How AI Meaningfully Helps In Transforming Retail Businesses

Azhaan Merchant Jun 07, 2022

As a result of the pandemic, we are witnessing a macroeconomic situation never seen before – inflation is at an all-time high, consumer behaviour has shifted massively from offline to online, and brands that we have all grown up with are going into bankruptcy. The average consumer today has evolved substantially compared to a decade ago – they live in the age of the convenience economy where everything is a click away. And at the same time, it is evident that a majority of brands on the high street have struggled to evolve and keep up with the changing expectations of this generation of consumers. This begs the question – what is the key to survival for a brick & mortar store today?

If you dive deeper into the reason why consumers spend a majority of their spend online, it’s not just because of the ease of the experience offered but it’s also because the ecommerce giants are utilising AI to make their users’ experience more hyper-personalised and immersive. Ecommerce giants are able to analyse a user’s profile by monitoring their shopping and browsing behaviour to understand their preferences and in turn, offer them suggested products and services that resonate with them on a much deeper level. This experience has “spoilt” the average consumer and in turn, caused a massive disruption in the high street retail landscape as brick & mortar stores struggle to stay relevant to the same degree. But, what if I told you that high street operators can now adopt AI to help them create a similar experience for their physical foot traffic?

Leveraging computer vision to transform the offline retail experience

Today a retailer with multiple labels and brands under their management will have a good understanding of what products are being sold within each of their stores and formats by analysing point-of-sale or loyalty data. However, what is equally important is for them to understand the consumers who came into their stores and didn’t make a purchase. Getting access to this information is pivotal for the brand to see what their conversion rates are across their different stores and formats. Operators can now unlock this data with the use of computer vision – a subsect of AI that analyses existing CCTV video footage to provide insights into how consumers behave within their stores.

An AI-powered intelligent video analytic solution like this can provide a store manager with detailed information on what the shopper journey truly looks like from entry to exit. Let’s jump into some specific use cases and how they relate to key business outcomes:

1) Know your customer

In order to create a more personalised experience, the first piece of data that a store manager needs access to is demographic data. Each brick & mortar store has a different enchantment around it that draws in a unique combination of customers – it is critical to understand this age and gender mix on a store level. Computer vision solutions can give access to this data which in turn permits store managers to customize the product mix in each store according to the customer segments that frequent that store the most rather than having a blanket product mix for a region.

2) Influence the customer's purchase

Imagine you walk into an electronics store with the intention to make a high-value purchase such as buying a new camera. The average customer wants to browse several options to ensure they are educated on available options so that they can understand key factors such as features and prices before they commit to any product. However, a customer who dwells around such high-value purchases without getting any assistance from sales representatives has a much higher chance of abandoning the purchase as they don’t get the “educational” experience they desire. To solve this problem of abandonment, operators can leverage computer vision to send real-time alerts directly to a sales associate's instant communication headset with a message informing them that they need to attend to a customer in a specific aisle.

3) Build the basket size

A key strategy utilised by retailers is to build the basket size of shoppers by extending the time they spend in the store so that they discover new products and make impulse purchases. In order to do this successfully, retailers need to understand the shopper journey for each demographic cohort – what is their dominant path in the store? Which isles and zones are they spending their time in? What products are they engaging with? Computer vision unlocks all of these data points that help store managers optimise the layout of each store to aid maximum product discovery for each segment of consumers.

Simplifying store operations while creating better customer experiences

The use of AI and machine learning to collect and analyse social, historical, and behavioural data will allow retailers to obtain a far more precise picture of their customers. Unlike traditional data analytics software, AI constantly learns and improves from the data it examines and can predict client behaviour. This enables organizations to give highly relevant experiences, expand sales opportunities, and enhance the consumer journey. Tools like computer vision will give operators a clear edge to simplify their store operations and amplify the impact of their teams while creating better experiences for their customers.