You Ask, I Answer: Making a Case for AI in Marketing?

Ashley asks, “How can I make the case for AI in marketing?”

You Ask, I Answer: Making a Case for AI in Marketing?
Watch this video on YouTube.

Can’t see anything? Watch it on YouTube here.

Listen to the audio here:

Download the MP3 audio here.

Machine-Generated Transcript

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

Christopher Penn 0:13

In today’s episode, Ashley asks, How can I make the case for AI and marketing? Well, here’s the this is a challenging question, here’s why it’s challenging.

That assumes that you have a problem for which AI is the best solution.

Right? And that means you need to understand what things AI is really good at, and what things AI is not good at.

Right? Fundamentally, artificial intelligence is good at two types of problems, right classification problems, and regression problems for the most part.

Now, there’s, there’s some flexibility in that, but those are the biggest, broadest categories.

If you got a bunch of stuff that you need to categorize and classify.

AI is actually really good at that.

particularly things like images, audio, video, text.

Classification, is a particularly strong area for AI to do its work.

And regression, or which leads to prediction AI is reasonably good at things like that forecasting, trying to understand what contributes to an outcome.

What are the drivers of an outcome? AI is pretty good at that.

So the question is, do you have a problem for which AI would be an appropriate solution? There are a whole bunch of problems where AI is not be answer.

For example, in some there’s some kinds of forecasting where simpler statistical methods like auto regressive integrated moving averages still deliver best in class performance, better than neural networks better than, you know, really complex regression algorithms and machine learning powered regression algorithms something sometimes, and ARIMA result is perfectly fine.

So a major challenge for somebody who is not a data scientist is to understand which problems are suited for AI and which are not.

If you’ve got a problem for which there is a lot of data, both for the problem itself, and for past solutions than AI is probably a good candidate, right? Say you’ve got a million pages of text, and that that have good examples and bad examples of writing.

And you got 10,000 new pages, you need to assess are these good or bad.

With that much data, AI is actually a really good solution.

Because there’s enough information to train a model, which is fancy for have AI write software for itself, that it can successfully build that model, and then run it against your production data.

AI is also really good at in general data where there are clear patterns, and clear repetition that requires scale, right.

One of the big challenges with AI problems with just throwing a at a problem is that there may not be enough data to create that model, right? If you’re trying to say build a model to write great corporate blog posts for your company, and you’ve only got like 200 blog posts, you’re gonna have a hard time, that’s not impossible, you can take an existing model and fine tune it.

But for the most part, that’s not enough information to really help it it build a good robust learning data set a training data set for to generate the outcomes you want.

On the other hand, if you’re a company that you’ve got 2 million blog posts, then you’re talking, right, and now you’ve got enough to cook with, it’s kind of like, there’s a minimum amount of ingredients you need for a recipe to work, right.

There’s some ingredients, some recipes, where if you don’t have a sufficient number of ingredients, it never reaches critical mass and it doesn’t work.

Like you can’t really bake a loaf of bread with a quarter teaspoon of flour.

I mean, if you scaled down all the ingredients, there’s just not enough mass there for the recipe to work properly.

You know, the same is true for like a model cars and stuff below a certain scale size.

An internal combustion engine simply doesn’t work.

Well if it’s like this big.

Right? And that’s the challenge you face with with artificial intelligence.

So big data in means that you’ve got a good case for AI.

Christopher Penn 4:43

If you find a problem has no repetition, it’s a bad candidate for AI.

Right.

So this is one of the reasons why we all often say AI will take tasks and not jobs because your job from day to day is wildly different.

Right? different meetings, different participants, different activities, different kinds of lunch you eat, there’s a whole bunch of these random variables.

But within your job is a series of tasks.

And sometimes those tasks are highly repetitive.

And if it’s highly repetitive, then there’s an opportunity potentially to bring in some, some machine learning to pick up that individual task and automate it.

When I am putting together my weekly newsletter, the process is exactly the same week after week and automated a substantial part of it because it’s so repetitive.

However, there’s still a decent chunk of it that is manual that is human because that part is wildly variable.

Things I feel like writing about that week, vary wildly from week to week.

So do you have a case to fit to make for AI? If you have a lot of data, and it’s highly repetitive? One of the things people are thinking about is does AI or machine learning? Does? Is there a case to be made based on cost savings.

And this is tricky, because it depends on the task.

It depends on the task.

And it depends on the complexity, and all the stuff that we’ve just mentioned.

There are definitely things where it doesn’t pay for a human to do it.

So like curating content for social feeds, right, that is a machine based task, for sure.

It’s relatively low value, highly repetitive, big data.

And solving for it is worthwhile, right, because you can take some, you know, two hours out of somebody’s calendar each week, and turn that into five minutes, that’s two hours back that you get that you can do something else with that time.

On the other hand, there are some tasks where the creation of the model and the maintenance of the model would be so vast, that you’d be better off doing it manually, right? Like shooting your video each week, if I were tried to have a machine do this entire video from beginning to end, the enormity of that task and the high variability of it would be so difficult that it would take me years to make it happen.

And it wouldn’t be worth the return on investment would be a negative.

So that’s a major part of this question is, when you’re making a case for AI, can you make a case for a positive return on investment for its application? This is one of the reasons why Artificial intelligence has not been widely adopted by many businesses.

Because in a lot of cases, people are treating it as this magic wand.

And as a result, they’re not doing a simple cost benefit analysis and saying actually, this is a problem that isn’t worth solving with with machine learning.

Better off solving with basic statistics or an Excel spreadsheet or just doing it manually.

Yeah, real simple example I influencer identification.

There’s a whole bunch of different ways to do it.

But if you’re validating the feeds of influencers, and you do it once a year, it’s probably not worth automating.

If you’re doing every day, then it’s worth automating.

So that would be my suggestion.

But how do you make the case for AI figure out if you’ve got an AI problem to begin with before anything else? Thanks for asking.

We’ll talk to you soon.


You might also enjoy:


Want to read more like this from Christopher Penn? Get updates here:

subscribe to my newsletter here


AI for Marketers Book
Get your copy of AI For Marketers

Analytics for Marketers Discussion Group
Join my Analytics for Marketers Slack Group!


Pin It on Pinterest

Shares
Share This