Innovations in technology such as AI and machine learning have changed the advertising industry as a whole. Leveraging modern improvements in data gathering has helped marketers not only target their consumers more accurately, but improve the overall consumer journey. Predictive analysis is next in line to change the way marketers apply technology to their overall strategy. Over 90% of top marketers are either fully committed to or already implementing predictive marketing. 

In this blog, we give a rundown on how predictive advertising improves overall user retention and how it’s changing the way marketers advertise today. 

What is Predictive Advertising?

Predictive advertising is a marketing application of predictive analysis. Predictive analysis harnesses customer data such as demographics, behavior, transaction, and subscription data, and uses it to predict how individual customers will behave in the future. The analysis uses a mix of artificial intelligence and statistical algorithms to predict trends. When using predictive analysis in advertising, it’s possible to identify new potential customers and target them with relevant advertising content on the right platforms at the right time.

There are four common types of predictive segmentation techniques:

  • Look-alike modeling: Look-alike audiences are users who have similar traits or behaviors to existing customers. Look-alike modeling is a process that helps marketers find lookalike audiences of their best, most profitable users. It is a modeling approach that can be used by marketers to define customers who are most likely to engage with their marketing messages or activities.
  • Classification modeling: This type of predictive model looks for users similar to a specified group, but adds another layer of negative examples. These are the users that marketers don’t want to target.
  • Click-based optimization: This uses AI to determine the likelihood that each user will take a certain action in the future, such as clicking on an ad or making a purchase.
  • Uplift modeling: Uplift modeling predicts how an intervention (such as delivering an ad) will impact each user’s likelihood of making a purchase.

Why Predictive Advertising?

Predictive advertising drives sales and growth and allows marketers to easily anticipate trends across channels. Being able to accurately anticipate future trends helps influence marketing decisions and drive results. Traditional advertising involves a human review of past performance data to draw conclusions about what went right and what could have worked better. Predictive advertising, however, leverages technology to expedite this process. 

Over 60% of global marketers say their customer and marketing data comes from too many sources to make sense of it. As a result, 82% say predictive marketing is essential for business success. With predictive advertising, marketers can draw insights from data on a bigger scale than the human brain and manual bandwidth can handle. Most companies use a combination of traditional digital advertising and predictive advertising to improve overall retention and user engagement. 

Benefits of Predictive Advertising

Machine learning and AI help marketers predict trends and anticipate future consumer needs. Applying this technology to advertising with predictive analytics can help app marketers create better messaging for their users, analyze vast amounts of data and improve retention rates. 

Better Messaging

App marketers frequently segment their audiences based on interests and demographics. Predictive analytics, however, goes beyond these basic features to see how these audiences behave at different points in the buyer journey. This kind of analysis goes beyond typical assumptions and standard data points to determine which users are likely to convert and what creatives and messaging are needed to activate them.

By understanding customer behavior, marketers can shape their messaging to better target the right audience. This messaging will be able to help retain and re-engaged lapsed users by taking a look at data trends and what these users resonate with. For example, if certain gaming users react positively to receiving a certain type of award, marketers can use that in their message to pique those users’ interest and engage with them.

Sort Through Data

With consumers constantly on social media or the web, there is more contextual and behavioral  data available to marketers than ever before. Data helps app marketers predict user behavior and allows them to more accurately and effectively create campaigns to re-engage these users. However, being able to synthesize and derive actionable insights from this data is a specialized skill set that many businesses don’t have the bandwidth to handle.

Almost 9 out of 10 marketers note that data is the most underrated resource in their organization. With predictive advertising tools, billions of buying signals are collected and analyzed from a combination of channels, such as social media, web, email, CRM, and offline data. The more data available about an audience, the more accurately app marketers can re-engage their users. 

Increase Conversions

Users resonate with more targeted and personalized advertisements. Predictive analytics can help app marketers better target these individuals and deliver individualized experiences to specific user segments. By analyzing user behavior and going beyond basic demographics such as age and gender, marketers can craft campaigns based on user behaviors and trends. Leveraging predictive advertising allows marketers to create a truly personalized experience for their target audience that’s timely, relevant and optimized for conversions. 

Takeaways of Improving Retention with Predictive Advertising

Leveraging modern improvements in data gathering has helped marketers not only target their consumers more accurately, but improve the overall consumer journey. Predictive analysis is next in line to change the way marketers apply technology to their overall strategy. 

  • What is Predictive Advertising? Predictive advertising is a marketing application of predictive analysis. Predictive analysis harnesses customer data such as demographics, behavior, transaction, and subscription data, and uses it to predict how individual customers will behave in the future. 
  • Why Use It? Traditional advertising involves a human review of past performance data to draw conclusions about what went right and what could have worked better. Predictive advertising, however, leverages technology to expedite this process.
  • Benefits: Applying predictive analytics to advertising can help app marketers create better messaging for their users, sort through vast amounts of data and improve retention rates.