No idea what you're doing

How marketing misses the point by ignoring Attribution

Photo by Klugzy Wugzy on Unsplash

If you’re spending $5,000/month on Google and $5,000/month on Facebook Ads and Google delivers you 100 clicks whereas Facebook delivers 250, what should you do assuming the Cost-per-Click is the same?

The answer of course is, “I have no idea”. Or at least, not if that is the only bit of data you have to go on.

Unless your business gets paid every time someone clicks on an ad to your site, then the number of clicks is only relevant if you know what they do next.

Of course, if all people who click on an ad then behave in the same way when they get to your site (ie, an equal amount of people from all ads go onto become a customer), then you might start to think that you should switch some more of your budget over to Facebook based on the above. But in my experience, this isn’t how it works at all.

Free Trials or Request Demos aren’t the end of the Journey

But it’s worse than this.

Often we see Marketing Teams measuring their success by either the number of free trials they have driven, the number of demos that they have driven or some other action (such as downloading a white paper) to demonstrate a Marketing Qualified Lead (MQL).

Don’t get me wrong, the MQL isn’t an inconsequential KPI, it’s just that on it’s own it doesn’t tell you the whole story. And it also kind of assumes that marketing’s role is up once they get the lead to the MQL stage. That it’s someone else’s problem after this point. Like it’s a relay race and your job is just to get it to the next person and keep your fingers crossed.

Marketing’s job is to work out the best way to spend the company’s money on promotion to increase revenue. And a lead is only good if ultimately they turn into a customer. I might be able to drive a load of leads if I used the strapline ‘Click here for free money’. But it’s unlikely that those leads would turn into customers unless they were the right sorts of leads and I was actually able to give them free money.

We’ve probably all seen the quote:

“Half my advertising budget is wasted; I just don’t know which half”

This is is often attributed to John Wanamaker, a 19th-century American retailer, or Lord Leverhulme, a British industrialist.

But as we also know, this view is much more nuanced, especially given what we can track nowadays.

Get really good at Attribution

The key to all of this is that marketing needs to get really good at attribution. And not just to the point of handing over the lead to sales. Attribution is the process of determining which marketing interactions or touchpoints influence a customer’s journey from lead ALL THE WAY to sale.

It’s not enough to say, I can get more clicks by spending more on Facebook.

It’s not enough to say, I can generate more MQLs.

It’s not even enough to say, I know how leads from Facebook move through the funnel, become a customer and what their Lifetime Value is going to be (although this is a much better place to be than only having visibility to the point that they become an MQL).

What really needs to happen is that we understand:

• all the touchpoints there are along the way to the point where they are an MQL (however that is defined)

• then what touch points there are along the way from MQL/SQL to Paid Customer (and possibly beyond).

Pre-MQL vs Post-MQL Attribution

Attribution often has different characteristics based on when the touchpoints happen:

Analytics Attribution
Pre-MQL, attribution is often tracked by looking at analytics as a result of contact information. It means that marketing teams need to create conversion goals that they believe are indicative of an influencing touchpoint.

CRM Attribution
Post-MQL (at least for enterprise), the attributions are going to be more trackable through your CRM. Did they attend the demo they asked for? Did they ask for a quote? What emails did the sales rep have to send?

Making sense of Attribution Scoring

How you calculate all of this is your Attribution Model and there are a few different ways to do it.

1. First or Last Touchpoint

This is what I alluded to above. You basically say something like “this customer came to us through a Google Ad’ and then you attribute 100% of the credit to the Google Ad in marketing terms.

The problem with this of course is that it ignores the fact that it might have taken a gargantuan effort to get them from clicking an ad to becoming a customer. If it costs you $5 for a click, but then another $500 in the production of supporting content, attending industry events, sales pitches and bespoke demos to get them over the line, standing up and proclaiming Google is the key to customer acquisition is like saying buying a bag of potatoes is the key to producing a Michelin Starred dinner.

2. Linear Attribution

This model seeks to track every single interaction and apply an equal weighting of credit to each.

At the end of a period you will be able to see that, of your new customers, 50% of them saw an ad, 30% of them watched a video, 5% of them signed up for a webinar etc.

This is a much better way of doing it than the first/last touchpoint approach, because you can at least see what is influencing leads as they pass through the funnel. If you can see that loads of people download a whitepaper, then maybe it’s a good time to check you are happy that it’s working hard enough for you.

But it can also be misleading: just because someone gave you their email address at a conference, did that influence their decision? Would they have bought anyway? If your claim is that a conversation with someone at a conference a year ago was equally as important as a demo request a month before they become a customer, could that be misleading?

3. Rules-based or Weighted Attribution

This is where you say that some touchpoints are more important than others and you give them a different weighting as a result. Often these are based on an assumption around how strong their intent is based on their action. So for example, someone downloading a ‘free checklist’ as a lead magnet might get less points than someone signing up for a demo request. This is because the demo request shows the customer has higher intent.

Within this you may place additional credit based on when the touchpoint happened:

U-shaped Attribution
This is where you place more weighting on the first and last touchpoint than you do on the middle touchpoints based on the assumption that getting them to know who you are is important and getting them over the line as a customer is important. The other parts are just part of the drip, drip, drip that pushed them towards the conclusion.

W-shaped Attribution
This assumes that you give say, 30% to the first, last and middle points on the funnel, and spread the remaining 10% equally across any other touchpoints.

Time decay Attribution
This approach says that credit gets more important the closer they are to becoming a customer. So, the very first touchpoint wouldn’t get much in the way of credit, but as the prospect gets closer to signing on the dotted line, so too does the weighting given to the interaction they have.

4. Machine-Learning/AI Attribution

I suspect this is where we are all heading. This is where we use AI to figure out what is the most influential marketing elements and as a result, where best to place our bets. These will inevitably give us the most accurate results and allow us to identify where we need to push harder.

If you have no idea about your attribution then you have no idea what you’re doing

That might sound harsh. It’s not meant to. It just means that you really are just chucking a load of money into the ether and hoping for the best. You might be lucky: you might notice that one bit of effort pays dividends, or at least your gut tells you that it does. But are you still potentially missing something?

Let’s say all of your customers met you first at an Annual Expo that you attend each year. You’re probably onto something. But, wouldn’t it be great to know what happens to the ones that convert the quickest after that so you can just repeat that behaviour more widely?

It’s complicated and maybe you don’t have enough data yet to really be able to action much of this. But even if it’s not relevant right now, it’s coming down the line, so get prepared!