How can you identify the most important factors that contribute to customer churn?

This step is extremely important and often marketing analytics experts take it for granted... Before anything else we must be able to define what churning means in our business and to do that we should have good knowledge of revenue models and channels and be able to interpret customer behaviors. Churn often means the customer or user stops doing what's important in the business and can affect business Growth. Churn can be categorized into different types, such as customer churn (losing customers) or revenue churn (losing revenue from existing customers).

To identify factors contributing to customer churn: Data Analysis: Study patterns in churned customers’ behavior. Feedback Surveys: Ask departing customers their reasons. Engagement Metrics: Track usage drop-offs or decreased interactions. Support Tickets: Monitor complaints or issues raised before churn. Market Trends: External factors may influence decisions. Billing Issues: Notice payment failures or disputes. Competitor Movement: Track if competitors offer better deals or features. Review regularly and adapt strategies accordingly.

To make your life easier, here are two most typical definitions of churn: 1. In products where access is limited to paying users only, churn = a user stopped paying and hasn't renewed in 2 weeks (the 2 weeks tenure is caused by expiring credit cards, and can be changed to 1, 3, or 4 weeks, but in my experience 2 weeks work best) 2. In products where a user can freely come and go: there were 28 days without any user activity recorded in data sources. The number of days can vary based on specifics of your products (e.g. money transfer app is used once in a month, so it makes sense to make it 60 days), but 28 is a market standard. Always, *always* make sure that everyone in the company who has access to metrics knows the exact definition.

Data quality plays a key role in the process of data analysis for identifying the churned customers. 6 data quality factors like data consistency, completeness and etc must be checked before starting the analysis. The reason for that is to prevent misjudging and misinterpreting what data says. Data integrity is the second most important thing to consider before starting the data analysis process.

Before visualization, EDA or Exploratory Data Analysis is a key step before analyzing data where analysts or data scientists examine and summarize the main characteristics of a dataset to gain a better understanding of its structure. It helps to understand the data and the information is provided. It also helps with the data cleaning process, data Visualization, pattern recognition, hypothesis generation and etc. In data visualization it's important to know what charts to use, for example charts like Histogram helps to see the data distribution and decide on what metrics to is good ? Mean? Median? Mode? For visualizing the data there are many tools like Google Looker Studio, Power BI, Tableau, Marplotlib( a library of python ).

Propensity Modeling can help understand important factors that leads to customer churn. By incorporating a good mixture of behavioral as well as demographic factors into the model, marketers can understand/predict future customer behavior. The model helps to predicts the likelihood that a customer would perform certain actions. The model predictions then can used in numerous ways such as audience targeting, bidding adjustments, and creative decisioning, to improve the overall effectiveness of paid media programs.

When presenting your data, stick to the following rules: 1. Begin your presentation with a brief summary of results for both documentation and information purpose. 2. Make sure you can talk about a single graph or table in a single sentence, at most a couple of sentences. If you cannot talk about it clearly, people won't understand you, and your knowledge will go to waste. 3. Set a structure to every step in presentation, e.g. a single slide should have 1 or 2 data visualisation to the left, and short commentary to the right. 4. Always take responsibility for other people understanding of your findings. If people don't understand you, it's your fault, not theirs. Your goal is to create knowledge and communicate it so that others can use it.

To identify additional churn factors, use Avinash Kaushik’s “Out-of-Sights” approach. It’ll allow you to go deeper and uncover factors that might otherwise go unobserved. Here are the criteria (N-A-C-R): ? Novel: Seek fresh, unexplored reasons for churn. ? Actionable: Ensure findings drive specific interventions. ? Credible: Use reliable data sources, and ensure rigorous analysis. ? Relative: Contextualize your churn rate against industry standards. By applying N-A-C-R, you’ll pinpoint not just the obvious, but the subtle factors causing churn, ensuring actionable and meaningful insights.