How AI is Helping Answer 3 Critical Questions in Retail

Artificial Intelligence (AI) is noticeably making a difference in how we live and work. Search engines predict words and translate text, social media connects people, smart appliances respond to ambient conditions, and public transportation is becoming autonomous. Similarly, the retail industry is set to unleash fascinating possibilities by embracing AI.

Imagine the retail value chain when machine learning is fully operational, with the power of natural language processing, image identification and voice recognition software working together to produce better experiences for consumers. The cumulative effect can be transformative for retailers, across and beyond their enterprises.

Here are three questions for retailers that can be enhanced by deep learning and predictive algorithms.

What Do My Customers Want?

It's difficult to address this fundamental question. Since customer needs differ based on demographics, unique preferences and location, providing shopping recommendations becomes a challenge. To compound matters, some products are one-time purchases, others are made frequently, still more are impulse purchases, and others can be seasonal choices. Accordingly, a store concierge, mobile shopping app or e-commerce portal need to make appropriate suggestions based on an intimate understanding of the shopper’s intent.

Machine learning (ML) is part of the AI technology stack which can integrate historical and current data to better understand customer needs, rank available products for a specific shopper, and apply the ranking to deliver personalized product recommendations. It can also help drive revenue by optimizing upsell and cross-sell. Innovative algorithms are continuously learning and analyzing massive data sets to understand customers’ intents and desires. Retail data analyzed within ML span across contextual data such as customer age, gender, etc., as well as variable (i.e., more specific) data such as purchasing history, browsing behavior and many others. The result is personalized insights that can serve retailers in better understanding their customers and tailoring exciting experiences.

Enhanced visibility into a shopper’s preferences permeates across all customer touchpoints: shopping via the website, mobile app and brick-and-mortar stores. Store associates become more productive when they're equipped with AI-powered apps on handheld devices. ML algorithms dynamically generate personalized product bundles and provide suggestions for complementary items with a higher conversion potential.

Do I Really Know My Customers?

Accuracy in segmenting your customer universe boosts sales planning, inventory management and supply chain operations. Traditionally, customer segmentation accounts for geographic, demographic, psychographic and behavioral attributes. It works for country, region and even store-level promotions. The "purchase anywhere, consume anywhere" dynamic facilitated by omnichannel commerce requires an amalgamation of segmentation criteria to serve the right customers with the right products at the right times.

AI techniques analyze characteristics of large segments to create smaller pools with near identical shopping styles and requirement patterns. These techniques can identify customers who may be a good fit for well-defined marketing buckets.

Accurate micro-segmentation enables retailers to better engage with customers and maximize their lifetime value via digital channels as well as brick-and-mortar stores. Retailers can gain a consolidated view and a nuanced understanding of market segments and products. ML facilitates a generation of marketing content that resonates with specific needs and situations. Effective communication through an appropriate channel drives conversion and revenue. Accordingly, retailers can text a promo code, email a product list showcasing new arrivals for a specific product category, or share a location-based advertisement with a mobile user in-store. Furthermore, smart segmentation enhances the online shopping journey through personalized site navigation.

Can I Foresee My Customer’s Next Action?

Store managers use conventional wisdom and rudimentary models for sales forecasting and returns management. Meanwhile, advanced predictive models calibrate the interplay of different factors that influence a transaction (purchase or return) and intelligently forecast volume. AI combines hundreds, even thousands, of seemingly unrelated variables to increase the accuracy of predictions. Even a small improvement in the accuracy of a prediction can make a meaningful difference to the bottom line via more accurate promotions, assortment, logistics and inventory management.

ML enhances channel conversion by providing a real-time definition of product category and subcategory. With showrooming and webrooming commonplace, insights into a customer’s digital trail and habits can influence the path to purchase. For instance, retailers can use learnings from AI-based technologies to customize the user interface; add features such as buy online, pick up in-store; plan promotions; modify the planogram or web page layout; and replenish inventory.

The ability to predict a return at or prior to purchase allows the retailer to take timely and informed action. The incidence of returns can be reduced by offering a discount to close a sale. For example, a fashion retailer may recommend a consultant, size chart or virtual dressing room to prevent a product return.

AI is set to become ubiquitous in every sphere of life. However, adoption will be determined by consumer needs as well as the business context. Retailers are already using AI-based technologies to make shopping journeys more personalized and goal-oriented. And consumers are also benefiting from the adoption of these new, powerful technologies, enriching the customer experience in both subtle and sweeping ways.

Arish Ali is the CEO and co-founder of Skava, an industry leader in providing innovative e-commerce solutions.