Photo by Oleg Moroz on Unsplash

Uplift modeling: how causal machine learning transforms customer relationships and revenue

Details of this series

This article is the first in a series on uplift modeling and causal machine learning. The idea is to deep dive into these methodologies both from a business and a technical perspective.

Introduction

Picture this: our tech company is acquiring thousands of new customers every month. But beneath the surface, a troubling trend emerges. Churn is increasing — we’re losing clients — and while the balance sheet shows impressive growth, revenue isn’t keeping pace with expectations. This disconnect might not be an issue now, but it will become one when investors start demanding profitability: in the tech world, acquiring a new customer costs way more than retaining an existing one.

What should we do? Many ideas come to mind: calling customers before they leave, sending emails, offering discounts. But which idea should we choose? Should we try everything? What should we focus on?

This is where uplift modeling comes in .Uplift modeling is a data science technique that will help us understand not only who might leave, but also what actions to take on each customer to retain them — if they’re retainable at all of course. It goes beyond traditional predictive modeling by focusing on the incremental impact of specific actions on individual customers.

In this article, we’ll explore this powerful technique with 2 objectives in mind:

• Firstly, sensitize business leaders to this approach so that they can understand how it benefits them.
• Secondly, give the tools for data scientists to pitch this approach to their managers so that they can be an instrument to their companies’ success.

We’ll go over the following:

• What is uplift modeling and why is it so powerful?
• High-Impact use cases for uplift modeling
• ROI: what level of impact can you expect from your uplift model?
• Uplift modeling in practice : how to implement it?

What is uplift modeling and why is it so powerful?

Usually, companies try to anticipate a customer behavior, churn for example. In order to do that they model a probability of churning per user. They are “outcome” modeling, meaning estimating the likelihood that a user will take a specific action.

For example, if an outcome model estimates a 90% probability of churn for a particular user. In that case, the company may try to contact the given user to prevent them from leaving them, right? This is already a big step, and could help significantly lowering the churn or identifying its root causes. But here’s a tricky part: what if some users we identify actually want to leave, but just haven’t bothered to call or unsubscribe? They might leverage this call to actually churn instead of staying with us!

Unlike outcome modeling, uplift modeling is a predictive modeling technique that directly measures the incremental impact of a treatment — or action — on an individual’s behavior. Meaning that we’ll model the probability of a user staying if contacted by the above company, for instance.

An uplift model focuses on the difference in outcomes between treated and control groups, allowing companies to assess the actual “uplift” at individual level, identifying the most effective actions for each customer.

Description of an uplift model vs an outcome model

More precisely, uplift modeling enables us to categorize our customers into 4 groups based on their probability of response to the treatment/action:

• Persuadables: these are the users who are likely to respond positively to the actions : they are the ones we want to target with our actions.
• Sure things: These are our customers who will achieve the desired outcome regardless of whether they receive the intervention or not. Targeting these users with the intervention is generally a waste of resources.
• Lost causes: These are individuals who are unlikely to achieve the desired outcome, action or not. Spending resources on these users is likely not cost-effective.
• Sleeping dogs: These customers may actually respond negatively to the treatment. Targeting them could potentially harm the business by leading to an undesired action (e.g., canceling a subscription when reminded about it).

The goal of uplift modeling is to identify and target the persuadables while avoiding the other groups, especially the Sleeping Dogs.

Coming back to our retention problem, uplift modeling would enable us not only to assess which action is the best one to improve retention, it would enable us to pick the right action for each user:

• Some users — Persuadables — might only need a phone call or an email to stay with us.
• Others — Persuadables — might require a $10 voucher to be persuaded.
• Some — Sure Things — don’t need any intervention as they’re likely to stay anyway.
• For some users — Sleeping Dogs — any retention attempt might actually lead them to leave, so it’s best to avoid contacting them.
• Finally, Lost Causes might not respond to any retention effort, so resources can be saved by not targeting them.

In summary, uplift modeling enables us to allocate precisely our resources, targeting the right persuadables with the right action, while avoiding negative impacts thus maximizing our ROI. In the end, we are able to create a highly personalized and effective retention strategy, optimizing our resources and improving overall customer lifetime value.

Now that we understand what uplift modeling is and its potential impact, let’s explore some use cases where this technique can drive significant business value.

High-Impact Use Cases for uplift modeling

Before jumping into how to set it up, let’s investigate concrete use cases where uplift modeling can be highly relevant for your business.

Customer retention: Uplift modeling helps identify which customers are most likely to respond positively to retention efforts, allowing companies to focus resources on “persuadables” and avoid disturbing “sleeping dogs” who might churn if contacted.

Upselling and Cross-selling: Predict which customers are most likely to respond positively to upsell or cross-sell offers or promotion, increasing revenue & LTV without annoying uninterested users. Uplift modeling ensures that additional offers are targeted at those who will find them most valuable.

Pricing optimization: Uplift models can help determine the optimal pricing strategy for different customer segments, maximizing revenue without pushing away price-sensitive users.

Personalized marketing campaigns: Uplift modeling can help to determine which marketing channels (email, SMS, in-app notifications, etc.) or which type of adds are most effective for each user.

These are the most common ones, but it can go beyond customer focused action: with enough data we could use it to optimize customer support prioritization, or to increase employee retention by targetting the right employees with the right actions.

With these powerful applications in mind, you might be wondering how to actually implement uplift modeling in your organization. Let’s dive into the practical steps of putting this technique into action.

ROI: In practice, what can you expect from your uplift models?

How do we measure uplift models performance?

This is a great question, and before jumping into the potential outcomes of this approach- which is quite impressive, I must say — it’s crucial to address it. As one might expect, the answer is multifaceted, and there are several methods for data scientists to evaluate a model’s ability to predict the incremental impact of an action.

One particularly interesting method is the Qini curve. The Qini curve plots cumulative incremental gain against the proportion of the targeted population.

Example of a Qini Curve

In simple terms, it helps answer the question: How many additional positive outcomes can you achieve by targeting X% of the population using your model compared to random targeting? We typically compare the Qini curve of an uplift model against that of a random targeting strategy to simulate what would happen if we had no uplift model and were targeting users or customers at random. When building an uplift model, it’s considered best practice to compare the Qini curves of all models to identify the most effective one on unseen data. However, we’ll delve deeper into this in our technical articles.

Now, let’s explore the potential impact of such an approach. Again, various scenarios can emerge.

What level of impact can I expect from my newly built uplift model?

Well, to be honest, it really depends on a lot fo different variables, starting with your use case: why did you build an uplift model in the first place? Are you trying to optimize your resources, for instance, by reaching out to only 80% of your customers because of budget constraints? Or are you aiming to personalize your approach with a multi-treatment model?

Another key point is understanding your users — are you focused on retaining highly engaged customers, or do you have a lot of inactive users and lost causes?

Even without addressing these specifics, we can usually categorize the potential impact in two main categories — as you can see on the above magnificent drawing:

• Optimization models: An uplift model can help you optimize resource allocation by identifying which users will respond most positively to your intervention. For example, you might achieve 80% of the total positive outcomes by reaching out to just 50% of your users. While this approach may not always outperform contacting everyone, it can significantly lower your costs while maintaining a high level of impact. The key benefit is efficiency: achieving nearly the same results with fewer resources.
• High-impact model: This type of model can enable you to achieve a greater total impact than by reaching out to everyone. It does this by identifying not only who will respond positively, but also who might respond negatively to your outreach. This is particularly valuable in scenarios with diverse user bases or where personalized approaches are crucial.

The effectiveness of your uplift model will ultimately depend on several key factors, including the characteristics of your customers, the quality of your data, your implementation strategy, and the models you choose.

But, before we dive too deeply into the details, let’s briefly explore how to implement your first uplift.

Uplift modeling in practice : how to implement it?

You might be wondering: if uplift modeling is so powerful, why haven’t I heard about it before today? The answer is simple: it’s complex to set up. It requires in-depth data science knowledge, the ability to design and run experiments, and expertise in causal machine learning. While we’ll dive deeper into the technical aspects in our next article, let’s outline the main steps to create, scale, and integrate your first uplift model:

Step 1: Define your objective and set up an experiment. First, clearly define your goal and target audience. For example, you might aim to reduce churn among your premium subscribers. Then, design an A/B test (or randomized controlled trial) to test all the actions you want to try. This might include:

• Sending personalized emails
• Calling clients
• Offering discounts

This step may take some time, depending on how many customers you have, but it will be the foundation for your first model.

Step 2: Build the uplift model. Next, use the data from your experiment to build the uplift model. Interestingly, the actual results of the experiment don’t matter as much here — what’s important is the data on how different customers responded to different actions. This data helps us understand the potential impact of our actions on our customers.

Step 3: Implement actions based on the model. With your uplift model in hand, you can now implement specific actions for your customers. The model will help you decide which action is most likely to be effective for each customer, allowing for personalized interventions.

Step 4: Monitor and evaluate performance. To check if your model is working well, keep track of how the actions perform over time. You can test the model in real situations by comparing its impact on one group of customers to another group chosen at random. This ongoing evaluation helps you refine your approach and ensure you’re getting the desired results.

Step 5: Scale and refine. To make the solution work on a larger scale, it’s best to update the model regularly. Set aside some customers to help train the next version of the model, and use another group to evaluate how well the current model is working. This approach allows you to:

• Continuously improve your model
• Adapt to changing customer behaviors
• Identify new effective actions over time

Remember, while the concept is straightforward, implementation requires expertise. Uplift modeling is an iterative approach that improves over time, so patience and continuous refinement are key to success.

Conclusion

Uplift modeling revolutionizes how businesses approach customer interactions and marketing. This technique allows companies to:

• Target the right customers with the right actions
• Avoid disturbing customers that might not want to be disturbed
• Personalize interventions at scale
• Maximize ROI by optimizing how you interact with your customers!

We’ve explored uplift modeling’s fundamentals, key applications, and implementation steps. While complex to set up, its benefits in improving customer relationships, increasing revenue, and optimizing resources make it invaluable for any businesses.

In our next article, we will dive into the technical aspects, equipping data scientists to implement this technique effectively. Join us as we continue to explore cutting-edge data science ideas.

Sources

Unless otherwise noted, all images are by the author

(1) https://en.wikipedia.org/wiki/Uplift_modelling

(2) https://growthstage.marketing/improve-marketing-effectiveness-with-ml/

(3) https://forecast.global/insight/understanding-customer-behaviour-using-uplift-modelling/

(4) https://dxc.com/us/en/insights/perspectives/paper/to-tackle-data-science-challenges-think-like-an-entrepreneur

(5) https://arxiv.org/pdf/1908.05372

(6) https://arxiv.org/abs/2308.09066

From insights to impact: leveraging data science to maximize customer value was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.