A.I. for Everyone in the Retail Org

In a successful retail operation, everyone can benefit from using AI tools — and for those tools to be most effective, everyone needs to be on board.

Artificial intelligence can supercharge the way a retail business functions, but its effectiveness suffers from two common misconceptions: one, that it’s a cure-all for the age-old problems bedeviling retail, like one-time buyers and customer attrition; and two, that it’s mainly useful for marketers. 

Let’s tackle these two misconceptions in hopes of making the most of AI to drive retail growth. 

 

Retail Artificial Intelligence Still Needs People

First, AI isn’t the only term that gets tossed around as a magical solution for retail problems — it’s used loosely and often interchangeably with other concepts like predictive analytics and machine learning. 

At the simplest level, artificial intelligence refers to a computer or machine performing tasks in a way that we humans would consider smart. Machine learning is a sub-category under this header, having to do with giving information to a computer and letting it learn on its own; and predictive analytics looks at what’s happened in the past and uses statistical modeling to forecast what will happen next. 

To a scientist, these and other related terms are all distinct. In the retail customer data world, they end up being used interchangeably because the broad category of AI and its various applications all involve computers performing complex math with great speed and accuracy.

Identifying profitable customer segments, isolating a single customer view, surfacing insights around purchasing patterns, cross-referencing shopping records and browsing history to predict likely next purchases — all of this is super useful, but it’s not magic; it’s just math. It’s work that people could be doing, only it would take us a lot longer. 

This means that machines are using their beefed-up computational power to do the heavy lifting so that we are free to think more strategically. Or, to put it another way, AI won’t solve your retail business challenges for you, despite any number of slick sales pitches to the contrary — but it can open up ways for you to work smarter and more effectively. AI in retail does not take the place of humans, because there still needs to be someone to steer it and act on the insights it produces.

 

Artificial Intelligence for All the Retail People

So who uses the tools and insights AI provides? The usual suspects are on the marketing team. Many retail AI platforms are built for marketing teams (hence the category of MarTech, which sounds like some kind of vengeful god), and for good reason. 

Marketers can use AI-generated segments and algorithms to know who to contact and when, to plan more effective welcome series and win-back campaigns, to target social media and ads, and to drive value by “following the customer wherever she goes” (but, you know, not in a creepy way).

What’s less common is seeing other teams in the retail organization using AI-powered customer data tools. But there’s plenty that teams outside of marketing can do to up their game with artificial intelligence. 

The finance team can use predictive analytics to forecast revenues, even for customers that have not yet been acquired. The merchandising team can get a granular view of what products are selling in which segments so they can make more precise decisions on stock and purchase timing. Executives can track critical KPIs that show overall client health, measure new opportunities, and understand what’s really driving revenues.

On top of the fact that individual teams can do their own work more effectively by drawing on AI-generated insights, sharing these tools helps everyone aim their retail beams in the same direction. Organizational silos are a major obstacle to a smooth-operating organization, and getting everyone on the same page about data-based strategy provides a common source of truth and promotes cross-functional effectiveness. 

Even business units that may not directly use AI tools will benefit when it is commonly applied through the organization. Customer service can provide a better experience based on what other departments are learning about customer behavior. IT can free up bandwidth for their higher-level projects by letting AI compile and sort segment data for them. 

AI-powered customer data can help you chop down churn, bring one-time buyers back for more, and find the very valuable customers you’d love to get to know better. But it’s not a magic button. It’s a stepladder that lets you work at a higher level. And in the competitive retail space where every stage of the customer experience is critical, it makes sense to get your whole organization to step up on that ladder and elevate their game. 

 

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