Data is a powerful thing. As technology advances, data science is changing everything from the way we police our streets to the way we farm. Our ability to collect and use data is at an all-time high, and it’s creating ripples across every industry.
Marketing is no exception. When it comes to marketing, data is king. The more data you have, the more informed your decisions will be when you put together your next campaign. Data can tell you how people are likely to react to your product, when you should launch it, and much more.
Using data to inform future decisions is called predictive analytics, and we’ll break that concept down in more detail here. We’ll also take a look at some best practices for predictive analytics so that you can utilize it effectively in your next marketing effort.
What Is Predictive Analytics?
Predictive analytics takes the data you have and uses it to make insights about the future. Technology journalist John Edwards put it this way in an article for trade publication CIO:
“Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. The science of predictive analytics can generate future insights with a significant degree of precision.”
Basically, this technology uses AI to generate future trends based on historical data, and it’s very accurate. The more data you’re able to feed into the software using this process, the better it works.
Technology like browser cookies and Google analytics allow marketers to gather vast amounts of data on people. It’s possible to collect data on who opens emails and how often, what websites people browse the most, where people like to shop, what their favorite bands are, and so on.
This allows for the granular targeting of marketing campaigns. The more personalized a campaign is, the more likely someone is to respond to it. Businesses are starting to take notice; the predictive analytics market is on track to grow to almost 11 billion in value by 2022 according to a report by Zion Market Research.
A classic example of predictive analytics at work in marketing would be the emails you get from companies like eBay or Amazon after looking at a laptop. They remind you that you looked at it, ask you if it’s still of interest, and recommend other products that might go with it, such as a case or a USB dongle.
The concept of predictive analytics has been around for a while, but it’s gaining more traction today due to:
- Growing volumes and types of data
- More interest in using data to produce valuable insights
- Faster, cheaper computers
- Increasingly user-friendly software
- Tougher economic conditions and a need for competitive differentiation
This technology makes it easier than ever for today’s marketers to see what people are buying, how they’re responding to it, and what products they should promote to them with cross-selling or upselling.
The Benefits of Using Predictive Analytics
This technology gives the marketers using it a better understanding of how their customers will behave by taking data in all its forms and determining which strategy would work best. Then, the software executes that strategy without a data scientist having to run the numbers.
Using past behavior, it’s possible to determine what kind of purchases someone is likely to make, and when. Because of that, businesses can more accurately decide how much to allocate to their marketing, and how that money should be spent.
These systems can monitor data and make decisions based on it in real-time. This automates marketing tasks like model generation, lead scoring, and updating customer insights to streamline the entire marketing process. That means easier segmentation of your customer base and more revenue generated.
Use Cases for Predictive Analytics
How marketers use this technology depends largely on what industry they’re in and who they’re trying to approach. Predictive analytics is particularly useful for:
- B2B customer prospecting
- Lead scoring
- Lead segmentation
- Proactive sales integration
- Managing customers through the full sales cycle
It can also take unstructured data like images, sounds, and text, and use them to determine patterns. Natural Language Processing (NLP) software, for example, can look at web page content to determine its theme, and then decide what ad would be most effective.
Getting the Most Out of Predictive Analytics
When you’re ready to make the leap to big data, there are a few best practices you can use with your predictive analytics engine to make sure you’re getting the most out of the software:
Make sure you have a large (and valid) dataset. The AI behind predictive marketing refines itself over time, but it still needs as much data as you can provide in order to do its job well, especially when you’re just getting started.
Hone in on the best data sources. Make sure the program is drawing from the right data to inform its models and decisions as it learns over time. An example of that would be integrating predictive analytics into an eCommerce platform that could see when a particular product was popular. It could then recommend that product to people more often.
Make predictions visually clear and easily understandable. A basic example of this would be the “you might like” or “customers also bought” sections on shopping sites. You could play with this to offer users features like interactive maps or product recommendations in an app.
Monitor performance. As the AI learns and implements solutions, it’s important to regularly check to make sure its recommendations are relevant and accurate. Users’ expectations can periodically shift, and it’s important that you keep up with them. Indicators of change could be anything from a spike in sales following an ad campaign to more subtle shifts like reduced page engagement.
Two factors determine how effective your predictive marketing campaign will be: your data and the way you present the recommendations or predictions derived from that data. For marketers, that data could be demographic information like age and geographics coupled with typical buying patterns for a specific product. Of course, while this depth of information is powerful, it’s important to use it ethically and abide by laws like the GDPR.