Zehra Cataltepe is the CEO of TAZI.AI an adaptive, explainable Machine Learning platform. She has more than 100 papers and patents on ML.

While many believe that growth comes from acquiring new customers, the real key to sustaining a company’s growth lies in retaining customers.

That’s why it is of huge importance to understand how you can prevent customer churn. Understanding when, where and why a customer churns is crucial because you can lose your customers without knowing it if your business fails to grasp this.

Among other things, machine learning (ML) can help to uncover insights about customer churn because it can process large amounts of data well beyond the ability of humans.

In this article, I will explore six steps businesses can take to succeed in their ML journey. I’ll start with customer churn prevention and then move on to other ML-powered strategies to foster business growth and ensure long-term success.

1. Evaluate and optimize the customer acquisition cost (CAC).

ML can assess the effectiveness of acquisition strategies and their impact on churn. By evaluating CAC, you can gain insights into the cost-effectiveness of different acquisition channels.

ML, for example, can track and analyze customer behavior, helping you understand which acquisition strategies are most cost-effective and where resources can be optimized. This can help you acquire customers who are more likely to stay with the company.

To succeed with this strategy, you need to track data such as customer demographics, behavior and acquisition channels. This data can be collected through CRM systems, customer surveys and website analytics.

2. Understand and optimize customer onboarding.

It’s important to identify potential barriers or risks that may hinder customer retention during the different stages of the customer onboarding process.

ML can detect and predict which of your onboarding processes cost the most time and resources and where customers have the highest churn likelihood. With this information, you can streamline and enhance those processes and ensure a more time- and cost-effective transition for new customers.

To accomplish this, you should collect data at every stage of the onboarding process, such as time spent on each step, customer feedback and dropout rates. For example, if customers are getting stuck at the account setup stage, you can “debug” and simplify the process or provide additional support. I have also seen ML identify customers struggling with payments, and enabling easier payments can make a big difference in that case.

3. Analyze and update pricing strategies.

Pricing plays a crucial role in customer acquisition and churn, especially for a mandatory purchase, such as an insurance policy, financial services, utilities and basic household items.

Once you’ve used ML models to predict churn, you can decide on the best pricing strategy by using scenario planning to explore different pricing and product choices. Incorporating profitability models can also be beneficial. ML, for example, might identify customer segments that are profitable and have a high retention rate, which means you should adjust your marketing strategy and try to reach more customers in those demographics.

It might also be a good idea to explore whether you are pricing your product right. Maybe you are underpriced? Identification of low retention and low profitability segments is crucial. Those segments might indicate strong competition or high risk or cost or even fraud. Once you have those insights, use the takeaways to explore price sensitivity or discounts, based on the industry and regulations.

4. Market to the right segments.

Understanding which customer segments are more profitable or have higher retention rates can help to ensure that you are marketing to those segments at the right time and place and with the right product. ML models can help to determine which marketing channels to invest in and how much, since the revenue almost always flattens after a certain amount of spend.

For example, if ML indicates that a customer segment is not responding to marketing efforts or has a high churn rate, companies should reevaluate their marketing strategies, including content, time, place and spending for that segment and consider focusing on more profitable segments.

5. Assess and mitigate risks.

Crucially, you need to understand where you are losing most customers or revenue. Risk (such as credit, payment, claims) or fraud (such as payment, claims) ML models can help to understand and mitigate where problems might arise.

Once a company has identified that they are losing customers due to one of these issues, they should analyze the data to understand the root cause and then develop strategies to address these issues. This could involve improving customer service, adjusting pricing, enhancing product features and using more business-aligned ML models for audit, fraud detection and even prediction.

6. Always reiterate on business results and people first—and then data.

ML models are successful only if they are created, deployed and maintained in alignment with your changing business goals and the people who are implementing those goals.

Break down your ML deployment process into parts where business is always aligned with the data, data scientist or software teams. Here are a few important considerations:

• Business Alignment: Define goals and metrics, identify target customer segments, and align strategies accordingly.

• Data Acquisition: Gather comprehensive customer data from various sources.

• Model Building: Develop ML models tailored to your needs, train them with historical data, and fine-tune for optimal performance.

• Dashboards: Visualize insights and key metrics to monitor churn rates and identify areas for improvement.

• Deployment: Integrate ML models into operations, automate processes, and take proactive actions to retain customers.

The customer is at the heart of every business and understanding, and serving them should be your first priority. With the right mindset and tools, you can implement these steps, integrate ML techniques and optimize your approach in order to improve acquisition and to prevent churn.

Zehra Cataltepe

Forbes Councils Member

Forbes Technology Council

Also Published on Forbes: https://www.forbes.com/sites/forbestechcouncil/2023/08/10/six-steps-to-starting-your-machine-learning-deployment-journey/