Predictive modeling techniques have become the ultimate tool in a marketing technologist’s belt for increasing the impact of campaigns and demonstrating return on investment.
With increasingly creative ways to splice data comes a more focused, driven approach to putting the right content in front of the right audience, at the right time. Provided teams have access to clean, quality data, the ability to dial-in campaigns to reach a target audience is better than ever.
This post examines some of the most prevalent predictive modeling techniques, with a specific focus on three different types:
- Segmentation Models
- Propensity Models
- Intelligent Recommendations
Within each broader category, you will see five predictive modeling techniques your business can start implementing as soon as you have the tools in place to make it happen.
The concept of customer segmentation has a storied history in the marketing world. What was once little more than an educated guess, akin to taking a swing at how many jellybeans are in a jar, has become a strategic initiative fueled and supported by hard data.
Here are several different predictive modeling techniques that allow you to splice customer data to create a refined audience for your campaigns.
Technique #1: Behavioral Clustering
As leads convert to customers, the path leading them to conversion becomes incredibly valuable for a marketer focused on leveraging predictive insights.
That information, called behavioral data, means fairly little alone. When combined with demographic (or firmographic, in the case of B2B companies) data, it empowers marketing teams to identify commonalities and trends that help create new target segments.
By prompting future leads that match the demographic background of that new target segment with the same sequence of behavioral actions, marketers can improve conversions and appropriately forecast the impact of their campaigns.
Technique #2: Product-Based Clusters
Like behavioral clusters, these segment customers together based on similar activity and engagement with your brand. Product-based clusters deviate slightly in that they focus specifically on tracking specific buying trends among target demographics.
For B2C customers, this has a fairly obvious application. Customers who buy similar items can all be prompted with the same promotional information. (A classic example would be the print-out coupons that trail your receipt at the supermarket.)
For B2B customers, the use case actually expands a bit. As you will see with a deeper dive into “recommendations” later in the piece, these product-based clusters also help marketing technologists accurately forecast the lifetime value of a customer.
By knowing the types of products or solutions a business prefers and comparing the data to similar existing customers, product-based clusters tell you which products in your portfolio of solutions you could actually sell to the customer over the course of their time as your client.
This concept leads nicely into the second type of predictive model, propensity models.
Propensity, by definition, means “an inclination to behave in a certain way.”
As you will see in the next two examples, that title quite accurately summarizes the purpose of these predictive modeling techniques.
Technique #3: Share of Wallet Estimation
“Share of wallet estimation” refers to the percentage of a customer’s budget that has been allocated toward your solution. Of course, the higher the percentage, the less likelihood that growth through upsell or cross-sell opportunities exists.
When combined with product-based clustering, an accurate share of wallet estimation tells you how much of your customer’s budget is with competitors (and therefore is yours for the taking), as well as what specific products or services you can sell to the customer to increase your share of wallet percentage.
Technique #4: Likelihood of Churn
Many marketers make the mistake of using predictive tools purely as a means of lead generation, when in fact, the defensive capabilities of strong predictive modeling techniques are some of the most exciting use cases.
Paramount among those would be the ability to measure the likelihood of churn. For SaaS companies, churn can be an immense problem. Considering it can be six times more expensive to acquire a new customer versus to maintain a current one, protecting your existing client-base should be a top priority across all industries.
Measuring a customer’s propensity to churn leverages much of the same methodology adopted to segment potential leads by behavior. If you can accurately forecast the likelihood of a lead converting a new customer, so can you accurately predict the chance of that customer leaving.
Churn forecasting looks to identify “red flag” behaviors from previously churned customers in your current client book. As customers exhibit troubling behavior, that information can be passed along to a sales or customer success team for further follow-up, and/or can be transitioned into an education-focused nurture campaign.
The impact can be tremendous when it comes to maintaining your current customers and increasing revenue by reducing churn.
Technique #5: Recommendations Filtering
Finally, there is recommendations filtering which, like product-based clustering, looks at a target segment’s buying behavior and asks an important question:
What opportunities exist to upsell or cross-sell this customer on existing solutions?
It is a model made famous by Amazon and its “recommended products” list on each page. It has since become a de facto way to create opportunity for increased revenue at the point of sale.
Predictive Modeling Relies on Quality Data
None of this is possible without clean, quality data supported by a good customer data platform. Quality data is, in fact, the foundation upon which accurate predictive insights rest. To learn more about how to use your data wisely, watch the webinar: Leverage Account Intelligence and Intent Data to Win.