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Structuring Limited-Information Data for Marketing Forecasts

Social Sciences Conference June 16, 2016 Madrid

Conference series "Customer experience in the 21st century"

General information

Venue: Aula Magna. IE Business School María de Molina, 11. Madrid

Organized by:

Fundación Ramón Areces

In cooperation with:

IE Business School

  • Description
  • Programme

The global economy has experienced profound transformations in the last decade. One of the worst economic and financial crisis has left lasting effects on the way how markets work. The old receipts struggle to foster a much desired recovery. However, at the heart of the market we still find the final customers as key actors. An important part of the economic activity depends on their needs, their preferences and the way that companies design to satisfy them. If clients buy, companies grow and, as a consequence, the economy should grow. It is fair to say that customer experience and its management in the 21st century are profoundly different. So we need to develop new ways to understand the customer and serve it. What is the best way to deliver high value added services to the final clients? How can we prevent customer misbehavior in the sharing economy? How can we organize data on customers in order to understand them better? Why are we growing so little after the big recession?

These are some of the questions that our invited experts will try to answer in these series of conferences. 

Data in marketing is typically characterized by many units of analysis with each having relatively short histories. Respondents in consumer surveys are limited in the time and attention they provide to survey questions before fatigue and boredom set in, household shopper panels frequently contain less than a dozen observed purchases in a particular product category, and the analysis of customer reviews is challenged by short narratives of experiences with a product or service. Moreover, the field of marketing embraces the notion of consumer heterogeneity, where differences among individuals drives segmentation, product development, pricing and promotional strategies. Forecasts of customer demand and satisfaction in a heterogeneous environment must therefore looks for ways of obtaining additional information in ways other than simple data pooling and aggregation. This presentation examines successful strategies for improving marketing forecasts by employing assumptions that successfully impart greater model structure and parsimony.

Thursday, 16


Greg Allenby
Ohio State University.

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