Digital transformation in the retail industry today relies heavily on the use of AI.
Most visibly, use of artificial intelligence (AI) in product recommendation has completely changed the way purchase occurs today.
Be it online or offline, AI can parse through large amounts of data from varied sources and come up with unique customer insights. The use cases of AI go beyond data analytics. AI is opening new ways to enhance customer experience, by improving customer satisfaction, providing customized services and formulating accurate responses to customer queries, among many others.
Personalization and Customer Insights
Brands that create a personalized experience by integrating advanced digital technologies have a better chance at customer loyalty thereby increasing the retailers’ return on investment (ROI). Whether it is a mobile application, a website or an email campaign, the AI engine can continuously monitor all devices and channels to create a unified customer view. This kind of integrated view enables the retailers to deliver a seamless customer experience across all platforms.
AI-based product recommendation in the retail industry incorporates data from various sources like:
- Customer clickstream behavior such as views, likes, products added to cart
- Transaction details such as date, time and price of purchase
- Stock data based on color, model, size and so on
- Customer review data
- Data from social media websites
- Retailer’s priorities such as preference of products or brands to be displayed
- Popularity of products
- Frequency and monetary value of customer
According to a study by Barilliance, 31% of ecommerce site revenue is generated from personalized
Advantages of an AI-based Product Recommendation System
Today’s consumers are constantly in search of better retail experiences and newer products. Customization of the shopping experience and digitization of the retail industry has been vital in advancing retail industry revenues. The disruptive impact of AI in retail is seen across the value chain and AI is emerging as a powerful tool for retail brands to gain a strategic advantage over their competitors. Huge amount of data is analyzed by the AI product recommendation engine and guarantees several benefits:
- Fulfills customer expectation
- Boosts conversion
- Improves customer service
- Increases sales and profitability
According to Personalized Commerce study, brands that offer personalized lifecycles not only have a raised ROI(300+%) but are also in the best position to quickly pivot their business to meet changing customer behavior.
Top three use cases of AI in product recommendation
The deep insights uncovered by AI systems are imperative to retail organizations. Applications of AI in retail are manifold today. The following use cases have seen high adoption and impact:
1. Product Discovery
Product discovery is one of the most crucial steps in a shopper’s journey. A simple discovery endears
the retailer to the customer and also ensures retention for future purchases. The result of search on a website needs to yield information specific to any given customer. The products of the search should be as close as possible to what the customer has in mind and desires. Shoppers expect a more personalized approach rather than being bombarded with a bunch of offers. The key is to offer fewer but most suitable choices to the customer, without overwhelming them with too many options. This can be done with retail personalization which takes into account historical data(AI-driven) and real time shopper intent. This kind of personalization is crucial not only for meeting customer needs with respect to a product but also makes the customer feel unique and emotionally connected.
2. Recommendations Along the Purchase Map
Different types of recommendations need to be placed, beginning from creating awareness and up to the buying journey, in order for a successful purchase to be completed. For instance, best sellers should appear on the homepage and at the top of the category page whereas related products should be listed on the product page. The following are some of the listings which can be adopted by sellers in order to boost their sales
- Homepage - Categories such as popular products or best sellers
- Category page - Category best sellers
- Brand page - Brand bestsellers
- Product page - Complementary and related products. Curating this page based on historical data of the customer works well.
- Cart page - Complementary products based on the cart and wish list items can be listed here
- Order Confirmation page - Wishlist items or complementary products could be listed here
- Recently Viewed page - The display should be customized based on the customer’s movements along the website
All these different categories of product recommendations serve to keep the customer up to date with the latest listings from the company along with products which might pique the interest of the customer. Shoppers who engage with AI-powered product recommendations have a 26% higher average order value (AOV).However, care should be taken during the journey so that the product recommendations do not restrict the purchase flow.
3. Understanding the Pricing Structure
A pricing model that scales business can help to control costs. For every product recommendation the AI tool uses different algorithms. Therefore, it makes sense to choose an AI tool which gives out recommendations based on different categories as seen in the previous point. The different algorithms can be directed towards various sections such as for
- Related products
- Popular products
- Recently viewed products
- Discounted products
- New Arrivals
- Frequently bought together items
- Cross sell
Demand-based pricing methods such as price skimming, price penetration and value-based pricing all take into account the above-mentioned points. Price skimming involves identifying and charging the highest price of a product consumers are willing to buy and charge less as time passes. Penetration pricing is the process of attracting new buyers to a product by undercutting its value upon initial offering. Value-based pricing on the other hand is the process of pricing a product depending on what value consumers attach to it. Such products are usually designed to enhance a customer’s self-image.
The categorization of products arrived at by AI systems can help the retailer to arrive at a price that drives the bottom-line of the business AI can efficiently scan through petabytes of data, that provides context around customers needs and behavior. This level of intelligence is vital in providing a personalized shopping experience. With unlimited possibilities, AI is helping with a gamut of functions making the digital experience of the retail industry a fulfilling one.