You Ask, I Answer: Most Common Biases in Marketing AI?

In today's episode, Elizabeth asks, what's the most common type of bias that you see that we as marketers should be aware of? Gosh, there are so many to choose from. Um I'll start with two of these. Uh, because these are the two that again are probably the most common and things that people don't think about. The first is uh confirmation bias.

So confirmation bias, uh, pretty simply is when you start any kind of data science or AI project, uh, with a intending to look for a result that fits a predetermined conclusion. Uh, and you see this a ton in uh research studies, especially. Um, but obviously it can extend to building models which are nothing but software based on data. Uh uh simple example of this. Let's say you're a SaaS-based company, right?

And you sell, I don't know, um, photo editing services. Make something up. And you want to promote your services, so you build a predictive model that has the marketing goal of telling people they should edit their photos more, right? You do some market research and and all this stuff, and of course you come up with a conclusion that yes, people should edit their photos a lot more. Better edited photos, um, perform better on social media, and you create an algorithm that you know predicts if only you had edited this photo, it would have earned this much more engagement.

Now, that's kind of a silly example, but you can see how that particular bias corrupts everything. Because you are working towards a predetermined conclusion rather than building a model that is truly open to being uh trained and can yield results that frankly might not support your business. Right? Again, you see this most often in uh research. There used to be a company not too far from here, uh, in in the Boston area, that would sell this as a service.

They were they were sort of the and to this of good research. You know, they would say, hey, you want to prove whatever thing, you know, hire our services, and we'll come up with a statistically validated way um that proves your thing. And anytime, you know, once once that was known, any time you saw that company's name in the in the methodology, if it was even listed, you know, yep, that survey is completely invalid. You you know that they the company that sponsored it bought the result that they wanted, and it ruined the re the research company's credibility and it ruins the credibility of any company that uses that research company. Um that's probably the first one.

That one is relatively easy to spot inside your own organization because your VP of marketing or CEO or somebody says, Hey, I need something that does this. That's not how research works. That's not how uh good models work either. Um, because one of the things that will happen is that uh if you are willing to tolerate bias like that, conscious bias in your model, um, then you are willing to tolerate biases that are discriminatory. And that's gonna get your butt kicked over time.

Second is selection bias, and this corrupts the process because it is uh a bias that exists in your data set that does not represent the population as a whole. So you're using data that is inherently skewed in some way, consciously or unconsciously, and it does not represent whatever population you're trying to serve. So, simple example. Uh number of years ago, uh HubSpot did a uh a research uh study saying, you know, we they made some claim about inbound marketing, which is their thing. And when you look at the methodology that says we emailed, uh it was a survey, and we emailed our list of you know however many people.

Well, you can see the problem here. You can't make a survey that claims to represent all marketers based on your own email list, because people who are on your email list inherently have a greater affinity towards your products and services, right? HubSpot was all about and still is about inbound marketing. Naturally, somebody who subscribes to their email list probably has at least some passing interest in inbound marketing, and as a result, you have a survey that is biased towards people who like inbound marketing, and so to make a claim that this represents all views of all marketers is incorrect because there's this whole group of people out there not on their email list who probably don't give two shakes of a of a fish at uh non uh at in my marketing, they don't care, they don't, it's not their thing, and so their views are not being represented in this survey methodology because it was it was uh they were selected out, right? They were not part of that population.

Now, what could that ha survey have represented? You could have certainly said all marketers who are in the HubSpot audience believe this, and that would be valid. Right. When you're collecting data for data science and and AI projects, the same thing applies. If you are collecting data from a specific focus group that you selected, uh did you select a group that was representative of your audience as a whole.

And B2C, that means balancing against things like age, gender, uh, physical location where that person is, uh, other demographics. Uh th this is one of those rare cases where you should be using protected classes like race, uh, gender identity, etc., to make sure that you are balanced against the actual population, that you don't have a uh non-representative sample. On B2B, you'd be looking at things like uh a balance of companies uh by size, by revenue, number of employees, um uh industry, and it should look like the the balance of as a whole. So if you do a survey and your initial data pool comes from sixty-one percent technology companies, comp technology companies do not make up sixty-one percent of the global economy or even one country's economy. Um they represent uh you know typically a much lower slice depending on the metric you choose on.

So you'd want to be sure that you are balancing your data set to represent the population that you're claiming to study. And selection bias tends to selection bias is not as intentional as confirmation bias. Confirmation bias is I want to put my thumb on the scale and I want it to say this, which is just a horrendous thing to do. Selection bias happens more often than not because of naivety. Uh a naive researcher or marketer or data scientist will just do something and not think, am I balanced?

And that's something that you can solve with things like checklists. Like, did you balance this data set? Did you balance the input collection methods? Uh are you uh are you truly representative? Have you checked?

Have you validated that your data is representative? If you can solve for both of these biases, your data will inherently across the spectrum of the ways that you use data from market research to data science and analysis projects to AI models, will inherently get better. It may not deliver the results you're expecting, particularly if you have a preordained conclusion in mind, even if you keep your processes clean, but it will be better. It'll be objectively better data. And if you're using that data to make decisions, which you should be, um, you'll make better decisions.

So really good question. There's a whole bunch of other biases, and there's there's a difference between a statistical bias and a human bias that we'll leave for another time. But these are the I would say the two most common statistical biases that we see in data science and AI. If you have follow up questions, leave them in the comments box below. Subscribe to the YouTube channel and the newsletter.

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