Taking Users From Unknown to Known to Known+

As the battle to acquire new users becomes increasingly difficult, the ability to derive more value from each of your current users becomes more important. This aligns with a recurring argument we’ve made here at AMO that the secret to thriving in the future is boosting the lifetime value of a reader.

The problem for much of the media industry is that, to increase the value of a visitor, we all need to get better at understanding the who behind a pageview or email subscriber. It’s no longer good enough to just have people consuming our content; we need to put ourselves in a place where we can articulate exactly who is consuming the content and then, in some instances, articulate why. We’ll come back to that why in a minute.

And this need is true irrespective of your business model. If you’re going to charge for content, knowing what kind of content to create for your audience is critical. On the market services side, being able to directly target the right sponsor messaging to the right user is equally impactful. In both cases, you increase the LTV of a reader.

But to do this, you need to know who your reader is and why they’re reading the content.

To break this down, it helps to visualize users in three buckets: unknown, known and known+.

• Unknown: these are the people who come to your site but have told you nothing about themselves.
• Known: these are the people who have told you something about themselves and you are starting to build a profile on who they are.
• Known+: these are your known users that have exhibited certain intent behaviors, such as being in the market for a piece of software or desiring more depth of information on a certain topic.

Each of these is important to running a media company, but the goal should be to move the users down the funnel as best as you can. You’d be shocked how hard it is to get to a known user. The reality is, it’s complicated and takes time to get information on your audience.

So, let’s spend some time talking about how we can move readers from unknown to known and then from known to known+.

But first… A note about our partner, Bombora.

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Unknown → Known

There are a number of reasons that publishers have such a low conversion rate from unknown to known, but the two major ones are:

1. They get the bulk of their traffic from platforms like Google and social media.
2. They are not creative enough in capturing an email address and asking for other information about the reader.

If you can solve for those two, you are already well on your way to solving the unknown to known conversion. Unfortunately, you don’t have much control over the first; platforms are getting stingier with traffic. So, let’s focus on what you can control, which is your website.

There are a number of things you can do to qualify who a reader is. You can experiment with any of these, but I’ve found there’s no single tactic that works perfectly. Gathering data about your audience is about using all available tactics to ensure you maximize the data available to you.

The most straightforward solution is on newsletter sign-up. A complaint I hear from many newsletter folks is that asking for data on newsletter sign-up hurts conversion rates. There is some truth to that. However, there’s an easy way around that: use a redirect.

Let’s use Retail Dive as an example. When you visit their site, there’s a clear CTA to sign up for the newsletter. All they ask is for your email and which newsletter you want to receive since they publish multiple.

But once you click submit, the system redirects you to a second page. I included a screenshot of it below.

At this stage, the user’s email has already been signed up for the newsletter, so you don’t have to worry about a drop in conversion rate. However, now you can start asking for more information. Many folks will simply close this page down; some won’t. By adding this extra step, you automatically move a fresh, unknown reader squarely into the known category.

This page can also be a great way to monetize that known user immediately. Retail Dive promotes three gated pieces of content that generate leads for their partners. You can do this in a more advanced way by showing offers based on the data fields inputted above. If they say they’re in marketing, show a retail marketing offer. If they say they’re in supply chain, show a supply chain offer.

In any case, you’ve solved two things here. First, you got their email. Second, you got some of those subscribers to qualify themselves.

But what if you only got them to give their email address? What can you do then?

I’ve long been a fan of progressive profiling of first-party audience, where a user gives you information incrementally so that they don’t feel burdened. The rule here is that once you have a piece of data about someone, you shouldn’t ask for it again. If you’ve got their first name and email—using the Retail Dive example above—you shouldn’t ask for that again.

Instead, when a user lands on something that you’ve data-gated—a soft gate that requires the user to give you information about themselves—don’t ask for first name. Instead, ask for company name. Fast forward a few days and when they land on another data-gated article, ask for job title. Over time, you’ll gather information about the reader.

But what do you do when you can’t qualify who the user is? Either they’ve only given you an email address or, worse, they’re stubbornly remaining anonymous.

This is where data enrichment comes into play. There are a number of third-party tools that, if you send them an email address, can send data back to you to help deepen your understanding of them. But as we all know, getting an email address is hard and all the tactics above might not be enough.

That’s what makes Bombora interesting. Unlike many data enrichment tools that require an email, Bombora can take other digital identifiers—in a privacy-compliant way—and return information to you about them. Now, you know more about the visitors consuming your content even if they haven’t told you anything about themselves.

By working with a data enrichment partner, you can start to better understand who your readers are, where they work and what their company is in market to purchase. Taking the user from unknown to known gives you an incredible opportunity.

But it doesn’t have to stop there…

Known → Known+

We’ve talked a lot about getting the user to declare information about themselves. But there’s a problem with basing decisions on who someone is: you assume, based on information about them, what they’ll want. The outcome is you might be promoting content or sponsored offers to people that are not interested.

And so, the transition from known to known+ is an exercise in understanding what a user cares about. To do this well, you typically need a customer data platform. In essence, you track what they’re reading on your site and infer their interests and intent from that.

Let’s assume that a director of marketing is reading a number of stories about email marketing in your publication. They might be in the market for an email service provider, so promoting an offer from one of your partners to this person makes sense.

But that’s still pretty limiting because it assumes that you have a breadth of content to make those informed decisions. Having a reader consume one article about email marketing doesn’t mean they need an ESP. Maybe it’s not even two or three. If you knew users from a particular company were reading dozens of articles about email marketing across the internet, that would give you a ton of signals about the intent exhibited by that company and user.

To do this is very complicated because you need to convince your competitors to share data with you. You want them to let you know when one of your readers is on their site and what content they are consuming. But why would they do that? Because you would do the same for them.

In essence, you’d be building what’s known as a data cooperative. In exchange for sharing your content consumption data with other publishers, they share with you. It’s all anonymized and privacy compliant, but in the end, both parties win. This can become more robust as you add dozens of publishers into the mix because you can track that known user’s behavioral activity across other publications.

Now when you look at your audience, you’re not just judging them based on what they do on your site; instead, you’re getting a more robust picture of what their interests are. They may never read an email marketing story on your site, but they read two dozen across the data co-op. Now you can promote an advertiser’s content to them and the results will be strong.

And if you’re running a subscription business, this might inform new potential coverage areas. AMO very rarely writes about programmatic advertising, but if I knew that my readers were consuming a ton of content elsewhere about this topic, I’d have one of my reporters spend more time covering it. Why wouldn’t I? It’s still relevant to a media audience, but I’ve learned more about their interests.

So, how do you start a data co-op? Like I said, you need to effectively convince all of your peers—either direct or tangential—to work with you on sharing data.

Or… you can reach out to our partner, Bombora. Over the past ten years, Bombora has built a B2B Data Cooperative that includes thousands of leading publishers and media destinations, broad-scale and vertically specific. As a trusted partner, Bombora ensures that all data in the co-op is completely anonymized with respect to the publisher identity and user privacy. This is a one-of-a-kind asset built to benefit the publishing community at large. Click here to have a conversation.