Propensity model features: The best B2C data for predicting customer actions

Propensity model features: The best B2C data for predicting customer actions

The most effective propensity model features in B2C leverage a combination of behavioral, transactional, product usage, and engagement data. These Signals collectively provide a comprehensive view of customer intent and likelihood, driving accurate predictions for churn, upsell, and cross-sell opportunities to maximize ARPU. Prioritizing these feature categories is critical for actionable insights.

Markin specializes in identifying these high-impact Signals and translating them into tangible Revenue Opportunities. By focusing on predictive analytics, we empower large B2C companies to automate and scale their data science efforts, directly impacting the bottom line.

Understanding propensity model features in B2C

A propensity model predicts the likelihood of a customer performing a specific action, such as purchasing a new product, churning, upgrading their subscription, or engaging with an offer. For B2C companies, these models are indispensable for personalizing customer journeys, optimizing marketing spend, and preventing customer attrition. The accuracy of these predictions hinges entirely on the quality and relevance of the input features, or Signals.

At Markin, we treat every data point as a potential Signal. The goal is to identify which of these Signals are truly predictive and which are noise or, worse, lead to data leakage. This precise selection of propensity model features is what transforms raw data into actionable intelligence for ARPU expansion.

Key propensity model features: High-impact Signals

Effective B2C propensity models draw on a diverse set of customer Signals. We categorize these into several high-impact groups, which represent the best propensity model features for B2C.

1. Behavioral Signals

Behavioral data captures how customers interact with your product, website, app, and communications. These are often the most dynamic and predictive propensity model features.

• Website/App Interaction: Pages viewed, time spent on site/app, click-through rates, feature usage, search queries. For example, a customer repeatedly visiting a premium subscription page is a strong Signal for upgrade propensity.
• Content Consumption: Articles read, videos watched, content categories engaged with. This can indicate interest in specific product lines or services.
• Engagement Frequency/Recency: Last login, frequency of app usage, recent interactions. A decline in these metrics can be a strong churn Signal.
• Marketing Interactions: Email open rates, clicks on push notifications, responses to ads. High engagement here might indicate receptiveness to new offers.

2. Transactional & Billing Signals

Financial and purchase history provides concrete evidence of past behavior and future intent. These are foundational propensity model features.

• Purchase History: Average order value (AOV), total spending, frequency of purchases, product categories purchased, last purchase date. A customer with a high AOV might be a good Candidate for premium upgrades.
• Subscription Details: Current plan, duration of subscription, past upgrades/downgrades, billing cycles.
• Payment Information: Payment method, billing issues (e.g., failed payments). A history of payment failures can be a churn Signal.
• Refunds/Returns: Frequency and value of returns. High return rates can indicate dissatisfaction or propensity to churn.

3. Product Usage Signals

For subscription-based or product-centric businesses, how customers use the product is paramount. These usage patterns are critical propensity model features.

• Feature Adoption: Which features are used, how often, and to what depth. Customers using advanced features may be less likely to churn and more likely to upgrade.
• Usage Volume: Data consumption, storage used, number of active projects/users (for SaaS). Exceeding usage thresholds can indicate upgrade potential.
• Error Rates/Performance Issues: Frequent encounters with errors or performance problems can be a strong churn Signal.

4. Customer Support Signals

Interactions with customer service often reveal underlying issues or satisfaction levels. These provide insightful propensity model features for retention decisioning.

• Contact Frequency: Number of support tickets, calls, or chat sessions. A sudden increase can be a churn Signal.
• Contact Reason: Category of issue (technical, billing, product feedback). Specific issues might correlate with churn or dissatisfaction.
• Resolution Time/Satisfaction: How quickly issues are resolved and customer feedback on support interactions. Poor satisfaction is a clear churn risk.

5. Demographic & Psychographic Signals

While often less dynamic, these provide a baseline for segmentation. They are useful, but generally less predictive than behavioral or transactional propensity model features.

• Demographics: Age, gender, location, income (if available).
• Psychographics: Interests, lifestyle (often inferred from behavioral data).

The synergy between these categories of Signals is where powerful insights emerge. Markin's Revenue Discovery process is designed to uncover these complex relationships and quantify their impact on ARPU.

Avoiding data leakage in propensity model features

Data leakage occurs when information that would not have been available at the time of prediction is inadvertently included in the training data. This leads to overly optimistic model performance during development, but poor performance in production. It's a critical pitfall to avoid when selecting and engineering propensity model features.

Mechanisms for Preventing Data Leakage:

1. Time-Based Splitting: The most crucial method. Always split your dataset based on time. Train the model using data from an earlier period (e.g., Q1-Q3) and test it on a subsequent period (e.g., Q4). Never allow data from the test period into your training set, especially features related to the target variable.
2. Feature Engineering Awareness: When creating new propensity model features, ensure they are only derived from data available *before* the prediction point. For example, 'average spend in the last 30 days' is acceptable, but 'total spend after next month's churn event' is leakage.
3. Target Variable Definition: Clearly define your target variable and ensure no feature implicitly contains future information about it. For churn prediction, a feature like 'number of support tickets *after* churn date' would be leakage.
4. Cross-Validation Caution: Standard k-fold cross-validation can introduce leakage if not handled carefully with time-series data. Use time-series cross-validation or a forward-chaining approach where folds are built sequentially in time.
5. External Data Source Verification: If integrating external data, ensure its timestamp aligns correctly with your internal data to avoid future information leaking into past observations.

Markin's platform enforces strict data governance and validation protocols to prevent leakage, ensuring that every Candidate Action is based on truly predictive and robust Signals.

What data do I need to predict customer propensity?

Predicting customer propensity demands a robust and integrated data infrastructure. You need consistent, timely access to a variety of data sources to extract high-quality propensity model features.

Here's a breakdown of essential data categories and their characteristics for building effective propensity models:

Data Category	Examples of Raw Data	Key Characteristics for Propensity Modeling
Behavioral	Web logs, app events, clicks, views, searches, session durations, content interactions	High volume, high velocity, time-stamped, requires real-time processing capability for optimal impact. Captures immediate intent and evolving engagement.
Transactional	Purchase records, order values, product SKUs, return requests, subscription changes, billing history, payment methods	Structured, historical, accurate, often linked to customer IDs. Fundamental for understanding past value and predicting future spend, churn, or upgrades.
Product Usage	Feature usage logs, data consumption, API calls, error logs, performance metrics, device types	Specific to product interaction, often requires custom tracking, indicates satisfaction, depth of engagement, and potential for upgrade/churn.
Customer Support	Ticket history, chat transcripts, call recordings, CSAT/NPS scores, resolution times, issue categories	Provides insights into pain points, satisfaction levels, and potential churn risks. Can be unstructured (text) requiring NLP for feature extraction.
Marketing Engagement	Email open/click rates, ad impressions, campaign responses, push notification interactions, survey participation	Indicates responsiveness to offers, preferred communication channels, and receptiveness to new products or services.
Demographic/Profile	Registration data, age, gender, location, income (if available), inferred interests, household size	Static or slow-changing, foundational for segmentation and personalization, but less dynamic than behavioral or transactional data.

The challenge for many B2C enterprises is not a lack of data, but the inability to unify, clean, and activate it effectively for predictive modeling and extraction of robust propensity model features. This is where Markin's platform excels, transforming disparate data into cohesive Revenue Opportunities.

From Signals to Revenue: The Markin Approach to propensity model features

Markin's agentic AI growth platform focuses on operationalizing propensity models by turning identified Signals into actionable Candidate Actions. Our process for leveraging propensity model features involves:

1. Signal Ingestion: Unifying data from all sources (CRM, CDP, product databases, billing systems, marketing platforms) to create a comprehensive customer profile.
2. Feature Engineering & Selection: Automatically generating and evaluating hundreds of propensity model features to identify the most predictive ones, while rigorously checking for leakage and ensuring relevance to the target variable.
3. Hypothesis Generation: Developing hypotheses based on identified Signals and potential Revenue Opportunities. For example, 'Customers who view upgrade pages thrice in a week have a high propensity to upgrade within 7 days, responding best to a personalized discount offer.'
4. Experimentation & Learning: Deploying A/B tests or multi-armed bandits to validate Candidate Actions, continuously refining models based on real-world outcomes. This iterative process ensures models are always optimized for maximum ARPU.
5. Layering: Orchestrating multiple propensity models and Candidate Actions across different customer segments and touchpoints, creating a personalized and dynamic customer experience that drives measurable growth.

By leveraging these principles, Markin helps large B2C companies move beyond theoretical models to practical, high-impact growth strategies. Explore our product to see how we multiply data science capacity.

Conclusion

The best propensity model features for B2C are a blend of timely behavioral interactions, historical transactional data, detailed product usage metrics, and critical customer support Signals. Avoiding data leakage through strict time-based validation is paramount for model integrity. By effectively gathering, processing, and deploying these Signals, B2C companies can accurately predict customer behavior, optimize interventions, and significantly increase ARPU. Markin provides the platform to achieve this, transforming raw data into direct revenue growth.