Machine Learning and omnichannel shopping experience

A U.S. study examining the behavior of over 46,000 customers during a 14-month period in collaboration with a retailer found that 73% of shoppers used multiple channels during their shopping journey.

By Valli Bollavaram

The future of retail lies in omnichannel sales. Shoppers today are demanding enthusiasts. They prefer to have multiple options across retailers to make the best shopping choices. They expect to shop via any channel, any time, and expect each channel to provide a customized yet seamless experience. A U.S. study examining the behavior of over 46,000 customers during a 14-month period in collaboration with a retailer found that 73% of shoppers used multiple channels during their shopping journey. For retailers, embracing this reality, engaging customers in their preferred shopping medium and personalizing these experiences are critical to retaining loyalty.

Machine Learning and its applications

The success of the omnichannel approach will depend on its execution. In an ideal scenario retailers choosing to go omnichannel will need to meet shoppers expectations upfront, while delivering a seamless, integrated experience. Machine Learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed to and has a major role in helping achieve this ideal shopping scenario. Gathering data received from customers at various touchpoints and leveraging this data using machine learning algorithms can help predict customer preferences in a scientific manner.

Many companies like Apple and Starbucks are optimizing and consolidating mountains of data that are generated at different points of interaction with the customer. This includes the customer’s purchase history, experience with customer service, and most preferred channel for shopping. Apple’s sales executives can access the customer’s history at all interaction points available via an iPad. Using this data the executive is able to personalize his interaction with customers and guide them on which product to purchase.

Starbucks launched the ‘My Barista’ app through which they can track customer preferences. This knowledge helps guide them to order their favorite drink. In addition, the app also provides an option to pick up their order from the nearest location, tracked based on their mobile phone and Google maps. All of this is possible due to machine learning algorithms that make sense of a variety of data generated, providing the capability to learn new patterns and predict future buying options.

Data drives omnichannel experience

The first step is to have customer data and shopping preferences consolidated across all channels. This includes data such as demographics, weather, sentiments, competitor pricing, shipping, macroeconomic trends, historic sales, assortments, planograms, etc. Providing free Wi-Fi for customers who walk into stores is common across all retailers. Once the customer logs in to the Wi-Fi, the retailer will be able to identify the customer, understand purchase patterns across all channels, identify which items were browsed and not bought, and identify any upcoming life events.

At this stage, machine learning algorithms can be applied to analyse data gathered from various customer touch points and understand life events. It can also be used to produce personalized marketing promotions on a product that the customer has shown interest in the past or based on previous purchases/browse behaviors. All purchase receipts can also directly e-mailed to customers for that extra digital edge. Even if the customer doesn’t directly interact with digital channels in the store, employees can utilize digital touchpoints to help provide a seamless experience for them. Other personalisations such as sending a customer their shopping list based on past purchases or providing coupons for products are some of the ways to deliver a seamless retail experience. .

The benefits of machine learning for customers extends beyond the point of purchase. It can be used to forecast sales more accurately at a local store level. Inventory can be stocked based on forecast, thus ensuring that there is the right level of stock for customers. This ensures that the chances of a customer walking into the store and not finding the productis significantly reduced.

Retail is no longer a business of buying products and selling it to customers at the most profitable price point. Retailers have to understand the needs of customers and personalize the assortment, optimize the delivery, provide personalized offers that match their needs and enrich their overall shopping experience, essentially marketing to individual customers. This is at the heart of a truly omnichannel experience. Machine learning plays a pivitol role in addressing this; helping tailor decisions to individual customer needs and influencing the way they shop.

The author is Vice President – Enterprise Data & Business Intelligence Engineering, Target India.

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