which article numbers belong to each product, which brand, which packaging size and supplier information etc. Inventory management and visual merchandising can be automated too, making it easier to see when products need to be replenished on the shelf — or giving a signal when the visual merchandiser needs to change the presentation of their products in store.
Customer data. The rise of the e-commerce industry cannot exist without cleaned-up and relevant data. Think of product/brand information data about the shopper and their preferences, but also the underlying data about the prices, discounts, payment processes etc. This information includes how the customer is interacting and experiencing the website where the product and service are being provided. In order to serve customers better, e-commerce players and retailers need to dive into the different customer segments. How do shoppers in each segment behave? What are their preferences?, which additional product are they likely to buy with the current item in their basket? Classifying or labeling these data points will help sharpen the customer segments and serve shoppers better.
Facial recognition technologies can be used to identify customer profiles & behaviours and create predictive styling for a more personalised customer experience. Consumer analysis can be done and stored for future visits and persona profiling. Unfortunately, not all customer segments have been well-represented in existing datasets, resulting in outliers, people being refused access or skewed data insights. Hence it’s important that datasets for facial recognition are unbiased, diverse in all aspects and representative of the people that actually visit that specific location or store.
Visual search is a growing technology, using recognition software to allow customers to take pictures of clothing or adverts and link them directly to product pages. This improves the customer experience drastically as it’s easier than ever to find the product you are searching for.
Receipt transcription Through receipt transcription, a large amount of data is being generated such as purchase, shipping and handling information. Automatic transcription and labeling of this information from the POS system will help reduce labour costs and streamline the back-end system. Thus data labeling will significantly reduce the workload of store representatives and personnel.
Sentiment Analysis helps classify text using natural language processing, text analysis and computational linguistics to identify and extract subjective information. This can be used to train a computer or model how to identify emotion around a certain brand or product, analyse customer reviews and develop competitive intelligence. Social media can also be monitored to highlight brand reputation threats or brand influencers.
All in all, these data labeling trends along with a wide span of others can be used to improve the overall customer experience, reduce operational costs and increase the efficiency of retail players and e-commerce platforms drastically.
Want to enhance your retail business? Enlabeler can offer you end-to-end solutions to improve your customers experience and/or your business’ efficiency. Reach out to us at info@enlabeler.com or visit www.enlabeler.com for more information.
