How to Use Predictive Analytics in Email Marketing?

The implementation of predictive analytics in email marketing has become a hot cake in the eCommerce industry. It is because predictive marketing has the capability to sell wisely, and sell more via the email channel. This trend will play a key role in identifying what kind of products your customers are looking to buy, and then you can send a personalized email with the right product.

This article will explain to you how to apply predictive analytics to get more personalized emails for email marketing campaigns.

What is Predictive Marketing?

Predictive marketing is a marketing technique that finds out the possibility of success of various marketing strategies. Here, data analytics is used to determine which marketing actions are more likely to convert based on customer behaviors. This plays a key role in making smart decisions. When applied in email marketing, it helps you target the relevant audience, enhances engagement, yield more conversions, and generate revenue from email campaigns.

What is Predictive Analytics?

Predictive analytics is a data-oriented process used by marketers to understand customers interactions, which can be helpful in creating more personalized, and related marketing campaigns.

For email marketing professionals, predictive data points provide a new door of opportunities that enable to predict and understand customer behaviors like:

• High risk of Churn
• Buying intent
• Date of upcoming purchase
• Favorite product category
• Customer Lifetime Value

These data points help you better understand your email subscribers and let you serve them exactly what they need at the right time.

Let’s discuss the top 4 favorite data points that can be useful to enhance the message, and measure overall email performance.

Buying intent

Understanding how likely a visitor is to buy can assist you to go ahead and deliver the right content in your message. Visitors who have a high level of interest are likely to convert, and preserving your discounts for such contacts will drive up LTV.

Predicted Date of upcoming purchase

Mid-range and more sophisticated ESPs have the ability to aggregate contact purchasing habits and anticipate when they might place their upcoming order, enabling you to automatically deliver an email with recommended products at the correct time.

Favorite product category

Identifying the category most preferred by every user lets you to better produce your emails with the product that is preferred by them.

Anticipated lifetime value

By looking at a historic value of a customer, his/her purchase frequency, and anticipated date of repurchase, a predicted lifetime value can be generated. This analysis helps you understand who among your customers is most loyal or most probably to convert at a higher AOV.

Implementing predictive analytics in your email marketing campaign will make your campaigns look more personal, suitable, and timely – improving your revenue.

Why Predictive Analytics is Important in Marketing?

The following factors explain why predictive analytics is important in marketing:

Access huge amounts of customer data easily

Availability of fresh automation tools

Further development of sophisticated programs

Faster than traditional analytics

Both the prescriptive and predictive analytics market was stood at USD 10.01 million in 2020 and is predicted to touch $35.45 billion by 2027, and grow at a CAGR of 21.9% between 2020 to 2027.

Use of Predictive Analytics in Email Marketing

When it comes to email marketing, predictive analytics supports an organization’s email service provider and integrates real-time behavior recognition with past customer data to create both automated and personalized email campaigns. Its added advantage is that it is helpful from acquisition and relationship-building to customer retention and win-back email campaigns.

Let’s look at how predictive analytics improves your email campaign strategies:

Acquiring fresh customers – Draft emails that convert more potential customers into customers:

When potential customers become the first subscribers to receive a promotional email from a company, they will receive a welcome email series to their inbox. Its objective is to motivate them to buy a product. Similarly, all-new prospects get such emails, and sometimes a quality promotional offer.

By implementing predictive analytics to both demographic, and behavioral data, you can segment potential customers – testing numerous messages, and offers – to create informative, relevant, and personalized emails improve conversions, and generate revenue.

Building relationships for customer retention – Draft emails that engage particular customers:

Predictive analytics can use product recommendations options for customer engagement, and retention. This data can help you target the right customers who have previously purchased your products or browsed them at your website. Adding various details like age, gender, order amount, location, etc. It is possible to identify what kind of products they would like to buy in the future. With this data, you send email content and offers to individual prospects.

Predictive analytics is also useful in determining how frequently customers make purchases, you can understand the optimum frequency to send your product-related emails to them.

Customer win-back strategy – reengage customers who didn’t make any purchase in a while sending a "we miss you" message in an email to all customers after a particular duration of time since they last purchased a product. With the help of predictive analytics, you can create personalized win-back emails, find out the best time interval to send emails to them, and offer some discounts or incentives to reengage them.

Predictive marketing is a powerful weapon for marketers to understand their target audiences and help them apply a powerful strategy in their email marketing campaigns. With this, you can impress your subscribers, and convert them into loyal customers, which ultimately leads to an increase in sales.

Senior Seo Specialist at Express Analytics. Has experience in Seo, Social Media, Blogging, Online Reputation Management, Google Ads, and YouTube Video Optimization.