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Customer Analytics

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Happy Customers, Happy Business
Harness the power of R and data analytics to keep your customers engaged and happy.

A successful business must manage relationships with a massive number of customers. As every manager knows, maintaining good customer relationships is a significant cost. Even minor failures of relationship management can escalate into damaging and expensive problems, and worst of all – unhappy customers.

Our customers provide us with all the data we need to keep them happy. Using the power of R we can draw essential insights from customer data. And with the insights of good data analysis, your business can build a great customer retention system, making it much simpler and less costly to keep your customers happy.

Course Outline

The course outlines an effective customer retention system based on insights drawn from customer data.

Three key concepts are introduced:

  1. Strategies for Customer Happiness – engage and retain your customers with simple techniques to boost happiness drawn from customer data.
  2. Analytics for customer retention in R – how to harness the power of the R programming language to gain valuable insights from customer data.
  3. Data engineering for customer analytics – optimizing a database infrastructure to provide customer data in the best form for R analysis.


The course is delivered in three sections, each correlating to a key role within a typical B2C organization:

  1. Commercial Manager – responsible for Sales and Profit by managing product offers, channel and customer segments.
  2. Business Analyst – drawing insights from customer purchase data with audience segmentation, clustering, market basket analysis and churn prevention.
  3. Database Developer – back-end development including loyalty systems architecture and building the customer analytics data mart.

Course Duration

6 days,  with two days dedicated to each role.



All code will be in R using R Studio and tidy verse package. Data engineering will require working knowledge of SQL.
Participants are required to complete “R Bootcamp for Business Analytics” or an equivalent foundation in R.


Role: Commercial Manager

Strategies for Customer Happiness
A systematic approach to engaging and retaining customers for Commercial Manager roles.

“With the power of R analytics our business has implemented an automated customer satisfaction program at substantially reduced cost.”

Your business is in a constant dialogue with its customers, from their first contact with outreach marketing, through product purchase, to customer service and follow up. Even a great product can be undermined by a failure of communication, and even great communication can suffer from inconsistency. With insights drawn from customer data via the power of R, your business can deploy cost effective strategies for customer happiness.

This section of the course is focused around forward facing commercial management roles, those responsible for sales and revenue and the management of product offers, channel and customer segments.

Course Outline

Day One Day Two
Customer Lifecycle

Understand the CRM cycle, and where R analytics can support customer progression from consideration, purchase, use and maintaining loyalty.


Use R analytics to divide your business’ customer base into subgroups for targeted campaign delivery.

  1. Demographic
  2. Purchase behavior
  3. Product affinity
  4. Clustering
Campaign Development

Customise your retention campaigns with insights drawn from R analytics.

  1. On Boarding
  2. Cross-sell
  3. Up-sell
  4. Win Back
  5. Random acts of kindness

Measuring Campaign Performance

Apply R analytics to assess the effectiveness of campaigns, based on the same insights used to shape your campaign.


Role: Business Analyst

Analytics for customer retention in R
Drawing valuable insights from customer data for Business Analyst roles.

“Not only do R analytics deepen our insight into our customers, they make that task much easier.”

Your customers already provide you with all of the information you need to better understand their needs, and by meeting those needs, keep them happy. But understanding the streams of data generated by a large customer base can be challenging, especially when different forms of data analysis can yield quite different insights. With the power of R analytics, we can develop an understanding of customer behavior that makes engaging and retaining customers simpler and more effective.

This section of the course is focused around Business Analyst roles, those developing technical solutions to business problems to support a company’s sales efforts.

Course Outline

Day One Day Two

Deepen your customer insights with advanced audience analysis techniques powered by R.

  1. Demographic
  2. RFM analysis
  3. Latency
  4. Behavior


Use object class similarity to develop alternative perspectives on your customer base.

Market Basket Analysis

Uncover otherwise invisible associations between products and services from customer purchasing patterns.

Churn Prevention

The dreaded churn! Understand the factors driving otherwise loyal customers to test your competitors.

Cases Studies


Role: Database Developer

Data Engineering for Customer Analytics
Optimising stored customer data for effective R analysis.

“A handful of changes to our data engineering processes unleashed a host of new possibilities in our R analysis.”

Your business has made heavy investments in technology to store valuable data derived from your interactions with customers. How that data is stored, organized and processed can make all the difference to how it is understood. For the best results from R analytics we need to optimize data engineering processes in your existing database. It’s at the foundation data level that the biggest gains can be made in customer happiness.

This section of the course is focused around Data Engineering roles, those responsible for managing and organizing valuable data harvested from customers.

Course Outline

Day One Day Two
Loyalty systems architecture

Optimize your loyalty system to harvest the best data for powerful R analytics.

  1. Functions of a Loyalty CRM system
  2. Analytics Sandbox

Building the Customer Analytics Data Mart (Part 1)

Unify data for disparate sources across your business into a single database for efficient processing ready for R analytics.

  1. Customer Masterdata
  2. Product Masterdata
  3. Location Masterdata
  4. Sales Fact Table
  5. Segmentation Fact Table
  6. Customer behavior Fact table
Building the Customer Analytics Data Mart (Part 2)

  1. Campaign Masterdata
  2. Message Fact table
  3. Campaign Analytics Fact table

Analytics Sandbox

Establish a safe space for exploring the meaning of data without affecting core data security.

  1. Visualisation of Campaign Performance
  2. Customer Analytics in R