Annika asks, "What are the prerequisites for using AI in marketing in a way that you can maximize Return on investment (ROI)?"
Let's begin with a reminder that ROI is a financial equation: return on investment. Earned - spent / spent is the calculation. Thus, in order to achieve positive ROI, we need to maximize earned income and minimize spent income. This brings up many considerations that belong in the business requirements phase of the AI lifecycle. Watch the video for full details.
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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, Anika 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 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 are spent in GM are spent spend funding.
So that brings up that brings up a lot of considerations when it comes to artificial intelligence.
And these are considerations that belong in the business requirements portion of the AI lifecycle.
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 decide when 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's earning money through its marketing and sales, but it's spending too much you can improve ROI overall by reducing how much you spent, right? Because it's simple equation of balance, the less you spend the better 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 things more efficient, reduce the spend side, or increase the earning side? So some common things that you would look at, for example, you 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, can integration, evaluation, purchase, ownership, loyalty, 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? Is it consideration to evaluation as an evaluation of purchase? Is it awareness? Where are you Where's 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 you can use AI? To fix this problem? Well, there's two levers, right, you can either use AI to, to make your outreach more impactful, or you can reduce your expenses.
A big thing in awareness marketing is that people spend a crap ton of money on 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, what things are direct contributors have awareness, what things are assisted contributors, meaning they help, and what things are just a waste of money, stop doing the things that are waste of money, and then and then you automatically prove your ROI there.
And then take some of that money, and reinvest it in the things that are working, immediate improvement and ROI.
If you if we take, for example, the end of the customer journey, or the end of the buyers 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 expect 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 be 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 a 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 SAS 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 you spend a lot of money on your cloud computing costs.
crappy sentiment analysis is super cheap, and has really fast.
So a good delivers a great user experience.
It keeps your costs slow.
It just delivers terrible results.
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 that 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 this models going into production is going to be running all the time.
And it's gonna be processing data all the time.
They're very, very different applications is 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.
That's a question I guarantee.
Not enough people are thinking about not enough people are considering when they built Yeah.
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