AI Segmentation Models for Media Networks

AI segmentation models are reshaping how media networks target audiences and optimize ad spend. Traditional demographic targeting is becoming less effective, with accuracy dropping by 34% since 2019. AI models, by analyzing user behavior and real-time data, deliver better results: 58% higher conversion rates and 44% lower customer acquisition costs compared to older methods. Here's a quick look at five leading platforms:

Salesforce Einstein: Predicts user actions and integrates CRM data for real-time insights.
Amplitude: Focuses on behavioral data and predictive cohorts to enhance engagement.
CleverTap: Combines rule-based logic with machine learning for intent-based targeting.
Delve AI: Generates detailed audience personas using psychographic and behavioral data.
Locatium: Leverages real-time location intelligence for precise, geography-driven segmentation.

Each platform offers unique strengths - whether it's predictive scoring, behavioral analysis, or location-based insights - making them valuable tools for refining audience targeting in a world moving away from third-party cookies. Below, we dive into their methodologies, integrations, and use cases to help you choose the right solution for your needs.

How to Build Customer Segments with AI (Real-World Use Case)

1. Salesforce Einstein

Salesforce Einstein brings artificial intelligence directly into the daily operations of media networks by providing predictive insights within Salesforce's Sales, Marketing, and Media Clouds.

Segmentation Methodology

Einstein tracks customer behavior across various touchpoints - like website clicks, email interactions, form completions, and purchase history - to create dynamic audience profiles. Each prospect is scored on a scale from 0 to 100, with updates every four hours to reflect real-time engagement. Unlike static demographic labels, this score acts as a live indicator of buying intent.

The platform goes a step further by predicting specific actions, such as whether a subscriber might cancel their service or upgrade to a premium tier. Additionally, Einstein uses natural language processing to scan text for trending topics and keywords, helping guide content and advertising strategies.

Integration Capabilities

Einstein runs on Salesforce Data Cloud, which merges data from clickstreams, app usage, and ad interactions into a single, cohesive view. This eliminates the need for manual data stitching. MuleSoft APIs further enhance its connectivity by linking it to external systems.

Accuracy and Performance Metrics

Einstein’s seamless data integration supports its strong performance metrics. To ensure accurate scoring, the system requires one year of historical engagement data and a minimum of 20 prospects linked to opportunities. It also includes a score decay feature that lowers a prospect's ranking when engagement levels drop.

Media Network Use Cases

Einstein’s analytics and integration capabilities help media networks fine-tune their ad spending by evaluating channel performance and reallocating budgets to strategies with the highest ROI. It also identifies clients at risk of churning, allowing sales teams to step in with tailored retention offers. On the content side, Einstein suggests the best channels and timing for customer engagement, boosting click-through rates for both editorial and advertising content.

2. Amplitude

Amplitude stands out by using behavioral data to predict user actions, offering a fresh take on segmentation. By analyzing past activities - like articles read, videos watched, or subscription activations - the platform groups users into cohorts and applies machine learning to predict future events, such as churn or upgrades.

Segmentation Methodology

Amplitude organizes users into three key types of cohorts:

Behavioral cohorts: Groups users by specific actions, such as binge-watching a series.
Predictive cohorts: Uses AutoML to continually update predictions about future actions, recalculating hourly.
Computed properties: Aggregates raw data, like total hours streamed, into actionable insights.

Integration Capabilities

Amplitude integrates seamlessly with advertising and email platforms, syncing cohort data in real time or hourly. This ensures messaging remains relevant to a user’s current journey. For instance, it can trigger a "Welcome Back" email when a previously inactive subscriber re-engages.

Media Network Use Cases

Several media networks have achieved impressive results using Amplitude:

Slate: By simulating paywall scenarios, they saw a 500% increase in conversions within a few months.
NBC: Tailored app homepages based on user history, doubling Day 7 retention rates.
iflix: Shifted from a single onboarding experience to seven targeted campaigns, resulting in a fourfold increase in conversion-to-view rates and ad revenue.
Le Monde: Discovered that 30% of paid subscribers experienced technical issues accessing gated content. Using Amplitude to analyze user journeys, they resolved this friction point.

3. CleverTap

CleverTap combines rule-based logic with predictive B2B AI tools to analyze user demographics, device types, and real-time actions. This approach allows media networks to go beyond basic demographic targeting and predict user behaviors, such as their likelihood to subscribe, engage with specific content, or churn.

Segmentation Methodology

CleverTap uses multiple segmentation methods to categorize users effectively:

User properties: Focuses on attributes like age, location, device type, and operating system.
Behavioral segments: Tracks event frequency and recency, distinguishing active users from inactive ones.
Interest-based profiling: Identifies users who frequently engage with specific content types, such as those who predominantly read technology articles.

Accuracy and Performance Metrics

CleverTap measures segment performance by comparing target groups with control groups using a "Boost or drop %" formula: (TG - SCG) / SCG * 100. Additionally, users are assigned RFM scores based on Recency, Frequency, and Monetary metrics.

Media Network Use Cases

CleverTap’s capabilities make it a valuable tool for media networks. For example, it can segment users near live events or retail locations for hyper-local targeting. The results speak for themselves: CleverTap retargeting delivers 147% higher conversions than standard display ads, while segmented audiences experience a 76% increase in click-through rates.

4. Delve AI

Delve AI focuses on creating detailed audience personas to provide deeper insights into what drives engagement. Its approach goes beyond just identifying the audience by digging into the reasons behind their behavior.

Segmentation Methodology

One standout feature of Delve AI is its use of Digital Twins - AI-generated personas that simulate market research interviews.

Integration Capabilities

Delve AI connects with tools like Salesforce, HubSpot, Google Analytics (GA4), Shopify, Klaviyo, and Stripe to pull in real customer data.

5. Locatium

Locatium takes a different approach to audience targeting by using mobility signals from telecom data. Instead of relying on generalized demographic profiles, it taps into real-time location intelligence to pinpoint high-value audience segments.

Segmentation Methodology

At the core of Locatium's capabilities is its AI Commercial Planning tool, which transforms raw location data into actionable business insights.

Integration Capabilities

Designed with tier-1 operators in regions like APAC, LATAM, MEA, and Europe, Locatium offers a streamlined experience.

Accuracy and Performance Metrics

Locatium delivers significant improvements in both speed and outcomes. It segments audiences 2.2 times faster than traditional methods, boosts ad revenue by 30%.

Conclusion

Choosing the right AI segmentation model comes down to aligning your network's unique challenges with the strengths of each platform. Ultimately, the key is finding the right balance between data volume, speed of deployment, and technical capabilities.