Attend any conference, read any publication, or talk to anyone in the retail industry, and you’re guaranteed to hear about about the challenges of reaching shoppers in today’s omnichannel world. Consumers engage with your brand through multiple channels — from online, to mobile, to in-store — which creates challenges for brands and retailers trying to unify their channels and reach shoppers with the right experience at the right time.

Customer experience has always been important, but, now, brands and retailers must think about the customer experience across many different touchpoints. A critical component of customer experience is treating each shopper like an individual. This is a lot easier to do in-store, right? You have sales associates that can speak with each shopper about their tastes and style. This becomes much more challenging when shoppers visit your website.

In an ideal world, the minute a shopper landed on your website, you would know exactly what they wanted, where they’d already been shopping, and what they’d already bought. If you had all of that information, your website could act like the digital version of a knowledgeable sales associate, serving up product recommendations and personalized promotions. The whole shopping experience would be smarter and more customized to what the shopper wants, not what you think they want. Unfortunately, that kind of data is hard to come by for many brands and retailers.

In its place, we have things like buyer personas that we base on theoretical characters combined with demographic data and consumer behaviors. But more often than not, the data we build these personas on is incomplete or outdated. And to make matters worse, we then build marketing strategies on top of of this data.

The result? We end up targeting personas, not people. As helpful as those personas are in centering our marketing messages, they strip us of that one-to-one customer interaction — and sometimes leave us blind to what people really are in market for.

You might have a 24-year-old woman buying a man’s shaving set for her boyfriend or a 60-year-old man buying kids’ toys for his grandchildren. If you relied on personas and demographic-based audience segments, would your website provide a positive customer experience for these people?

Probably not. But this isn’t a marketing problem — it’s a data problem.

Use buying signals to reach people instead of personas

There’s a better way for marketers to reach actual people, not personas: focus on buying signals, instead of demographics or psychographics.

Buying signals — like when someone looks at a lot of product pages or reads a bunch of customer reviews — show purchase intent. And purchase intent tells you a lot more about what a person’s interested in than demographic or higher-level personas alone.

In our research, we found a prime example: a 44-year-old male who was in the market for a rice cooker. Nothing about his age, gender, location, or background would make you guess he could be sorted into a “small kitchen appliance audience,” but his shopping behaviors made his interest abundantly clear.

Smart marketers are making purchase intent data a centerpiece of their e-commerce strategies, but it’s hard to get the full picture of what people are in market for and what their complete shopping journey looks like. Whether you’re working with a data provider or have an internal team focused on customer data, here a few guidelines to follow when creating or purchasing audience segments for your campaigns.

1. Ask what constitutes a buying signal 

On its face, the term “buying signal” seems simple and intuitive: it’s any signal that indicates someone’s purchase intent. But in the world of e-commerce, that could be anything and everything. If someone’s reading an article about the latest smartphone that’s been released, does that mean they want to buy that phone? Maybe. Or maybe not.

Being clear about what you mean — and what any third-party vendor means — when you talk about buying signals is crucial. Don’t be afraid to ask questions and drill down into what someone means. Ask how long someone has to look at something for that to count as a buying signal. If someone looks at a product page for 10 seconds, they’re probably less interested in that product than someone who looks at the product page for two minutes or more.

2. Understand how many buying signals or actions cause a user to be placed in an audience segment

If a data provider is looking to sell you users that fall into a particular interest segment, it is important to know what factors have led them to place the users in that specific segment. Is a user added to an audience segment after a single data signal, or does it take multiple signals before the user is added?

Here’s an example: A data provider might market their segmented audiences with jazzy names like “home improvement enthusiasts” or “handy husbands.” That’s great, but how many signals does it take to put someone in that category? One signal doesn’t qualify as purchase intent. Looking at the product page for a toolbox once doesn’t make you a home improvement enthusiast.

Generally, a higher number of signals in a given time period indicates a stronger level of user intent, though you should ensure that those data signals are both fresh and relevant.

3. Use the right kind of signals to place users into an audience segment

I mentioned the “home improvement enthusiast” and “handy husbands” audience segments. If these are built with the wrong kinds of data signals — like reading a DIY article once or visiting Home Depot’s website one time — then they’re not representative of the audience you’re trying to reach.

Someone who has looked at 5 toolsets in the past 30 days, read multiple reviews, thoroughly researched them, and then purchased a toolset is much more likely to engage with and be influenced by timely home improvement product ads. Your audience segments should be built using data signals that indicate actual purchase intent.

4. Understand how long a user remains in an audience segment and what action (or period of time) causes them to be removed

Consumer desires fluctuate based on their mood, interests, what they want or need to buy, and even the time of day or month in the year. If a user is placed into an audience segment but does not continue to display interest and intent, they should be removed from that segment.

You should be looking for a data provider who has a strategy for keeping their audiences up to date with current engaged users, not with old information. Audience segments with old data can cause your marketing to be irrelevant, for example a recommendation or advertisement for a piece of clothing you’ve already bought.

By paying attention to users’ real actions instead of demographics or theoretical characteristics, you better ensure that your campaigns reach the right people at the right time. It’s not that segmentation or buyer personas are flawed strategies in e-commerce. It’s just that even the best strategies can be made even better when you use the right data.


For more information about using shopper intent to build out marketing and advertising programs, check out our e-book: Separating Signal from Noise: How to Find, Reach, and Win Today’s Shopper.

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