Everyone’s talking about the benefits of Artificial Intelligence (AI) in marketing, especially Machine Learning, which is proving itself as a tool to support business growth. It can appear to be a very complex process, but the concept can be quite simple. Our approach is to keep the complexity in the technical algorithms, so you don’t need to be a data scientist to understand Machine Learning at a strategic level.
Machine Learning promises to greatly enhance the effectiveness of marketing strategies, but as adoption grows throughout the industry, what are some of the common misconceptions to avoid?
Whether you’re already using Machine Learning to drive incremental uplift, or curious about the benefits it can bring, it’s easy to defer all thinking around it to the technical experts.
But understanding what it can actually do for you is not the same as understanding all the details under the hood. The same applies to almost all technology we use every day; we know what it can do but rarely care about how it actually works.
So don’t worry about algorithms and technical indicators, worry about what you want from a Machine Learning model and how it’s going to help you communicate more effectively
with your customers. Most Machine Learning models will simply give you an output
which you can utilise according to your needs. You should be engaging in conversations about what that output should be and how it’ll be useful to you.
This leads to the question ‘what can Machine Learning actually be used for?’
Historical analysis on customer behaviour can tell us all kinds of things about individuals and business performance but what if we are more interested in the future?
The basic approach is to look for some patterns in our datasets and define some rules which split customers into segments, which we then assume will behave similarly in the
future.
For example, if we’re looking to market to customers who are likely to be high value in the near term, we could use a standard segmentation model to assign a value band to each
customer based on their cumulative spend to date. We then add the assumption that the future high value customers are the same as those in the highest value band now.
But what if that assumption is not always true? It may be for some customers but we’re likely to miss out on a large number of good prospects we could have identified also using
spending patterns, engagement, demographics, and any other data we have available.
We could manually define a huge list of rules to consider all these factors and continuously review it or, we could get a machine to learn all these relationships for us.
A Machine Learning model like this does essentially the same thing as a standard segmentation model but is much more powerful in its ability to accurately predict and can adapt to evolving behaviours over time.
Now you’ve figured out what Machine Learning can do and what you want to use it for, let’s get the tech guys to build some models and we’ll start making loads more money.
Not exactly.
A Machine Learning model is going to give you an output which tells you something you didn’t previously know about your customers, but you then need a strategy for how you’ll actually use this information.
Even the world’s best Machine Learning model that can predict everyone’s behaviour with 100% accuracy is going to have absolutely no effect on the business’s bottom line, if that knowledge is not leveraged in some way.
Although knowing which customers are most likely to be high value may form the backbone of a marketing strategy, you still need to develop the appropriate content and choose how and when to surface it to each individual customer.
Conveniently, appropriately designed Machine Learning solutions can help with these other factors but as a Marketeer, it’s up to you to define how all this works together
in an effective marketing strategy.
Richard Fletcher
Lead Data Scientist
Horizon Powered By XCM