Shea asks, “Wondering your perspective on how to measure the financial impact of earned media when it doesn’t include a backlink to the company website?”
This is a fairly common question. There are several ways to peck away at this and arrive at a conclusion that’s on reasonably solid ground. Let’s dig into those methods.
The first thing we need to understand are the values of our digital channels. To do this, you need a robust attribution model, based on reliable software like Google Analytics. In that software, you need to have goals and goal values set up; goal values confer a dollar value on the activities inside Google Analytics.
Why do we need this? Activities like earned media show up in other ways. Rarely, it’s direct traffic; more often than not, it’s through things like organic search or referral traffic. In the case of Shea’s question, it’s very likely to be organic search. With a good attribution model, we’ll be able to infer the value of an organic search visitor.
The second calibration step we’ll need, besides ensuring goal values, is to ask people how they heard about us in our customer touchpoints. This question helps reveal some of the precursors to organic search. Ideally, if we had a successful earned media campaign and someone read about us in, say, Fast Company, they would put “Read about you in Fast Company” as their reason.
You can see a more detailed example of this calibration step in this blog post.
This calibration step alone can help understand the impact of good earned media campaigns. Keep track of the number of times someone responds with things like “I saw you on…” or “I read an article…” and you’ll begin to pick out where those offline or disconnected interactions occur the most. You’ll also gain more insight into connected channels that may not be yours; for example, if an influencer talks about you in their Slack or Discord community, you likely would never know until a customer mentions it.
Modeling Earned Media Lift
Because there’s no way to do an A/B test (the preferred method usually) for seeing the impact of a campaign, we have to resort to statistical techniques that essentially reconstruct A/B tests retroactively.
Why? Rarely do any campaigns ever operate in a vacuum. At the same time that an earned media campaign is occurring, chances are many other things are happening as well – search ads running, email campaigns going out, Instagram ads running, etc. a customer will likely be impacted by many different methods of communication, so we have to essentially remove the effects of other marketing methods to see what impact our earned media campaign had.
If we don’t do this, then we run the risk of attributing impacts to the wrong things. For example, suppose at the same time an earned media campaign was occurring, a new Google Ads branding campaign was running. Which deserves credit for a boost in traffic and conversions?
The best practice in this case, for those companies with a sufficiently robust CRM, is to track and log every touchpoint a prospective customer has – including those “how did you hear about us” responses – and then build either a propensity scoring model or a binary classification model based on that information. We specify those people who responded with earned media campaigns as the “treatment” group, and everyone else as the control group, then analyze the likelihood of someone converting based on that “treatment”. This requires access to machine learning tools, be they free like R or paid like IBM Watson Studio.
For companies that don’t have that level of data, we can still use propensity score models in a lower accuracy version. Instead of tracking individuals, we track the days and times our earned media campaign has run, and then measure against similar days when earned media campaigns weren’t running (our control data). As with the best practice version, this creates a “treatment” of our marketing with earned media while removing some of the noise of other channels.
Let’s look at a practical example. Few would argue that having company executives on stage would be earned media, especially if you didn’t pay to have them there. Using the propensity score model on a day-level basis, here’s what the difference was in terms of my website traffic by source between the days I was speaking (and the three days following) versus other similar time periods:
Of the channels, I consistently see more traffic from LinkedIn on days when I’m speaking compared to days when I’m not speaking. That makes intuitive sense as well as analytical sense; people who are watching me speak are likely checking out who I am as well.
Putting Together the Financial Impact
Using this model, we can ascertain the exact number of visitors to our site from different sources – and the delta, the difference, for earned media campaigns. In my case, I earned 2.4x more visitors from LinkedIn during periods when I was speaking compared to periods when I was not. If I extract the actual data, the actual number of users, I can find the delta between those two. Again, from the example above, that was something like 125 users’ difference on speaking days compared to non-speaking days.
In other words, earned media got me 125 visitors more during those time periods than not.
This is where our Google Analytics goal values come into play. If we’re able to extract the average monetary value of users from each given channel, then we multiply that value times the difference, the delta, of earned media. In the example above, if LinkedIn users are worth, say, 10 on average, and I have a model that shows I got 125 more users from LinkedIn because of my earned media, I can infer the value of those users at1,250 – and that’s the value of earned media in this example.
That’s one of the ways we can determine the value of any channel.
When This Won’t Work
There are situations where this methodology doesn’t work, especially for the time-based model, which I showed above. Propensity score modeling in particular requires there to be enough control data to find good matches with the treatment data, usually 2x more control data than treatment data.
That means if you’re running “always on” campaigns, you won’t be able to measure their impact because there will be no “off” days to compare them to.
The best way to do this is at the individual level; the aggregated level does work but it’s not nearly as accurate.
This method also doesn’t work if there are two synchronous campaigns; if an earned media campaign always occurs at the exact same times as a different campaign, disambiguating between the two is not possible. You see this happen most often during things like major product launches where everyone’s going full steam on everything all at once.
There is nothing in marketing that cannot be measured. The question always boils down to, how much is your organization willing to invest in time, money, and resources to conduct the level of measurement that you want to achieve? Market research and data science paired together can achieve very high levels of confidence, but at high costs (though not as high as wasting budget on things that don’t work).
When someone says something in marketing can’t be measured, what they’re really saying is they’re unwilling to make the commensurate investment to measure the thing. Earned media is one of those areas where people seem perennially unwilling to invest in measurement, even though proven methods for measuring earned media have existed for years. The techniques outlined above are just newer additions to an already robust toolkit.
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