The rapid explosion of technology has led to a new age where the checks and balances of ethical data collection and self-regulation that grew organically over the past 70 years are now often 'overlooked' in favor of speed and cost-efficiency -all these with detrimental effects. As part of ESOMAR's continued commitment to better understand how data is being used within and outside of organizations, how it is being controlled, and who 'owns' that data, we partnered with Kadence International to conduct a study amongst executives working in marketing, advertising, market research, and IT in North America, Europe and Asia. The results showed that although business leaders around the world put a high commercial value on consumer data, they pay far less emphasis on ensuring its security.
The evolution of the insights function and its contribution to business growth is addressed in this presentation, which provides a specific example of how brand & business insights can be an integrated, future focussed, and growth-driving function within a profit-driven organisation. The case study demonstrates the brand and business insight function's ability to collaborate closely with senior marketing and commercial teams while also playing a proactive and crucial role in making strategic decisions that drive business growth. Through analysis of existing consumer and industry data, future business growth targets are defined and validated, leading to approval of the business case for change in long-term strategy in a key European market.
This paper will look at two markets as case studies (microwave ovens and 3-in-l vacuum cleaners) and attempt to show that an accurate prediction could have been made of when the markets would peak. In each case study the prediction is made when the markets were growing at their fastest - the most difficult time to make such a forecast. The experiences gained from the case studies are then used to make a prediction for the camcorder market in Great Britain - a market which itself is currently undergoing a period of rapid volume growth. The principal device used to forecast the market peaks is a mathematical equation often referred to as the logistic equation. More commonly this particular tool is used in population forecasts but it works very well here too, providing the input data is accurate enough. Accurately predicting the market peak is one thing, but what happens after the peak also needs to be understood. Analysis of continuous consumer data can highlight whether the market is likely to go into rapid decline or to stabilize into a mature replacement market. The data used throughout comes from two sources: 1) Lek-Trak GfK's census based monthly retail audit of the electrical durables markets in Britain and 2) Home Audit GfK's quarterly consumer measurement. Home Audit acquisitions data is based on 100 postal/telephone interviews per year; the ownership comes from 10 face-to-face interviews per year.
Since 1983, United has maintained a continuous tracking survey for measuring customer satisfaction. In 1990, United began serving European markets and this program was extended to our European flights. This paper describes how United uses this consumer data to ensure quality service in Europe. Methodology for the survey program will be described in detail. Case histories will be presented demonstrating four uses of the data in maintaining Uniteds service level: monthly reviews of trends in customer service ratings, ad hoc analyses, service concept testing, and ongoing custom analysis. With United's ongoing tracking system United can test new concepts, monitor trends in service, and analyze the success of any product changes. This system allows United to monitor and improve our service to the customer which should ultimately lead to increased market share and higher profits.
In this paper I want to consider a new way of analysing consumer purchasing data. In brief the method consists of classifying consumers not by the usual demographic characteristics, but in terms of their purchasing behaviour in some previous period. We shall be considering the way in which a comprehensive mathematical model of consumer purchasing behaviour is useful, firstly in suggesting the form of analysis referred to above, and secondly in providing the necessary background for a proper interpretation of the analysis when carried out. The mathematical model referred to is the negative binomial model of consumer purchasing and the work to be described in this paper is part of a long-term programme of research and can be regarded as a development of the work previously reported to this conference in September 1962.