How Can Causal Inference Models Like Uplift Drive Smarter Subscription Strategies

In today’s hyper-competitive subscription economy, businesses face a critical challenge: how to effectively allocate marketing resources to maximize subscription conversions while minimizing wasted spend. Traditional targeting approaches often fall short because they can’t distinguish between customers who would subscribe regardless of an incentive (the “sure things”) and those who will only convert when offered a special promotion (the “persuadables”). This is where causal inference models, particularly uplift modeling, are revolutionizing subscription strategy.

Understanding Causal Inference and Uplift Modeling

Causal inference represents a paradigm shift from traditional predictive analytics. While conventional models ask “What will happen?”, causal models ask “What will happen if we take a specific action?” This distinction is crucial for subscription businesses making strategic decisions about promotional offers and discounts.

Uplift modeling, a specific application of causal inference, quantifies the incremental impact of an intervention on individual customers. In the subscription context, it answers the question: “How much more likely is this specific customer to subscribe if we offer them a promotion versus if we don’t?”

Dmitry Zolotukhin, Chief Data Officer at more.tv, defines uplift as “the difference in revenue from a user when targeted versus not targeted.” The fundamental challenge? You can’t simultaneously observe both outcomes for the same user — they either receive the promotion or they don’t. This is the classic “counterfactual problem” in causal inference.

The Business Case for Uplift Modeling

Subscription businesses have compelling reasons to adopt uplift modeling:

1. Resource Optimization: Marketing budgets are finite. Uplift models help allocate promotional spend only to customers who truly need it to convert.
2. Reduced Cannibalization: By identifying customers who would subscribe at full price, businesses avoid unnecessary discounting that erodes profit margins.
3. Personalized Targeting: Different customers respond differently to various incentives. Uplift models enable more nuanced targeting strategies.
4. Increased ROI: By focusing promotional efforts on the most responsive segments, businesses can achieve higher returns on marketing investments.

Implementation Framework: From Theory to Practice

Step 1: Experimental Design

The foundation of uplift modeling is a properly designed experiment. According to Dmitry Zolotukhin, more.tv’s approach involved:

• Identifying a cohort of trial users
• Randomly assigning users to test and control groups
• Offering the test group a 30-day trial extension
• Tracking conversion rates in both groups

This randomized control trial (RCT) setup is essential for establishing causality rather than mere correlation.

Step 2: Data Collection and Feature Engineering

Effective uplift models require robust user data. Key features might include:

• Engagement metrics (active days, content viewed)
• Interaction patterns (search behavior, content discovery)
• Consumption habits (completion rates, genre preferences)
• Platform usage (device types, time-of-day patterns)

More.tv’s model incorporated numerous behavioral metrics, including:

• Number of active days
• Projects watched and favorited
• Completion rates for content
• Search interaction patterns

Step 3: Data Preprocessing and Model Stability

A significant challenge in uplift modeling is ensuring model stability. As Zolotukhin notes, businesses need models that perform consistently across different data splits. His team encountered instability issues when using random data splitting approaches.

To address this, they developed sophisticated preprocessing techniques:

1. Temporal Splitting: Dividing data by registration date, with older users in the training set and newer users in the validation set.
2. Stratified Sampling: Ensuring consistent distribution of key features across training and validation sets.
3. Thompson Sampling: Using a probabilistic approach to select samples that optimize model performance.
4. Combined Approach: Ultimately, combining temporal splitting with Thompson sampling provided the most stable results.

Step 4: Model Training and Evaluation

Unlike traditional predictive models, uplift models require specialized evaluation metrics. The Qini coefficient emerged as the preferred metric, measuring the difference in conversion rates between test and control groups across different segments of the model’s predictions.

A model with good uplift performance will show:

• Higher conversion rates in the test group for segments predicted to have high uplift
• Similar conversion rates between test and control for segments predicted to have low uplift

Step 5: Implementation and Continuous Refinement

Once trained, the uplift model can guide targeting decisions:

1. Score new customers based on their predicted uplift
2. Target high-uplift customers with promotions
3. Preserve margin by not discounting to low-uplift customers
4. Continually test and refine the model

More.tv’s implementation strategy involved:

• Selecting trial users
• Reserving 5% as a control group (no offer)
• Using the model to identify the top 20% of users most likely to respond to the offer
• Targeting only those high-potential users
• Comparing conversion rates between targeted and control groups

Advanced Strategies: Beyond Basic Uplift

As subscription businesses mature in their uplift modeling capabilities, they can implement more sophisticated strategies:

Tiered Promotional Approaches

Rather than a binary decision (offer vs. no offer), businesses can create a spectrum of promotions based on uplift scores:

• High uplift: Moderate discount (e.g., 15% off)
• Very high uplift: Deeper discount (e.g., 30% off)
• Extremely high uplift: Premium offer (e.g., 50% off plus added features)

Multi-Treatment Uplift Models

Some customers might respond better to different types of incentives. Multi-treatment uplift models can determine:

• Who responds best to price discounts
• Who prefers extended trials
• Who values premium features
• Who responds to combination offers

Temporal Optimization

The timing of an offer can be as important as the offer itself. Advanced uplift models can determine:

• Optimal timing during the customer journey
• Best day of week/time of day for offer presentation
• Ideal cadence for follow-up promotions

As more.tv’s research indicates, experimenting with offer timing (1-2-3 weeks after start) represents a promising direction for optimization.

Implementation Challenges and Solutions

Despite its potential, implementing uplift modeling comes with challenges:

Challenge 1: Data Requirements

Uplift models require substantial data to establish statistical significance. Smaller businesses may struggle with sample size limitations.

Solution: start with simpler models and key segments, then expand as data accumulates. Consider pooling data across similar campaigns where appropriate.

Challenge 2: Technical Complexity

Uplift modeling is more complex than traditional predictive modeling and requires specialized expertise.

Solution: Begin with established packages and frameworks like CausalML or pylift. Consider partnerships with analytics providers specializing in causal inference.

Challenge 3: Organizational Adoption

Moving from traditional targeting to uplift-based approaches requires organizational buy-in and process changes.

Solution: start with small pilot programs to demonstrate ROI. Build internal knowledge through training and demonstration projects.

The Future of Causal Inference in Subscription Businesses

As the subscription economy continues to mature, causal inference models are becoming increasingly sophisticated. One major direction is the integration with large language models (LLMs), which can enhance uplift modeling by incorporating unstructured data such as customer support interactions or social media sentiment.

Another is the use of reinforcement learning, enabling continuous optimization of targeting strategies by adapting to customer behavior in real time. Advances in counterfactual explainability will also improve our ability to understand why certain customers respond to specific offers, adding transparency to decision-making processes.

Finally, uplift models will evolve beyond offering content alone, enabling cross-channel optimization that includes delivery timing, channels, and creative elements for more effective personalization.

About Author

Dmitriy Zolotukhin is a seasoned data leader with a strong background in analytics, machine learning, and business strategy across streaming, e-commerce, and fintech sectors. As Chief Data Officer at START, he leads the development of data products, drives cross-functional collaboration, and delivers measurable business impact through data-informed decision-making.