Customer data is everywhere. With every customer interaction, marketers are collecting enormous quantities of information that can be utilized to inform strategic decisions and shape future behavior. The process of evaluating all of your available data to drive decision making and predict future business outcomes is known as predictive analytics. Rather than just reporting the data, predictive analytics help guide future planning and more confidently anticipate long-term business outcomes.
Predictive analysis helps marketing teams invest their resources wisely and set KPIs that align with total business value. Learn more about the different types of predictive models to use in marketing and examples of how these models can be applied to your own marketing efforts.
There are three types of predictive models marketers should know about:
- Clustering models (segments)
- Propensity models (predictions)
- Collaborative filtering (recommendations)
I’ll go through each and give you a definition, as well as a total of 13 examples:
A. Clustering Models
Clustering is the predictive analytics term for customer segmentation. With clustering, you let the algorithms, rather than the marketers, create customer segments. Think of clustering as auto-segmentation. Algorithms are able to segment customers based on many more variables than a human being ever could. It’s not unusual for two clusters to be different on 30 customer dimensions or more. We call these dimensions the cluster DNA. See below for an example of some of the factors that could make up a cluster’s DNA.
The most used clustering algorithms are behavioral clustering, product based clustering (also called category based clustering) and brand based clustering.
Predictive Model 1: Behavioral Clustering
Behavioral clustering informs you how people behave while purchasing: do they use the web site or the call center? Are they discount addicts? How frequently do they buy? How much do they spend? How much time will go by before they purchase again? This algorithm helps set the right tone while contacting the customer. For instance, customers that buy frequently but with low sized orders might react well to offers like 'Earn double rewards points when you spend $100 or more.
Predictive Model 2: Product-Based Clustering (also called category based clustering)
Product-based clustering algorithms discover what different groupings of products people buy from. See the example below of a category (or product) based segment or cluster. You can see people in one customer segment ONLY buy sweaters, whereas those in another customer segment buy different types of activewear products, such as outerwear, sportswear, swimwear and watches – but never kids’ clothes, intimates or jewelry. This is useful information when deciding which product offers or email content to send to each of these customer segments.
Predictive Model 3: Brand Based Clustering
Brand based clusters tell you what brands people like. Now you know what specific brands to pitch to certain customers. When a brand releases new products - you know who is likely to be interested. See the example below of brand based clusters. As you can tell, the algorithm has discovered that customers who, like Tahari, also tend to like Calvin Klein and Nine West, but would not be interested at all in Desigual or 6126.
B. Propensity Models
Propensity models are what most people think of when they hear “predictive analytics”. Propensity models make true predictions about a customer’s future behavior. With propensity models, you can truly anticipate a customers’ future behavior.
Model 4: Predicted Lifetime Value
Algorithms can predict how much a customer will spend with you long before customers themselves realize this. At the moment a customer makes their first purchase you may know a lot more than just their initial transaction record: you may have email and web engagement data for example, as well as demographic and geographic information. By comparing a customer to many others who came before him (or her) you can predict with a high degree of accuracy their future lifetime value. This information is extremely valuable as it allows you to make value-based marketing decisions. For example, it makes sense to invest more in those acquisition channels and campaigns that produce customers with the highest predicted lifetime value.
Model 5: Predicted Share of Wallet
With predicted share of wallet models, you can estimate what percentage of a person’s category spend you have currently achieved. For example, if a customer spends $100 with you on groceries, is this 10% or 90% of their grocery spending for a given year? Knowing this allows you to see where future revenue potential is within your existing customer base and to design campaigns to capture this revenue.