AI innovations in retail demand effective data strategies

AI’s transformative powers are being realized across industries. From self-driving cars to robots in manufacturing, AI is slowly capturing market share. Retail is among the industries being transformed by this technology and by 2027, the market for AI in retail is expected to reach a staggering $23.2 billion.

Every business that adopts AI tools needs to be aware of the different data-powered innovations within their industry and the types of data needed to bolster efficiency and decision-making. Below are key examples of AI-led innovations in retail and steps leaders can take to deploy an effective data strategy.

The benefits of AI

AI technology delivers numerous benefits to the retail sector. The primary reason retail has become a hotbed for deploying AI innovations lies in AI’s ability to improve operations, enhance customer experience and increase profits. Prominent examples of AI innovations include:

Computer vision systems

Today, retailers are able to automatically categorize inventories by color, shape, type and a variety of other subjective categories and then let customers filter products using those categories. For example, if you are looking for a particular style of chair in blue, you can search for similar results and the AI algorithm will automatically be able to find these for you. This is made possible by the area of study within AI called computer vision.

Other very useful areas within computer vision include image recognition and motion detection. In retail spaces, these tools can be particularly effective to count foot traffic or inventory on display shelves. AI applications can also help customers with recommendations and map out their end-to-end journey from a device to the store, using tools like augmented reality, also made possible by computer vision systems.

Natural language processing (NLP) systems

NLP systems process human language to enable machines to understand natural conversations. An intuitive example of this comes in the form of human-machine interactions through chatbots and dialog systems.

Over the years, chatbots have become a landmark achievement in retail, especially in customer service roles. They are capable of attending to customer queries, thus slashing human workload and reducing human error.

Chatbots can also be a great resource to understand what customers enquire about. These responses can then be used to make an agile sales strategy based on current demand, or supplement other decision-making in business.

Since most online data is text-based, there are many other use cases of NLP, such as sentiment analysis.

Data-powered personalization and predictive systems

When consumers are shown suggestions that automatically align with their preferences, they are more likely to enjoy the shopping process. AI-powered personalization tools hold the key to understanding which products a customer can easily be persuaded to buy, which is essentially the power to bridge the gap between want and need. In fact, more than 35% of Amazon’s consumer purchases are credited to its recommendation engine, which has been a critical part of its success.

Predictive systems are also widely used in sales forecasting as well as for price and demand predictions and inventory and supply chain optimization. Similarly, machine learning (ML) algorithms can be of great help when predicting product performance and demand, based on a range of factors. Purchase history,  location of the customers, upcoming holidays and seasonal purchases are some factors that can be accounted for by the algorithms.

Furthermore, with available data on sales, customer demographics and distance from competitor outlets, AI applications can also predict optimal locations for outlets. Data and AI also allow for the convergence of digital and in-store sales strategy.

Data strategies to make the most of these innovations

Clearly, AI innovations are beginning to make retail experiences more seamless, personalized and engaging. But how can retail businesses map out a strategy to tap each of these innovations?

Data needs for computer vision

A data strategy to deploy computer vision systems requires a large number of pictures and videos. To give an intuitive example, if we are building a system to recognize faces, there are a multitude of factors that we need to consider: What is the lighting? Do they have sunglasses or hats on? Have they aged? Do they have a different hairstyle? Are there two people in the picture? Is it a video of the person, and not the actual person?

As a result, it’s important to have a large amount of annotated data in place to account for all the variations and have a clear mapping of the information. But sometimes, we do not possess enough data. One technique often used in deep learning is to train on another dataset that is slightly similar, and then as a final step, train it on our own dataset. This gives the machine learning algorithm a “head start” by using a larger dataset to identify common traits, like what a human looks like, or what shapes they are made up of. This is called transfer learning.

While collecting the large amount of data that is required, retail businesses also need to pay heed to privacy issues around people on camera. Similarly, the cost of storing continuous feed of cameras from all the stores can quickly add up. As a result, cataloging these feeds is important.

Data needs for NLP systems

Since NLP systems also need to deal with unstructured data, such as data from call center tickets, customer feedback forms, emails and phone calls, retail businesses need to find mechanisms to process and categorize these datasets to draw actionable insights.

From processing audio data from customer interactions to extracting insights from speech and transactional purchase history, businesses can gain an edge over their competition only if they are equipped to process vast pools of unstructured data, and find repeatable patterns on some level, which the machine will be able to learn.

Data needs for predictive systems

To make the most of predictive systems, retail businesses should remove internal data silos and create better access to these datasets. Similarly, they should combine information from structured and unstructured data to create a repository of information that is as large as possible, so that they can then pick and choose the data to feed into ML algorithms.

Businesses should combine structured data, such as sales data from various sources, with audio and text data from customer calls and video data from stores, all in one place. This can produce extremely powerful combined insights into products, sales and demand, which will allow for even more powerful ML models.

Conclusion

In today’s digital landscape, every business needs to have a future-proof strategy to deal with data. As retail is a customer-facing industry, the amount of data it produces is copious. Machine learning tools are primed to turn this data into insights and automated applications, freeing humans from rote tasks and allowing them to focus on more strategic endeavors.

Digital transformation will continue to disrupt traditional retail modalities. Properly dealing with data is the only way for businesses to benefit from AI in retail and stay ahead.

Sameer Maskey is the founder and CEO of Fusemachines and serves as an adjunct associate professor at Columbia University.