3 Predictive Models Email Marketers Should Know


3 Predictive Models Email Marketers Need to Know About

How can you effectively use predictive analytics to engage your customers and increase revenue? Understanding your predictive analytics platform — how to use it and what it’s best used for — is key. To get you started, here are three predictive models you should know about and some examples of how they can be used to improve your email performance results.

Predictive analytics modeling is a statistical process that involves analyzing current and historical data to predict future behavior and outcomes. By applying predictive analytics to your email marketing, you can help ensure that you’re getting the right customers the right offer at the right time. For example, insights garnered from predictive models can help you segment your email list and personalize your email messages (e.g., with appropriate incentives and product recommendations) based on such customer attributes as likelihood to purchase and lifetime value.

The following are three predictive analytics models that are especially important for email marketers:

  1. Clustering Models
    In email marketing, clustering refers to the segmentation of your email list. Algorithms used in predictive clustering models allow marketers to segment customers based on numerous variables. Types of clustering models that are useful in email marketing include:

    • Behavioral-based
    • Product-based
    • Brand-based.

    Variables analyzed in a behavioral clustering model could include such behaviors as how frequently customers purchase from your e-commerce website, how much they spend, and if they buy your merchandise only when it’s on clearance. Using this information, you can create more targeted messaging for customer segments that gives them the right discount offers at the right frequency and cadence to motivate them to purchase.

    On the other hand, product- and brand-based clustering analyzes the types of products and brands of merchandise that your customers are most interested in purchasing. This helps you determine email content, such as which product and brand offers to send to individual customer segments.

  2. Propensity Models
    Propensity models analyze such customer data as past purchases and online behavior to predict a customer’s future behavior. Using this information, you can target and segment these customers to communicate with them more effectively. The following are few examples of propensity models that are valuable to email marketers:

    • Customer lifetime value — This is one of the most useful metrics in predictive analytics, especially for e-commerce retailers. Algorithms are used to compare a new customer with customers who have purchased from your company in the past (e.g., analyzing such information as purchase behavior, demographics, and geographic location), to predict the new customer’s future lifetime value with your company. Then, you can focus your marketing campaigns on effectively reaching and capturing those customers who will make the biggest impact on your company’s bottom line.
    • Propensity to engage — How likely is a customer to click on the links in your emails? With this information, you can determine which segments of customers to send (or not send) specific email campaigns.
    • Propensity to buy — Which customers are ready to purchase from your company? Using this information can help you craft and send the most relevant email messages to customer segments. For example, customers who aren’t at the ready-to-buy stage may need more nurturing with product information and customer testimonials to help convince them. Or they may need an added incentive, such as a discount coupon, to convince them to purchase.
    • Propensity to unsubscribe — Knowing how likely customers are to unsubscribe can help you determine the optimal frequency and cadence of your email send to them. For example, you may want to decrease the frequency of emails sent to customers who are more likely to unsubscribe. Plus, you may want to take the preemptive step of giving them the opportunity to update their preferences and reduce the number of emails they get from you. On the other hand, if customers are deemed not likely to unsubscribe, sending more emails to this segment may help drive more sales.
    • Propensity to churn — What is the likelihood that customers are ready to churn (i.e., cease their relationship with your company)? Answering this question through propensity modeling can help you identify customers to whom you should be sending a win-back email campaign — especially if they are customers predicted to have a high lifetime value with your company.
  3. Collaborative Filtering Models
    In e-commerce, collaborative filtering is an automatic recommendation process in which software analyzes customers’ profiles and buying patterns, and offers suggestions for other products that customers may be interested in purchasing. The e-commerce company Amazon’s recommendations of products “liked” by customers who also liked a recently purchased product is a great example of how to effectively use collaborative filtering. Cross-sell and upsell recommendations are two examples of collaborative filtering models.

    Typically occurring at the time of purchase, upsell recommendations may include suggestions for a bigger, better version of the same product about to be purchased. Or in some cases, upsell recommendations may provide the opportunity to purchase several of the same items at a lower cost per item.

    Cross-sell recommendations may also be made in an email campaign at the time of purchase or soon after purchase. These recommendations would include items that are often bought along with the purchased product (e.g., car charger and screen protector for a mobile phone). Recommended items could also be bundled at a discount price to help drive more revenue.

Ultimately, you don’t need to internalize all of these predictive analytics models. That’s because a number of tools on the market have already done the heavy lifting in terms of integrating these types of predictive algorithms right into their platforms. So, with the help of these tools, automated emails sent to each recipient are personalized — including the right products, the right links, the right recommendations, etc. — based on that individual’s behaviors and unique interests.

If you’re interested in learning more about how to best use predictive analytics tools to increase the profitability of your email-marketing efforts, contact FulcrumTech today and we’ll show you how it’s done!

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