How to Talk to Your Data Scientist
Machine learning is poised to help marketers garner phenomenal new insights and results, and to change many processes and jobs along the way. We discussed this potential in “Machine Learning is About to Turn the Marketing World Upside Down.”
Machine learning can’t provide better results alone, of course. Marketers need to collaborate with data scientists to identify important questions to explore, accelerate tests, improve the accuracy of answers, and make better decisions. And to effectively collaborate, they need a common language.
Because data analytics has roots in statistics and computer science, it’s packed with specialized terminology. And because few machine-learning glossaries and textbooks frame terms in a marketer’s context, we’ve created a shortlist below.
I’ve selected the terms below based on my own experience as a journalist, interviewing machine-learning researchers and experts. And I’ve framed the definitions and examples based on input from marketing expert Steven Wong, chief marketing officer of Ready State, and data scientist Christopher Doyle, director of pricing and market analysis at a national health services company.
This list covers some basics to help you have those productive conversations with your data scientists.
Machine learning is the process through which a computer learns with experience rather than additional programming.
Let’s say you use a program to determine which customers receive which discount offers. If it’s a machine-learning program, it will make better recommendations as it gets more data about how customers respond. The system gets better at its task by seeing more data.
An algorithm is a set of specific mathematical or operational steps used to solve a problem or accomplish a task.
In the context of machine learning, an algorithm transforms or analyzes data. That could mean:
• performing regression analysis—“based on previous experiments, every $10 we spend on advertising should yield $14 in revenue”
• finding relationships between SKUs—“people who bought these two books are very likely to buy this third title”
Each of these analytical tasks would require a different algorithm.
When you put a big data set through an algorithm, the output is typically a model.
The simplest definition of a model is a mathematical representation of relationships in a data set.
A slightly expanded definition: “a simplified, mathematically formalized way to approximate reality (i.e. what generates your data) and optionally to make predictions from this approximation.”
Here’s a visualization of a really simple model, based on only two variables.
The blue dots are the inputs (i.e. the data), and the red line represents the model.
I can use this model to make predictions. If I put any “ad dollars spent” amount into the model, it will yield a predicted “revenue generated” amount.
Two key things to understand about models:
1. Models get complicated. The model illustrated here is simple because the data is simple. If your data is more complex, the predictive model will be more complex; it likely wouldn’t be portrayed on a two-axis graph.
When you speak to your smartphone, for example, it turns your speech into data and runs that data through a model in order to recognize it. That’s right, Siri uses a speech recognition model to determine meaning.
Complex models underscore why machine-learning algorithms are necessary: You can use them to identify relationships you would never be able to catch by “eyeballing” the data.
2. Models aren’t magic. They can be inaccurate or plain old wrong for many reasons. Maybe I chose the wrong algorithm to generate the model above. See the line bending down, as you pass our last actual data point (blue dot)? It indicates that this model predicts that past that point, additional ad spending will generate less overall revenue. This might be true, but it certainly seems counterintuitive.
