Avoiding Data Paralysis and Getting Actionable Information

Hey everyone – Before I jump in, I wanted to let you know that AMO will be going dark starting Wednesday and will return for the Tuesday edition on September 13th. I am going on vacation. But I’ll be back, recharged, and ready to keep going!

Now let’s jump in…

In an age of analytics, there is a bias toward as much data as humanly possible. There’s a belief that everything needs to be tracked. If there’s something unknown, the first question people ask is, “what data do we have?” As a result, people have learned to lean on data as a crutch in many respects.

And it happens in all departments. Edit teams do it. Sales teams do it. Unfortunately, there is an over-reliance on data. None of this is to say that I am against data, analytics, or whatever term you want to use. On the contrary, I believe it’s paramount to running an efficient business.

However, I believe 95% of the data that we collect is pointless. That’s not because the data isn’t interesting. I think it’s pointless because it distracts from what matters. And as the number of data points increases, you’re more likely to fall into a world of data paralysis.

At the core of it, data paralysis is a scenario where you have so much data in front of you that you’re afraid to make a decision. As we’ve become more dependent on data, we’ve become more afraid to make decisions. And we are left asking ourselves, “what data am I missing that might make this easier?” This becomes a torturous process that leaves you unable to act.

So, how do we avoid this and focus on what matters?

You need to ask yourself what really moves the needle for the business. What activities are going to make the business grow? And that doesn’t have to be revenue. For edit teams, it can be what are the most impactful stories. For growth teams, it’s what brings in new audiences. But you have to ask yourself what matters to the business.

I’ll give you some examples.

I’m a big believer in owned audiences. I don’t view someone as my user until I can control the means of communication. There’s a reason I’m so bullish on newsletters. And so, what I care about is what drives these owned audiences. Here are a few of the data points I might want:

1. Which external sources drive conversions?
2. Which internal sources drive conversions?
3. What is the conversion rate from different areas of the site?

I know exactly which sources give me the desired conversion with the first two questions. And with the last one, I know what types of units are performing. This data is actionable.

Let’s say you’re running a subscription business. When getting someone to pay, you might ask yourself similar questions to those above. But then, you also want to know things after they’ve signed up. For example:

1. What content are paid subscribers reading?
2. When does a subscriber become a zombie?
3. What percentage of zombies do you reengage?

As a quick aside: a zombie is someone who is still paying but doesn’t consume any content. They’re dangerous because you never truly know when they will churn.

Let’s dig deeper into acquiring subscribers because I think it’s essential. Data points that you might want to know include:

1. How does a user obtained from one source perform against another? (You’d have to define performance)
2. What is the LTV of subscribers by source?
3. What content performs best on other platforms?

You’ll notice that all nine of these questions I asked are very specific to a part of the business that you’re focusing on. It’s not to look for the sake of looking at them. Instead, you want to look at data that will actually help improve the business. Knowing how many pageviews you have is nice to know, but what are you going to do with that? Compare that with asking which sources drive the most engaged subscribers. One is clearly better.

I want to share one final example, which takes what I described above and amps it up 10x. Back in 2019, Ned Berke, Chief Strategy Officer at BlueLena, created the Audience Explorer dashboard when he was part of the Center for Cooperative Media. To this day, I think it is one of the most impactful dashboards created in analytics.

Why do I think that?

Because it’s very clear that there is a problem being posed and then the right data points are being sought out to help inform a solution to that problem. What’s that problem? I know very loyal people are likely to be my best monetized, so how can I get more loyal users?

How does it help solve that problem? It does it elegantly by breaking your audience into three buckets: casual visitors, prospective loyalists, and brand lovers. According to the Medium post:

Casual Visitors are aware of your publication, visiting no more than once per month. As their interest in your coverage increases (perhaps because you do great coverage, or do a better job distributing it — or both), they move down the funnel to become Prospective Loyalists. These visitors are much more engaged with your site, visiting two to five times each month. On average, they consume more pages per session as well. And as they find more that they love, your publication becomes an indispensable part of their routine and they visit six or more times a month. At that point they are considered Brand Lovers — in many cases, this is less than 10 percent of your audience yet drives up to 60 percent of pageviews each month.

The dashboard clearly articulates these three layers based on how many times a person visits the site. Of course, brand lovers are the end goal. But you want to understand the various steps it takes to get people to that point.

This is not looking at data for the sake of it. Instead, it is looking at a lot of data in a structured way with an apparent problem in mind. At a growing media company, there might be hundreds of issues or questions that you are dealing with. Each of them should have obvious data points that you track to help inform decisions.

And that’s the ultimate point. Being handed a bunch of data does nothing for you. It’s infinite. You, the operator, must know what you are looking for. Be clear about the problem and seek what you need to make that decision. Otherwise, you’ll stare at a bunch of charts with no idea how to activate the information. And that’s what leads to data paralysis.

Thanks for reading. If you have thoughts, hit reply or join the AMO Slack. I’ll send Tuesday’s newsletter, and then I’ll see you on September 13th.