Let’s start with a definition.
Machine learning is the use of computerized algorithms to analyze large amounts of data; for the machine to learn from this data; and, to make predictions and continually apply learning to new data — all accomplished faster and more efficiently than humanly possible.
In a marketing context, machine learning identifies what outcomes deliver better performance, measured in clicks, leads, sales and revenue.
While the results are real, some hyperbole is not. Here are seven common misconceptions on working with machine learning – plus strategies to optimize your efforts.
Myth One: You do not need a clear objective.
The most basic: start with a business objective, a reason for leveraging machine learning. What do you want to achieve or solve? The objective is the way to tell the machine what it needs to learn.
Myth Two: You do not
need to form a hypothesis.
Let’s clear this up early: simply loading bundles of data into your marketing platform is not an effective strategy. A more logical starting point is to form a hypothesis. More granular than the objective, the hypothesis is an assumption that you want to test against alternatives. Of course, one beauty of machine learning is the ability to test multiple assumptions and alternatives simultaneously.
Myth Three: You do not need to calculate sample size and test duration.
Like any type of marketing analytics, the sample size must be large enough to have confidence in the statistical significance and the performance results. How long does this take? The answer depends on the amount of data, number of variables, and the degree of consumer response – and, ultimately, the “learning curve” from the machine itself.
Myth Four: Eventually machine learning will determine a
A tough one for marketers with backgrounds in disciplines like direct mail, where, in simplistic terms, you are working to determine a new control. Think differently regarding machine learning, with emphasis on targeting, personalization and experience. With the machine’s ability to ingest consumer attributes and test multiple experiences, the goal is to determine the best outcomes for each customer type, not a one-size-fits-all experience.
Myth Five: Machines can learn to target immediately.
Building from your hypothesis, think of the first phase of machine learning akin to a random test. By serving different experiences, the machine learns what consumer attributes and factors correlate – and what is effectively engaging customers. This experimentation takes time, while the machine learns and targeting capabilities improve.
Myth Six: Machine learning takes the place of
random A/B split testing.
In the world of machine learning, there is room for both A/B and multi-variant testing. A/B testing may be all that is required when decisions are simple, data is not available in real time or you simply want initial insights before starting more complex testing.
Myth Seven: Machine learning can always outperform.
Machine learning often delivers amazing insights and outcome – but it doesn’t always achieve more. The quality of inputs is critical to achieving performance outcomes. Four areas where machine performance may go amiss: input attributes are not relevant; too many attributes prevents statistical significance; target audience is too homogeneous; and, creative execution is not relevant to targets.
One last myth: machine learning will eventually replace the need for marketing and analytics experts. Quite the contrary: machine learning simply enables us to be more strategic and empowered as we make very human decisions on products, media, positioning and customer experience.