people shopping at a supermarket

An Evolving AI retail experience: Transforming the way consumers shop

The retail experience of a shopper is the latest area that AI and machine learning are causing disruption. Most retailers recognizing the potential of these technologies have started aligning them into their business goals. Two crucial aspects - data and computing power have changed in the past few years in the space of AI, which has opened up new opportunities for retailers today.

Computing power is easy to see, with the advent and rise of smartphones which have phenomenal computing power when compared to the bulky phones and computers used decades ago. Businesses today have unlimited computing access to train their AI algorithms. Furthermore, the data available today is extremely rich and scalable. AI systems that leverage learning techniques such as Machine learning thrive on large, rich data sets. When fed appropriately, these systems discover patterns and correlations that would be otherwise difficult with a human intervention. These machine learning approaches automate data analysis, enabling users to create models that can then be used to make useful predictions about other similar data.

Retail is a perfect fit for AI, here’s why -

The speed at which AI can be deployed depends on specific critical factors. The first is the ability to test and measure. Retail giants can effectively deploy AI and test and measure consumer response. They can also leverage AI to measure the effect on their entire supply chain.

There is some innovative and interesting robot technology taking place in retail such as Grocery giant partnering with Nuro.AI to deliver groceries to the customers’ doorsteps. But most significant changes will come from the deployment of AI rather than the use of physical robots or autonomous vehicles.

Here are 3 AI-based scenarios that will transform the retail experience -

Shopping habits

AI can detect underlying patterns in the shopping behavior of shoppers from the products that they buy and the method used to buy them. This could be a simple weekly purchase of groceries from the supermarket, the sporadic purchases of wine from the liquor store or the complex midnight icecream cravings from the local convenience store.

At a larger scale, analysis of the behavior of millions of consumers would enable supermarkets to predict the number of households that cook pasta every week. This would then inform the inventory management systems, and automatically optimize the stock of pasta. This information can also be shared with the suppliers, enabling more efficient inventory management and organized logistics.

Pricing dynamics

The pricing challenge for supermarkets involves applying the right price and the right promotion to the right product. Retail pricing optimization requires data analysis at a granular level for each customer, product and transaction. To be effective, many factors need to be considered such as the impact of sales due to the changing price over time, seasonality, weather and competitors’ promotions.

A well-defined machine learning program can factor in all variations, including details such as purchase histories and product preferences to develop deep insights and pricing tailored to maximize revenue and ultimately, profit.

Customer feedback

In the past, customer feedback was collected through forms and feedback cards that were filled out and placed in a suggestions box. The feedback had to be manually read and acted upon appropriately. With the rise of social media, the platforms were leveraged to express feedback publicly. Retailers subsequently engaged in social media scraping software to respond, resolve and engage with customers.

With the growing innovations, machine learning will play a larger role in this space. Machine learning and AI systems will be able to analyze unstructured data from multiple sources such as verbal comments or video content.  

The evolving retail experience

As a shopper moves through various stages in life, the circumstances and spending habits change. AI and machine learning models will adjust and be able to predict the needs of the consumer before the consumer even searches for a product.

This shift to predictive marketing would change the way shoppers purchase products, bringing in suggestions and recommendations that they would not have even considered. The possibilities would widen, all because of AI - for both consumers and retailers alike.

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