You Ask, I Answer: Advancing Analytics Maturity?

In today's episode, Shelley asks, I understand the general idea behind the analytics maturity model, but how do you advance? Where are the instructions on how to move to higher stages than descriptive analytics? Really good question. So the five layers of the marketing analytics maturity model are descriptive, which is answering what happened, diagnostic, which answers why did those things happen, predictive, which answers the question, well, what happens next? What should we do next?

Uh, what's likely to happen next? Prescriptive, which is what should we do about what we're predicting, and then proactive, which is when you have systems that permit you to have some of it automated. So an example would be like Google Ads, where the system simply just takes care of making changes based on data without your intervention. Now, there's no there's no simple pat answer uh for how you move from one layer to the next. It really is dependent on three things.

It's dependent on the people you have in your organization, right, and their level of skill. It depends on the processes that you have in place in your organization that uh codify your analytics practices and how uh agile those are, how responsive to change those are, and of course the technologies, the platforms that you're using as to whether those platforms enable you to do additional types of analytical work, right? If you just have Google Analytics and nothing else, you're kind of stuck in descriptive analytics. It's not until you introduce things like surveying that you would get to diagnostic analytics and start to introduce statistics and machine learning in uh programs like Watson Studio or uh R or Python, that you could start doing uh predictive and prescriptive analytics. The number one thing, though, that will get you to move towards a higher level in your analytics is curiosity, right?

The ability to ask questions. For example, suppose you open up your Google Analytics account and you see that website traffic to uh your blog is up 40%. If you just nod your head, go, cool, put that in your PowerPoint for your stakeholders, and you're done, right? That's I would call it being very incurious. You're not particularly interested in in digging in.

You just want to get your work done, which is understandable, uh, and move on to the next item on your to-do list. That in curiosity precludes you from moving up a level in the uh hierarchy of analytics, right? You looked at the data, you analyzed it, you've clearly determined what happened, but then it stopped there. There was no, well, why was traffic up 40%, right? That would be the logical question.

Uh was it just a fluke? Did we get uh uh pieces of coverage on Reddit? Uh did somebody mention us on Twitter, an influencer? Why did that happen? That would be diagnostic analytics.

If you in your analysis and your diagnostics understand why it happened, then you can start to say, you know, is this something that is is cyclical, is this seasonal, is this something that we can explain uh as a trend? And if so, can we then forecast it happening again? Um that would be your next step from diagnostic to predictive. If after that you say, okay, well, we know that you know every May there's going to be interest in our blog. Uh it's just a one of those seasonal things, then the logical thing to do would be to say, okay, well, from a prescriptive analytics perspective, what should we do about it?

Right? Should we run a campaign? Should we uh hire another influencer? Should we send a whole bunch of email? What can we do that would take advantage of that natural trend, right?

If there is a trend. Or if you find out there isn't a trend, but in the diagnostic phase, it turns out that it was just an influencer whose ear you uh caught, the logical question would be, well, great, can we do that again? Can we do that differently? Can we do that better? Um, can we accomplish more if we put some budget behind it?

So you don't necessarily need to uh linearly move from diagnostic to predictive if the data we're talking about is not predictable, but you can move straight to prescriptive to ask the question, what should we do? What is the action we should take? What is the decision we need to make? Each of the stages and the migration up to the next level in each of the stages is contingent on curiosity. It is contingent on asking questions and legitimately wanting to know the answers and being willing to invest in the answers.

You know, it'd be super easy if your CMO is like, oh yeah, I want to know the answer to that. Get it to me for tomorrow. Like, oh no, this is going to require some research and some budget and some people. And if after you present your business case and say, hey, you know, we think we can increase our results 20%, but we're going to need 50 grand to do it. If there's if your the powers of the beer are like, okay, that's a worthwhile investment, then you can move up to the next level, right?

You can say, okay, we've we've analyzed our data and we've found a predictable trend, but we need budget to buy some predictive analytics software or hire an agency to do it for us. And the st if the stakeholders say yes, then congratulations, you move up another rung on the ladder. On the other hand, if the stakeholders are like, oh no, no, I thought you could do that for free, then you're constrained, right? So that's that's how you advance. It is, it's like anything, right?

If you are curious, if you are willing to ask the questions, uh, if you are willing to be wrong, and if you're willing to invest uh time, people, money, uh to get answers, then you stand a good chance of evolving your analytics practice to those higher levels in the marketing analytics maturity model. Really good question. Thanks for asking.