Customer retention analysis is an important part of customer churn modeling. Understanding why customers stay or leave helps companies identify at-risk customers earlier and implement targeted retention strategies. Here are some examples of SQL queries that can help analyze customer retention and churn:

— Query to find the overall customer retention rate by counting active customers in the current month who were also active in the previous month, divided by the total number of customers in the previous month.

SELECT COUNT(CASE WHEN active_current_month = 1 AND active_prev_month = 1 THEN 1 END) / COUNT(DISTINCT cust_id) AS retention_rate
FROM customer_data;

— Query to find the monthly customer churn rate over the last 12 months. This helps analyze churn trends over time.

SELECT DATE_FORMAT(billing_month, ‘%Y-%m’) AS month,
COUNT(DISTINCT CASE WHEN active_current_month = 0 AND active_prev_month = 1 THEN cust_id END) / COUNT(DISTINCT cust_id) AS churn_rate
FROM customer_data
WHERE billing_month >= DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
GROUP BY month;

— Query to analyze retention of customers grouped by various demographic or usage attributes like age, location, subscription plan, usage frequency etc. This helps identify at-risk customer segments.

SELECT age_group, location, plan, avg_monthly_usage,
COUNT(DISTINCT CASE WHEN active_current_month = 1 AND active_prev_month = 1 THEN cust_id END) / COUNT(DISTINCT cust_id) AS retention_rate
FROM customer_data
GROUP BY age_group, location, plan, avg_monthly_usage;

— Query to find customers who churned in the last month and analyze their profile – age, location, when they onboarded, previous month’s usage/spend etc. This helps understand reasons behind churn.

SELECT cust_id, age, location, date_onboarded, prev_month_usage, prev_month_spend
FROM customer_data
WHERE active_current_month = 0 AND active_prev_month = 1
LIMIT 100;

— Query to analyze customer lifetime value (CLV) based on their average monthly recurring revenue (MRR) over their lifetime as a customer until they churn. Customers with lower CLV could be prioritized for retention programs.

WITH
customer_clv AS (
SELECT
cust_id,
SUM(monthly_subscription + transactional_revenue) AS total_spend,
DATEDIFF(MAX(billing_date), MIN(billing_date)) AS months_as_customer
FROM customer_transactions
GROUP BY cust_id
)
SELECT
AVG(total_spend/months_as_customer) AS avg_monthly_mrr,
COUNT(cust_id) AS number_of_customers
FROM customer_clv
GROUP BY active_current_month;

— Query to analyze customer churn by subscription end-dates to better plan and reduce non-renewal of subscriptions.

SELECT
DATE(subscription_end_date) AS end_date,
COUNT(cust_id) AS number_of_expiring_subs
FROM subscriptions
GROUP BY end_date
ORDER BY end_date;

These are some examples of SQL queries that companies can use to analyze and model customer retention, churn and non-renewal. The data and insights from these queries serve as valuable inputs for targeted customer retention programs, resolving customer service issues in a proactive manner, optimizing pricing and packaging of offerings based on customer lifetime value assessments, and much more. Regular execution of such queries helps optimize the customer experience and reduces unwanted churn over time.

Some additional analysis that can benefit from SQL queries includes:

Predicting customer churn by building machine learning models on historical customer data and transaction patterns. The models can be used to proactively reach out to at-risk customers.

Linking customer data to other related tables like support tickets, product usage logs, payment transactions etc. to gain a holistic 360-degree view of customers.

Analyzing effectiveness of past retention campaigns/offers by looking at retention lifts for customers who engaged with the campaigns versus a control group.

Using SQL to extract subsets of customer data needed as input for advanced analytics solutions like R, Python for more customized churn analyses and predictions.

Tracking key metrics like Net Promoter Score, customer satisfaction over time to correlate with churn/retention.

Integrating SQL queries with visualization dashboards to better report insights to stakeholders.

The goal with all these analyses should be gaining a deeper understanding of retention drivers and pain points in order to implement more targeted strategies that improve the customer experience and minimize unwanted churn. Regular SQL queries are a crucial first step in the customer data analysis process to fuel product, pricing and marketing optimizations geared towards better retention outcomes.