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Using Omnichannel Behavior to Maximize Contextual Relevance

[To learn more about the research we’ve conducted on the marketing strategies of today’s top brands, click here.]

One of the key cornerstones of behavioral marketing is contextual relevance: the ability to deliver the right omnichannel campaign at the right time to each customer, depending on where they are in their individual customer journey. This creates a great customer experience, no matter the marketing campaign.

Not only does successfully delivering a contextually-relevant campaign demonstrate a level of insight into each customer’s needs and preferences, but it can also encourage engagement across multiple channels and drive repeat purchases. In fact, a single missed signal or inaccurate campaign is enough to turn your customer away — potentially for good.

To test this out, we looked at the strengths and weakness of a real campaign from a retail brand, analyzing their cross-channel marketing strategy in detail. We created a customer account with a retailer that sells casualwear and workwear for men and women. For three weeks, we took note of all campaigns and communications the retailer delivered at numerous touchpoints in a typical customer lifecycle. We also made note of any potential engagement opportunities that the brand missed out on.

In this case, the brand missed a huge change to provide a seamless experience with contextually relevant product recommendations. Here’s a look at the positive and the negative sides of a real-life omnichannel approach to customer experience.

Analyzing a real omnichannel marketing campaign

In the immediate moments of our journey with the brand, things started out with promise. In addition to asking for a name and an email address, the brand asked us to note if we’d prefer to receive recommendations on products for men or for women. Right off the bat, the brand made it clear that they wanted to try to deliver product recommendations that aligned with our personal preferences. But the problem with this approach is that solely using demographic data to deliver campaigns and product recommendations isn’t nearly as effective as sourcing customer data directly from interactions – and, in fact, it’s rarely as accurate.

The marketing strategy mistakes became clear

Once we switched from browsing items on the store’s webpage to placing an item in a cart on mobile, we immediately saw a few missteps in customer experience. While we did browse women’s products during both sessions (women’s socks on the webpage on Day 1, women’s T-shirts on mobile on Day 7), as we promised we would, the brand unfortunately didn’t go beyond asking for that surface level data.

Instead of understanding that we were interested in looking at both socks and T-shirts, and then sending campaigns that recognized both browsing sessions, the brand continued to deliver emails that had little to do with the products we’d looked at on their site. To the brand, it was enough that they’d recognized we were chiefly browsing products for women – the campaigns they sent didn’t do much beyond announcing that they were “for women.”

A need for personalization

With the signals of intent we were displaying across multiple devices and multiple channels, the brand missed a fairly sizable opportunity to stitch together those signals into a more cohesive customer profile. If they’d been able to unify all our actions together and source insights from what they observed, they would have been better equipped to deliver the kind of product recommendations that we actually wanted to see, and they could have honored the preferences we demonstrated from our actions in a way that would have led to increased contextual-relevance and promoted engagement. Behavioral marketing is, after all, all about behavior.

Click here to read an overview of our project timeline and learn more about the best and worst of retailer’s digital marketing strategy.

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