What data do I need for customer analytics?

At a minimum, you need transaction data (what customers bought, when, and how much), customer identification data (to link transactions to individuals), and interaction data (website visits, support tickets, communications). Additional data like demographic information, survey responses, and social media engagement enriches the analysis. Start with what you have and build from there.

What is RFM analysis?

RFM analysis segments customers based on Recency (how recently they purchased), Frequency (how often they purchase), and Monetary value (how much they spend). Each customer is scored on each dimension, creating segments like "Champions" (high on all three) and "At Risk" (previously high but declining). It is a simple but effective starting point for customer analytics.

How does customer analytics differ from web analytics?

Web analytics focuses on website behaviour (page views, sessions, bounce rates) and is typically anonymous and session-based. Customer analytics ties behaviour to identified individuals across all channels (web, email, phone, in-store), focuses on business outcomes (revenue, retention, lifetime value), and takes a longer-term view of customer relationships.

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Customer Analytics

Also known as:customer insightscustomer data analysisclient analytics

The practice of collecting, analysing, and interpreting data about customer behaviour, preferences, and interactions to improve customer acquisition, retention, and lifetime value.

In-Depth Explanation

Customer analytics uses data science and analytical techniques to understand customers deeply - who they are, what they want, how they behave, and how to serve them better. It transforms customer data into insights that drive personalisation, retention, and growth.

Key customer analytics domains:

Acquisition analytics: Understanding which channels and campaigns attract valuable customers
Behavioural analytics: Tracking how customers interact with products and services
Segmentation analytics: Grouping customers by characteristics, behaviours, or value
Retention analytics: Identifying churn risk and drivers of loyalty
Lifetime value analytics: Predicting the total value a customer will generate
Satisfaction analytics: Measuring and improving customer satisfaction and experience

Customer analytics techniques:

RFM analysis: Segmenting by Recency, Frequency, and Monetary value
Customer lifetime value (CLV) modelling: Predicting future customer value
Churn prediction: Identifying customers likely to leave
Next-best-action modelling: Recommending optimal interactions for each customer
Customer journey analysis: Mapping and optimising the end-to-end customer experience
Sentiment analysis: Understanding customer feelings from text data

Data sources for customer analytics:

Transaction and purchase history
Website and app behaviour
Customer service interactions
Survey responses and feedback
Social media engagement
Email and communication interactions
CRM data and notes

Privacy considerations are critical in customer analytics - all analysis must comply with the Australian Privacy Principles and any applicable data protection regulations.

Business Context

Customer analytics enables businesses to make data-driven decisions about how to acquire, serve, and retain customers, directly improving revenue and reducing the cost of customer acquisition and support.

How Clever Ops Uses This

Clever Ops implements customer analytics solutions for Australian businesses, building unified customer data platforms, segmentation models, and predictive analytics that drive personalised engagement. We help clients understand their customers at a deeper level and use those insights to grow sustainably.

Example Use Case

"A mid-market retailer implements RFM segmentation to identify their most valuable customers, creating targeted loyalty campaigns that increase repeat purchase rate by 23%."