It’s not too surprising to me that digital targeting is starting to be labelled discriminatory. When applied just to marketing messages and pushing specific brands,targeting by gender, race, political affiliation has been accepted. However, as digital targeting has been used for other purposes rather than just marketing, it raises questions of egalitarian principles and in this case, legality. It’s a good reminder for those of us in the field to consider the ramifications of what we do as we start using accepted marketing methods on other problems.
There are so many types of Attribution and the terminology can be seemingly interchangeable. I’ve watched three people discuss attribution – each meaning a different type – for a full five minutes before they realized that they weren’t discussing the same thing. Imagine how challenging this can be if one company is attempting to implement an attribution project, but not everybody at the company is trying to implement the same thing. I’ve been there! I was once brought in to complete the “attribution” project where multiple key stakeholders on the project all thought it meant something different. Clear definition setting was critical to getting that project to success.
So what are the different definitions of attribution?
- Digital only attribution (most common)
- Online to Store
- Multiple screens
- Offline (often TV but can be for other offline channels as well) to Online
- Unified Measurement
- Campaign experimentation tests
- Causal lift
- Marketing Mix Modeling
- Generic data connections that can support some of these methods
In future posts, I’ll dive into each of these for greater definition and link through this post. Realize that software vendors often offer multiple combinations of these attributions which can increase the confusion in the market. I have found that creating these definitions – even if one tool or client is considering multiple types — helps to give everyone a shared, clear understanding and lets you focus on the potential goals of each.
Attribution requires focusing on evolving and improving.
Inevitably when I deliver a first version of attribution to a client, I find that a strong advocate for the project suddenly questions if it’s worthwhile. It’s happened regularly enough that I’ve nicknamed them “The Perfectionist”. The Perfectionist suddenly thinks of the data missing from the first version – lack of facebook views, or that the sales team phone calls aren’t being considered, or even that direct mail isn’t integrated — and wants all of that included before we could ever consider publishing our findings and making decisions from it. And what we have for our first version isn’t perfect. But you have to remember that your Marketing Attribution methods cannot and will not be perfect. Both data limitations and the ever-changing nature of marketing technology and campaigns make it nigh-impossible to build the “perfect” attribution method. And that’s okay. The lack of perfection should not stand in your way of progress.
Just like when a person attempts to improve their health by changing their diet and exercise, you don’t consider it a failure if you don’t have the perfect body right away! And they may never have the perfect body — most of us won’t. But they will see improvement. You want to be in a that mindset — improving the present, not focusing on the perfect. If you only think about what attribution is NOT doing, not using it to make any decisions until it’s ‘perfect’… then you might never benefit from it at all.
And realize today that you are already discussing attribution within your organization. When you discuss how well a campaign is doing, most likely your organization looks at some numbers which are already based on attribution (often last touch). Even companies that lack reporting on campaign performance have a method of defining success around campaigns and channels in order to make changes to those campaigns and channels. Instead of focusing on how to build the perfect attribution, I encourage clients to first think about where they believe their attribution methods are letting them down. If the data capture in these areas isn’t too difficult or costly, then focus on these! They are often the most actionable and productive ways to improve attribution.
In the end, the most successful analytics projects in my experience focus on improvement, rather than trying to build the best solution ever. You’ll improve attribution through iterations, allowing you to get better one step at a time, while you both make better decisions today AND justify investments for tomorrow’s improvements.