How AI is Creating a Customer Intelligence Pool for Businesses
How AI is Creating a Customer Intelligence Pool for Businesses

In the age of Retail 4.0, Artificial Intelligence with connected technology enables retail enterprises to understand customers with multi-dimensional data, prediction, prescription models, and recommendation algorithms.

Starting with new customer acquisition, retailers are using location analytics with GIS and demographics to help them find ways to grow their business using AI algorithms. For instance, location analytics help them identify potential new locations for their stores.

With existing customer purchase history, buying behavior of existing customers through spending patterns, and lifestyle can be predicted at an individual level and aggregated level by store, location, etc. AI algorithms sense demand and feed information to the Supply Chain Network. AI algorithms predict demand and recommend pricing using multivariate algorithm techniques. Price optimization, which is a function of many complex parameters like supply chain margins, assortment optimization, market mix optimization, trade promotions, fulfillment costs, etc are looking at AI to provide recommendations and predictions.

AI-based programs on trade promotion optimization and market mix optimization have been around in the marketing analytics space. Retailers have started using AI programs to predict spending and returns on types of channels like digital or physical banners in the store. In addition, recommendation engines help personalization of marketing ads by profiling persona, lifestyle, and demographics.  

Customer experience is taken to a new level with omnichannel interactions and touchpoints that involve AI Bots for eg: ‘fill in a shopping cart’. The programs provide insights like who bought a product and also summarise the feedback from social media. Interestingly, in the retail soft lines, computer vision-based algorithms (like Lenskart) help customers wear a particular product and see how it looks. Furthermore, they are able to try only those that are available in the backroom or a nearby store.

Amazon for long has been using recommendation engines to display products based on browsing history, affinity algorithms, and also cross-sell and upsell products. In modern stores with beacons and IoT sensors, heatmaps are able to not only track buying behavior but are able to assist store staff with coaching on what customers are looking for. AI logistic algorithms help in market basket analysis and primarily in the assortment optimization space.

Discounts and coupons have moved from traditional to intelligent ways by enabling dynamic real-time discounts depending on loyalty, and referrals. AI-based recommendation engines are able to prescribe the attractive level of discounts at the product level and store visit level. AI also recommends a personalized list of new and popular products. The customary gift register is replaced by AI-based recommendations for gift registry which eliminates the need for a shopper to create his own gift registry.

Overall, AI and data-based social analytics empower customers with information thereby leading retailers to intelligent conversations. Retailers use social media to understand not only the products that customers desire but also product issues after service. AI helps communicate this social media information to the supply chain from sourcing, and merchandizing through fulfillment. Sentiment analysis based on social media feeds is used by AI algorithms to prescribe product specs, competitor strengths, and opportunities and feeds information to adjacent industries like insurance and healthcare.

Data strategy is vital for retailers in identifying the variety of structured and unstructured data within the enterprise and seeks data from external agencies, social media, and syndicated data. After identification, setting up a system to collect, store, and curate the data is essential for the AI algorithms to describe, predict and prescribe intelligence.

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