Customer Analytics for Marketers (TRA104)
Monday, April 21, 2014 at 9:00 AM - Wednesday, April 30, 2014 at 1:00 PM (PDT)
Please make a note that the training is divided into 4 sessions and each session is conducted on different dates.
Session1 - 04/21/2014 from 9:00AM to 1:00PM
Session2 - 04/23/2014 from 9:00AM to 1:00PM
Session3 - 04/28/2014 from 9:00AM to 1:00PM
Session4 - 04/30/2014 from 9:00AM to 1:00PM
Analysis of customer data is at the front-and-center of the data revolution. Organizations have the opportunity to better understand and predict customer behavior than ever. However, the data, tools, and techniques are complex and understanding how to most efficiently do customer analytics is important. In this course, we will use a hosted analytics solution LityxIQ powered by Revolution R Enterprise to easily build and deploy customer analytics without the need for programming or complex IT solutions.
Introduction to Customer Analytics
The course will begin with an overview of standard customer analytics requirements and solutions.
- Introduce the technical environment to be used in the course.
- Introduce sample problems and datasets to be addressed.
Data Preparation and Cleaning
Data is at the heart of any customer analytics programs. But preparing corporate data assets for advanced analytics is often a significant part of an analytics implementation.
- Review datasets that often play a role in customer analytics, such as transactional, marketing data, and external demographics.
- Learn how to summarize and merge datasets together to create a final dataset ready for advanced analytics.
- Discuss advanced issues such as timing of data collection and measurements and data cleaning.
Generating Insights about Customer Behavior
An important step in the customer analytics process is learning more about the conditions that drive customer behaviors and segments.
- Build charts, graphs, and dashboards that identify and track key behavioral patterns and trends.
- Learn how to track marketing effectiveness.
Predictive models allow an organization to predict future customer and prospect behaviors, such as their likelihood to respond to a campaign, buy a certain product, or cease being a customer altogether (churn).
- Introduce common models in customer analytics, such as response, churn, customer value, and cross-selling.
- Techniques for setting up models.
- Building, validating and implementing models.
- Reviewing model performance and using the information to create a business case.
Optimizing Marketing Efforts
Predictive models are often the first step in efficient marketing programs, but optimizing your efforts provides tremendous additional bang for your marketing dollars.
- Introduce marketing campaign optimization problems and potential solutions.
- Solve and implement an optimization case study
Business and marketing analysts or experienced modelers with a desire to learn more about how to leverage customer data most effectively.
Knowledge of customer data and common customer and marketing activities. Knowledge of R or programming is NOT required.
Access to LitixIQ platform will be provided during the duration of the course