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What are the most effective ways to apply causal inference in customer service?

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Causal inference is the process of determining the causal relationship between variables based on observational or experimental data. It can help you understand how your customer service actions affect your customer satisfaction, retention, and loyalty. In this article, you will learn some of the most effective ways to apply causal inference in customer service using machine learning techniques.

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Jani Chetankumar

Co-Founder at Mechwat l Co-Founder at Commalinn

1 Why causal inference matters

Customer service is not only about resolving issues, but also about creating positive experiences that influence customer behavior and outcomes. However, measuring the impact of customer service on these outcomes can be challenging, as there may be many confounding factors that affect them. For example, how do you know if a customer stayed with your company because of your service, or because of other factors, such as price, convenience, or inertia? Causal inference can help you answer these questions by isolating the effect of your service from other influences and estimating the counterfactual outcomes, that is, what would have happened if you had done something differently.

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Jani Chetankumar

Co-Founder at Mechwat l Co-Founder at Commalinn
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In my experience, ensuring data quality, interpretability, and ethical considerations is crucial. Identifying confounding variables, validating results, and collaborating with domain experts have been key in achieving accurate causal inference in customer service using machine learning.

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2 How to use machine learning for causal inference

Machine learning can be used to perform causal inference in customer service, which involves identifying causal relationships, estimating causal effects, and testing causal hypotheses. Common techniques include matching, which involves finding pairs of customers who are similar in all relevant characteristics, except for the treatment variable; propensity score, which involves estimating the probability of receiving a treatment given the observed characteristics of each customer; and instrumental variables, which involves finding a variable that affects the treatment variable but not the outcome variable. For example, matching can be used to compare the outcomes of customers who received a proactive service call with those who did not. Propensity score can be used to create groups of customers who have similar propensity scores but different treatment statuses. Instrumental variables can be used to measure the effect of agent quality on customer satisfaction, controlling for other factors that may affect both variables.

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Heather Leffew, PhD

Senior Applied Research Science Engineer
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To make this a bit easier to understand, machine learning in customer service can be thought of like detective work to figure out what really makes customers happy. It's about finding cause and effect. One way is 'matching,' where you compare customers who are almost identical but one got a special service and the other didn't. Another method is the 'propensity score,' where you group customers based on how likely they are to get a certain service, then see how they react differently. Lastly, 'instrumental variables' help isolate one specific factor, like whether the quality of a service agent really makes a customer happier, while making sure other things don’t muddle the results.

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3 How to evaluate causal inference results

Once you have applied machine learning techniques to estimate the causal effects of your customer service actions, you need to evaluate the validity and reliability of your results. It's essential to check if the assumptions of your causal inference technique are met, such as the independence of the treatment and control groups, the absence of hidden confounders, and the consistency of the treatment effect. You should also test how sensitive your results are to changes in your data, model, or parameters, and assess the robustness and stability of your estimates. Moreover, it's important to interpret your results in a meaningful and actionable way. Consider the ethical and practical implications of your findings, and how they can inform your customer service strategy and decisions. Avoid making causal claims that are not supported by your data or method.

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4 How to improve causal inference with machine learning

Machine learning can help you improve your causal inference by enabling you to explore causal relationships in your data, optimize causal interventions, and learn from causal experiments. For instance, graphical models, such as Bayesian networks or causal diagrams, can represent the causal structure of your data and identify direct and indirect effects of your variables. Additionally, reinforcement learning can be used to design and evaluate customer service policies that maximize reward functions such as customer lifetime value or net promoter score. Furthermore, online controlled experiments, such as A/B testing or bandit algorithms, can be used to test the causal effects of your customer service actions in real time and update models and estimates based on the feedback.

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5 How to get started with causal inference in customer service

If you want to get started with causal inference in customer service, there are several steps you can take. Firstly, define the causal question: What customer service action do you want to evaluate and what outcome do you want to measure? Moreover, consider what data sources you have access to and how they can be combined to create a comprehensive dataset. Additionally, choose the machine learning technique that is most suitable for your causal question and data. Finally, analyze and communicate your results. Think about the main findings and insights from your causal inference, as well as how to present and visualize your results in a convincing manner. Furthermore, consider how to communicate the uncertainty and limitations of your results, as well as how to translate your results into actionable recommendations for customer service.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most effective ways to apply causal inference in customer service?

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1 Why causal inference matters

2 How to use machine learning for causal inference

3 How to evaluate causal inference results

4 How to improve causal inference with machine learning

5 How to get started with causal inference in customer service

6 Here’s what else to consider

Machine Learning

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What are the most effective ways to apply causal inference in customer service?

1

2

3

4

5

6

1 Why causal inference matters

2 How to use machine learning for causal inference

3 How to evaluate causal inference results

4 How to improve causal inference with machine learning

5 How to get started with causal inference in customer service

6 Here’s what else to consider

Machine Learning

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