Part 2—Anatomy of Predictive Analytics in Email Marketing

A better understanding of your customers’ interests and habits, smarter segmentation, and a greater return on marketing spend—that’s what predictive analytics can do for your email-marketing program. How exactly does predictive email-marketing work? Find out the answer to that question in this second article of a 3-part series on predictive marketing.

Predictive marketing is one of the hottest trends in email today. And it’s likely to get even hotter as marketers look to improve their 2018 strategies, which is what we talked about in “Part 1—What Is Predictive Marketing and How Does It Fit Into the Email-Marketing World?” The reason predictive marketing is such a big buzzword in email marketing is because more and more marketers are realizing that leveraging predictive analytics to make data-driven decisions can have a huge positive impact on driving customer engagement and conversion.


Understanding the Basics of Predictive Modeling

Making data-driven marketing decisions goes beyond simply looking at past customer behaviors; it takes into account real-time input from current data. In other words, predictive analytics are most valuable for predicting future customer behaviors when email marketers successfully connect multiple data points and interconnections across multiple sources in real time. This involves collecting data from all of the marketing channels that customers use to interact with a company, including website browsing behavior (that infers a customer’s interests), purchase behavior (both online and point-of-sale), customer relationship management (CRM) data, email engagement metrics, and social media interactions.

In addition to data mining, predictive analytics also leverage statistical algorithms and modeling, artificial intelligence, and machine learning to analyze customer data to predict future customer behaviors. There are a number of different predictive-modeling techniques for analyzing data. Here are a few examples:

  • Clustering algorithms that create segments of an audience based on such variables as demographic information and displaying certain customer behaviors (e.g., how often they purchase from your company online, how much they spend, which products/brands they purchase, the types of promotional email offers they open and click).
  • Propensity modeling analyzes customer data to predict future behavior; for example, it gauges how likely a customer is to engage with your email content, buy from your company, or unsubscribe.
  • Collaborative filtering that analyzes customers’ profiles and purchasing patterns to generate recommendations of products in which they are likely interested (e.g. cross-sell and upsell recommendations).
  • Marketing spend analysis takes a look at how current customers were acquired and kept to help determine the most successful acquisition and retention techniques.

To read more about these models, we encourage you to check out Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data.

Source: The two types of machine learning methods provide different algorithms tailored for different problems. Copyright: © 1984–2017 The MathWorks, Inc.

What Can Predictive Analytics Do for Your Email-Marketing Program?

So, what can you do with all of the analytics, and which ones are key to maximizing your email-marketing return on investment (ROI)? The following are some examples of the ways that you can leverage predictive analytics in your email programs:

  • Segmenting subscribers—Identify how different customer segments are most likely to respond to specific email content and promotional offers. In addition, predict and reach the target audience that is most likely to engage and convert. This information can then be used to craft email campaigns that are relevant and personalized for each customer segment.
  • Assessing Customer Lifetime Value—Who are your best customers, the ones who have the potential of spending the most with your company over time? Determining which customers have the highest lifetime values will help you focus your email-marketing efforts on engaging and converting them.
  • Product Recommendations—Using such data as customer engagement rates (e.g., open, click, and conversion rates) and past shopping behavior, predictive analytics can help you determine which products to recommend. Then, deliver the product recommendations in such email campaigns as abandoned cart, post-purchase, and win-back.
  • Lead Scoring—Based on a prospect’s profile and behavioral data, predictive analytics can help you assign scores to prospects to prioritize the most promising leads, improve conversion rates, and decrease buying cycles.
  • Tone-of-voice prediction—This is another area in which predictive analytics tools can be useful in crafting successful email campaigns. Tone-of-voice predictions are based on analyses of a brand’s communications and the target audience’s responses to those communications, which can help determine the tone of messaging that you should use to effectively engage your customers.
  • Reducing subscriber churn—Although it’s inevitable that some people will unsubscribe from your email list, wouldn’t it be great to be able to identify those who are most likely to unsubscribe? With this predictive information, you can take preemptive actions to prevent subscriber churn, such as designing and sending a reengagement campaign or win-back email campaign.
  • Subject line and content optimization—You can also use predictive analytics to help you perform more robust multivariate testing. Then, armed with the results of such analyses, you can identify customer trends and determine the subject lines, email content, and calls to action that will get the best open, click, and conversion rates.

With predictive analytics solutions, marketers can begin to quickly and easily answer questions—in real time—to the age-old marketing questions of What happened, When did it happen, How many times has it happened, and Where did it happen? Marketers can then take that information to accurately predict and shape the future behaviors of their prospects and customers.

The great news is that predictive-marketing tools are not only becoming easier to access and use, but also more affordable. Be sure to watch for the third and final article in our predictive-marketing series that will take a look at the leading predictive analytics tools in the email industry today.

Looking to learn more about how to best use predictive analytics tools to increase the profitability of your email-marketing campaigns? Contact FulcrumTech and we’ll help you harness the power of predictive marketing to successfully engage your prospects and customers and give a big boost to your email-marketing ROI.

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