Have you ever received an email that seemingly showed the perfect product at the perfect time? How can your favorite brand possibly know what you want before you even realize it yourself?

The perfectly timed message is no coincidence: the magic lies in predictive analytics.

Here are four simple ways that you can apply predictive analytics to generate more personalized emails for your email marketing campaigns.

“You don’t need a full data science team to leverage predictive analytics effectively. Look for out-of-the-box tools to develop your program and test the effectiveness of predictive analytics before you invest in expensive data analytics resources. Chances are for most programs, basic to intermediate predictive capabilities provide a solid ROI without getting bogged down in the build.”

emily collins tinuiti

— Emily Collins, Senior Manager of CRM & Email at Tinuiti

 

What Is Predictive Analytics?

 

Predictive analytics is a data-driven process used by marketers to predict customer behavior, which can be used to create highly personalized and relevant marketing campaigns.

For email marketers, using predictive data points opens up a whole new world of capabilities that enable you to anticipate and understand key customer behaviors, including:

  • Risk of churn
  • Purchase intent
  • Date of next purchase
  • Favorite product category
  • Customer lifetime value

 

Most importantly, they help you as a brand better understand your subscribers at a more granular level, enabling you to serve them what they want or need at exactly the right time.

 

The Four Predictive Data Points That Are Essential For Email

 

Here are four of our favorite predictive data points that can be used to improve messaging, cadence, and your overall email performance.

 

1. Purchase intent

 

Knowing how likely a user is to purchase can help you to tailor the cadence and content of your messages.

“An under-recognized benefit of using artificial intelligence to power your email campaigns is the ability to control margin. Predictive analytics tools can analyze a user’s level of purchase intent to help dictate the incentives served.”

— Mandi Moshay, Director of CRM & Email at Tinuiti

 

“Those with high interest are likely to convert with a gentle nudge of the shininess of a new product. Preserving your discounts for those contacts who need them most will drive up AOV and LTV across your customer base.”

Testing different approaches based on intent can help to control margin and drive up AOV by allowing brands to pull back on discounting.

 

2. Predicted date of next customer order

 

Mid-range and more sophisticated ESPs are able to aggregate contact buying habits and predict when they might place their next order, allowing you to automatically deliver an email with suggested products at the right moment.

Need proof this works? One Tinuiti client saw a 433% lift in placed order rate when testing a predictive trigger on their win-back series versus a static 90-day post-purchase milestone.

3. Preferred category

 

Determining the category most preferred by each user allows you to better merchandise your emails with the product that most interests them.

One Tinuiti client leveraged preferred category data to better tailor emails for their customers who prefer products from the full-figure category.

Emails were created for full figure shoppers with imagery of plus-size models more representative of the audience, leading to a 105% increase in conversion rate.

 

 

4. Predicted lifetime value

 

By examining a contact’s historic value, purchase frequency, and predicted date of repurchase, a predicted lifetime value can be generated. This can allow you to better understand who among your contacts is most loyal or most likely to convert at a higher AOV, or conversely who potentially needs a push to convert.

Consider tailoring your messages based on predicted future purchase value: if a contact is projected to spend under $100, they may need an additional incentive to convert, like free shipping or a gift with purchase.

Customers who are predicted to spend $300+ probably don’t need an incentive to make a second purchase; they’ve given enough indication of intent and loyalty that nurturing content will do the trick.

 

Apply Predictive Data to Understand and Anticipate Customer Engagement

 

Understanding and anticipating customer behavior can open up a whole new world of marketing capabilities for your email program.

Applying predictive analytics to your email program will make your campaigns more personal, relevant, and timely — increasing your engagement and revenue when applied at scale.

 

Want to learn more ways you can personalize your emails?

Download our The Ultimate Guide to Email Personalization here.

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