AI in Retail: Serving millions with 0s and 1s

Retailers need to adapt to rapid changes in consumer attitudes and the marketplace. Every aspect of retail operations has the potential to make or break the customer experience. AI technologies are breaking down barriers and making it imperative for retailers to adopt a globally competitive retail business model. The power of machine learning has made it possible for businesses to continuously scrutinise customer behaviour data and generate alerts when the time is right for the next best action. FMCG (Fast Moving Consumer Goods) companies are already rich with data. Successful transitioning to AI can be achieved through a gradual approach, the right combination of people, processes and tools. By upscaling AI efforts, companies can glean valuable and actionable business insights. With data-driven intelligence at their disposal, organisations can strengthen four key pillars crucial to delivering a next-level retail experience— the ability to understand their customers, smart merchandising, hyper-relevant marketing and optimised operations- that centres around the products and services their customers crave.

  1. Customer lifetime management • Derive factors that influence purchase behaviour Purchase decisions are now influenced by a variety of factors that were not envisaged. Customer segmentation based on behaviour now takes in account not just frequency-recency-monetary but also diversity of purchases, potential to experiment with a new product, seriousness, profitability (amount bought/amount returned) and other such variables. These aspects map differently to different product categories. AI can help in bringing clarity in what really drives experience. It can also help determine the production quantity and potential consumers. • Track and prevent customer attrition Customer behaviour can be tracked to glean insights into their loyalty. Creating customer analytical records and detailed journey maps help in identifying which customers have deviated from their usual behaviour and what should be the next best action. Customer look-alike mapping aids in quickly gauging the time to action and in identifying the action that is most likely to achieve the objective. • Drive higher revenues per customer by running targeted cross-sell and up-sell Customers are willing to share their data if they enjoy a higher quality shopping experience. By offering differentiated shopping journey, retailers and brands can demonstrate that their relationship is authentic. Past purchase behaviour of similar customers can be combined with product features and pricing to derive a propensity model for future needs.

  2. Merchandising • Pricing plans and promotions to optimise certain KPI To remain profitable, optimal pricing is everything. There are inherent trade-offs between balancing growth in volume vs driving return on investment. A combination of metrics has to be modelled so that it can be achieved in unison. Being able to simulate scenarios and measure probable impact of business decisions can be achieved through detailed modelling of available data and algorithms that learn from that data. • Creating customer-centric assortments Good assortments which give ‘instant gratification’ to customers are at the heart of retail. They yield better sell-through, lower chances of mark downs and help optimise the inventory store by store. Understanding customer demographics, behaviours, tastes and millions of transactions requires a mix of data analytics and machine learning. Edge sensors can be deployed to gather in-store data points such as duration for which a customer looked at a certain item, and if the item was picked up from the shelf. Scalable AI solutions can anticipate customer behaviour and replicate market conditions to run complex analysis on large datasets to help decision-making. AI can help retailers deliver outstanding customer experience and meet their financial goals. • Location Scoring Retailers need to see, at an aggregate level, who is moving around in cities and where are they going, so that they can market products and services more effectively to consumers, and also identify the prime locations for new stores. Retailers are also tapping into location data to find out more about the potential customers that are walking by their storefronts every day. AI leverages open data sources to analyse the interplay between demographic factors of a certain location and the success of their operations in that area.

  3. Marketing • Market Mix Modelling (MMM) An effective MMM takes in account the marketing spending across product base and their impact on the revenue. With a sharp increase in customer touch points, there is a corresponding rise in the data that needs to be analysed. Without ironing out the collinearities and correlations in the data, such analysis can become inadequate. Powerful AI algorithms can aid ‘what-if’ analysis and highlight correlations in the data. • Personalised Assistance in Real-time According to a Salesforce report, 70% of consumers say a company’s understanding of their individual needs influences their loyalty. Retailers are now equipped with the next level of personalisation where they are able to provide just-in-time assistance and proactive care. AI can identify potential customers based on purchase history to promote and share focussed offerings in a brand. When such customers come in vicinity of the store, a message with an offer can be sent on a mobile phone. • Improving Adoption of Loyalty Programs Loyalty programs seek to go beyond registration and occasional cashing in offers. Inactive memberships can result in relationship breach. Demographic look-alike mapping, targeted efforts can be made to convert this into an active membership by sending right offers at the right time.

  4. Operations • Supply Chain Retailers can now obtain a holistic view of their supply chain covering raw material acquisition, production and last-mile delivery by adopting intelligent, data-driven processes. This perspective is essential not only for stock forecasting and managing customer expectations but also for a shared common understanding among all the members of the chain. Advanced analytics can predict demand spikes, identify bottlenecks and reduce supply shortages for dozens of products. • Invoice Processing Digital processing of invoices requires extracting textual information from images of invoices. Formats of invoices may vary depending on issuer of the invoice. Cloud-based, OCR enabled service can help in expediting information extraction and improve SLA compliance significantly.

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