A man sitting at a restaurant table, while shopping on his smartphone - Mobile commerce concept

The familiar line from the American Express commercials, “Don’t leave home with it,” was meant to have consumers remember their credit cards. It is an especially useful reminder when purchasing online. With the advent of cashless retail (or cashierless retail, as some experts are claiming), consumers can shop via the convenience of their smartphones now, which can render a website less important in an ecommerce strategy. That shift alters the analytics strategy as well.

Smartphones are at the center of the cashless movement. Consumers have grown reliant on them for more than calls — the new Lincoln Aviator can be unlocked and locked through a smartphone. Consumer adoption of mobile devices and additional self-service options such as ordering kiosks has blended the retail experience in-store with online convenience.

As a consequence, marketers must consider predictive models that can stitch together retail behavior across devices and determine how sustainable the retail activity is. As I mentioned in my post on Amazon Go, digital analytics is a building block for planning a predictive analytics stack. The solutions have begun to offer better attribution features, such as the recently introduced Web + App feature in Google Analytics. But overall, the analytics strategy for most businesses, let alone retailers, have focused on media — activity on a webpage and, in recent years, on an app page. 

With cashless retail, marketers have to incorporate more context that surround the media customers use. This means inducing more statistical applications to the metrics. For example, the frequency of conversion activity can serve as initial data for predictive analytics machine learning models. The data can be exported into a data language like R or Python to develop an advanced model. The effort can also leverage the latest machine learning frameworks such as TensorFlow.

A few useful models can arise from this trend, such as:

  • A forecast on sales based on analytics conversion data from a particular customer segment. The end result is a better inventory maintenance or guidance on the duration of a sales activity.
  • Applying Market Basket Analysis, a statistical technique to determine what combination of products and services are being frequently purchased together. The benefit is being able to upsale the right products or provide the right combinations of products that can be used in personalization among a customer segment.
  • A retailer can establish a quality recommendation engine based on purchase data from analytics to suggest preferred choices and help to optimize sales.

Related Article: Is Omnichannel Retail the Future of Ecommerce? 

Retailers Experiment With the Cashless Trend

Retailers have been experimenting with cashless retail. Adweek reported a number of retailers are gaining traction with the cashless trend. Microsoft announced a cashless trial partnership with retailers — certainly the largest bid against Amazon Go yet.

Marketers should also expect more discussion on the societal impact of cashless tech. Researchers are concerned that a significant percentage of urban populations have a smartphone, but do not have access to credit or standard banking that can be linked to a digital device for transactions. The consequence is a potential to deprive people of vital products and services from stores that have adopted cashless tech.

Some metropolitan areas like Philadelphia have banned cashless retail. Meanwhile, CNBC reported in April that Amazon will allow cash purchases at its Go stores. Marketers should monitor these debates and learn what they can do to avoid economically redlined neighborhoods with cashless retail while still targeting the customers they want to serve. 

With these concerns in mind, marketers should still feel confident that developing analytics to account for cashless retail experience will benefit customers with agile retail experiences. Competitors are finding ways to adopt analytics for a strategic advantage, so marketers should use the current rethinking of the checkout experience as an opportunity to refine their analytics and machines learning strategies.