The ability to predict outcomes has a huge benefit to marketers. Here’s how predictive analytics can transform your marketing fortunes, and how to kick off…
The concept of leveraging your data to predict what your customers will do next can seem a daunting prospect. As customer journeys become increasingly complex, even getting to a Single Customer View can be a challenge, let alone using your SCV to predict the future.
However predictive analytics doesn’t have to involve complex algorithms or Artificial Intelligence. Simpler analytical techniques have produced strong uplift time and again. A fully integrated SCV, incorporating everything from your customer’s most recent Instagram ‘like’ to what they had for breakfast last Tuesday, usually isn’t valuable.
Read about best-fitting your analytics approach here.
It’s well worth considering how predictive analytics techniques – both simple and more complex – might improve your marketing performance and how they could be incorporated into your marketing strategy.
But first, let’s take a step back. What is predictive analytics, and why should marketers be using it?
Predictive analytics applies analytical techniques to current and historical data in order to generate insight into what might happen in the future.
What sorts of insights could you garner? Here are some examples
A monthly forecast of next year’s e-bike sales
A customer’s likely next car model purchase
Predicted ROI on your multi-million-dollar marketing campaign
Which packaging artwork will generate the most sales
Insights like these can be crucial to decision-making around production requirements, marketing strategy and creative. And that’s just the tip of the iceberg. It’s easy to see how even a few well thought through predictive analytics projects can lead to incremental gain.
It’s highly likely most companies already employ predictive analytics to some degree, but there’s also probably room to be doing more and up sophistication. Next we’ll explore a selection of proven techniques that will get you on your way, and some more complex ideas if that’s more relevant to you.
Regression analysis investigates the relationship between a target variable and one or more predictor variables. Several types of regression analysis exist – suitability depends on what you’re trying to predict. Here we’ll cover off Linear and Logistic regression.
Linear regression models the significance and strength of a relationship between your target and predictor variables. The key point here is that the method models a linear relationship, for example, for every additional £1,000 spent on PPC, sales increase by 0.05%.
This is simple linear regression, where there’s only one predictor variable. If more predictor variables are added, for example social media spend or unique website visitors, it becomes a multiple linear regression, often increasing a model’s accuracy.
It’s important to remember that linear regression plots a line of best fit, or overall trend, that minimises the difference between actual results and modelled results. A model will never be 100% accurate – this would indicate overfitting is an issue – but modelling outputs indicate whether the model is sufficiently statistically robust to use for identifying trends and steering decision-making and spend.
Logistic regression predicts the likelihood of a specific event happening. Applications within marketing include modelling the likelihood that a customer will defect from your brand (churn modelling), the probability of a customer engaging with a specific marketing channel or product, or the chances of enticing a prospect to spend with a brand for the first time if they’re sent a promotional offer.
This information can be used to inform targeting, customer-level messaging, and loyalty schemes.
Machine learning techniques are used to build and refine regression models, from selecting the optimum combination of variables, to iterative improvements using your most recent data. Once you’ve mastered the basics, regression therefore offers a great springboard for exploring more advanced analytics techniques.
So, hopefully this gives an insight into how Predictive Analytics addresses real business problems. We might even have sparked some ideas as to how you could use predictive analytics techniques to drive better results in your work…
If that’s the case – do get in touch. We’ve delivered predictive analytics solutions for brands including Nissan, ITV, Harvey Nichols and many more. We’d love to help.