Predict Customer Behavior Like Never Before

Alexandra Simion knows a thing or two about innovation in the world of marketing technology. She also knows, first-hand, the challenges of being a busy brand marketer in today’s e-commerce ecosystem.

Figuring out how to track, respond to, and proactively get the right offers in front of millions of database members is a mind-boggling task to even the most savvy digital marketer.

Over the last three-and-a-half years with BrandAlley UK, Alexandra has worked cross-functionally to adopt new tech, help connect systems, and ensure brand consistency. But everything has changed for the retailer since adopting AI about a year ago.

As she reflects back, BrandAlley’s astute and pragmatic Head of Marketing knew that she would be taking a risk by adopting artificial intelligence.

“As a business, you can’t succeed if you don’t take risks… imagine that you’re adopting something that you’ve never used before, maybe no one else has used before… there’s definitely a risk, but you have to make a decision — is it more important not to take the risk, or is it more important to try and see what happens?”

          — Alexandra Simion, Head of Marketing, BrandAlley UK

AI has reached a tipping point. Retail companies worldwide are employing various use cases to affect enterprise-wide impact more quickly than ever before.

For e-commerce, AI shines brightly (and is getting much better), specifically, around predicting customer events. This might include purchase probability, likelihood to convert, who will churn, who will take action on certain offers, anticipated revenue, and more.

“We were able to use #AI to see when an individual would lapse, & communicate with that person” says @AlexandraSimion of @BrandAlleyUK       CLICK TO TWEET

Why the sudden and widespread interest in AI? The answer is three-pronged:

• Key AI-related technologies (machine learning, AR and VR) have passed the peak of inflated expectations (Gartner), and are well-understood than ever.
• The AI Revolution is underway, and digital marketers are awakening to the reality that scaling 1-to-1 interactions is impossible to achieve without automation and AI.
• AI makes sense. Nothing has carried higher potential than AI when it comes to efficiency, automation, and data analysis.

AI in Action: Understanding Tangible Benefits

If AI can drive autonomous cars and help us fly people into outer space, it should be able to — and certainly can — change the way e-commerce brands both dissect their data and communicate with customers.

Machine learning, deep learning, and predictive analytics (subsets of AI) connect data, decisioning, and marketing automation in a way we simply haven’t been able to do before.

Three big ways this is all coming together for e-commerce teams include: targeting lapsing or churning customers, converting first-time to second-time buyers, and predicting revenue for lifecycle segments.

How AI Targets Churning Segments

If you know that Customer I buys every 60 days and Customer II buys every three weeks, you know each has very different purchase cycles. AI can help understand which customer is a higher-value customer, and, if they go cold, it can deliver the content/offer most likely to convert them before it’s too late.

This requires testing and optimization over time, as Simion describes:

“#ArtificialIntelligence told us when to communicate w/ ‘likely to churn’ customers,” says @AlexandraSimion of @BrandAlleyUK       CLICK TO TWEET

BrandAlley generated a 24% increase in conversions among “churning” segments with AI as an ally.

How AI Increases First- to Second-Time Buyers

AI also helps to identify first-time buyers who are likely to convert again, and encourages a second purchase with the right offer based on what they bought the first time. It identifies these active buyers who are likely to convert, then provides an irresistible offer to get them to keep coming back. Over time, expect to see incrementally higher conversion rates and AOV of this  segment.

In their first-time to active buyer program, for example, BrandAlley saw a 10% increase in average basket value.

First- to second-time buyer campaigns are arguably the most important to get right. By its nature, this segment is at high risk for being a “one and done.” So, using AI, you can maximize the chances that first-time buyers return.

How AI Predicts Quarterly Revenue

For marketing executives who want to project customer lifetime value (CLV) and quarterly revenue, AI can be helpful in forecasting dollars for upcoming events.

For example, BrandAlley anticipated revenue for the business for all lifecycle segments for the quarter ahead.

Final Thoughts About How E-commerce Companies Are Predicting Customer Behaviour Like Never Before

Companies like BrandAlley showcase what enterprise AI implementation looks like in practice. They’re driving tangible impact using AI, and go way beyond abstract benefits like “AI saves time,” and “AI automates messaging.”

AI helps predict important aspects of customer behavior, including which segments are most likely to take action and on which kinds of offers.

When we talk about AI fueling the Fourth Industrial Revolution, with respect to e-commerce,  this is exactly what we’re talking about — and why we’re so high on AI.

Handpicked Related Resources:

• Customer-Centricity for Retail Survival: BrandAlley’s Story [Video]
• Alexandra Simion of BrandAlley and Alex Timlin of Emarsys Share How to Leverage AI Martech [Video]
• [Revolution Series] Alexandra Simion, Head of Marketing, BrandAlley

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About the Author

Michael is Digital Content Manager at Emarsys. In conjunction with his team, he manages the Marketer + Machine content hub and podcast – creating best-of-breed, educational material for e-commerce and digital marketers. Michael is a published author on industry publications including Content Marketing Institute, JeffBullas.com, Business2Community, and others. 

Connect with Michael: LinkedIn • @mjbecker_