It’s been estimated that the average American sees about 4,000 ads per day.
As a marketer, let that sink in for a second. Amazing but daunting time to be alive, isn’t it?
Your work is cut out for you: ensure your marketing efforts are reaching the right customers at the right time. And as you likely well know, there are at least two fundamental questions to answer:
The practice of predictive analytics can yield sights on these questions, but like any advanced, powerful tool, it needs to be properly understood and applied to be effective.
You’ve probably read a few blog posts, watched a few webinars and maybe even suffered through a lengthy whitepaper or two, but may still be thinking:
“Predictive analytics is too complex but my team doesn’t have the resources or time to learn and use it”
If you’re having thoughts like this, you’re in the right place. The goal of this post is three-fold:
But first, let’s quickly cover some definitions to clear up any confusion
SAS states it quite nicely as:, “Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.”
In marketing-layman terms, a predictive analytics – or a predictive analysis – can provide insights by predicting unknown values related to customer behavior that lead to reducing cost and/or increasing profit margins. Thus, a predictive model is a formula for estimating a specific unknown value of interest, called the target.The target could be a prediction of things like:
Let’s get even more specific with a couple examples, shall we?
Here are two applications of predictive analytics for marketing:
A successful predictive analytics project is like baking a cake: you get out of it what you bake into it. Delivering value is most common when you have the following ingredients:
While predictive analytics isn’t for the faint of heart with arithmetic, if you properly understand your business objectives and questions then you can provide value by managing a predictive analytics initiative since a model is only as strong as the assumptions it makes. In other words, a racecar is only as fast as the driver behind the wheel – who doesn’t also need to be the mechanic!
A dataset is clean when we have consistent labelling and values throughout our dimensions. For example, we should have:
A quality dataset means we have:
For example, if we were using the Google Analytics API to extract data for a customer-level predictive model (e.g. – predicting a customer’s probability of conversion based on view history), we would require at least the following custom dimensions implementedl:
An alternative to a clean, quality dataset extracted from the Google Analytics API, are the Google Analytics 360 and Firebase data exports to BigQuery, an enterprise ready data warehouse solution. Google also offers access to several other valuable, hit-level datasets via BigQuery Data Transfer Services:
Just like your favorite Italian restaurant and their traditional, marinara sauce, we at Cardinal Path have our own recipe for success or methodology for predictive modeling:
Hopefully you now have a better grasp on what predictive analytics is and some concrete use cases for improving your marketing ROI and bottom line.
Predictive analytics can yield valuable insights, but like any advanced, powerful tool, it needs to be properly understood and applied to be effective.
At Cardinal Path we have the expertise and can help with the entire process. From defining the business objectives, the nuances of collecting and storing clean, quality data and of course, selecting and using the right predictive models that deliver value to your business.
Contact us to learn more about how we can help with your data collection, Predictive Modeling, and more!
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