Acquiring a new customer costs time, money and lots of effort, while retaining customers is essential to the health of your businesses. That’s why understanding customer churn patterns is critical to the growth and profitability of your business. Certainly, the pandemic has not made things any easier. According to Gartner, half of C-Suite executives at companies of $100 million in revenue are concerned about economic disruption and customer churn related to changing consumer behavior due to the pandemic. At Deep Tech, we understand that this can be overwhelming. Customer retention teams in the retail industry generally have limited resources. That’s why using AI to understand which customers are more likely to churn is important for your team, allowing them to properly allocate retention efforts.
With more options available than ever before, consumers have more power and access to information. Companies that understand and embrace this new zeitgeist are ready to adapt, with the help of AI. According to Forbes, companies who utilize and understand the power of AI now will avoid losing customers in the future and improve their own offerings to better serve their customers’ needs. Customer churn modeling with AI can accurately predict which of your current customers are most likely to defect to your competitors. Having this knowledge empowers your retention team to focus their resources on the customers most at risk of churning and offer them incentives to stay. You can’t afford to lose loyal customers. By using AI to reduce customer churn, you can keep your existing customers happy while focusing efforts on onboarding new customers.
What is the Churn Rate?
Wikipedia states that the churn rate (also called attrition rate) measures the number of individuals or items moving out of a collective group over a specific period. It applies in many contexts, but the mainstream understanding of churn rate is related to the business case of customers that stop buying from you.
Software as a service (SaaS) with its subscription-based business model and membership-based businesses are at the forefront of innovative customer retention strategies. Analyzing growth in this space might involve tracking metrics (like revenues, new customer ratio, etc.), performing customer segmentation analysis, and predicting lifetime value. The churn rate is an input of customer lifetime value modeling that guides the estimation of net profit contributed to the whole future relationship with a customer. Independently, it calculates the percentage of discontinuity in subscriptions by customers of a service or product within a given time frame.
This translates to revenue loss via customer cancellation. Market saturation is quite evident in the SaaS market, there are always plenty of alternatives for any SaaS product. Studying the churn rate can help with Knowing-Your-Customer (KYC) and effective retention and marketing strategies for subscription-driven businesses.
Jeff Bezos once said, “We see our customers as guests to a party, and we are the hosts. It’s our job every day to make every important aspect of the customer experience a little bit better“. Improving customer retention is a continuous process, and understanding churn rate is the first step in the right direction.
You can classify churn as:
- Customer and revenue churn
- Voluntary and involuntary churn
Customer and Revenue Churn
Customer churn is simply the rate at which customers cancel their subscriptions. Also known as subscriber churn or logo churn, its value is represented in percentages. On the other hand, revenue churn is the loss in your monthly recurring revenue (MRR) at the beginning of the month. Customer churn and revenue churn aren’t always the same. You might have no customer churn, but still have revenue churn if customers are downgrading subscriptions. Negative churn is an ideal situation that only applies to revenue churn. The amount of new revenue from your existing customers (through cross-sells, upsells, and new signups) is more than the revenue you lose from cancellations and downgrades.
Voluntary and Involuntary Churn
Voluntary churn is when the customer decides to cancel and takes the necessary steps to exit the service. It could be caused by dissatisfaction, or not receiving the value they expected. Involuntary churn happens due to situations such as expired payment details, server errors, insufficient funds, and other unpredictable predicaments.
The Importance of Customer Churn Prediction
Customer satisfaction, happiness, and loyalty can be achieved to a certain degree, but churn will always be a part of the business. Churn can happen because of:
- Bad customer service (poor service quality, response rate, or overall customer experience),
- Finance issues (fees and rates),
- Customer needs change,
- Dissatisfaction (your service failed to meet expectations),
- Customers don’t see the value,
- Customers switch to competitors,
- Long-time customers don’t feel appreciated.
0% churn rate is impossible. The trick is to keep the churn rate as low as possible at all times.
The impact of the churn rate is clear, so we need strategies to reduce it. Predicting churn is a good way to create proactive marketing campaigns targeted at the customers that are about to churn.
Thanks to big data, forecasting customer churn with the help of machine learning is possible. Machine learning and data analysis are powerful ways to identify and predict churn. During churn prediction, you’re also:
- Identifying at-risk customers,
- Identifying customer pain points,
- Identifying strategy/methods to lower churn and increase customer retention.
The Challenges of Building an Effective Churn Model
Here are the main challenges that might make it difficult for you to build an effective churn model:
- Inaccurate or messy customer data,
- Weak attrition exploratory analysis,
- Lack of information and domain knowledge,
- Lack of a coherent selection of a suitable churn modeling approach,
- Choice of metrics to validate churn model performance,
- Line of business (LoB) of services or products,
- Churn event censorship,
- Concept drift based on changes in customers behaviour patterns driving churn,
- Imbalance data (class imbalance issue).
Churn Prediction Use Cases
Customer churn prediction is different based on the company’s line of business (LoB), operation workflow, and data architecture. The prediction model and application have to be tailored to the company’s needs, goals, and expectations. Some use cases for churn prediction are in:
- Telecommunication (cable or wireless network segment),
- Software as a service provider (SaaS),
- Retail market,
- Subscription-based businesses (media, music and video streaming services, etc.),
- Financial institutions (banking, insurance companies, Mortgage Companies, etc.),
- Marketing,
- Human Resource Management (Employee turnover).
Designing Churn Prediction Workflow
The overall scope to build an ML-powered application to forecast customer churn is generic to standardized ML project structure that includes the following steps:
- Defining problem and goal: It’s essential to understand what insights you need to get from the analysis and prediction. Understand the problem and collect requirements, stakeholder pain points, and expectations.
- Establishing data source: Next, specify data sources that will be necessary for the modeling stage. Some popular sources of churn data are CRM systems, analytics services, and customer feedback.
- Data preparation, exploration, and preprocessing: Raw historical data for solving the problem and building predictive models needs to be transformed into a format suitable for machine learning algorithms. This step can also improve overall results by increasing the quality of data.
- Modeling and testing: This covers the development and performance validation of customers churn prediction models with various machine learning algorithms.
- Deployment and monitoring: This is the last stage in applying machine learning for churn rate prediction. Here, the most suitable model is sent into production. It can be either integrated into existing software, or become the core of a newly built application.
Why Deep Tech: The Modeling Your Team Needs
Deep Tech allows you to stay ahead of the game by automatically identifying the individual reasons why each customer is likely to churn. This knowledge allows you to understand the factors driving your churn rate and adjust business processes accordingly, as well as customize your retention efforts. The age of the consumer is upon us and there are key opportunities for growth coming out of the pandemic. Knowing what your customers want and what drives them to potentially look elsewhere for their needs keeps your sales and marketing teams in the know, helps to drive campaign narratives, and ultimately improves customer retention and your bottom line.