Estimated brand reach is the most important high-level metric that everyone seems to either interpret incorrectly, or ignore altogether.

Why? Because it’s a tough nut to crack.

By definition, brand reach is a headcount of unique “individuals” who encounter your brand, and you cannot de-anonymize all the people on every one of your web channels. Simply put, two “sessions” or “users” in your analytics could really be from one person, and there’s just no way you could know.

Nevertheless, you can and most definitely should estimate your brand reach. And you should, and most definitely can, use that data in a meaningful way.

For instance, it’s how we confirmed that:

• It was time to abandon an entire paid channel in favor of a different one.

• There’s a near-perfect correlation between our engaged reach and our lead generation.

And that’s just the tip of the iceberg. Let’s dive in.

What is reach?

Reach counts the number of actual people who come in contact with a particular campaign. For example, if 1,500 people see a post on Instagram, your reach is 1,500. (Warning: Take any tool claiming to give you a “reach” number with a grain of salt. As we covered earlier, it’s really hard to count unique individuals on the web).

Impressions, on the other hand, is , specifically.

The only reason we know this is that we meet as a team regularly to look over this data, and we’re always debriefing one another on the types of actions we’re taking on different campaigns. This structured, frequent communication helps us pull insights from the data, and from each other, that we’d otherwise never uncover.

Why this work is so worth doing

If at some point while reading this article you’ve thought, “dang, this seems like a lot of work,” you wouldn’t necessarily be wrong. But you wouldn’t be right, either.

Because most of the actual work happens upfront — figuring out exactly which channels you’ll track, and how you’ll track them, and building out the pivot tables that will help you visualize your data month after month.

Pulling the data is a monthly activity, and once you have your methods documented (write down EVERYTHING, because a month is a long time to remember precisely how you’ve pulled data), it’s pretty easy.

One person on our team spends about one hour per month pulling this data, and then I spend maybe another two hours analyzing it, plus 15 minutes or so presenting it at the start of each month.

We’ve only been doing this for about half a year, but it’s already filled gaps in our reporting, and it’s provided us with clues on multiple occasions of where things might be going wrong, and where we should be doubling down on our efforts.

Eventually, we even hope to help use this as a forecasting tool, by understanding the relationship between reach and sales meetings, but also reach and the most meaningful metric of all: revenue.

How cool would that be?