Monthly Archives: January 2015

ICMA activities

The ICMA activities are organized around three pillars:


  • A research agenda focusing on innovative methodologies and applications in marketing analytics.


Training & Education


Company collaborations

  • Innovative research projects in collaboration with companies in different sectors (e-commerce, insurance, financial services, retail etc.)

Kristof Coussement, PhD.

Dr. Coussement teaches several marketing related courses including Strategic Marketing Research, Customer Relationship Management and Database Marketing in which students are taught the theoretical principles of all aspects in marketing research, operational and analytical CRM and the methodological foundations of predictive marketing modelling.

Dr. Coussement is publishing in international peer-reviewed journals like Computational Statistics & Data Analysis, Decision Support Systems, European Journal of Operational Research, Information & Management, Expert Systems with Applications, among others. Moreover, his works has been presented on various conferences around the world. His main research interests are all aspects in customer intelligence, B-to-B intelligence, direct marketing and analytical CRM. Improving his ‘practical’ experience over the years by doing several real-life research projects in a different number of industries, his main focus lies on doing profound academic research with a high-added value to business.

Dr. Coussement is founder and committee member of BAQMaR, i.e. the largest online European Association for Quantitative & Qualitative Marketing Research.

Connecting business and
academics results in
better, fresher ways of
leveraging customer data.

This is what we strive for on a day-to-day basis at the IÉSEG Center for Marketing Analytics (ICMA): placing business-driven applications at the heart of innovative research projects and training in marketing customer analytics.

– Kristof Coussement, Ph.D.,
ICMA founders.

Why Marketing Analytics?

Marketing analytics recognizes the value of a company’s data. All information that a company collects about its customers – transactional data, profile information such as demographical and psycho-graphical information, customer communication, web site usage etc. can potentially be deployed to increase the ROI of your marketing efforts by making them more effective. Many companies have access to valuable data. Only few use it to their full benefit.

As the following figure shows, taken from a recent industry survey by Forrester Research, many drivers motivate companies to adopt marketing analytics, but the ultimate shared end-goal of marketing analytics is an increase of marketing efficiency.

Which of the following drivers encourage your organization to make use of customer analytics?

Source: Forrester Research, 2012, Q1 2012 Global Customer Analytics Online Survey (Base: 90 customer analytics professionals)

Through advanced analytical methods and the use of customer data, it aims at making intelligent selections of customers so that marketing actions can concentrate only on those customers that are most likely to respond in a desired way to a marketing campaign. Instead of wasting money on unprofitable or uninterested customers, marketing budgets can be spent where they matter the most.
The benefits of marketing analytics have been proven in numerous academic studies, and are now universally accepted in industry. This is shown in the following figure, showing the perceived benefits of marketing analytics in a research survey targeted at marketing professionals:

What benefits have you seen from your marketing analytics efforts?

As shown, marketing analytics have a major role to fulfill in making marketing efforts more relevant and personalized towards customers.

Due to widespread usage of CRM systems and especially the role of the Internet for commerce and marketing, more and more diverse forms of data related to customer behavior becomes available. This triggers a vivid need for adapted big data methods that enable marketing analysts to effectively store, handle, integrate and analyze these huge quantities of both structured and unstructured data.  Big data technology and analysis techniques and marketing analytics are rapidly developing in tandem nowadays. As a recent Accenture study demonstrated, the major expectation of big data technology managers foresee in the near future are improved customer relationships.

Where will Big Data have the biggest impact on your organization in the next 5 years?

Source: Accenture, 2014, Accenture Big Success with Big Data Survey, April 2014

Applications & Methods

During recent years, the field of marketing analytics has substantially evolved on two dimensions. First, new methodologies have been developed and rigorously tested in the conventional disciplines of database marketing. Examples of these innovative techniques are ensemble learning, Bayesian modeling or text mining. Second, the principles of marketing analytics have found their way into new domains and applications, such as direct marketing for non-profit, social media marketing, search engine marketing and mobile marketing. The key word within these new developments is creativity. Creativity is found in the methods that analysts use and on the application side. The ICMA focuses on both aspects, and it fills a large gap in the business and academic environment by digging in into the hottest trends in database marketing!



While the intensified use of the Internet as a marketing channel has widened its scope, disciplines within marketing analytics are traditionally categorized along three pillars of Customer Relationship Management (CRM) following the customer lifecycle.
Customer acquisition refers to the persuasion of individuals or businesses to engage in a relationship with the company, i.e. to become customer
Customer development aims at a widening or deepening of the relationship with the customer. In the former case, the customer buys other products and services within the range of the company, while in the latter, the customer purchase more or more expensive products
Customer retention comprises efforts to ensure that customers continue to do business with the company.

Marketing Analytics Applications / MethodologyExplanation

Marketing Analytics Applications / Methodology
Prospect Identification The process of managing customer prospects and inquiries in order to extend the current customer base
Cross-Selling The task of selling additional products or services to existing customers in order to extend their current product portfolio
Up-Selling The task of extending/replacing existing products in a customer’s product palette by an upscale extension/version
Response Modeling The process of sending out targeted mailings in order to increase the response rate on catalogue campaigns
Lifetime Value The process of estimating the present value of future cash flows that a customer will generate according to the customer’s relationship
Churn Prediction The process of identifying whether or not a customer will leave the company
Re-Activation Modeling The process of activating “sleeping” customers or recapturing lost customers out of the database
Profiling The process of describing different customer groups (e.g. complainants versus non-complainants) based on available customer information
Segmentation The process of splitting up the customer database in homogeneous groups of customers in order to set-up different marketing actions to each of them
Predictive Analytics The task of predicting future customer behavior based on replicating past customer behavior assuming that different types of customers behave equally over time
Text Mining The process of converting and analyzing textual customer information like customer emails into a quantitative format in order to extract relevant knowledge out of it
Social Media Analysis The task of scraping, converting and analyzing data from social media like Twitter, Facebook, Linkedin, product review websites, etc. to gain customer knowledge.
Web Analytics The process of using website usage data like browsing paths, visitor origin data, sales funnels, etc. to optimize the functioning of a website resulting in increased sales
Clickstream Analysis The task of analyzing website visit and page request server log data in order to profile web users resulting in an optimized website and advertising personalization
Recommendation system The task of employing past behavioral data to personalize customer offerings