Causal Inference Applications in Growth Marketing
Utilizing causal inference techniques to accurately assess the efficacy of marketing campaigns.
Imagine you're a growth marketer at Netflix, and you've just brewed your morning coffee. As you sip, you ponder over a challenge: how to measure the impact of a new campaign you've introduced. In the last meeting, you know about A/B testing, but someone just mentioned Difference-in-Differences (Diff-in-Diff) analysis. Intrigued, you decide to dive deeper.
1. The Genesis of Diff-in-Diff
The Difference-in-Differences (Diff-in-Diff) method has its roots in econometrics. This field often grapples with understanding the causal impact of policies or interventions in non-experimental settings. Over the years, as researchers faced challenges in establishing causality due to the absence of randomized control trials, the need for a method that could mimic experimental conditions using observational data became evident.
Diff-in-Diff emerged as a solution to this challenge. It was designed to estimate causal effects by comparing the changes in outcomes over time between a group that received an intervention (treatment group) and a group that did not (control group). Its adoption increased, especially in policy evaluation, labor economics, and healthcare research, where randomized experiments were often impractical or unethical. However, it can also be applied widely in marketing and growth.
2. The Mechanics of Diff-in-Diff:
Theoretical Framework:
The core premise of Diff-in-Diff is the Parallel Trends Assumption. This assumes that, in the absence of the treatment, the difference between the treatment and control groups would have remained constant over time. Thus, any deviation from this parallel trend post-intervention can be attributed to the treatment.
Mathematical Representation:
Y1t - be the outcome for the treatment group after the treatment.
Y0t - be the outcome for the control group after the treatment.
Y1b - be the outcome for the treatment group before the treatment.
Y0b - be the outcome for the control group before the treatment.
The Diff-in-Diff estimator is:
DID=(Y1t−Y1b)−(Y0t−Y0b)This equation captures the difference in changes between the treatment and control groups, isolating the effect of the intervention.
3. Diff-in-Diff in Action: A Lifecycle Marketing Scenario
Background:
Netflix has observed a consistent drop in engagement among users who have been subscribed for over a year. The hypothesis is that these long-term subscribers are overwhelmed by the vast content library and often struggle to find new shows or movies that align with their interests.
To address this, Netflix has introduced a new email campaign: "Rediscover Classics." This campaign curates classic movies and shows personalized based on the user's viewing history. The goal is to re-engage these long-term users by nudging them towards content they might have missed but are likely to enjoy.
Implementation:
Given the vast user base, Netflix decided to roll out this campaign only in Europe initially, keeping Asia as a control group. Both regions have a similar mix of long-term subscribers and have shown parallel engagement trends over the past year.
Data Collection:
Over the next three months, Netflix will collect data on the following:
Average weekly watch hours per long-term subscriber.
The number of unique titles watched per subscriber.
User feedback scores on content recommendations.
Diff-in-Diff Analysis:
Step 1: Preliminary Analysis Before diving into Diff-in-Diff, Netflix plots Europe and Asia's average weekly watch hours over the past year. The trends seem parallel, reinforcing the validity of using Diff-in-Diff.
Step 2: Calculating Differences: For Europe (treatment group) and Asia (control group), Netflix calculates the difference in average weekly watch hours before and after the feature's introduction.
Step 3: Finding the Difference-in-Differences The difference from the control group (Asia) is subtracted from the treatment group (Europe) to get the Diff-in-Diff estimate. This value represents the average weekly watch-hour change attributable to the "Rediscover Classics" campaign.
Results:
Upon analysis, Netflix finds that the Diff-in-Diff estimate is optimistic, indicating that the "Rediscover Classics" led to an increase in average watch hours among long-term subscribers in Europe compared to their counterparts in Asia.
Furthermore, the number of unique titles watched also increased in Europe, suggesting that users were exploring more content. The feedback scores in Europe showed a noticeable uptick, reinforcing the feature's success.
Conclusion:
The Diff-in-Diff analysis provided Netflix with a clear understanding of the "Rediscover Classics" feature's impact. The positive results in Europe, backed by the control observations in Asia, made a compelling case for a broader rollout of the part to other regions.
4. Diff-in-Diff in Action: A Subscription Optimization Scenario
Background:
Netflix has been facing a challenge with its mid-tier subscription plan. While the basic and premium plans have steady subscriber numbers, the mid-tier plan has been experiencing higher churn rates. To address this, Netflix considers introducing a new feature: "Family Sharing," which allows users of the mid-tier plan to share their subscription with one additional family member, giving them simultaneous access.
Implementation:
Given the potential implications on server loads and content delivery, Netflix decides to test this feature in South America, keeping North America as a control group. Both regions have a similar distribution of subscription tiers and have shown parallel churn rates for the mid-tier plan over the past year.
Data Collection:
Over the next three months, Netflix collects data on the following:
Churn rates for the mid-tier subscription plan.
Average monthly watch hours per mid-tier subscriber.
User feedback scores, particularly concerning the "Family Sharing" feature.
Diff-in-Diff Analysis:
Step 1: Preliminary Analysis Before applying Diff-in-Diff, Netflix plots the churn rates for the mid-tier plan in both South America and North America over the past year. The trends appear parallel, suggesting that Diff-in-Diff would be a suitable method for this analysis.
Step 2: Calculating Differences For both South America (treatment group) and North America (control group), Netflix calculates the difference in churn rates before and after the introduction of the "Family Sharing" feature.
Step 3: Finding the Difference-in-Differences The difference from the control group (North America) is subtracted from the treatment group (South America) to get the Diff-in-Diff estimate. This value represents the change in churn rates attributable to the "Family Sharing" feature.
Results:
Upon analysis, Netflix finds that the Diff-in-Diff estimate is negative, indicating that the "Family Sharing" feature led to a decrease in churn rates for the mid-tier plan in South America compared to North America.
Additionally, the average monthly watch hours increased in South America, suggesting that users were more engaged, likely due to the added value of the new feature. Feedback scores from South American users also reflected a positive reception to "Family Sharing."
Conclusion:
The Diff-in-Diff analysis provided a clear insight into the impact of the "Family Sharing" feature on the mid-tier subscription plan. The reduced churn rates in South America, when compared to the control observations in North America, made a compelling case for introducing the feature across other regions to optimize the subscription model further.
5. A/B Testing vs. Diff-in-Diff
A/B testing is the gold standard for causal inference. It's a direct method where subjects are randomly assigned to two groups, ensuring a clear causal relationship. However, it requires a controlled environment and is not always feasible. Diff-in-Diff, on the other hand, offers an indirect measure. It doesn't need a pristine environment, making it perfect for real-world scenarios where controlled experiments aren't feasible. However, it does rely on certain assumptions, and its validity is contingent upon those assumptions holding true.
A/B Testing: The Controlled Experiment
Precision and Directness: A/B testing, often referred to as split testing, is a controlled experiment where two or more variants are compared to determine which performs better in achieving a desired outcome. Its strength lies in its directness; by randomly assigning subjects to different groups, it ensures that any observed differences in outcomes can be attributed to the variant itself, eliminating confounding variables.
Environment: A/B testing thrives in controlled environments. Whether it's a website interface change or a new app feature, the conditions remain consistent, ensuring that external factors don't skew the results.
Limitations: While A/B testing is powerful, it's not always feasible. For instance, when evaluating the impact of a policy change or a region-specific feature rollout, random assignment becomes impractical. Moreover, ethical considerations can sometimes prevent the use of A/B tests, especially in sensitive areas like healthcare or public policy.
Diff-in-Diff: The Quasi-Experimental Approach
Adaptability: Diff-in-Diff shines in situations where controlled experiments are challenging. By comparing the changes in outcomes over time between a treatment group and a control group, it seeks to isolate the effect of an intervention, even in messy real-world settings.
Assumptions: The core of Diff-in-Diff is the Parallel Trends Assumption, which posits that in the absence of the intervention, both the treatment and control groups would have followed similar trends over time. This assumption is crucial; if it doesn't hold, the results can be misleading.
Flexibility: Diff-in-Diff can be combined with other techniques, like propensity score matching or fixed effects, to bolster its robustness. This flexibility allows it to adapt to various scenarios and datasets.
6. Potential Pitfalls and Challenges
While Diff-in-Diff is a powerful tool, it's not without its challenges:
Violation of Parallel Trends: As emphasized, the Parallel Trends Assumption is central to Diff-in-Diff. If this assumption is violated, the Diff-in-Diff estimator can be biased. Researchers often use historical data to validate this assumption, but it's not always foolproof.
Temporal Spillovers: If the treatment indirectly affects the control group over time, it can lead to misleading results. For instance, if a new Netflix feature in the U.S. becomes popular and is discussed in global forums, it might influence user behavior in Canada, even if the feature isn't available there.
Dynamic Treatment Effects: If the effects of the treatment change over time, it can be challenging to capture a consistent effect. This is especially true for digital platforms where user behavior can evolve rapidly.
7 . Additional Resources
For those keen on mastering DID:
Econometrics in Python, Difference-in-differences — Multiple groups and periods (FE-DiD model)
A Practitioner’s Guide To Difference-In-Differences Approach
P.S. All the examples provided in the article, including those related to Netflix or any other entity, are purely hypothetical and fictional. They are crafted for illustrative purposes and do not reflect the actual strategies, data, or decisions of the mentioned companies.