Enhanced Marketing ROI

Budget constraints are a constant reality for most SMBs. Every marketing dollar must be spent wisely. Predictive audiences directly address this challenge by improving the efficiency of marketing campaigns. By targeting users with a high propensity to convert, SMBs can significantly reduce wasted ad spend and increase conversion rates.

Imagine running a Google Ads Meaning ● Google Ads represents a pivotal online advertising platform for SMBs, facilitating targeted ad campaigns to reach potential customers efficiently. campaign specifically targeting users predicted to purchase in the next 7 days. The click-through rates and conversion rates for such a campaign are likely to be far higher than a campaign targeting a generic audience. This translates directly to a better ROI and more effective use of limited marketing budgets.

Improved Customer Engagement

Generic marketing messages often fall flat. Customers today expect personalized experiences that resonate with their individual needs and preferences. Predictive audiences allow SMBs to create more relevant and engaging customer interactions.

For example, a clothing boutique could use predictive audiences to identify users likely to churn and proactively send them personalized offers or style recommendations to re-engage them. This level of personalization fosters stronger customer relationships, increases customer loyalty, and ultimately drives repeat business.

Proactive Growth Strategies

Traditional marketing often focuses on reacting to past customer behavior. Predictive audiences enable a shift towards proactive growth strategies. By anticipating future customer actions, SMBs can get ahead of the curve and proactively shape customer journeys.

For instance, a subscription box service could use predictive audiences to identify users likely to upgrade to a premium subscription and proactively offer them incentives to do so. This proactive approach to growth allows SMBs to capitalize on opportunities before they arise and build a more sustainable and scalable business.

Automation and Efficiency

Manual audience segmentation and campaign optimization are time-consuming and resource-intensive tasks, especially for SMBs with lean teams. Predictive audiences automate much of this process. GA4 continuously analyzes data and updates predictive audience lists in real-time, ensuring that marketing efforts are always targeted at the most relevant users. This automation frees up valuable time for SMB owners and marketing teams to focus on higher-level strategic initiatives and creative campaign development, boosting overall operational efficiency.

Defining Key Conversion Events

Conversion events are actions that you want users to take on your website or app that contribute to your business goals. For an SMB, these events are often directly tied to revenue generation or customer acquisition. It’s essential to identify the 2-3 most important conversion events that are critical to your business success.

Focus on events that are frequently tracked in GA4 and provide meaningful insights into user behavior. Here are some examples of key conversion events for different types of SMBs:

• E-Commerce Store ● Purchase (completed transaction), Add to Cart, Initiate Checkout, Product Page View
• Service-Based Business ● Form Submission (contact form, quote request), Appointment Booking, Phone Call, Live Chat Engagement
• Restaurant with Online Ordering ● Online Order Placement, Menu Item View, Reservation, Loyalty Program Sign-up
• SaaS Business ● Free Trial Sign-up, Demo Request, Subscription Start, Feature Usage (e.g., creating a project, inviting users)
• Local Business (e.g., Gym, Salon) ● Appointment Booking, Class Registration, Membership Sign-up, Contact Form Submission

It’s important to choose conversion events that are accurately tracked in GA4. Ensure that your GA4 setup is correctly configured to capture these events. You can verify this by checking your GA4 reports and ensuring that conversion data is being recorded consistently.

If you are unsure about your GA4 setup, consult Google’s official documentation or seek assistance from a GA4 expert. Accurate conversion tracking is the bedrock of effective predictive audience targeting.

Setting SMART Predictive Goals

Once you have identified your key conversion events, the next step is to define SMART goals for using predictive audiences. SMART goals provide a framework for setting objectives that are clear, actionable, and measurable. This ensures that your predictive audience strategy is focused and that you can track your progress effectively. Here’s a breakdown of the SMART criteria in the context of predictive audiences:

1. Specific ● Clearly define what you want to achieve with predictive audiences. Instead of saying “improve marketing performance,” be specific like “increase online order conversion rate.”
2. Measurable ● Establish quantifiable metrics to track your progress. For example, “increase online order conversion rate by 15%.”
3. Achievable ● Set realistic goals that are attainable within your resources and timeframe. Don’t aim for unrealistic targets that will demotivate your team. Consider your current conversion rates and historical data to set achievable benchmarks.
4. Relevant ● Ensure your goals are aligned with your overall business objectives. Your predictive audience goals should directly contribute to your broader growth and revenue targets. For instance, if your business priority is to increase online sales, then your predictive audience goals should focus on driving online conversions.
5. Time-Bound ● Set a specific timeframe for achieving your goals. For example, “increase online order conversion rate by 15% within the next quarter.” Having a deadline creates a sense of urgency and helps you track progress over time.

Here are some examples of SMART goals for SMBs using predictive audiences:

• E-Commerce Store ● Increase purchase conversion rate from predictive audience campaigns by 10% in the next month.
• Service-Based Business ● Increase form submission rate from users in the “high lead probability” audience by 5% within two months.
• Restaurant with Online Ordering ● Reduce cart abandonment rate among users predicted to purchase online by 8% in the next three weeks.
• SaaS Business ● Increase free trial to paid subscription conversion rate for users in the “high conversion probability” audience by 12% within the next quarter.
• Local Business ● Increase appointment bookings from users in the “high booking probability” audience by 7% in the next month.

By clearly defining your key conversion events and setting SMART goals, you lay a solid foundation for successful predictive audience implementation. This upfront planning ensures that your efforts are focused, measurable, and aligned with your business objectives, maximizing the potential ROI of your predictive audience strategy.

Understanding GA4 Predictive Audience Templates

Each predictive audience template in GA4 focuses on a specific type of predicted user behavior. Understanding the nuances of each template is crucial for selecting the right ones for your business goals. Here’s a breakdown of the three core templates:

• Purchase Probability ● This template identifies users who are predicted to purchase on your website or app within the next seven days or 28 days. GA4 analyzes past purchase behavior and user characteristics to determine the likelihood of a user making a purchase. This audience is ideal for SMBs focused on driving sales and increasing revenue. It can be used to target users with promotional offers, product recommendations, or special deals to encourage immediate purchases.
• Churn Probability ● This template identifies users who are predicted to be inactive or not return to your website or app within the next seven days. Churn, or customer attrition, is a significant concern for many SMBs, especially subscription-based businesses. This audience allows you to proactively identify users at risk of churning and implement retention strategies. You can target these users with re-engagement campaigns, personalized content, or special offers to encourage them to stay engaged and prevent churn.
• Spend Probability (Top 28-Day Spenders) ● This template identifies users who are predicted to spend the most money on your website or app within the next 28 days. This audience is valuable for SMBs looking to maximize revenue from their most valuable customers. By identifying high-potential spenders, you can tailor marketing efforts to encourage larger purchases or upsells. You can target these users with premium product recommendations, exclusive offers, or loyalty program incentives to increase their average order value and overall spending.

GA4 also offers variations within these templates, such as “likely 7-day purchasers” and “likely 28-day purchasers” for the Purchase Probability Meaning ● Purchase Probability, within the context of SMB growth, automation, and implementation, quantifies the likelihood that a prospective customer will complete a transaction. audience. These variations allow you to fine-tune your targeting based on the urgency of your campaigns and the desired timeframe for conversions. For example, the “likely 7-day purchasers” audience is suitable for short-term promotional campaigns, while the “likely 28-day purchasers” audience is better for longer-term engagement strategies.

It’s important to note that predictive audiences require a certain volume of data to function effectively. GA4 needs historical data on conversions and user behavior to train its machine learning models and make accurate predictions. If your GA4 property is new or has limited historical data, predictive audiences may not be immediately available or as accurate. Google provides specific data thresholds that must be met for predictive audiences to be generated.

These thresholds typically involve a minimum number of positive and negative examples of the predicted behavior (e.g., purchases vs. non-purchases). As your GA4 property accumulates more data over time, the accuracy and effectiveness of predictive audiences will improve.

Activating Predictive Audience Templates in GA4

Activating predictive audience templates in GA4 is a straightforward process that can be completed in just a few steps. Here’s a step-by-step guide:

1. Navigate to Audiences ● In your GA4 property, go to Admin (bottom left corner) > Audiences (under Property settings).
2. Create New Audience ● Click on the New Audience button.
3. Explore Templates ● Select Suggest Audience, then navigate to the Predictive tab.
4. Choose a Template ● Select the predictive audience template that aligns with your goals (e.g., Purchase probability, Churn probability, Spend probability).
5. Configure Template (Optional) ● Review the template settings. For most SMBs, the default settings will be sufficient. You can adjust parameters like the prediction window (e.g., 7 days or 28 days for Purchase probability) if needed.
6. Save Audience ● Give your audience a descriptive name (e.g., “Likely 7-Day Purchasers”) and click Save.

Once you save a predictive audience, GA4 will begin to populate the audience list. This process may take some time, depending on the volume of data in your GA4 property. You can check the status of your audience in the Audiences report.

Once the audience is populated, you will see the estimated audience size and other relevant metrics. You can then use this audience for targeting in Google Ads, explorations, and other GA4 features.

It’s recommended to start by activating one or two predictive audience templates that are most relevant to your immediate business priorities. For example, if your primary goal is to increase online sales, start with the Purchase probability audience. If customer retention Meaning ● Customer Retention: Nurturing lasting customer relationships for sustained SMB growth and advocacy. is a major focus, activate the Churn probability audience. You can always activate additional templates later as you become more comfortable with using predictive audiences.

Activating predictive audience templates is a quick and easy way for SMBs to tap into the power of machine learning without requiring any coding or complex configurations. This step sets the stage for leveraging these audiences in your marketing activations to achieve your defined goals.

Activating GA4’s pre-built predictive audience templates is a simple, no-code process that unlocks powerful machine learning insights for SMB marketing.

Applying Predictive Audiences in Marketing Channels

GA4 predictive audiences can be seamlessly integrated with various marketing channels to enhance targeting and personalization. Here are some key channels where SMBs can effectively apply predictive audiences:

• Google Ads ● This is a primary integration point for GA4 predictive audiences. You can use predictive audiences to refine your Google Ads targeting and reach users who are most likely to convert. For example, you can create a Google Ads campaign specifically targeting the “likely 7-day purchasers” audience with tailored ad copy and offers. This ensures that your ad spend is focused on high-potential users, maximizing your click-through rates and conversion rates. You can also use predictive audiences for exclusion targeting, for instance, excluding “likely 7-day churners” from acquisition campaigns to avoid wasting ad spend on users likely to disengage.
• Email Marketing ● Predictive audiences can significantly enhance email marketing Meaning ● Email marketing, within the small and medium-sized business (SMB) arena, constitutes a direct digital communication strategy leveraged to cultivate customer relationships, disseminate targeted promotions, and drive sales growth. personalization. You can segment your email lists based on predictive audiences and send highly targeted email campaigns. For example, send personalized product recommendations Meaning ● Personalized Product Recommendations utilize data analysis and machine learning to forecast individual customer preferences, thereby enabling Small and Medium-sized Businesses (SMBs) to offer pertinent product suggestions. to the “likely 28-day purchasers” audience or send re-engagement emails with special offers to the “likely 7-day churners” audience. This level of personalization increases email open rates, click-through rates, and ultimately conversions.
• Website Personalization ● Predictive audiences can be used to personalize the website experience for different user segments. Using website personalization Meaning ● Website Personalization, within the SMB context, signifies the utilization of data and automation technologies to deliver customized web experiences tailored to individual visitor profiles. tools that integrate with GA4, you can display tailored content, offers, or calls-to-action based on a user’s predictive audience membership. For example, show a special discount banner to users in the “purchase probability” audience or display helpful resources and onboarding guides to users in the “low spend probability” audience to encourage engagement and increase their value.
• Social Media Advertising ● While direct integration with social media platforms may be less seamless than with Google Ads, you can still leverage predictive audience insights to inform your social media targeting strategies. You can create lookalike audiences in social media platforms based on the characteristics of your GA4 predictive audiences. For example, create a Facebook lookalike audience based on your “likely purchasers” audience to expand your reach to new users with similar profiles.

When applying predictive audiences in marketing channels, it’s crucial to tailor your messaging and offers to resonate with the specific predicted behavior of each audience segment. For example, for the “purchase probability” audience, focus on conversion-oriented messaging, product benefits, and clear calls-to-action. For the “churn probability” audience, focus on re-engagement messaging, highlighting new features, or offering incentives to stay. Relevance and personalization are key to maximizing the impact of predictive audiences in your marketing activations.

Measuring Results and Optimizing Predictive Audience Campaigns

Measuring the results of your predictive audience campaigns is essential to demonstrate their effectiveness and identify areas for optimization. Tracking the right metrics and analyzing campaign performance data will allow you to refine your strategies and maximize your ROI. Here are key metrics to track and strategies for optimization:

• Conversion Rate ● This is a primary metric to track for campaigns targeting “purchase probability” or “lead probability” audiences. Compare the conversion rate of campaigns targeting predictive audiences to the conversion rate of your general campaigns or campaigns targeting non-predictive audiences. A significant uplift in conversion rate for predictive audience campaigns indicates their effectiveness.
• Click-Through Rate (CTR) ● Monitor CTR for ad campaigns and email campaigns targeting predictive audiences. Higher CTRs suggest that your messaging is resonating with the targeted audience. Compare CTRs to benchmark data and identify opportunities to improve ad copy and email subject lines.
• Customer Retention Rate ● For campaigns targeting “churn probability” audiences, track customer retention rate. Measure the retention rate Meaning ● Retention Rate, in the context of Small and Medium-sized Businesses, represents the percentage of customers a business retains over a specific period. of users who were targeted with re-engagement campaigns compared to a control group who were not targeted. An improvement in retention rate demonstrates the impact of your churn prevention efforts.
• Average Order Value (AOV) ● For campaigns targeting “spend probability” audiences, track AOV. Compare the AOV of users in this audience segment to the overall AOV. An increase in AOV indicates that you are successfully encouraging higher spending among high-potential customers.
• Return on Ad Spend (ROAS) ● For paid advertising campaigns using predictive audiences, calculate ROAS. This metric provides a clear picture of the revenue generated for every dollar spent on advertising. Compare ROAS for predictive audience campaigns to your overall ROAS to assess their efficiency.

Regularly analyze your campaign performance data to identify trends, insights, and areas for optimization. A/B test different messaging, offers, and creative elements within your predictive audience campaigns to determine what resonates best with each segment. Continuously refine your targeting criteria and campaign parameters based on performance data to improve results over time. GA4 provides robust reporting and analysis tools to help you monitor your predictive audience campaigns and gain actionable insights.

By diligently measuring results and optimizing your campaigns based on data, you can ensure that your predictive audience strategy delivers tangible and measurable improvements in your marketing performance and business outcomes. Step three is not just about applying predictive audiences; it’s about continuously learning, iterating, and maximizing their potential to drive sustainable growth Meaning ● Sustainable SMB growth is balanced expansion, mitigating risks, valuing stakeholders, and leveraging automation for long-term resilience and positive impact. for your SMB.

Applying predictive audiences across marketing channels and rigorously measuring results is crucial for SMBs to realize tangible ROI and optimize campaign performance.

Customizing Predictive Audience Templates

GA4’s pre-built predictive audience templates are not rigid; they offer a degree of customization to align them more precisely with your SMB’s specific context. While the core predictive logic remains, you can adjust certain parameters to refine the audience definition and enhance targeting accuracy. Customization options vary slightly depending on the template, but common adjustments include:

• Prediction Window Adjustment ● For the “Purchase Probability” template, you can choose between a 7-day or 28-day prediction window. The 7-day window targets users with a higher immediate purchase intent, while the 28-day window broadens the audience to include users with a slightly longer purchase consideration cycle. SMBs with shorter sales cycles might prefer the 7-day window, while those with longer cycles or higher-value purchases may opt for the 28-day window.
• Inclusion of Behavioral Filters ● You can add behavioral filters to pre-built templates to further refine the audience. For example, within the “Purchase Probability” template, you could add a filter to include only users who have viewed a specific product category or visited a particular landing page. This adds a layer of behavioral targeting on top of the predictive layer, ensuring that you are reaching users who are not only likely to purchase but also have shown interest in specific products or services.
• Exclusion of User Segments ● Similarly, you can exclude specific user segments from pre-built templates. For instance, you might want to exclude existing customers from a “Purchase Probability” audience if your goal is to acquire new customers. Or, you could exclude users who have already made a purchase in the past week to avoid targeting recent converters with acquisition-focused campaigns.

To customize a predictive audience template, follow these steps:

1. Navigate to Audiences ● Go to Admin > Audiences.
2. Edit Existing Audience ● Find the predictive audience you want to customize (you may need to create one from a template first if you haven’t already), and click on the three dots (…) to the right of the audience name, then select Edit.
3. Modify Conditions ● Within the audience builder interface, you will see the pre-set conditions for the predictive audience. Click Add Filter to add inclusion or exclusion filters based on dimensions and metrics like page views, events, demographics, or technology.
4. Adjust Prediction Window (If Applicable) ● For templates like “Purchase Probability,” you may see options to adjust the prediction window (e.g., 7 days or 28 days). Modify this setting as needed.
5. Save Changes ● Click Save to apply your customizations.

Customizing predictive audience templates allows SMBs to tailor them to their specific business context and marketing objectives. It’s a step beyond basic template activation, enabling more precise targeting and potentially higher campaign performance.

Customizing GA4 predictive audience templates allows SMBs to refine targeting by adjusting prediction windows and adding behavioral filters, enhancing campaign precision.

Combining Predictive Audiences with Custom Segments

The real power of GA4’s Audience builder emerges when you combine predictive audiences with custom segments. Custom segments allow you to define audiences based on a wide range of dimensions and metrics beyond the pre-defined predictive criteria. By layering custom segments on top of predictive audiences, you can create highly granular and targeted audiences that are exceptionally relevant to your specific marketing campaigns. This combination approach enables SMBs to move beyond broad predictive categories and reach very specific user niches with tailored messaging.

Here are some examples of how SMBs can combine predictive audiences with custom segments:

• Predictive Audience + Demographics ● Combine “Purchase Probability” with demographic segments like age, gender, or location. For example, create an audience of “Likely 7-day Purchasers who are women aged 25-34” to target a specific demographic with tailored product recommendations or offers. This is particularly useful for SMBs whose products or services appeal to specific demographic groups.
• Predictive Audience + Technology ● Combine “Churn Probability” with technology segments like device category (mobile, desktop, tablet) or browser. For example, create an audience of “Likely 7-day Churners who primarily use mobile devices” to investigate if mobile user experience issues are contributing to churn and address them proactively.
• Predictive Audience + Acquisition Source ● Combine “Spend Probability” with acquisition source segments like Google Ads campaigns, organic search, or social media. For example, create an audience of “Likely Top 28-day Spenders acquired through Google Ads ‘Brand Campaign'” to understand which acquisition channels are driving the most valuable customers and optimize ad spend accordingly.
• Predictive Audience + Website Behavior ● Combine “Purchase Probability” with website behavior segments like pages visited, events triggered, or time spent on site. For example, create an audience of “Likely 7-day Purchasers who have viewed product pages in the ‘New Arrivals’ category” to promote new product lines to users with high purchase intent and demonstrated interest in new products.

To combine predictive audiences with custom segments in the Audience builder:

1. Navigate to Audiences ● Go to Admin > Audiences.
2. Create New Audience ● Click New Audience, then Custom Audience.
3. Add Predictive Condition ● Click Add Condition, then select Predictive Conditions and choose your desired predictive audience (e.g., Purchase probability).
4. Add Custom Segments ● Click Add Condition again and add segments based on dimensions and metrics like demographics, technology, acquisition, or behavior. You can use AND/OR logic to combine multiple segments.
5. Define Time Scope (If Applicable) ● Set the membership duration and lookback window for your audience.
6. Save Audience ● Give your audience a descriptive name and click Save.

Combining predictive audiences with custom segments allows for incredibly precise targeting, enabling SMBs to create highly personalized marketing experiences for niche user groups. This advanced segmentation strategy can significantly improve campaign relevance and drive higher conversion rates and ROI.

Layering custom segments onto predictive audiences in GA4 enables SMBs to create highly granular audiences for pinpoint marketing and enhanced personalization.

Leveraging Sequence Segments with Predictive Audiences

For even more sophisticated audience definition, GA4 allows you to use sequence segments in conjunction with predictive audiences. Sequence segments enable you to define audiences based on the order in which users perform specific actions on your website or app. Combining sequence segments with predictive audiences allows SMBs to target users based not only on their predicted behavior but also on their journey and interaction history. This advanced technique is particularly useful for understanding complex user paths and tailoring marketing messages based on where users are in their customer journey.

Examples of using sequence segments with predictive audiences for SMBs:

• Abandoned Cart Recovery with Purchase Probability ● Target “Likely 7-day Purchasers” who have sequentially added items to their cart but did not complete a purchase. This audience represents users with high purchase intent who experienced cart abandonment. You can target them with abandoned cart recovery Meaning ● Abandoned Cart Recovery, a critical process for Small and Medium-sized Businesses (SMBs), concentrates on retrieving potential sales lost when customers add items to their online shopping carts but fail to complete the purchase transaction. emails or ads featuring incentives to complete their purchase. The sequence would be ● 1. Add to cart, 2. Did not purchase (within a specific timeframe).
• Re-Engaging Inactive Users with Churn Probability ● Target “Likely 7-day Churners” who sequentially visited the website multiple times in the past but have been inactive recently. This audience identifies users who were previously engaged but are now showing signs of disengagement. You can target them with re-engagement campaigns highlighting new content, features, or exclusive offers to rekindle their interest. The sequence could be ● 1. Multiple website visits in past month, 2. No website visits in past week.
• Upselling High-Potential Customers with Spend Probability ● Target “Likely Top 28-day Spenders” who have sequentially purchased a specific product category in the past. This audience identifies high-value customers who have already shown interest in a particular product area. You can target them with upsell or cross-sell offers for related premium products or services within that category. The sequence might be ● 1. Purchase in ‘Category A’, 2. No purchase in ‘Category B’ (related premium category).

To create audiences using sequence segments with predictive conditions:

1. Navigate to Audiences ● Go to Admin > Audiences.
2. Create New Audience ● Click New Audience, then Custom Audience.
3. Select Sequence Segment ● Choose Add Sequence.
4. Define Sequence Steps ● Define the sequence of user actions you want to track. For each step, you can add conditions based on events, dimensions, and metrics. Ensure you use the “is followed by” option to define the sequential order.
5. Add Predictive Condition ● Within the sequence definition, or as an additional condition outside the sequence, add a predictive condition by clicking Add Condition and selecting Predictive Conditions.
6. Define Time Constraints (If Applicable) ● Set time constraints for the sequence steps (e.g., “within the same session,” “within 7 days”).
7. Save Audience ● Give your audience a descriptive name and click Save.

Sequence segments, when combined with predictive audiences, offer a powerful way to understand and target users based on their journey and predicted future behavior. This advanced segmentation technique enables highly personalized and contextually relevant marketing interventions, leading to improved customer engagement Meaning ● Customer Engagement is the ongoing, value-driven interaction between an SMB and its customers, fostering loyalty and driving sustainable growth. and conversion outcomes for SMBs.

Combining sequence segments with predictive audiences in GA4 allows SMBs to target users based on their journey and predicted behavior, enabling highly personalized marketing.

Dynamic Website Personalization Based on Predictive Audiences

Website personalization goes beyond simply displaying a user’s name. Dynamic website personalization, driven by predictive audiences, involves tailoring website content, layout, and offers in real-time based on a user’s predicted behavior. This creates a highly relevant and engaging experience for each visitor, increasing the likelihood of conversion and fostering stronger customer relationships. Predictive audiences provide the intelligence layer to power truly dynamic personalization for SMBs.

Examples of dynamic website personalization Meaning ● Dynamic Website Personalization for SMBs is the strategic implementation of adapting website content, offers, and user experience in real-time, based on visitor behavior, demographics, or other data points, to improve engagement and conversion rates. using predictive audiences:

• Personalized Product Recommendations for Likely Purchasers ● For users in the “Purchase Probability” audience, dynamically display product recommendations on the homepage or product pages based on their browsing history and predicted purchase intent. Highlight products they have previously viewed or products that are frequently purchased by users with similar profiles. This personalized product discovery can significantly increase click-through rates and sales.
• Proactive Chat Support for Churn-Prone Users ● For users in the “Churn Probability” audience, proactively trigger a live chat window offering assistance or addressing potential concerns. This proactive support can help resolve user issues, improve customer satisfaction, and prevent churn. The chat message can be tailored to address common churn reasons or offer personalized solutions.
• Tailored Offers and Incentives for High-Value Spenders ● For users in the “Spend Probability” audience, dynamically display exclusive offers, discounts, or loyalty program incentives on the website. Recognizing and rewarding high-potential spenders can encourage larger purchases and build customer loyalty. The offers can be personalized based on their past purchase history or preferred product categories.
• Content Personalization Based on Predicted Interests ● If you can predict user interests based on their browsing behavior (e.g., using custom predictive metrics), you can personalize website content accordingly. For example, for users predicted to be interested in “sustainable products,” display content highlighting your eco-friendly product lines or sustainability initiatives. This content personalization enhances user engagement and positions your brand as relevant to their values.

Implementing dynamic website personalization requires a website personalization platform that integrates with GA4 audiences. Several tools on the market offer this integration, ranging from enterprise-level platforms to more SMB-friendly solutions. The key is to choose a platform that aligns with your technical capabilities and personalization goals.

Start with simple personalization use cases and gradually expand as you gain experience and see positive results. Dynamic website personalization, powered by predictive audiences, can transform your website from a static brochure to a dynamic, customer-centric experience that drives conversions and builds lasting relationships.

Dynamic website personalization based on GA4 predictive audiences allows SMBs to create real-time, tailored website experiences, enhancing engagement and conversions.

Optimizing Marketing Spend Allocation with Predictive Audience Insights

Predictive audiences provide valuable insights into the potential value and behavior of different user segments. This information can be strategically used to optimize marketing spend allocation across channels and campaigns, ensuring that resources are directed towards the most promising opportunities. Instead of spreading marketing budgets evenly, SMBs can leverage predictive audience insights to prioritize high-potential segments and maximize ROI.

Strategies for optimizing marketing spend based on predictive audience insights:

• Increased Ad Spend on High Purchase Probability Audiences ● Allocate a larger portion of your Google Ads budget to campaigns targeting “Purchase Probability” audiences. These users are most likely to convert, so investing more in reaching them can yield a higher return. You can bid more aggressively for keywords relevant to this audience and allocate a larger daily budget to these campaigns.
• Reduced Ad Spend on Churn Probability Audiences (for Acquisition Campaigns) ● For acquisition-focused campaigns, consider excluding “Churn Probability” audiences or reducing bid amounts for these segments. While re-engagement campaigns for churn-prone users are valuable, spending heavily on acquiring users who are likely to churn may not be efficient. Reallocate those resources to acquiring users with higher predicted lifetime value.
• Channel Prioritization Based on Spend Probability ● Analyze which marketing channels are most effective at acquiring “Spend Probability” audiences. For example, if you find that users acquired through social media tend to have higher spend probability, consider increasing your social media advertising budget and focusing on channels that attract high-value customers.
• Personalized Budget Allocation within Campaigns ● Within Google Ads campaigns, use audience bid adjustments to allocate budget dynamically based on predictive audience membership. Increase bids for “Purchase Probability” audiences and decrease bids for lower-potential audiences within the same campaign. This ensures that your budget is automatically optimized towards the most valuable segments.

To effectively optimize marketing spend using predictive audience insights, regularly analyze the performance of campaigns targeting different predictive segments. Track metrics like conversion rate, ROAS, and customer lifetime value Meaning ● Customer Lifetime Value (CLTV) for SMBs is the projected net profit from a customer relationship, guiding strategic decisions for sustainable growth. (LTV) for each audience segment. Use these data-driven insights to make informed decisions about budget allocation and channel prioritization.

GA4’s reporting and exploration features provide the necessary data to analyze audience performance and guide spend optimization. By strategically allocating marketing spend based on predictive audience insights, SMBs can significantly improve marketing efficiency Meaning ● Maximizing marketing ROI for SMBs through strategic resource allocation and data-driven optimization. and maximize ROI, ensuring that every marketing dollar works harder.

Predictive audience insights enable SMBs to strategically allocate marketing spend, prioritizing high-potential segments and maximizing ROI across channels.

Predictive Audiences for A/B Testing and Experimentation

A/B testing and experimentation are crucial for continuous marketing optimization. Predictive audiences provide a powerful framework for designing and targeting A/B tests, ensuring that experiments are conducted on relevant user segments and that results are more statistically significant and actionable. Using predictive audiences for A/B testing Meaning ● A/B testing for SMBs: strategic experimentation to learn, adapt, and grow, not just optimize metrics. allows SMBs to refine their marketing strategies with greater precision and accelerate the optimization process.

Examples of A/B testing scenarios using predictive audiences:

• A/B Testing Website Offers for Purchase Probability Audiences ● Test different website offers (e.g., discount codes, free shipping, bundled deals) specifically on the “Purchase Probability” audience. Compare the conversion rates of different offers within this high-intent segment to identify the most effective incentive for driving purchases. This targeted A/B testing ensures that you are optimizing offers for the users most likely to convert.
• A/B Testing Email Subject Lines for Churn Probability Audiences ● Test different email subject lines for re-engagement campaigns targeting “Churn Probability” audiences. Experiment with subject lines that emphasize different value propositions, urgency, or personalization. Measure open rates and click-through rates to determine which subject lines are most effective at re-engaging churn-prone users.
• A/B Testing Landing Page Variations for Spend Probability Audiences ● Test different landing page variations for campaigns targeting “Spend Probability” audiences. Experiment with landing page layouts, content, and calls-to-action to optimize for higher average order values and increased spending. Focus on elements that highlight premium products or services and encourage larger purchases.
• Personalization Element Testing for Different Predictive Segments ● Test different personalization elements (e.g., product recommendations, content blocks, website banners) for different predictive audience segments. What resonates with “Purchase Probability” audiences may not be as effective for “Churn Probability” audiences. Tailor your A/B tests to the specific needs and motivations of each segment.

When using predictive audiences for A/B testing, ensure that you have a clear hypothesis for each experiment and that you are tracking the right metrics to measure success. Use GA4’s Experiments feature or third-party A/B testing tools that integrate with GA4 audiences Meaning ● GA4 Audiences, in the context of Small and Medium-sized Businesses (SMBs), represent a specific group of users identified within Google Analytics 4 based on shared attributes, behaviors, or events triggered on their website or app. to set up and manage your tests. Segment your audience based on predictive audience membership and randomly assign users within each segment to different test variations.

Analyze the results of your A/B tests to identify statistically significant improvements and implement the winning variations. Continuous A/B testing, guided by predictive audience insights, is a powerful approach to data-driven marketing optimization for SMBs, leading to ongoing improvements in campaign performance and customer experience.

Leveraging predictive audiences for A/B testing allows SMBs to conduct more targeted experiments, refine strategies with precision, and accelerate marketing optimization.

About The Cozy Bookstore

The Cozy Bookstore is a small online retailer selling a curated selection of books across various genres. They primarily rely on Google Ads, social media marketing, and email marketing to drive traffic and sales. Like many SMBs, they faced challenges in optimizing their marketing spend and personalizing customer experiences with limited resources. They were looking for a way to improve their marketing ROI and drive sustainable growth.

Step 1 ● Cozy Bookstore Defines Conversion and Goals

The Cozy Bookstore started by defining their key conversion event as “Purchase” (completed online transaction). Their primary business goal was to increase online book sales. They set the following SMART goal for using predictive audiences:

Increase online purchase conversion rate from Google Ads campaigns targeting predictive audiences by 15% within one month.

This goal was specific (increase conversion rate), measurable (15%), achievable (based on past campaign performance), relevant (directly related to sales growth), and time-bound (one month).

Step 2 ● Cozy Bookstore Activates Predictive Audiences

Next, The Cozy Bookstore activated the “Purchase Probability” predictive audience template in GA4. They chose the “Likely 7-day Purchasers” variation, as they wanted to focus on users with a higher immediate purchase intent. They followed the steps outlined in the Fundamentals section of this guide to activate the audience in their GA4 property. The audience began populating within a few hours.

Step 3 ● Cozy Bookstore Applies Predictive Audiences and Measures Results

The Cozy Bookstore implemented the “Likely 7-day Purchasers” audience in their Google Ads campaigns. They created a new Google Ads campaign specifically targeting this audience, using the same keywords and ad copy as their existing general campaigns. They also increased their bid amounts for this audience segment to prioritize reaching high-potential purchasers. They closely monitored the performance of this new campaign compared to their general campaigns over the next month.

Results ●

• Conversion Rate Increase ● The Google Ads campaign targeting the “Likely 7-day Purchasers” audience achieved a 20% Increase in purchase conversion rate compared to their general Google Ads campaigns. This exceeded their initial goal of 15%.
• Improved ROAS ● The Return on Ad Spend Meaning ● Return on Ad Spend (ROAS) gauges the revenue generated for every dollar spent on advertising campaigns, critically important for SMBs managing budgets and seeking scalable growth. (ROAS) for the predictive audience campaign was 25% Higher than their general campaigns, demonstrating a significant improvement in marketing efficiency.
• Reduced CPA ● The Cost Per Acquisition (CPA) for the predictive audience campaign was 18% Lower, indicating that they were acquiring customers more cost-effectively by targeting high-probability purchasers.

Conclusion ●

The Cozy Bookstore successfully used GA4 predictive audiences to significantly improve their Google Ads campaign performance and drive online sales growth. By following a simple three-step implementation process, they were able to leverage the power of predictive analytics without complex technical expertise. The case study demonstrates the tangible benefits that SMBs can achieve by adopting predictive audiences and focusing on data-driven marketing strategies. The Cozy Bookstore plans to expand their use of predictive audiences to email marketing and website personalization to further enhance their customer engagement and drive continued growth.

Identifying Custom Predictive Metric Opportunities

The first step in creating custom predictive metrics is to identify opportunities where prediction can provide significant business value. Think beyond standard e-commerce conversions and consider other user behaviors that are crucial for your SMB’s success. Brainstorm potential predictive metrics that could inform strategic decisions and improve operational efficiency. Here are some examples of custom predictive metric opportunities for different types of SMBs:

• SaaS Business ●
  • Feature Adoption Probability ● Predict the likelihood of a user adopting a specific premium feature within a certain timeframe. This can inform targeted feature promotion and onboarding efforts.
  • Upgrade Probability ● Predict the probability of a free trial user upgrading to a paid subscription within the trial period. This can optimize trial nurturing and conversion strategies.
  • Customer Support Ticket Probability ● Predict the likelihood of a user submitting a support ticket in the next week. This can help proactively allocate support resources and identify users who might need extra assistance.
• Content-Driven Website (e.g., Blog, News Site) ●
  • Subscription Probability (Newsletter or Premium Content) ● Predict the likelihood of a user subscribing to a newsletter or a premium content subscription. This can optimize subscription prompts and content gating strategies.
  • High-Engagement Probability ● Predict the likelihood of a user becoming a highly engaged user (e.g., frequent visits, high time on site, multiple page views). This can help identify and nurture potential loyal readers or community members.
  • Content Share Probability ● Predict the probability of a user sharing a specific piece of content on social media. This can inform content promotion and social amplification strategies.
• Lead Generation Business ●
  • Lead Qualification Probability ● Predict the likelihood of a lead converting into a qualified sales opportunity. This can help prioritize lead follow-up efforts and optimize lead scoring models.
  • Demo Request Probability ● Predict the probability of a website visitor requesting a product demo. This can optimize demo request calls-to-action and demo scheduling processes.
  • Contact Form Conversion Probability (Specific Form) ● Predict the probability of a user completing a specific contact form (e.g., quote request form, consultation form). This can optimize form placement and design.

When identifying custom predictive metric opportunities, consider the following factors:

• Business Impact ● How will predicting this behavior contribute to your business goals? Will it improve revenue, efficiency, customer satisfaction, or other key metrics?
• Data Availability ● Do you have sufficient historical data in GA4 to train a predictive model for this behavior? Predictive metrics require a reasonable volume of positive and negative examples of the predicted event.
• Actionability ● How will you use the predictions once you have them? Will you be able to take concrete actions based on these predictions to improve your marketing or operations?

Focus on identifying 1-2 custom predictive metrics that have the highest potential business impact and are feasible to implement based on your data availability and actionability. Start small and iterate as you gain experience with custom predictive metrics.

Identifying custom predictive metric opportunities requires SMBs to think beyond standard conversions and consider unique business-specific behaviors for prediction.

Defining Custom Predictive Metric Events and Parameters

Once you have identified a custom predictive metric opportunity, the next step is to define the events and parameters that will be used to train the predictive model in GA4. This involves specifying the positive and negative examples of the behavior you want to predict and identifying relevant user characteristics that might be predictive of that behavior. Accurate event and parameter definition is crucial for the success of your custom predictive metric.

Here’s a breakdown of key considerations for defining custom predictive metric events and parameters:

• Positive Event Definition ● Clearly define the GA4 event that represents a positive instance of the behavior you want to predict. This is the event that indicates the user has exhibited the desired behavior. For example, for “Feature Adoption Probability” in a SaaS business, the positive event could be a custom event named “feature_adopted” triggered when a user uses a specific premium feature for the first time.
• Negative Event Definition (Implicit or Explicit) ● Define what constitutes a negative instance, or the absence of the desired behavior. In some cases, negative examples are implicit ● users who did not trigger the positive event within a specific timeframe are considered negative examples. In other cases, you might have explicit negative events. For example, for “Churn Probability,” a negative event could be “subscription_cancelled.”
• Timeframe for Prediction ● Specify the timeframe over which you want to predict the behavior. Is it within the next 7 days, 28 days, or a custom timeframe? The timeframe should be relevant to the business context and the actionability of the prediction. For example, predicting “Feature Adoption Probability” within the next 7 days might be relevant for onboarding campaigns, while predicting “Upgrade Probability” within the 30-day trial period is crucial for trial conversion strategies.
• User Parameters (Predictors) ● Identify user dimensions and metrics in GA4 that might be predictive of the desired behavior. These parameters will be used by GA4’s machine learning model to build the predictive model. Consider a wide range of parameters, including:
  • Demographics ● Age, gender, location, interests (if available).
  • Technology ● Device category, operating system, browser.
  • Acquisition ● Source, medium, campaign.
  • Website Behavior ● Pages viewed, events triggered, session duration, page depth, recency, frequency.
  • Custom Dimensions and Metrics ● Any custom data you are collecting in GA4 that might be relevant (e.g., user role, industry, customer segment).

When defining events and parameters, start with a clear understanding of the user journey and the factors that might influence the behavior you want to predict. Collaborate with your marketing, sales, and product teams to identify relevant events and potential predictors. Document your event and parameter definitions clearly to ensure consistency and accuracy in your predictive metric implementation.

Defining custom predictive metrics involves carefully selecting positive and negative events, prediction timeframes, and relevant user parameters for model training.

Implementing Custom Predictive Metrics in GA4 Using Audiences

Currently, GA4 does not offer a direct interface for creating fully custom predictive metrics in the same way it provides pre-built templates. However, you can effectively implement custom predictive metrics by leveraging GA4’s audience builder and exploration reports in a creative and strategic way. This approach involves using audience definitions to represent different probability ranges for your custom metric and then analyzing these audiences to gain predictive insights.

Here’s a step-by-step approach to implementing custom predictive metrics using GA4 audiences:

1. Define Probability Ranges ● Based on your custom predictive metric (e.g., “Feature Adoption Probability”), define probability ranges that are meaningful for your business. For example:
   • High Feature Adoption Probability ● Users with a predicted probability of 70% or higher to adopt the feature in the next 7 days.
   • Medium Feature Adoption Probability ● Users with a predicted probability of 40-70%.
   • Low Feature Adoption Probability ● Users with a predicted probability below 40%.
2. Create Audiences for Each Range ● In GA4’s Audience builder, create separate audiences for each probability range. While you cannot directly specify probability thresholds, you will need to use your understanding of user behavior and relevant dimensions/metrics to create audience definitions that approximate these probability ranges. This might involve using combinations of behavioral segments, demographics, technology, and acquisition source segments that you believe correlate with higher or lower probabilities of the predicted behavior. Example Audience Definitions (for “Feature Adoption Probability”) ●
   • High Feature Adoption Probability Audience ● Users who have recently engaged with related features, have high session duration on feature-related pages, and are acquired through specific onboarding campaigns.
   • Medium Feature Adoption Probability Audience ● Users who have shown some interest in related features but have lower engagement levels and are acquired through general marketing campaigns.
   • Low Feature Adoption Probability Audience ● Users who have not shown any interest in related features, have low overall engagement, and are acquired through broad acquisition channels.
3. Analyze Audience Performance in Exploration Reports ● Use GA4’s Exploration reports (specifically, the Free Form exploration) to analyze the performance of these audiences. Create reports that compare the actual feature adoption rates, conversion rates, or other relevant metrics across these different audiences. This analysis will help you validate if your audience definitions effectively approximate the desired probability ranges. You can refine your audience definitions iteratively based on these performance insights.
4. Use Audiences for Targeted Actions ● Once you have validated your custom predictive metric audiences, you can use them for targeted marketing activations, website personalization, and resource allocation, similar to how you would use pre-built predictive audiences. For example, target the “High Feature Adoption Probability Audience” with personalized onboarding messages and feature promotion campaigns.

This approach requires a more manual and iterative process compared to using pre-built templates. It relies on your business knowledge and analytical skills to create audience definitions that effectively proxy for custom predictive metrics. However, it allows SMBs to extend the power of predictive analytics beyond the standard templates and address their unique business needs within the current capabilities of GA4. As GA4 evolves, it’s possible that Google will introduce more direct support for custom predictive metrics, further simplifying this process.

Implementing custom predictive metrics in GA4 involves creatively using audience definitions to represent probability ranges and analyzing audience performance in explorations.

Benefits of CRM and Sales Platform Integration

Integrating GA4 predictive audiences with CRM and sales platforms offers several key benefits for SMBs:

• Enhanced Sales Lead Prioritization ● Predictive audiences, especially “Lead Probability” or custom lead qualification metrics, can be directly fed into CRM systems Meaning ● CRM Systems, in the context of SMB growth, serve as a centralized platform to manage customer interactions and data throughout the customer lifecycle; this boosts SMB capabilities. to prioritize sales leads. Sales teams can focus their efforts on leads with the highest predicted conversion probability, improving sales efficiency and conversion rates. CRM systems can automatically rank leads based on their predictive audience membership, ensuring that high-potential leads are addressed promptly.
• Personalized Sales Interactions ● CRM systems enriched with predictive audience data can provide sales representatives with valuable insights into each lead’s predicted behavior and interests. This enables sales teams to personalize their interactions, tailor their sales pitches, and address specific customer needs more effectively. For example, if a lead is in the “High Feature Adoption Probability” audience for a SaaS product, the sales rep can highlight those specific features during the sales conversation.
• Automated Sales and Marketing Workflows ● Integration allows for the automation of sales and marketing workflows based on predictive audience membership. For example, leads in the “High Purchase Probability” audience can be automatically enrolled in personalized sales email sequences or assigned to specific sales representatives. Similarly, users in the “Churn Probability” audience can trigger automated re-engagement campaigns in both marketing and sales platforms.
• Closed-Loop Reporting and Optimization ● Integrating CRM data back into GA4 creates a closed-loop reporting system. Sales conversion data from the CRM can be imported back into GA4 to measure the true ROI of marketing campaigns Meaning ● Marketing campaigns, in the context of SMB growth, represent structured sets of business activities designed to achieve specific marketing objectives, frequently leveraged to increase brand awareness, drive lead generation, or boost sales. and predictive audience targeting. This closed-loop feedback enables more accurate attribution modeling and continuous optimization of both marketing and sales strategies.
• Improved Customer Lifetime Value (CLTV) Prediction ● By combining GA4 predictive audiences with CRM data on customer purchase history and value, SMBs can develop more accurate CLTV prediction models. This enhanced CLTV prediction can inform long-term customer engagement strategies and resource allocation Meaning ● Strategic allocation of SMB assets for optimal growth and efficiency. decisions.

CRM and sales platform integration Meaning ● Platform Integration for SMBs means strategically connecting systems to boost efficiency and growth, while avoiding vendor lock-in and fostering innovation. with predictive audiences fosters a more customer-centric and data-driven approach across marketing and sales functions, leading to improved lead conversion, sales efficiency, customer retention, and overall business growth.

Integrating GA4 predictive audiences with CRM and sales platforms enables SMBs to prioritize leads, personalize sales interactions, and automate workflows for enhanced efficiency.

Practical Approaches to Integration

Several practical approaches can be used to integrate GA4 predictive audiences with CRM and sales platforms, depending on the specific tools and technical capabilities of your SMB:

• Direct Integrations (If Available) ● Some CRM and sales platforms offer direct integrations with Google Analytics 4. Check the documentation of your CRM and sales platforms to see if they offer built-in integrations with GA4 audiences. Direct integrations typically provide the most seamless data flow and automation capabilities. For example, some CRM platforms allow you to directly import GA4 audiences as lead segments or use GA4 audience membership as triggers for CRM workflows.
• Google Analytics 4 Export API ● GA4 provides an Export API that allows you to programmatically access audience data and other GA4 data. You can use this API to extract predictive audience membership data and import it into your CRM or sales platform. This approach requires some technical development effort to build the API integration, but it offers flexibility and control over the data transfer process.
• Google Sheets or Cloud Storage as Intermediary ● A simpler approach for SMBs without extensive technical resources is to use Google Sheets Meaning ● Google Sheets, a cloud-based spreadsheet application, offers small and medium-sized businesses (SMBs) a cost-effective solution for data management and analysis. or cloud storage services like Google Cloud Storage or Amazon S3 as an intermediary. You can export GA4 audience data to Google Sheets or cloud storage (e.g., using GA4’s BigQuery export and then exporting from BigQuery to Sheets/Cloud Storage). Then, you can import this data into your CRM or sales platform, either manually or using data import features or APIs offered by your CRM.
• Zapier or Integromat (Workflow Automation Platforms) ● Workflow automation Meaning ● Workflow Automation, specifically for Small and Medium-sized Businesses (SMBs), represents the use of technology to streamline and automate repetitive business tasks, processes, and decision-making. platforms like Zapier or Integromat can act as a bridge between GA4 and various CRM and sales platforms. These platforms offer pre-built connectors for GA4 and many popular CRM and sales tools. You can create automated workflows (Zaps or Scenarios) that trigger actions in your CRM or sales platform based on GA4 audience membership. For example, you can create a Zap that automatically adds leads in the “High Purchase Probability” audience to a specific CRM list or sends them a personalized sales email sequence.

When choosing an integration approach, consider your SMB’s technical resources, budget, and desired level of automation. Start with a simple integration method and gradually explore more advanced options as you gain experience and see the benefits of CRM and sales platform integration with predictive audiences. Data privacy Meaning ● Data privacy for SMBs is the responsible handling of personal data to build trust and enable sustainable business growth. and security are also crucial considerations when integrating data between platforms. Ensure that you comply with all relevant data privacy regulations Meaning ● Data Privacy Regulations for SMBs are strategic imperatives, not just compliance, driving growth, trust, and competitive edge in the digital age. and implement appropriate security measures to protect customer data during the integration process.

Practical integration approaches for SMBs include direct integrations, GA4 Export API, Google Sheets/Cloud Storage intermediaries, and workflow automation platforms like Zapier.

Transparency and User Consent

Transparency is paramount when using predictive audiences. Users should be informed about how their data is being collected, analyzed, and used for predictive purposes. Obtain informed consent from users for data collection and processing, especially for data used to build predictive models. Be clear and concise in your privacy policies and consent notices about your use of predictive analytics.

Explain how predictive audiences work in simple terms that users can understand. Avoid using overly technical jargon. Provide users with control over their data and the ability to opt out of data collection or personalized experiences based on predictive audiences. Make opt-out options easily accessible and user-friendly.

Avoiding Discrimination and Bias

Predictive models are trained on historical data, and if this data reflects existing societal biases, the models can inadvertently perpetuate or amplify these biases. Be mindful of potential biases in your predictive models Meaning ● Predictive Models, in the context of SMB growth, refer to analytical tools that forecast future outcomes based on historical data, enabling informed decision-making. and take steps to mitigate them. Regularly audit your predictive models for fairness and bias. Analyze the predictions for different demographic groups and ensure that they are not discriminatory or unfair.

Avoid using sensitive attributes like race, religion, or political affiliation directly in your predictive models, unless you have a legitimate and ethical justification and have obtained explicit consent. Focus on using behavioral data and user interactions for predictions, rather than relying on potentially biased demographic data. If you identify biases in your models, take corrective actions to re-train the models with debiased data or adjust the model parameters to reduce bias.

Data Security and Privacy Protection

Protect user data used for predictive audiences with robust security measures. Implement strong data encryption, access controls, and data anonymization techniques to safeguard user privacy. Comply with all relevant data privacy regulations, such as GDPR, CCPA, and other applicable laws. Ensure that your data collection, storage, and processing practices are compliant with these regulations.

Regularly review and update your data privacy policies and security measures to adapt to evolving regulations and best practices. Conduct privacy impact assessments to identify and mitigate potential privacy risks associated with your use of predictive audiences. Train your team on data privacy principles and responsible use of predictive analytics. Foster a culture of data privacy and ethical AI within your SMB.

Responsible Use of Predictions

Use predictive audience insights responsibly and ethically. Avoid using predictions to manipulate or exploit users. Focus on using predictions to improve customer experiences, provide relevant offers, and enhance service quality. Do not use predictive audiences for discriminatory pricing or service denial based on predicted characteristics.

Use predictions to personalize and improve customer interactions, not to create unfair or disadvantageous outcomes. Regularly review the ethical implications of your predictive audience strategies and make adjustments as needed. Engage in ongoing ethical reflection and dialogue within your organization about the responsible use of AI and predictive analytics.