Understanding Estimated Brand Reach as a Marketing Metric




The most crucial high-level metric that everyone appears to either understand incorrectly or neglect entirely is estimated brand reach.

 

Why? since it is a difficult nut to crack.

 

Brand reach is a count of distinct “individuals” who come into contact with your brand; you cannot de-anonymize every user on every one of your web channels. Simply put, there’s no way to determine if two “sessions” or “users” in your analytics are indeed from the same person.

 

You may, and you should, however, make an estimation of your brand’s reach. And you should and can absolutely make significant use of that data.

 

For example, here is how we verified that:

 

It was time to switch from one premium channel to another entirely.

 

Our engaged reach and our lead generation have an almost perfect correlation.

 

Just the tIp of the iceberg, really. Let’s start now.

 

Reach what?

 

Reach measures the number of actual individuals who interact with a certain campaign. Your reach is 1,500, for instance, if 1,500 people view a post on Instagram. (Warning: Take any tool claiming to offer you a “reach” number with a grain of salt. As we mentioned before, it’s incredibly impossible to count distinct persons on the web).

 

On the other hand, impressions are a count of views. Instagram posts can be seen many times by the same user. If every single one of the 1,500 individuals who are in its reach sees it twice, a post with that many impressions can easily exceed 3,000.

 

By keeping track of every individual who has come into contact with any and all of your company’s campaigns across all of your channels over a specific time frame, brand reach takes this a step further.

 

In order to accurately measure brand reach, each individual must be counted just once, which is not possible, as far as we are aware.

 

You may see exactly how many impressions your website has received on Google Search over time, for example, by using Google Search Console. However, it won’t include unique people during that time. Your brand could appear twice on Google if someone searches for two different keywords that your site ranks for. There is no way to connect those various sessions to a single user.

 

Even more challenging would be to follow that person across all of your channels. How would you, for example, prevent someone who found you on social media and then again on search from being tallied twice?

 

You cannot, is the short answer.

 

However, estimating brand reach is a task worth taking on. It will a) enable you to link actionable metrics to your overall brand awareness efforts, and b) provide you with a wealth of knowledge into how your deeper-funnel outcomes are impacted by your high-level brand awareness, which is sadly lacking in most marketing initiatives.

 

using impressions in place of actual reach

 

We are aware that we are unable to count the number of users who come into contact with our brand. However, we are certain that we can accurately count all impressions, and more importantly, we have concluded that there is a significant correlation between impressions and reach.

 

According to logic, if you notice changes in your brand’s overall impressions, your reach has probably also changed.

 

We put this theory to the test using one of the few channels—our email marketing program—where we can actually distinguish between pure reach and impressions.

 

Email advertising:

 

Reach is the total population of individuals who receive at least one email from us each month.

 

Impressions are the total number of emails sent each month to everyone in our database.

 

And as we had anticipated, there is a 0.94 almost perfect link between the two.

 

It’s also interesting to note that there is a 0.87 association between email impressions and email engagement (a person clicking on that email).

 

Email, it must be said, is a much more controlled route than, say, search or social media.

 

We’ll use Google Analytics’ count of “New Users” over the course of one year (which we’ll use as a stand-in for pure reach because it only counts people once in a particular timeframe) as a proxy for pure reach, so I went one step further and examined how our “impressions” in Google Search Console coincided with that figure:

 

The association between impressions and GA’s New Users has a very high Pearson Correlation Coefficient of 0.69. In other words, greater impressions often translate into more reach (also known as unique users).

 

The connection between GSC clicks and GA’s New Users is an astounding 0.992, which is only 0.008 away from being a perfect match.

 

People who are far smarter than I am have repeatedly noted that GA’s user data should not be trusted, for reasons I won’t go into here. However, the point is that there is enough of evidence to support a very close connection between reach and impressions.

 

TL;DR: If impressions shift in a bad or positive direction, reach is likely to follow suit, and vice versa.

 

What we arrived at

 

With all of this information in mind, we began monitoring impressions across all channels—except email, where we can truly utilize pure reach—to assist in calculating our anticipated brand reach. What happened? This graph shows the evolution of our brand’s reach over time:

 

Even though it is an estimate, having this kind of volume for your business is incredibly fulfilling.

 

The most essential information in this case, however, is not the number itself but rather how and, more crucially, why it fluctuates from month to month (more on this later in this post).

 

How to monitor projected reach

 

The estimated reach of our brand across all of our known marketing channels is shown in the graph above. The data may be obtained by simply logging into the analytics properties of each of these channels once a month and extracting the impressions from the previous month.

 

Let’s perform each step.

 

1. Use a spreadsheet to keep track of everything. Here is a sample that you might use. Update the data in the two leftmost columns as necessary to reflect your channel preferences. The information you enter in columns C through F will automatically fill in columns G through L. It will be simpler for you to generate pivot tables to aid in your analysis if you use this style and track the data on a monthly basis.

 

2. Access the impression information. Although every marketing mix is unique, the following is how we might acquire impression data for the channels we use:

 

Draw impressions for the month from Google Search Console for organic search.

 

Email marketing: The total number of distinct contacts who have successfully opened and read at least one email from you in the most recent month (this is one of the few channels where we utilize reach rather than impressions).

 

Impressions gleaned through Sprout or the built-in social media analytics services. Likewise with paid impressions.

 

Impressions taken from the ad-management platform of your choice for Google Ads, Adroll, or another ad platform.

 

Website referrals: The total monthly anticipated page traffic resulting from our hyperlinks. For this, we employ Ahrefs. Any backlink is considered a possible chance for someone to interact with our brand. Each referring page’s traffic is estimated by Ahrefs. To estimate the number of impressions we are making on other websites, we can export this information and sum it all up in a spreadsheet.

 

Impressions obtained from YouTube Analytics.

 

With a few exceptions, the most of what was just stated is self-explanatory.

 

Because employing impressions for email will greatly increase the number of people we believe we can contact. We send 3 million or more emails per month, yet we only get to about 400,000 people. By its very nature, email involves often messaging the same people. Although similar (your followers are your primary audience), social media has a considerably smaller reach (we reach fewer than 30,000 people each month).

 

Referral traffic comes in second. This refers to traffic that enters your site from other websites; however, email, search engine, and social media traffic are not included. These are tracked differently.

 

More than any other channel, the referral source is an educated guess. It ignores additional traffic sources such as social media, email, and other channels that website owners may be employing to promote a page since it only considers predicted organic page traffic.

 

But once more, rather than as an absolute figure, reach is most meaningful when seen as a relative metric, or how it varies month to month.

 

This is due to Ahrefs’ backlink tool’s unfortunate inability to accept custom dates. Although my method involves a few more steps, once you get the feel of it, it’s very easy (plus, I prepared a video to guide you).

 

Exporting the data to a spreadsheet is the first step. Next, eliminate backlinks from your sheet that were first viewed after the month’s end day or last viewed prior to the month’s first day. Add together all of the Page Views to get the total “impressions” that came through referral traffic.

 

How to assess projected reach

 

That is sufficient to begin building very simple pivot tables (such as tallying up your monthly total reach). But have you noticed all the 0s and holes?

 

By importing your engagement numbers, you may fill those in. Let’s go over them in order:

 

Pull clicks from Google Search Console for organic search. (Optional: I also suggest pulling branded clicks and impressions, which we measure as engagements in our spreadsheet. Clicks can be replaced by New Users from GA (remember that almost perfect relationship?) but you won’t be able to filter for your branded impressions and clicks using this method.

 

Total number of “clicks” from your emails, as measured by email marketing. Since openings have lost some of their reliability and certain email clients are now technically opening your emails before you are, we prefer this to opens. Your email automation platform can be used to retrieve email click data.

 

Social media: Sprout or each social platform’s native analytics are used to pull engagements (link clicks, comments, likes, and reposts). Likewise with paid assignments.

 

Interactions or clicks taken from the ad platform of your choice, such as Google Ads, AdRoll, or another.

 

Website referrals: According to Google Analytics, referral traffic refers to users who found your brand on an external website and subsequently interacted with it.

 

Views on YouTube sourced from Youtube Analytics.

 

1. Interested reach

 

This represents the percentage of your expected total reach that has interacted with your brand. You desire to observe this ascent each month.

 

2. rate of engagement

 

The proportion of your expected reach that is interacting with your brand is shown here. This is possibly your most crucial metric, and you should strive to improve it each month. The greater that percentage, the more effectively you are utilizing your reach.

 

3. Participation rate by channel

 

This displays the channels that have generated the most engagement for you this month. This allows you to mark channels that are providing you with what we may refer to as “bad” or “inefficient” reach. It supported our choice to switch from AdRoll, a full-featured display channel, to Google Display. We observed low engagement rates on the former month after month. Our cost per thousand impressions increased a little bit as a result of shifting our spending away from that display channel, but the additional expense was more than made up for by a greater engagement rate.

 

4. Month-over-month winners and losers

 

This can be used as a direct comparison for engagement or reach. The chart below is a comparison of engagements between October (blue) and November (red). We always want the red (most recent color) to be bigger than the blue (unless, of course, you’ve pulled resources or spend from a particular channel, e.g., paid Instagram in the chart below):

 

5. Correlation data

 

This is where we get a little deeper into the funnel, and find some fascinating insights. There are many ways to search for correlations, and some of them are just common sense. For example, we noticed that our YouTube reach skyrocketed in a particular month. After looking into it, we determined that this was a result of running video ads on Google.

 

But reach and engagements’ most important relationships are to leads and, better yet, leads assigned to sales reps.

 

More reach usually means more engagement. There’s a strong relationship between reach and engagement.

 

More reach usually means more lead gen. There’s a moderate relationship between reach and lead gen.

 

More engagement almost always means more lead gen. There is a very strong relationship between engagement and lead gen.

 

More engagement almost always means more assigned leads. There’s a strong relationship between engagement and leads that actually get assigned to sales people.

 

More lead gen almost always means more assigned leads. There’s a very strong relationship between lead gen and leads getting assigned to sales people.

 

This is just one of the ways we’ve sliced and diced the data, and it barely skims the surface of how you can evaluate your own brand reach and brand engagement data.

 

6. Collaborating with other marketers on your team

 

Some of the relationships and correlations are subtler, in the sense that they relate to specific levers pulled on specific channels.

 

For example, we were able to figure out that we can increase branded search by running broad-match-keyword Google paid search campaigns, 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.






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