In the era of “Retail 4.0,” connected technologies and artificial intelligence help retailers to better understand their customers through multidimensional prediction, data analysis, recommendation algorithms and prescription models.
Retailers are leveraging Location Analytics with GIS and Demographics to help them uncover methods to grow their business using AI algorithms, starting with new customer acquisition. For instance, location analytics aids in the discovery of prospective new store sites.
The purchasing behaviour of current customers can be forecasted at both the individual and aggregate levels by business, location, etc. using their past purchases, spending patterns, and lifestyles. The Supply Chain Network receives information from AI algorithms that detect demand. Using multivariate algorithm approaches, AI systems forecast demand and suggest pricing. Price optimization, which depends on a number of intricate factors like supply chain margins, assortment optimization, market mix optimization, trade promotions, fulfilment costs, etc., is looking to artificial intelligence (AI) for suggestions and forecasts.
In the field of marketing analytics, there are trade promotion optimization and market mix optimization AI-based tools. Retailers have been utilising AI programmes to forecast consumer spending and profits on various channels, such as internet or in-store banners. Additionally, through profiling personas, lifestyles, and demographics, recommendation engines aid in the tailoring of marketing advertisements.
Omni channel engagements and touchpoints that use AI Bots, such as “fill in a shopping cart,” raise the bar for the customer experience. The systems give information on who purchased a product and summarise social media comments. It’s interesting to note that in soft lines for retail, computer vision-based algorithms (like lenskart) assist buyers in trying on a specific item to see how it appears on them. Additionally, they can only test those that are in the backroom or a nearby store.
To display products based on browsing history, affinity algorithms, as well as cross-sell and upsell products, Amazon has long used recommendation engines. Heat maps are possible to track purchasing behaviour in contemporary businesses equipped with beacons and IoT sensors, and they can also help store employees by providing coaching on what customers want. Market Basket Analysis, particularly in the area of Assortment Optimization, benefits from the use of AI logistic algorithms.
By offering dynamic real-time discounts based on loyalty and referrals, discounts and coupons have evolved from traditional to intelligent ways. AI-based recommendation algorithms are able to suggest the most alluring discounts for both individual products and store visits. A tailored selection of recent and well-liked products is also recommended by AI. A customer no longer has to make his own gift registry because AI-based recommendations for gift registries have taken the role of the conventional gift register.
Overall, social analytics powered by AI and data arm customers with knowledge, enabling retailers to have informed discussions with them. Retailers use social media to understand both the things that customers want and the problems that arise after purchasing them. AI aids in spreading this social media information throughout the supply chain, from sourcing to fulfilment. AI algorithms perform sentiment analysis based on social media feeds to recommend product specifications, competition advantages, and business prospects. They also provide information to related businesses like healthcare and insurance.
Retailers must use data strategy to identify the many types of structured and unstructured data already present in the organisation and to look for data from outside sources such as social media, syndicated data, and external agencies. The creation of a system to gather, store, and curate data is necessary for the AI algorithms to characterise, forecast, and prescribe intelligence after identification.