Advanced Customer Analytics
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Over the last decade, marketing practice has gone through a radical transformation. At the heart of it is the availability of data. There is a common belief among marketing practitioners that analytics will pave the way for marketing decision making. Despite recognising the potential of analytics, there is a considerable skill gap that exists among practicing managers and recent developments in the field. While there are multiple programs in analytics which have surfaced, most of them do not delve deeper how managers themselves can engage in data driven decision making.
The objective of our program is to provide hands on experience in customer and marketing analytics. The program takes a step by step approach to develop a holistic approach to elucidate the core concepts used in this emerging domain, and train managers to apply some of the advanced modelling techniques appropriate for the decision context. We start with a basic module on marketing analytics and move towards developing more advanced models of customer profitability. Thus, this course will provide managers the skill sets necessary for making a difference in the real world.
The course will train managers to build a strong proficiency in data analytics. The design of the program is to enable managers to be able to use marketing/consumer data more proficiently, create customized models, and make more relevant data driven decisions for contexts specific to their organizations/businesses.
Day 1: Basics of Customer Analytics
The objective of this module is to look at basics of marketing analytics. We introduce the conventional models of marketing analytics such as multi-attribute models, models to understand customer recommendation systems etc. This module lays the foundation for more advanced models. With digital marketing becoming more prominent, this module is focused on blending both conventional models of marketing as well as models more suitable to understand the digital world.
Day 2: Data Driven Decision Making
In continuity with the earlier module, this module focuses on more advanced models for customer decision making. We start with sentiment analysis, text analysis and topic modelling to understand consumers through conversations they engage an digital world. Then, we move towards assessing customers through conventional analytical tools such as customer life time value.
Day 3: Advance methods for Customer Profitability Assessment
While most organizations have built capabilities to access customer profitability, in most cases, the models used only focus on direct measures of profitability. In the third module, we incorporate both direct and indirect measures of customer profitability. We look at customer influence value and customer referral value. Customer influence value helps organizations to identify key opinion leaders of a product or a service in social media platforms whereas customer referral value helps a firm to understand profitability of a customer acquired through referrals.
Who should attend
The course is designed for managers and organizations willing to explore and exploit data analytics for effective decision making.The target audience is expected to have basic understanding of programming preferably some in general understanding of "R".