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How to Transform Customer Experience with Explainable AI

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Read more about author Bulat Lutfullin.

In today’s competitive landscape, customer experience can make or break a business, and companies need to know more about their customers than ever before. One way to do so is by using artificial intelligence (AI) and machine learning (ML).

Companies collect all types of data from customer interactions, use that data to build AI/ML models, and apply those models for better customer engagement, personalization, and retention.

There is one problem, however. While AI can help businesses gain valuable insights into customer behavior, the results of its work are not always clear. For example, your marketing team can have a dashboard displaying AI’s predictions of customer churn, but no understanding of the factors causing it.

This article looks at how AI and ML are transforming the customer experience. We’ll dive deep into AI’s ability to not only predict, but to also explain the results of its work, enabling businesses to adjust their strategies based on clear insights, and not just raw numbers coming out of a black box.

Explainable AI: Insights That Make Sense

AI is a complex set of algorithms that enable machines to learn from data and make decisions without human intervention. But humans should have the ability to intervene – to look into the inner workings of AI/ML systems.

Reverting back to our churn rate example: Your sales and marketing team has a dashboard with churn rates for every customer. Let’s say Customer A has a churn rate of 80%. Does your team know how the AI algorithms came up with this number? What factors are influencing Customer A? What is the overall trend for customers like Customer A?

A good AI system should be able to answer all these questions. More than that, it should operate as a customer feedback engine that generates 360-degree customer-centric insights.

In the grand scheme of things, customer experience is a journey that consists of multiple touchpoints. If any inconveniences or service failures occur at any point in that journey, AI should be able to catch and report them to the responsible teams, or to generate an automatic reply or action, to recuperate any damage as quickly and smoothly as possible. 

For instance, your AI system can detect that Customer A spent 50% more time than average searching for a product with specific characteristics but did not complete the purchase. AI can analyze every touchpoint of Customer A’s journey and generate a report listing the most probable “failure to purchase” factors, so your sales and marketing team can craft a personalized offering.

In other words, AI and ML provide businesses with the ability to gain a deeper understanding of specific factors that impact customer decisions along the customer journey. By understanding these factors, product and marketing strategies can be adjusted accordingly, to optimize results.

Solutions for AI-Enabled Customer Experience

Given the number of customer journeys that any organization may have, it is almost impossible to develop a universal AI/ML-powered customer experience solution. However, such solutions can be built by following best practices (principles) and sharing common components.

Let’s consider some development and implementation principles:

  • An AI/ML solution should be designed and built only after the customer journey has been thoroughly researched, analyzed, and mapped.
  • A dedicated customer experience (CX) team of data scientists, data and ML engineers, DevOps, business managers, and domain specialists should be responsible for the project.
  • Both the CX team and the customer success team should be well aware of customer needs and preferences and be dedicated to resolving their pain points.
  • The solution should be developed and operated as a continuous feedback system, enabling the responsible teams and business units to monitor and improve the customer journey in near real time.
  • The buy-in of leadership and management is a must because AI/ML projects require high capital investment, and lead to considerable changes in processes and operations.

Now, let’s look at which components an AI/ML-enabled CX solution should have:

  • Data Hub. Regardless of the solution (batch vs streaming processing; predictive vs real-time analytics), the storage, processing, and evaluation of customer data should follow privacy best practices. Raw data and predictions should be well-integrated and easily accessible. For more advanced AI projects, a feature store is a must-have.
  • AI/ML Engine. A reproducible end-to-end ML infrastructure should be used as a robust foundation for AI. It has to include components to train, test, deploy, monitor, re-train, and fine-tune models on new customer data and feedback from business units. Having all of these and other tasks automated with MLOps and CI/CD is highly recommended.
  • UI and HITL. Business units should be able to check out and use the detailed and explainable results of AI/ML work in an easily accessible, user-friendly interface. They should also have the ability to enhance the solution via a human-in-the-loop (HITL) mechanism, which establishes the continuous feedback that AI/ML solutions need.

Bear in mind that the volume and quality of data – along with the ways data is discovered, observed, and governed – play crucial roles. Data drift, model drift, and other algorithmic biases should also be accounted for when designing and building an AI/ML solution for a specific use case.

Conclusion

Explainable AI is the future of customer experience. The world’s leading companies are already unlocking insights into what drives customer decisions, to continually adjust their customer-facing strategies and improve conversion rates, sales, retention, and customer satisfaction. These insights are used as feedback for enhancing products and services over time, by understanding how customers engage with them at any given moment in time.

With explainable AI insights, you can improve existing strategies and create new ones for your product, sales, marketing, and customer success teams. Explainable models can help you prove or disprove hypotheses faster than ever before, allowing you to perpetually optimize the customer journey.

Artificial intelligence and machine learning offer tremendous opportunities for businesses looking to improve the customer experience. Investing in the data-driven, AI-enabled customer experience should be a top priority for every business hoping to stay ahead of the competition in the digital age.

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