Machine Learning to Multiply Multi-channel Shopper Opportunity

Posted on: January 2, 2019 | Posted by: Sona Sharma

Driving Cross-Channel Engagement & Revenue Online and Off

Today’s shoppers are masters of multitasking: their on-the-go lifestyles and ever-shortening attention spans demand it of them. For retailers, this has traditionally presented a significant obstacle. How can stores keep customers captivated long enough to drive transactions?   

Luckily, they don’t have to. Retailers should not aim to capture customers’ attention for extended periods of time—instead, they must learn to embrace the multitasking nature of modern shoppers, catering to the many channels and contexts in which they operate in order to drive sales in real-time. In fact, when augmented by the power of Machine Learning, this solution leads to the creation of a new and improved category of retail customers: multi-channel shoppers.

Who Are Multi-Channel Shoppers?  

Multi-channel shoppers are customers who are willing to interact and transact with retailers on both online and in-store channels, depending on what is most convenient for them in-the-moment. Today’s shoppers are naturally shifting their shopping habits to embracing the expediency of this multichannel approach: according to a recent survey by the National Retail Federation (NRF), 54% of U.S. consumers say that they shop via both online and in-store channels. With more than half of consumers already embracing multichannel shopping patterns, its popularity is sure to grow as lifestyles become increasingly busy.

How does Multi-Channel Shopping Benefit Retailers?

Multi-channel shoppers drive engagement, satisfaction, and revenue for retailers. The NRF found that, when shopping, multi-channel customers spent an average of $93 more than single channel shoppers. This is due to their willingness to interact on multiple channels, which naturally presents them with more offers, opportunities, and touchpoints that encourage transaction.

A prime example of multi-channel shopping is the “buy online, pick up in-store” approach that many customers choose in order to avoid stress in the checkout line. This scenario presents retailers with a multitude of opportunities to engage shoppers across online and in-store channels:

To save time, Jenn is shopping for her husband’s birthday gift online. She selects a watch from a large retailer’s site and adds it to her cart. When she reaches the checkout page, an offer appears: “Jenn: Save on shipping costs with our in-store pick up option.” Pleased with the idea of costs savings—and realizing she’ll be running errands in the right area tomorrow—she selects in-store pick up and checks out online.

When Jenn arrives at the store the next day, she makes her way to the customer service counter to pick up her gift. As she waits in line, her phone buzzes with a notification from the store’s app: “Jenn: Redeem loyalty points for 25% any full-priced item. Click here for code.” After picking up the watch, Jenn decides to use her points for a discount on another watch band to complete the gift for her husband.

In this scenario, the retailer leveraged multi-channel customer trends to create and optimize a multi-channel shopping experience—but it wouldn’t have been possible without the assistance of Machine Learning (ML).

How can Machine Learning Optimize Multi-channel Marketing?  

In order to accelerate the development of multi-channel shoppers who interact across online and in-store channels—and optimize the experiences of existing multi-channel shoppers—an intelligent solution is needed. Retailers are increasingly leveraging ML and other advanced technologies to act on customers’ history and current context in real-time to drive multi-channel sales.

For example, in the scenario above, Jenn received an in-app notification while she was at the store to pick up her husband’s watch. The store’s ML-powered Customer Engagement Hub (CEH) knew her location as soon as her phone connected with the stores’ Wi-Fi, and strategically crafted and delivered the personalized offer most likely to resonate with Jenn based on her shopping history, loyalty points, and current context (an in-store pick up).

Machine Learning models like these can track and act on customer data in real-time, ensuring that each shopper continues to move their customer journey forward—even if it takes multiple channels and spans multiple days. This strategic continuation systematically encourages the creation of multi-channel shoppers and optimizes their online and in-store experiences.

 

The ZineOne CEH features robust Artificial Intelligence (AI) and ML models in order to strategically encourage and cater to today’s multi-channel shoppers. Learn more about how the ZineOne CEH optimizes multi-channel retail experiences.

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