Tag Archives: analyzing


Microsoft Power BI: Power BI is a powerful and popular BI tool that allows users to connect various data sources like Excel, SQL databases, online analytical processing cubes, text files or Microsoft Dynamics data and perform both standard and advanced analytics on customer data. With Power BI, you can visualize customer data through interactive dashboards, reports and data stories. Some key capabilities for customer analytics include segmentation, predictive modeling, timeline visualizations and real-time data exploration. Power BI has intuitive data modeling capabilities and strong integration with the Microsoft ecosystem and Office 365 which has led to its widespread adoption.

Tableau: Tableau is another leading visualization and dashboarding tool that enables effective analysis of customer data through interactive dashboards, maps, charts and plots. It has an easy to use drag-and-drop interface for quickly connecting to databases and transforming data. Tableau supports a variety of data sources and database types and has advanced capabilities for univariate and multivariate analysis, predictive modeling, time series forecasting and geospatial analytics that are highly useful for customer insights. Tableau also offers analytics capabilities like account profiling, adoption and retention analysis, next best action modeling and channel/campaign effectiveness measurement.

SAP Analytics Cloud: SAP Analytics Cloud, previously known as SAP BusinessObjects Cloud, is a modern BI platform delivered via the cloud from SAP. It provides a rich feature set for advanced customer data modeling, segmentation, predictive analysis and interactive data discovery. Some key strengths of SAP Analytics Cloud for customer analytics are predictive KPIs and lead scoring, Customer 360 360-degree views, customizable dashboards, mobility and collaborative filtering features. Its connectivity with backend SAP systems makes it very useful for large enterprises running SAP as their ERP system to drive deeper insights from customer transaction data.

Qlik Sense: Qlik Sense is another powerful visualization and analytics platform geared towards interactive data exploration using associative data indexing technology. It allows users to explore customer datasets from different angles through its Associative Data Modeling approach. Businesses can build dashboards, apps and stories to gain actionable insights for use cases like customer journey modeling, campaign performance tracking, Churn prediction and more. Qlik Sense has strong data integration capabilities and supports various data sources as well as free-form navigation of analytics apps on mobile devices for intuitive data discovery.

Oracle Analytics Cloud: Oracle Analytics Cloud (previously Oracle BI Premium Cloud Service) is an end to end cloud analytics solution for both traditional reporting and advanced analytics use cases including customer modeling. It has pre-built analytics applications for scenarios like customer experience, retention and segmentation. Key capabilities include embedded and interactive dashboards, visual exploration using data discoveries, predictive analysis using machine learning as well as integration with Oracle Customer Experience (CX) and other Oracle cloud ERP solutions. Analytics Cloud uses in-memory techniques as well as GPU-accelerated machine learning to deliver fast insights from large and diverse customer data sources.

Alteryx: Alteryx is a leading platform for advanced analytics and automation of analytical processes using a visual, drag-and-drop interface. Apart from self-service data preparation and integration capabilities, Alteryx provides analytic applications and tools specifically for customer analytics such as customer journey mapping, propensity modeling, segmentation, retention analysis among others. It also supports predictive modeling using techniques like machine learning, statistical analysis as well as spatial analytics which enrich customer insights. Alteryx promotes rapid iteration and has strong collaboration features making it suitable for both analysts and business users.

SAS Visual Analytics: SAS Visual Analytics is an enterprise grade business intelligence and advanced analytics platform known for its robust and comprehensive functionality. Some notable capabilities for customer intelligence are customer value and portfolio analysis, churn modeling, segmentation using R and Python as well as self-service visual data exploration using dashboards and storytelling features. It also integrates technologies like AI, machine learning and IoT for emerging use cases. Deployment options range from on-premise to cloud and SAS Visual Analytics has deep analytics expertise and industry specific solutions supporting varied customer analytics needs.

This covers some of the most feature-rich and widely applied business intelligence tools that organizations worldwide are leveraging to perform in-depth analysis of customer and consumer data, driving valuable insights for making informed strategic, tactical and operational decisions. Capabilities like reporting, visualization, predictive modeling, segmentation and optimization combined with ease-of-use, scalability and cloud deployment have made these platforms increasingly popular for customer-centric analytics initiatives across industries.


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
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.

customer_clv AS (
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
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.

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.