4 Simple Ways Predictive Analytics Can Improve Your Email Performance


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

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

— 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.”

What Is Predictive Analytics?

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

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

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.

The Four Predictive Data Points That Are Essential For Email

Want to learn more ways you can personalize your emails?

1. Purchase intent

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

Download our The Ultimate Guide to Email Personalization here.

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.
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.
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:
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?

2. Predicted date of next customer order

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.
“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.”
Customers who are predicted to spend 0+ 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.

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.
Here are four of our favorite predictive data points that can be used to improve messaging, cadence, and your overall email performance.
Here are four simple ways that you can apply predictive analytics to generate more personalized emails for your email marketing campaigns.

4. Predicted lifetime value

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

Apply Predictive Data to Understand and Anticipate Customer Engagement

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.
Knowing how likely a user is to purchase can help you to tailor the cadence and content of your messages. Consider tailoring your messages based on predicted future purchase value: if a contact is projected to spend under 0, they may need an additional incentive to convert, like free shipping or a gift with purchase.
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.

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