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Predictive audiences: How CDPs leverage machine learning to find your best customers

Your e-commerce company has a customer data platform. You’re using it to store and analyze every aspect of your customer’s interactions with your brand. But what if you could use that same information to predict who’s most likely to buy from you? That’s where predictive audiences come in. They allow you to create groups of people based on their likelihood of taking specific actions. It doesn’t matter whether those actions are buying something, signing up for a subscription service, or completing an application form.

Understanding the concept of predictive audiences

A predictive audience is an ad group that contains people who are most likely to convert. This can be based on past behaviors, such as purchases or visits to specific products or pages on your website.

It’s important to note that a lookalike audience isn’t the same as a predictive one. While they both target users based on similar interests, the difference is those lookalike audiences are built from existing customer profiles and use them as inspiration for creating new ones. Predictive audiences are built from data collected by machine learning algorithms. The software takes into account all sorts of factors in order to determine which customers fit best into each bucket/audience.

The two kinds of audiences are useful in different ways. For example, you can target your lookalike audience with specific ads and see how they perform over time. If they aren’t converting as well as you’d hoped, you can use that information to improve the campaign. On the other hand, predictive audiences are great for testing new things—such as products or services. Because they’ll give you a better understanding of what people want before spending too much money on creating them.

Predictive audiences are a great tool for marketers because they help you target the right people with your advertising. For example, if you have a website that sells running shoes and you want to sell more products. Then building a predictive audience is an ideal way to identify customers who have already purchased running shoes in the past. You can then use this information to target those individuals on Facebook with ads. For example about your new line of sneakers or other athletic wear. Predictive audiences are one of the key features of Google Ads. They allow you to create custom groups of people that meet certain criteria and then target those groups with your ads. This can help you find new customers who are likely to convert into paying customers. Something that can improve your overall ROI on PPC campaigns.

Why should you care about predictive audiences?

Predictive audiences help you find your best customers. They can help you personalize the customer experience and better target your marketing efforts, helping you improve your sales process.

In this blog, I will help you understand what predictive audiences are and how they can be used to improve your business.

What is a predictive audience? A predictive audience is a group of people who have similar characteristics and behaviors as your existing customers. These groups can be used to create highly personalized customer experiences, which helps increase sales.

How do predictive audiences work?

Predictive audiences use machine learning to identify customers with a high propensity to buy. Machine learning is the process of using algorithms that can “learn” from data and make predictions based on it. For example, if you have a sample set of 100 people who have bought your product in the past and another 100 who haven’t bought it, then machine learning can be used to determine which characteristics are most likely associated with purchasing history.

For predictive audiences, this means identifying your most valuable customers by examining their purchase patterns across different channels (e-commerce sites, mobile apps, etc.). This helps companies identify which people are more likely than others to convert into paying customers.

How to find your predictive audience

To find your predictive audience, you need to consider several factors. Here are some of the most important considerations:

  • What is your goal? Are you trying to increase sales? Get more customers? Improve retention? If you have a clear idea of what you want from your predictive audience. Then it will be easier for your team to create one that meets those expectations.
  • How many people do you have access to in your database? The larger the sample size, the better and more accurate any kind of statistical analysis will be. For example, if a company has 1 million customers in its database but only 200 records in its CRM (customer relationship management) system and wants to know who’s likely to buy an expensive product like a car or motorcycle within six months based on their past purchases. It’s going to have trouble creating an accurate predictive model with such limited data.
High spending customers

With machine learning, your CDP can identify customers with a high propensity to buy and help you personalize their experiences with your brand.

We know you’re busy, so we’ll make this quick. With machine learning, your CDP can identify customers with a high propensity to buy and help you personalize their experiences with your brand. To do so, you need to:

  • Create a predictive audience based on the customers who responded or interacted with your business in some way recently. For example, if you want an audience of people who bought something from your website within the past month or two weeks, but haven’t made any purchases lately (i.e., they’re inactive). You might also want an audience of people who purchased one product but are likely to purchase more products from that same category because they’ve already shown interest in it (i.e., active), or maybe even customize their purchase experience by offering them recommendations based on their previous purchases (i.e., segmented).
  • Set up personalized emails for each customer segmentation group that reflect what’s most important for them at the time of purchase (i.e., timely offers). For example: “Hi Jane—you’ve been inactive for three weeks but have purchased several items over time; would you like us to send some suggestions?”. Or “Hello Mr. Smith—you recently purchased product X from us; would we be able to help answer any questions about its use?”. When these emails go out automatically based on user behavior patterns & preferences gleaned from past activity data within an individual’s profile without any manual intervention needed! And this is where things start getting really exciting!

Conclusion

We’re just scratching the surface of what machine learning can do for your e-commerce. The most important takeaway is that it gives you access to a huge amount of valuable data and lets you use it to make smarter decisions. You can start by finding new customers or better understanding your existing ones. From there, we hope this article has given you some ideas on where else machine learning and a customer data platform might be useful for your store.

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