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Digital Analytics

How to Transform Data Into Marketing Insights Using Predictive Analytics

Jun 21, 2022

4 minutes read

Predictive analytics has carved a niche for itself and is now making rapid strides in the world of digital marketing.

It uses data, statistical algorithms, and machine learning techniques to predict future outcomes based on historical data. By analyzing patterns in data, predictive analytics help identify trends and behaviors in an industry. These predictions provide valuable insights that are used to make better-informed business and investment decisions.

An increasing number of organizations are using predictive analytics to help solve a wide variety of problems and unravel new opportunities.

In this blog post, we’ll discuss how predictive analytics can give you a huge edge and why it should be a part of your business strategy.

You’ll also learn in detail what it is and what are the three metrics that exist to drive predictive analytics.

Let’s begin!

What is Predictive Analytics?

Predictive Analytics is the use of historical data and events to predict future outcomes and make accurate predictions for unknown future events.

Sophisticated tools, statistical information, big data machine learning, AI models, etc can help predict forecast trends.

Professional big data scientists use predictive models to determine correlations between different elements of datasets. After data is collected, a statistical model is created and trained to generate predictions.

Google Analytics 4 supports predictive metrics and hopes to make future predictions about industry trends and customer behaviors easier for organizations. GA4 is designed to revamp the world of data collection, analysis, and processing.

Predictive metrics in Google Analytics 4 are derived from machine-learning algorithms that measure the process it took to convert.

These algorithms help GA4 collect user data on the datasets and predict their future behavior based on that.

GA4 supports the following three predictive metrics at present:

1. Purchase Probability: The probability that a user who has been active in the last 28 days is expected to generate a purchase/conversion event within the next 7 days. Currently, only purchase, ecommerce_purchase, and in_app_purchase events are supported in Google Analytics 4.

2. Churn Probability: The probability that a user who has been active on the website within the last 7 days is expected not to be active within the next 7 days.

3. Revenue Prediction: The revenue expected from all purchase events within the next 28 days from the users who have been active on the website in the last 28 days.

Prerequisites for Predictive Metrics

Predictive metrics are derived from Google machine-learning algorithms. Therefore, it is essential to train the predictive models as described below:

a. Configure the purchase event and send it to the GA4 property to be eligible for ‘purchase probability’ and ‘churn probability’ metrics.

b. About 1000 positive and negative samples of purchasers and churned users are required at the minimum.

c. Model quality is determined by regular traffic generating purchase events. This quality should be sustained for at least 28 days.

How Does Predictive Analytics Help Transform Business Data Into Marketing Insights?


1. Personalization

In today’s highly competitive marketplace, providing contextual customer experiences is no longer a “nice to have” — it’s an expectation. Personalizing your content and customer experiences is the modern way to connect with your audiences and pay attention to their specific needs and demands.

Predictive metrics can help you cater to high-value customers. Since these metrics in GA4 show the progress of a customer’s conversion, they can facilitate decisions of marketing the right offers throughout the customer journey.

Predictive analytic models like affinity analysis, churn analysis, and response modeling can help you learn the most effective marketing channels for your audience.

These metrics go beyond creating meaningful audience segments, lead scoring, and content recommendations to make your marketing activities more relevant.

2. Lead Qualification

Predictive analytics can help businesses with lead qualification. Here are the top three forms of B2B marketing that help the sales team convert more leads efficiently.

a. Identification Models: Businesses can use existing customers and their journey to acquire new prospects.

b. Predictive Scoring: This includes a list of potential leads, accounts, and prospects. Sales can prioritize who they want to nurture so they can convert.

c. Automated Segmentation: Here leads are segmented to identify the order of priority for personalized messaging.

3. Analyze Customer Behavior

Predictive modeling can help businesses predict customer preferences. Take eBay and Amazon for instance. These platforms use predictive modeling to analyze customer behavior and personalize their buyer journeys accordingly.

AgilOne, a cloud-based predictive marketing platform, uses predictive modeling as:

a. Predictions (Propensity Models): It includes parameters for prediction – propensity to convert, propensity to unsubscribe, predictive lifetime value, the likelihood of engagement, and propensity to churn.

b. Segments (Cluster Models): This model focuses on customer segmentation. Product-based clustering, behavioral clustering, and brand-based clustering are some of the popular cluster models.

c. Recommendations (Collaborative Filtering): This type of predictive analytics model is used for sending services, and product recommendations to customers. Netflix and Amazon use this model to upsell, cross-sell, and next-selling.

4. Product Development & Marketing Guidance

Predictive metrics also help you decide which products or services might work well in the market.

It doesn’t come with complete accuracy, but you can draw insights from the historical data on your customers’ purchasing habits, behaviors, and geographical location.

Predictive analytics can also help you decide which marketing channel will work best for your next campaign. Your social media data, and behavior scoring with customer data can help you make the most of predictive analytics.

You can integrate sentiment and text analysis with your social media data to facilitate product creation and future marketing campaigns.

Key Takeaway

Predictive analytics can help you understand the specific needs of your customers using business data generated over the years.

Therefore, it is time for marketers to leverage predictive metrics in Google Analytics 4 and boost their business.

Want to Learn More About Predictive Metrics? Contact Us!

Grazitti Interactive has a team of experts who help their clients with innovative and best-in-class digital marketing services. To reach out to the digital marketing experts at Grazitti, you can drop us a line at [email protected] and we’ll take it from there.