At the tactical level, you’ll hear digital measurement folks moan constantly about how business goals drive measurement and how essential it is that the business know what they want to accomplish before they can hope for effective measurement. I moan about it myself. There is probably nothing more frustrating that being asked to measure something for which no purpose can be ascribed!
The exact same truth holds at the strategic level. Knowing where you are is great - that's the "here" in going from "here to there". But what's the “there”? It IS NOT the people, process and technology required to do effective digital measurement. That’s resources and logistics, not strategy. “There” is what you need to know to run every aspect of your digital channel effectively and optimize it on an ongoing basis.
How do you describe what you need to know to run
your digital channel effectively? Build a model. This isn't a predictive model or a statistical model - it's just a simple, high-level conceptual model of how the digital channel works.
The modeling starts at a very high-level with the core functions of the digital channel:
Within each core function, you need to map out the high-level process that takes place. I'm going to show how we might expand one piece of this by walking through an expansion of the Digital Advertising function:
In this particular case, Digital Advertising is focused on Google Adwords, which drives visitors to the site. Some visitors convert leads off the site. Others move from the site to the call-center and convert there.
This is an example of a very simple but not uncommon digital advertising ecosystem.
With the basic flows in hand, the next step in building the model is to figure out what you MUST know at each step to understand how the business is functioning.
For PPC advertising, you start with what you are buying and how much you are spending on each keyword:
The next step is to understand how much traffic that keyword spend level generated and who that traffic consisted of:
Knowing how much traffic you generated seems (and is) trivial. Audience mix, on the other hand, is frequently ignored. To successfully measure and optimize campaigns you have to understand whether the targeting was successful and whether the offer/creative treatments were working. When a campaign succeeds or fails it can be hard to separate out the causal impact of these factors. To isolate targeting effectiveness, we think it’s critically important to understand the Audience Mix by source.
The next step in the Digital Advertising Process is the Website:
To measure campaign effectiveness you obviously need to measure Website Conversion. But if, like many businesses, you have significant Call-Center conversion then it’s absolutely imperative that you also measure the % of traffic sourced by campaigns that go from the site to the Call-Center and the subsequent Call-Center conversion on those campaigns. If you don’t do this, you simply cannot optimize your system.
For this particular business, not every application was accepted. So matching acceptance rates to campaigns is essential. While most campaigns tend to have similar acceptance rates, it’s perfectly possible – even likely – that a few campaigns will vary significantly from average and be either more or less effective than conversion rates would suggest.
Finally, there was a significant retention component to this business. From a digital
advertising perspective, what’s important isn’t measuring retention (that's a separate function that needs it's own model) but
predicting quality of customer. As with acceptance rates, different campaigns
are highly likely to source different types (and hence quality) of customer. If
your business has any ongoing customer component, and most do, optimizing for
Predicted LTV is essential to effective digital advertising.
This is the high-level model for one aspect of the Digital Channel. And using it, it’s easy to highlight exactly where the gaps and needs in a measurement program are:
It should be obvious how a model like this (expanded across all the core functions of your digital channel) is essential to creating a good strategy. By describing the system and the ideal measurement state, the model provides a complete map of where you want to get to. And by highlighting gaps in the measurement system, it provides a map of the ground that needs to be covered.
I have no doubt that most analysts inside a company carry a
complete (and probably much more detailed) model like this around in their
heads. But by making the model explicit and embedding it in the strategy, you
have a tool for making measurement requirements explicit and understandable.That's critically important. Because just as I have no doubt that most analysts carry a model like this around in their heads, I also have not doubt that the majority of enterprises we engage with have critical missing pieces like the ones I've described above that no one is addressing!
Why do you need online survey tools? To measure Audience Mix so you can understand and optimize targeting effectiveness of campaigns. Why do you need dynamic 800% numbers? So you can understand Web to Call-Center routing and optimize campaigns by conversion rate. Why do you need data enrichment on applicant emails? So you can build a better LTV model to optimize digital campaigns by quality of customer generated.
The model not only provides transparency about why specific technologies and analytics projects are necessary. It also provides direction (Strategy!!) about how to deploy your technologies and people within the measurement system.I hope from this it’s clear why there is no more important or central step in creating a digital measurement strategy than this modeling exercise. It is not quite the “whole of the moon”, but it is the single element without which no part of the strategy can really be deeply understood.