You Ask, I Answer: How to Improve Marketing ROI with AI?

In today's episode, Annika asks, what are the prerequisites for using AI and marketing in a way that you can maximize return on investment? Hmm. So let's begin with a reminder that ROI is a financial equation. First and foremost, a lot of marketers operate in a very confused state where they conflate results with ROI. They are not the same thing.

ROI is a financial equation. It is earned minus spent divided by spent. That's the calculation. And that means that if we want to maximize our ROI on anything, AI or not, we need to maximize our earned income and minimize our spent income, our spent uh our spent funding. So that brings up that brings up a lot of considerations when it comes to artificial intelligence.

Um, and these are considerations that belong in the business requirements portion of the AI life cycle. So before you look at one byte of data, before you consider any models, you have to have those business requirements. What are the parameters and the expected outcomes of the project? What do you expect to earn? What do you expect to spend?

What are your limits? How much can you invest on the spend side? Um you're tackling the business problem to tackle, you have to tackle the one that is likely to change one of those two levers. So let's say that your company is earning money through its marketing and sales, but it's spending too much. You can improve ROI overall by reducing how much you spend, right?

Because it's a simple equation of balance. The less you spend, the better your ROI. Vice versa, conversely, I should say. If your company is not earning enough money, even if you've managed expenses well, you'll still have poor ROI, and so you need to increase the amount of money you earn. So that begs the question, what problems do you have in your company that you can solve by using artificial intelligence to either make it things more efficient, reduce the spend side, or increase the earnings side?

So some common things that you would look at, for example, uh you would look at your sales and marketing operations funnel. Now remember, for the purposes of the customer, the funnel doesn't really exist. For the purposes of our internal companies, the funnel is how we divide labor. So at what stage in the funnel from awareness or awareness, consideration, evaluation, purchase, uh, ownership, loyalty, uh retention, loyalty, and evangelism. At what stage do you have the lowest transition ratio?

The lowest ratio from one stage to the next. Is it conversion? Uh is it consideration to evaluation? Is it evaluation to purchase? Is it awareness?

Where are your weakest spot? That gives you a sense of the business problem, and then you can dig into specific applications of AI for those problems. So let's say you have an awareness problem. People don't know who your company is. So, what are the ways that you can use AI to fix this problem?

Well, there's two levers, right? You can either use AI to attract to make your uh outreach more impactful, or you can reduce your expenses. Uh, a big thing in awareness marketing is that people spend a crap ton of money on uh stuff that may or may not work. So, this is a case where you would use machine learning to identify the mathematical relationships between all the things you're spending money on and the outcomes that you're getting to find out okay, what things are direct contributors of awareness, what things are assisted contributors, meaning they help uh, and what things are just a waste of money. Stop doing the things that are waste of money, and then and then you automatically improve your ROI there, and then take some of that money and reinvest it in the things that are working, immediate improvement in ROI.

If you if we take, for example, the end of the customer journey or the end of the buyer's journey from that evaluation to purchase, when does somebody make the jump? There's a case where machine learning could help you identify and predict people who are likely to purchase, and so you'd spend more money, more effort, more time on those people who are, according to your models, more likely to convert than the people who aren't. If you do that, you will of course increase your earnings. Now, a key consideration with artificial intelligence, is that there is a cost to it. A system that is in production is going to need servers, it's going to need its models to be trained and retrained and continuous improvement, monitoring, and all these things.

And those things add to the spend side. So as you're developing your models, as you're developing your software, one of the things you have to keep in mind is what when you're doing model selection in that part of the AI journey, you have to consider what are the costs of those models. This is something that many SaaS providers contend with on a regular basis. It's one of the reasons why sentiment analysis is uniformly terrible because great sentiment analysis is incredibly compute intensive, which means that you spend a lot of money on your cloud computing costs. Crappy sentiment analysis is super cheap and is really fast.

So it delivers a great user experience. But since no one seems to care, vendors don't have much of incentive to improve. So that's a case where, as you're doing your own analysis of what you should be selecting for models, for algorithms, for techniques, you have to keep in mind these are the things that are costs, and these costs, just like any software project, need to be managed and need to be controlled. So choose with a balance of objectives in mind, not just the best overall result, which is great in a data science context where you're doing the experiment once and you're delivering the result, versus uh this models going into production, it's going to be running all the time, and it's going to be processing data all the time. Okay, they're very, very different applications.

It's important to understand that distinction. So that's how you use AI in marketing to improve ROI. You either increase what you earn, you decrease what you spend. Ideally, you do both. And you have to remember that some of your savings on the spent side will be offset by the compute costs of the AI solution.

So you have to build with that solution in mind. Great question. Important question. That's a question I guarantee not enough people are thinking about. Not enough people are are considering when they build.

If you have follow up questions, please leave them in the comments. Please subscribe to the YouTube channel and to the newsletter. I'll talk to you soon.ai today and let us know how we can help you.