At Growth Tribe, we train business people in their first steps with A.I. for marketing and growth. In this video, we run you through the first steps of applying artificial intelligence and machine learning for marketing & growth.
Just like the industrial revolution took us from 1 horse to 450 horses, artificial intelligence and machine learning are taking us from 1 brain to thousands of brains working with us to answer critical questions.
A.I. can answer many questions for your growth and business.
* How likely is that person on the website to sign up or buy our product/service?
* How much is this customer likely to buy this year?
* Who’s going to stop using your product?
* What characteristics should you segment your customers by?
* Or even, what are the personality traits of your customers?
Now, although A.I. and Machine Learning can help answer all these questions, these applications are more or less mature.
So, we’ve mapped out how mature or advanced each application is in the marketing and growth world.
And, in the end, we’ll tell you what the must-haves are, what the should-haves are and the could haves.
At the very top, we have predictive analytics. Predicting outcomes (often future outcomes) based on historical data.
This allows marketers and business owners to predict a customer’s lifetime value,
identify customers more likely to be loyal customers, predict if a lead is of good value or not and predict how much a customer is likely to be worth over his or her lifetime..
The reason it’s at the top? It quite easy to implement and it’s been proven very powerful. In this 2016 Forbes article, 89% of B2B marketers had it on their roadmap:
Next up is clustering and customization.
Also referred to as unsupervised learning, which is basically being able to uncover patterns from large sets of data. Patterns that your human brain can’t see.
In our case, we use it to identify patterns of the most important characteristics that differentiate customers into segments. Based on this segmentation, you can then tailor content to customers. This is what we call customization or advanced personalisation.
Recommendation engines come in at third.
Recommendation engines are usually built through a healthy mix of the two discussed above.
Although they’ve been popularized by Amazon and Netflix, they are not a mature form of A.I. for marketing.. We also see that many ecommerce, media, transactional and content companies still build recommendation engines by hand.
Natural Language Processing (or NLP as we call it), is basically asking algorithms to understand and sometimes reproduce human language.
The applications in marketing and growth are already quite interesting although not as mature.
We currently use NLP to see what customers say about us and our competitors and obtain an indication of how a customer is currently feeling on a chat.
It’s also being used by some companies to create content like business articles or product descriptions.
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