From 0 to AI for Marketing Hero: 3 Easy Use-cases
By: Frederik Bussler
Tags: Artificial Intelligence, Marketing, Machine Learning, Data Science, Big Data

You’ve heard that AI is transforming marketing, but you may not know how to separate hype from real value.

I’ll cover the 3 easiest, fastest, and most profitable AI use-cases to get started with.

The main business metric in each of these is Return on Marketing Investment (ROMI).

1. Re-targeting

How many customers have expressed interest in what you offer? And how many of those ended up making a purchase? Chances are, you experience a massive drop-off between interested leads and people who end up buying.

Your goal in marketing is a 2-step process:

Get people interested.

Close them.

To minimize the drop-off of leads (between steps 1 and 2) and convert more interest, you need to re-target customers who have already expressed interest in your business, balancing brand repetition with novelty. Realize that…

… while also understanding that

“The definition of insanity is doing the same thing over and over and expecting a different result.”

If users aren’t interested in your content (engaged), then don’t repeat it. But if they are, find new approaches and angles, and repeat the same style across channels. This is how marketers find their niche.

Regarding specific parameters, you can target based on features such as past behavior, customer journey phase, real-time intent, geolocation, age, gender, purchase status, and more.

Users can be segmented on the above, using what’s known as a clustering algorithm, and then re-targeted differently based on the cluster (or group) they fall in.

The parameters you can use depend on the data you have, or are reasonably able to acquire. From there, follow best practices in feature engineering to derive the most valuable parameters for your retargeting model.

2. Recommendation Engine

“Already, 35 percent of what consumers purchase on Amazon and 75 percent of what they watch on Netflix come from product recommendations based on such algorithms ( McKinsey).”

These engines leverage customer data to offer personalized recommendations and ultimately increase revenue. Depending on your business model, this can mean many different things. On Facebook, it means spending more time on the platform. On Amazon, it means buying more products. On Netflix, it means watching more shows. Ultimately, it comes down to harnessing user attention and putting it on your product or service.

As above, the best parameters are decided after you’ve done exploratory analysis of your data.

Some parameters may include user app behavior, user site behavior, past purchases, and so on. For instance, you could create a user profile, and compare profiles that made one purchase with similar profiles that made additional purchases.

3. Social Analytics

If you’re doing your marketing right, you’re engaging users and clients among many channels with massive volume and a variety of content.

Do you know exactly who the influencers are? Do you know the theme of the posts that work the best? Their length? Content? Style?

You want to leverage all data assets available to identify influencers and key players, while monitoring, engaging, and reporting across all channels.

If you don’t figure out who’s driving your social and target them directly, while maintaining the rest of your following, you’ll experience drop-off like a hole in the bucket.

Attributes such as emotion, sentiment, topics, and demographics can be classified with machine learning, such as by using a clustering recipe as mentioned above. Further, natural language processing (or NLP) can be used for emotion analysis and to detect nuances in communication regarding your offerings.