A.I. for Marketing & Growth – Where do I start?

Hey everybody in this video we want to
run you through the first steps of applying Machine Learning and Artificial
Intelligence for business and growth We're gonna look at what are the
"must-haves" of AI and machine learning for business and growth, What are the
"should haves" and what are the "nice to haves" So at growth tribe we train people in
their first steps with AI for marketing and growth Let's use a small analogy. Now
just like the Industrial Revolution took us from one horse to 450 horses, AI and
Machine Learning are taking us from one brain to thousands of brain working
simultaneously to help us answer business questions The first step to
using AI or machine learning for business is typically to know what
questions you need to answer.

For example "How likely is that person on our website
to buy our product or service?" "How much is this customer likely to buy this year?" "Which one of our customers is going to stop using the product soon?" "What characteristics should I segment my customers by?" or even more recently "What are the main personality traits of my customers?" Now although AI and machine learning are like a thousand brains helping you
inside your company to answer these questions In real time All of these
different questions are actually more or less mature in the marketing and growth
sphere Some of them are really mature they've been used a lot there's many use
cases, some of them are less mature they're up-and-coming and there's
actually not that many use cases So we've actually gone ahead and mapped out
for you how mature each of these applications is for marketing and growth Just to make it visual and simple Now at the very top we have predictive

Predicting outcomes – often future outcomes – based on historical data.
Predictive analytics allows Marketers and Heads of Growth to predict the
customers life time value, to identify customers that are more likely to be
loyal and, of course, to predict whether a lead is a good value or not – how much
resources and time should I spend on each specific lead. It's also allowing us
to predict how much a specific customer or group of customers will be worth
throughout their whole customer lifetime The reason it's at the top of our chart
is that it's quite easy to implement and it's been proven again and again and
again. What's also fantastic is that you don't need that much data to run predictive

500 600 700 customers – and looking at the right historical data – is
enough to yield some results. All right Next up is clustering and customization.
Whereas predictive analytics was a form of what we call "supervised learning"
– where you know what you're looking for Clustering and customization is actually
a form of "unsupervised learning" It's throwing a lot of data at the
problem and asking the machine learning algorithm to find the patterns for you. This is an important part. In marketing and growth we use it to identify patterns to
find the characteristic that allow us to segment our different customers. What are the main characteristics that are important to differentiate my customer
base. We call this data-driven segmentation Whereas we used to sort of
guess what the main characteristics were of our different customer segments…now
we've got an extra vote in the room in the form of machine learning. The recommendation engines are usually built through a mix of the two that we saw
just above. A mix of unsupervised learning and a mix of supervised
learning. We call it hybrid models Now with the maturity that we've seen above
in supervised learning and unsupervised learning, companies have started to use
machine learning to build a recommendation engines whereas before we
used to use "if-then-that" statements Although we hear a lot about them in the
press like Netflix's recommendation engine or Amazon's recommendation engine
You also see that many ecommerce, content media or transactional companies aren't
actually using them yet and they're still building recommendation engines by
hand OK now let's go to number four Natural language processing or NLP, as we
call, it is basically asking computers to understand and sometimes reproduce human language The applications in marketing and growth are also quite interesting Although they're not that mature yet we currently use natural language processing for things like sentiment analysis: understanding what customers say about us our product or brand or our competitors We can also use it to
uncover an indication of how a customer is currently feeling right now on a chat
or on an online forum OK next up is psychographic personas.

Demographic s egmentation and behavioral segmentation have a new friend and this friend is
called psychographic segmentation The psychographic is a mix of your
personality your interest your attitudes and your behavior. This is a field we're
extremely excited about and we've only scratched the surface so far. The way I
like to summarize it is that if you can understand the psychographics of your
customers you won't understand "who they are" but actually "why they buy" which is a
great way of delivering the right message and the right product to the
right people And this is different to the clustering I mentioned before
because in this case we're actually using machine learning to uncover the
psychographics of our customers to discover what are the personality traits of
our customers are Finally the last one at the bottom of our chart is image recognition. This is actually the one that's making the most waves and it's getting the most press on the product side of things
Self driving car, facial recognition the stuff you hear about every day in the
news For the marketing and growth side we're
still currently exploring applications Few companies have actually started to
apply this for growth and marketing We're super excited about image
recognition for marketing and growth but we don't believe it should be one of
your first steps In what order should you start applying this? Well ever since
we started training machine learning for marketing and growth we've actually put
these two at the top as the "must-haves" secondly we have the "should haves" once
you've already covered the "must-haves" and then finally the "nice-to-haves" once
your company is more mature with AI and machine learning.

Of course depending on
which question you want to answer, you're going to have to start collecting data
to be able to answer that question Now of course this doesn't apply to every
single business All businesses are different and maybe the order is
different for you, but we hope that this can help you in the first steps to
applying narrow Artificial Intelligence and Machine Learning to your business
and grow.

As found on YouTube

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