You Ask, I Answer: Successes and Failures of AI in Marketing?

In today's episode, Annika asks, Can you give some examples of digital marketing projects that use AI in some way that failed and succeeded, and the reasons behind the successes are failures? Some of the uh the more prominent uh failures in the space are those uh in which uh people didn't keep their eyes on the systems after deployment. One of the uh the major faults uh uh reasons for failure in many AI projects is that after you deploy your model, uh you don't keep tuning it, you don't keep adjusting it, and the model drifts, which means that it begins to behave unpredictably. Uh it receives data that it doesn't know what to do with or uh forecasts poorly. So that uh is sort of the root reason why many uh AI projects kind of go off the rails.

Uh the most prominent uh example of this was uh I think Microsoft's Tay Twitter chatbot. I think it was from 2017, w in which uh Microsoft deployed this chatbot on Twitter, and uh it was unsupervised. They wanted to see how it would interact with users, and within twenty-four hours, users had turned it into a racist porn bot. And that is a function of model drift, where uh they didn't anticipate the uh uh the lesser aspects of human nature, and the model drifted and it eventually uh became unusable. What they should have done in that in that particular instance is had a constant cycle of tuning, a constant cycle of uh reinforcement learning with active learning, which means that a human uh should have been in the loop at all times, uh, continuing to tune the model and saying, yeah, it's that you just got there reject that.

Um when Microsoft when IBM had its um project debater AI uh this past February, uh one of the ten uh AIs under the hood was an ethics module that had um in addition to being trained on a bunch of what not to say actually had some rules hard coded into it that it was never allowed to do to uh do it was never for example allowed to at any point to reference uh Hitler so that is a really good example of of one of the core things one of the many many things that can go wrong with uh any kind of AI is model drift and if you want to uh read more about uh the ways AI projects fail there's actually a blog series I'm doing over at the Trust Insights blog uh you can see it there on uh uh ways AI projects fail another way that is very marketing specific is that uh a lot of companies have done a relatively poor job with sentiment analysis uh they tend to rely on very very old approaches for sentiment analysis bag of words approaches you know this word is generally bad uh this word is generally good and of course uh it fails to take into account any kind of context and the reason for that is not um AI based in a lot of cases. That is just uh computational cost based. The uh the cost to uh do more advanced types of machine learning on uh language uh in many cases outstrip what that company is able to s to spend and still be profitable um from a compute perspective or they don't have the code and talent to implement uh modern sentiment analysis in terms of successes in AI in marketing there are they are legion because many of them are very tactical and address um very solvable manageable problems for uh marketing uh uh again I these are something that uh we call the five use uh untapped data being able to use uh machine learning to classify and categorize unstructured data like call center calls, emails etc and be able to extract information uh classify it uh very very quickly uh dimension reduction which is a huge uh problem for most marketing data sets they have many many dimensions many uh columns in the spreadsheet if you will and using techniques to uh do feature selection and dimension reduction understand what's really important uh there are uh many many examples of being able to do uh driver analysis multiple regression in which uh you are trying to figure out what is at the heart of a problem so if you have all this data say posts on social media and you have all the reactions likes and wow and whatever and post views and shares and comments. Which one of those metrics or which combination of those metrics uh is really impactful uh would be a a an example of that. Um you have techniques like network graphing, which is a type of machine learning, uh being able to identify the relationships between entities, very useful for sentiment anal uh for social media monitoring, uh for influencer analysis, very, very powerful stuff.

Uh and of course, uh you have predictive analytics, uh time series forecasting, being able to forecast forward when is a a specific search term going to trend, uh and that that helps you then set your editorial calendar. So there are many, many uh tactical successes uh for using AI and machine learning in marketing. Um they are they are legion. Uh, I can refer you to a uh any number of talks that you've done over again on the Trust Insights website, uh detailing this in in much greater depth. But in terms of successes and failures, what sets apart successes and failures in AI and marketing more than anything else, this is something that my CEO constantly says is all about the planning.

Um do you have a clear business goal? Do you have a well-defined approach? Have you done your technical requirements well? At the end of the day, when you're doing AI, when you're doing machine learning, the output is software. So if you are not treating it like a software development project, you're setting yourself up to fail.

On the other hand, if you follow decades-long best practices for software development, knowing that you know a portion of the prod of the development will be done by a machine, uh, you will be much more likely to succeed. So those are some of the the basic basics. Uh as always, if you have uh follow-up comments, leave them in the comments box below. Please subscribe to the YouTube channel and to the newsletter. I'll talk to you soon.

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