Using responsible machine learning to drive customer-centric insights

In today’s digital transformation era, organizations rely heavily on data to make business decisions. However, data alone isn’t enough to drive positive outcomes for customers and the business. Analytics solutions, including statistical models, machine learning models, and deep learning models, offer a convenient way to turn data into meaningful business insights.

To lead from a customer-centric position, organizations need a comprehensive view of the full customer journey and the ability to gain granular insights into what drives customer experience.

A well-defined, data-oriented solution strategy can be broken down into four types of analytics:

1. Descriptive Analytics: Identify what’s happening now and what’s happened in the past. For example, determining the percentage of customers who are unhappy and may churn.
2. Diagnostic Analytics: Diagnose why things happened. For example, identifying pain points that made customers unhappy and caused them to churn.
3. Predictive Analytics: Predict what may happen in the future based on diagnostic analysis. For example, predicting the happiness of a customer and whether they will stay or churn.
4. Prescriptive Analytics: Prescribe actions to be taken to affect outcomes. For example, triggering actions that might turn an unhappy customer into a happy one.

This strategy requires proactive signals to take action “in the moment” and create relevant experiences for each customer.

According to McKinsey & Company, half of the companies that embrace AI in the coming years can increase efficiencies and create business opportunities that could lead to doubling cash flow.  Manufacturing leads all industries in this study due to a heavy reliance on data.

Data about customer interactions is key to predicting satisfaction and behaviors, allowing us to take proactive actions to personalize experiences and improve customer outcomes.

However, according to the 2020 Gartner AI in Organizations Survey, only 53% of machine learning prototypes are eventually deployed to production. One constant reason is that companies do not understand that machine learning is an iterative process, not a one-time development. Without a well-defined strategy or metric that determines success, projects can go in loops. In cases where resources are limited, a “proof of concept” project may be a good option.

The general ML practice includes seven steps:

1. Collect the data
2. Prepare the data
3. Choose the model
4. Train the ML model
5. Test and evaluate the model
6. Tune the parameters
7. Predict the outcome

Several questions should be considered at each iteration: Is this model performing well? Is it stable and learning accurately, or is the learning biased? Is the model interpretable and explainable? Common failures include unaccountable black-box mechanisms, poor data quality, and fair model quality.

Some pitfalls can be avoided by building simpler glass box models using responsible machine learning principles, a framework put together by the Institute for Ethical AI & Machine Learning.

As the name suggests, a glass box model provides more transparency and clarity based on principles such as human augmentation, bias evaluation, explainability with justification, reproducible operations, displacement strategy, practical accuracy, trust by privacy, and data risk awareness. All these are critically important for machine learning models across most industries.

ML models should be developed in collaboration and with a human-centered design approach at each development and deployment stage. This will enable and empower data scientists to provide deeper insights, make predictions, and identify actions that should be taken.

Responsible Machine Learning practices should be given top consideration to create a holistic view of the satisfaction and value potential of every customer. A more responsible ML will drive action-oriented insights that help shape an effective strategic approach in the era of digital transformation.

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