You Ask, I Answer: Proving the ROI of Analytics and Data Science?

Gautam asks, “What is your recommendation for proving the ROI of analytics and marketing data science?”

Proving ROI of analytics is one of the thorniest problems you’ll run into in marketing data science. The reason for the difficulty is that analytics is one degree removed from everything; analytics itself never makes a company any money – only the actions you take based on the data. Recall that ROI is (earned – spent)/spent – proving the ROI of the earned part is the difficult part. Watch the video for three different models.

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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

In today’s episode, Gautam asks, What is your recommendation for proving the ROI of analytics and marketing data science.

So proving the ROI of analytics of data science of anything data related is going to be one of the thorniest problems you run into in marketing data science.

And the reason for this is think about what ROI is.

It’s a very straightforward mathematical formula.

It is earned minus spent the essentially sort of the the net earnings, right, divided by what you spent.

That’s ROI.

Now, here’s why this is tricky.

Analytics, data science, big data, whatever you want to call it.

is one step removed from the things that actually generate profit, the things actually generate revenue, right? When you use Google Analytics, Google Analytics never ever generates you money.

Right? Your website generates you money in Google Analytics measures that when you are doing a B testing using basic data science, the testing doesn’t generate money.

It’s the result that you get the knowledge that you then make a decision on.

Remember, data without a decision is a distraction.

But that means that essentially analytics and data science if you think about these things like tools to fill their measurements of tools, so there they are technically tools but they are measurements of tools.

It’s like the frying pan is what you cook with and the thermometer that you use a little laser thermometer measures the frying pan right But the laser the thermometer doesn’t cook the food, the frying pans, one cooking the food.

So it’s very difficult to understand the value of the thermometer as a discrete dollar amount, you understand the value intuitively right? If you if you’re trying to get the frying pan to a certain temperature and you don’t want to burn the oil is at the pan it says it’s, you know, 380 degrees, great.

Let’s go ahead and start cooking or it’s 500 degrees in it too hot turn down the heat, otherwise, we will burn the oil and, you know, make a terrible tasting dish.

What’s the ROI of that thermometer? Well, it’s not ruining the dish, or it’s making the dish better.

But how do you quantify that right? Because again, is one order removed.

So in a situation like this, you are measuring the measurement of the actions you’ve taken based on data.

And that’s essentially the definition of how you measure the ROI of analytics and data science.

So the spent part is easy, you know Know what you spend on analytics in terms of hard dollars, you know what you spend on analytics in terms of labor and soft dollars, that’s your spent part.

That’s pretty straightforward.

The earned part is the hard part.

So there’s three different ways to tackle the Earned part.

And they’re in decreasing order of usefulness, but also tactically decreasing order of complexity.

The most accurate and the most complex way to to measure the ROI is when you make a decision, like, should we increase our decrease our Facebook spend, should we run ads on YouTube? You take you you create one version and a B test saying Okay, let’s use data to inform this decision.

And the B test is let’s just wing it, right? Let’s just do what we’ve always done.

And you carefully document and you run these campaigns and parallel one campaign with the best analytics.

You’ve got The other campaign with just kind of winging it and hoping it all works out.

And you measure the difference and you do this for many, many different campaigns.

Now this requires extremely good data governance and marketing governance to be able to carefully document both scenarios, and keep them separate and distinct in a laboratory conditions to the best of the abilities you can’t.

And you do this for all any kind of campaign we have the ability to do a B testing for some channels.

This is impossible to do, right you you cannot really organic search as a whole for SEO as a whole.

You cannot just store Well, I guess I guess you could have optimize a website, but that’s probably detrimental to the overall nature of the company, right.

The second way is to AB test different groups and teams.

And this works if you have a larger company and larger teams, you have one team that uses the best analytics practices.

The other team that doesn’t, at most large companies, you can easily find a team that does does not use data to make decisions.

And over time you measure each team’s performance.

You know, if you have two e commerce teams, maybe for slightly different business lines, one uses analytics one doesn’t, you can relatively easily differentiate the two smaller companies, that’s not going to really work out very well.

The third and least good way to measure that impact of analytics is period over period comparison.

So if you are, say, a company that is not especially data driven, and you go ahead and implement analytics, and data science and machine learning, and all these fancy things, you make an annotation in your analytics, Google Analytics, your CRM, whatever, saying on this date, we did this on this date, we did this and you and you start to really know when you’ve Become a data driven company.

We’ve made that commitment.

And then you start doing period over period comparisons on, you know, July of 2020.

We were a data driven marketing team July of 2019.

And we were not how does how do things compare? The reason this is the least good method is because there is a tremendous amount of potential interference.

market changes, if you’re running on campaigns, advertising changes.

Certainly we all know, Facebook’s algorithm could have changed dramatically, then.

And so it’s very difficult to come up with a clear case that analytics was the reason for all of the changes that happened when there’s so much interference.

So again, the A B testing of decisions with careful documentation is the best choice.

You can do the teams if you have the capability and then period over period comparison, if you just have nothing else, it’s not going to be great.

So that’s the the methodology for proving the ROI of analytics and data science.

The funny part You’re actually using data science to prove the value of data science, right? Is the scientific experimentation, a hypothesis that analytics has an ROI and then creating the testing conditions to do that? So Good question.

complicated question.

It can be a very frustrating question.

So I’m just remember that too, it’s going to be, it’s going to be frustrating.

As always, please leave your comments in the comments box below.

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