10 41 ab_tns_mc_book_03-03-2010

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10 41 ab_tns_mc_book_03-03-2010

  1. 1. 1| MulticulturalResearch Edited by Stephanie Herold Emil Morales A selection of expertise from TNS colleagues around the world
  2. 2. A selection of expertise from TNS colleagues around the world MulticulturalResearch
  3. 3. Idea and coordination | Stephanie Herold and Emil Morales Layout and design | Linda Ingwersen For further information please contact: Emil Morales | emil.morales@tns-global.com Stephanie Herold | stephanie_herold@danste.de Important note: Authors take responsibility for the content of their papers. We thank ESOMAR for providing permission to republish selected contributions.
  4. 4. Contents GLOBAL Dr. Susanne O’Gorman | Director | TRI*M Centre of Excellence | Germany Anne Uekermann | Project Manager | TRI*M Centre of Excellence | Germany Stakeholder Management across the Globe – How Benchmarking can enhance our understanding of cultural differences David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France How consumer engagement can help develop sales forecasting models Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa Should we adjust for cultural response bias in questionnaire surveys? Silja Maria Wiedeking | Research and Marketing Consultant | TNS Business Intelligence | Germany Understanding and Researching Culture – A Secondary Approach Linda VerPlanck | Senior Vice President Kantar Health | USA Stephen Potts | MD Kantar Health Asia-Pacific, Middle East & Africa | Singapore Dr. Vince Grillo | GM Kantar Health | Australia Healthcare in emerging markets – Marketing research implications AFRICA Isaac Ngatia | Account Director | TNS Middle East and Africa | Dubai Multicultural research – The African perspective 11 23 37 59 71 91
  5. 5. ASIA Lee Ryan | Regional Director | Qualitative Specialists ALM | Singapore Mark Leong | Project Director | Qualitative Specialists ALM | Singapore Engaging the new consumer Ashok Sethi | Head of Marketing Science and Consumer Insights Emerging Markets | China Market research – A cultural perspective Lee Ryan | Regional Director | Qualitative Specialists ALM | Singapore Lisa Li | Research Manager | China Dreaming of red mansions Alison Dexter | Research Director | Vietnam Bach Ngac Hieu An | Associate Research Manager | Vietnam From Bricolage to Pho LATIN AMERICA Marcelo Queiroz | Senior Project Manager | TNS Consumer | Australia The role of socio-economic criterion in delivering predictive research in Latin American countries EUROPE Pat MacLeod | Director | TNS System Three | UK Managing Migration – Challenges for Policy and Research Cynthia Vega | Research Executive | TNS Consumer | UK Fish and Chips – Culture and consumers in the UK Dr Reinhold Horstmann | Director | TNS Emnid Media Research | Germany Migrants and Media 2007 101 125 133 155 173 181 197 209
  6. 6. Welcome! As this book goes to print the notion of the marketing to new consumer is taking root in the minds of marketers across the globe. While there are many factors that are helping to define this new consumer, at the core of many definitions is the role of a deeper understanding of culture and how it shapes both attitudes and behaviors. With the relatively easy free flow of individuals prior to 9/11 across borders the stage was set for this consumer re- shaping to take hold. However, in some parts of the world it appears that the mergence of cultures was not really viewed as engendering a fundamental shift to within the host culture. Our aim in creating this compendium is to inform and provide perspective and experience for marketers across the globe as they wrestle with how to apply successful approaches to research in order to adapt their brands in response to these dynamic shifts of culture. In order to effectively do this we have leveraged the TNS global footprint to provide perspective from every region of the world. Not surprisingly, Asia is well represented given its emergence as an economic engine for growth. This book provides practical approaches and leading edge thinking on conducting market research among differing cultures and seeks to do so in a way that is engaging for you the reader. Some topics are more global in nature touching on topics such as the importance of benchmarking across countries, whether adjustments should be made in response to cultural response bias. Other sections dive more deeply into regions or specific countries for practitioners seeking to understand those audiences. There are many people to thank for their contributions to this compendium.
  7. 7. Foremost among those individuals is Stephanie Herold of Australia. Her passion for Multicultural understanding and research led her to reach out to me as soon as she learned of our division dedicated to this type of research in the US. It was Stephanie who felt we had a story that would be of interest to marketers and research practitioners across the globe. I eagerly shared in that vision, and as we communicated this interest to TNS employees across the globe we received many eager responses to participate. As this book goes to print, Stephanie will be moving on to a new role with a new employer but still pursuing her interest in cultures. I wish her continued success and know we will remain friends and colleagues. I would also like to thank all of the contributors on the following pages for their professionalism and passion as marketers. I am certain as you read each article you too will feel the unique perspective on their cultural topic of interest. They should certainly be commended for their efforts. A final note of thanks to the TNS leadership who provided the latitude, infrastructure, and freedom of thought to help make this work come to life. Emil Morales Executive Vice President and General Manager TNS Multicultural, USA
  8. 8. |11 Stakeholder Management across the Globe – How Benchmarking can enhance our understanding of cultural differences “For those who work in international business, it is sometimes amazing how different people in other cultures behave. We tend to have a human instinct that ‘deep inside’ all people are the same - but they are not. Therefore, if we go into another country and make decisions based on how we operate in our own home country - the chances are we’ll make some very bad decisions.” Prof. Geert Hofstede, Emeritus Professor, Maastricht University (1983) Differences between cultures have been analyzed and studied throughout past centuries but receive a more and more important status as the world, and especially the business world, is becoming more and more globalized.When receivingmarket researchresultsfrommulti-country-surveys,companiesareoftenchallengedwith theinterpretation of these results. Are differences in regions or countries due to variation in performance, client expectations, response- styles or other variables? With this article, we will try to show which differences between countries and geographical regions exist when looking at Customer Retention and Employee Commitment. Why worry about Stakeholders? The world is shrinking – in a hypothetical meaning: Products, and even services, that were designed in Germany and France, are being produced in China, India or Malaysia to be shipped to and sold in New Zealand, the United States and South Africa. We are operating in truly globalized markets. But how does this affect a company’s ability to service its stakeholders? Let’s take a step back to clarify what a stakeholder is and why he or she is important: “The basic and most cited definition by R.E. Freeman (1984) says that a stakeholder is ‘any group or individual who can affect or is affected by the achievement of an organization’s purpose’. Hence stakeholders are all relevant groups of people who are important for the organisation’s value creation, as their input (work, capital, resources, buying power, word-of-mouth etc.) is vital for the organisation’s success. Some have a direct, short term relevance, others tend Dr. Susanne O’Gorman | Director | TRI*M Centre of Excellence | Germany Anne Uekermann | Project Manager | TRI*M Centre of Excellence | Germany
  9. 9. 12 | to be more important in the long run. Without employees, for instance, a company wouldn’t be able to run any operations. Without suppliers – dependent of the company’s amount of storage – it could probably survive for some time. And without journalists even much longer.” [Hermann, S. (2006)] Many years of research have shown that Stakeholder Management is decisive in determining whether or not a company is, or will remain, successful. This holds true for small companies operating on national or regional level as well as large international corporations. Profitable companies have strong relationships with the relevant stakeholder groups in their business, be it customers, employees, distributors or shareholders. Stakeholder Management is concerned with actively and effectively managing these important relationships. Strong customer relationships, committed employees and successful relationships with suppliers, shareholders and other stakeholder groups define the winners in today’s global economy. How can Stakeholders be managed? TRI*M™ as a management information system for Stakeholder Management was developed in the early 1990s and stands for Measuring, Managing and Monitoring. The TRI*M™ management information system incorporates three key dimensions: Measuring: You can only manage what you measure Managing: Translating measurement into action, by implementing fact-based change Monitoring: Continuous evaluation of cause and effect The three basic tools of the TRI*M™ system offer the TRI*M™ Index as an indicator of what level of customer retention a company operates on. It is not only a measure of how satisfied customers are with an organisation’s service or product, it also shows the degree of loyalty towards their current supplier. The TRI*M™ Typology is a base to understand the relationship of customers towards the company, and the TRI*M™ Grid a key driver analysis, which helps to determine the true drivers of a customer retention, as well as strengths and weaknesses. The TRI*M™ Index is based on four questions which envelop the dimensions of satisfaction and loyalty to portrait the level of customer retention. These four Index questions are used in every TRI*M™ study and have been validated to work in different languages as well as across different industries and markets. TRI*M™ is working with a verbalised 5-point-scale. This verbalisation anchors the meaning behind a certain answering scale. To ensure that the semantics are internationally comparable, the answering scales are translated and approved by native speaking TRI*M™ consultants from the respective countries. The design of the TRI*M™ Index (as an absolute measure) is specifically not based on top or bottom boxes-results, because it is known that these can be culturally biased. Dr. Susanne O’Gorman | Director | TRI*M Centre of Excellence | Germany Anne Uekermann | Project Manager | TRI*M Centre of Excellence | Germany
  10. 10. |13 With the TRI*M™ Index being a key performance indicator which is derived by using the same four questions in each study, a benchmarking database was created, where results from all sorts of different studies are entered to create benchmarks for different regions, industries and target groups. Today, the TRI*M™ Benchmarking Database contains the largest number of Stakeholder Management results worldwide. A three-year period has been chosen for presenting the benchmarking data in order to avoid results being distorted through short term external effects on specific data and changes in the structure of the database. In the past 20 years, TRI*M™ has been used by more than 1600 clients worldwide in over 16.000 studies and more than 17mio. Interviews have been conducted using the TRI*M™ method. TRI*M™ is widely used by market leaders as well as niche providers in all industries, like Finance, Automotive, IT and Telecommunications, Healthcare, Consumer Goods, and further more. How does a benchmarking database support companies to interpret their multi-national results? The benchmarking database not only includes survey results, but also important background information like country and geographical region, industry field, target group, application and many more. This enables a company to compare themselves not only with a general norm, but with specific benchmarks which match the company’s situation in a certain country or industry. Different markets can therefore be better understood and actions tailored accordingly. The following graphic shows Customer Retention levels in different geographical regions throughout the past seven years and their changes within their region. The values presented show the mean Index, which is the average of all projects conducted in this particular region. In order to give companies the opportunity to not only compare themselves with the average, TNS also offers benchmarks which show Indexlevels of the Top 10% of companies (and above), Top 33% of companies (and respectively Bottom 33% and Bottom 10%). The higher the TRI*M Index, the higher the customer retention level. On first sight the regional comparison shows striking differences between the single regions. It soon becomes obvious that while the European and Asian customer retention results are on a moderate mean level, customers in North America show a higher retention. This level is even exceeded by Latin American customers and topped by customers located in the Middle East. Middle East and Latin American customers are not only more satisfied on average, also the spread between the best operating and the worst operating organisations is less wide than in European or Asian markets. North America shows the greatest divergence with the best companies becoming Dr. Susanne O’Gorman | Director | TRI*M Centre of Excellence | Germany Anne Uekermann | Project Manager | TRI*M Centre of Excellence | Germany
  11. 11. 14 | better over the past couple of years and the worst companies becoming worse in their customer orientation. Looking at the development over the last couple of years adds additional insight: While average customer retention levels in Europe and Latin America show a negative tendency and levels in North America stagnate, a raise in customer retention levels can be noticed in Asian and Middle East markets. Middle Eastern and Asian companies also show that the lowest scores have increased, going hand in hand with the average raise and proclaiming an overall increase in focus on customers’ needs. Looking at these differences in the regions shown above, immediately leads to the question on how those differences can be explained. A closer look at the answers to the single TRI*M Index Questions can giver a better understanding on where the differences between single regions can be seen. While Latin America reaches high average levels of customer retention, it is interesting to note that the overall performance of Latin American companies is rated lower than in North America where retention levels are on average lower. Organisations in Latin America, however, achieve high results for questions regarding recommendation of products or services, repurchase and also having an advantage compared to competitors. These questions are rated much lower in European and North American markets. Western markets, in Europe and North America, show the same pattern for recommendation and repurchase. Retention levels in North America, however, are much higher because the overall performance is rated better and respondents see a stronger competitive advantage. The competitive advantage question makes for the most interesting comparison between Top 10% Bottom 10% Base: TRI*M Benchmarking Database Customer Retention 2002 – 2008 Europe North America 52 50 47 55 87 88 87 90 Mean Comparison of customer retention values in different geographical regions 70 74 70 69 54 94 75 55 96 74 TRI*M Index Customer Retention by region 46 86 68 53 97 74 2002 - 2005 2003 - 2006 2004 - 2007 2005 - 2008 Middle East Asia 55 45 49 104 86 89 75 67 69 57 102 77 61 104 84 49 88 69 63 105 85 51 91 70 Latin America 65 100 82 60 100 81 62 100 80 63 98 79 Dr. Susanne O’Gorman | Director | TRI*M Centre of Excellence | Germany Anne Uekermann | Project Manager | TRI*M Centre of Excellence | Germany
  12. 12. |15 regions, as a major difference exists between European companies, which show the lowest ratings and Middle East companies, which have the highest rating. Very often the results of the “competitive advantage question” are also an indicator of the maturity of markets. In highly saturated markets, companies find it hard to establish a competitive advantage as products and services are becoming more and more interchangeable. While these companies usually do well in increasing overall performance level, they still fail to create real loyalty amongst their customer base, due to the fact that they cannot establish USPs which act as barriers for customers to try alternative offers. How does employee engagement vary across regions? TRI*M™ Employee Commitment measures the strength of engagement of employees towards their employer. The TRI*M™ Employee Commitment Index is comprised of five questions which entail the overall satisfaction of the respondent with his or her employer organisation. It also incorporates the aspect of “word-of-mouth” (likelihood to recommend the organisation as an employer) and the likelihood of the respondent to apply again for a position with this employer. The last two questions comprise the motivation of colleagues and respondents are asked to rate the overall (business) success of their employer. The first thing noteworthy in comparing employee commitment benchmarks is the range between the bottom 10% and top 10% of employers. Whereas Europe still shows substantial differences, in North America the range between companies with highly engaged employees and companies with a lesser degree of employee engagement is fairly narrow and also decreased over time. This is not only an indicator of North American companies being able Overall performance Competitive Advantage Base: TRI*M Benchmarking Database Customer Retention 2006 – 2008 Europe North America Repurchase Comparison of TRI*M Index questions for Mean Benchmark Index Customer Retention TRI*M Index questions Customer Retention by region Middle East AsiaLatin America Recommendation 3,1 3,6 4,0 3,3 3,8 4,1 3,8 3,9 4,2 3,6 3,6 3,9 3,5 3,7 4,3 4,1 4,1 4,2 4,04,3 Mean Index 68 74 85 7079 Footnote: The figures show the means of the single Index questions on a verbalised 5-point scale with “5” being the best answer, “1” being the worst answer Dr. Susanne O’Gorman | Director | TRI*M Centre of Excellence | Germany Anne Uekermann | Project Manager | TRI*M Centre of Excellence | Germany
  13. 13. 16 | to engage employees on a higher level; it is also a sign of the cultural diversity within Europe. Taking a closer look at different regions within Europe, one can see substantial differences in employees’ commitment levels. The separation of Northern, Southern, Western and Central Europe shows strong variation especially in Southern Europe, where the mean commitment scores are substantially lower than in the other Europe regions. Also within the European markets one can see that engagement levels have not managed to pick up much over the past years. Despite the often proclaimed high importance of “human resources” it is obvious that many companies have difficulties in actually establishing and enhancing a greater degree of employee orientation. Employee Commitment is influenced by a multitude of variables. Most important of course is each company’s ability to ensure satisfaction and providing employees with a motivating environment to work in. But other aspects also play a role: Country-specific labour laws set the framework for many issues which directly influence employee satisfaction, the economic environment (especially job-markets) play a major role, but also country specific corporate cultures which influence the way how employees act in their daily working life. It has also been seen that countries have developed certain leadership-cultures [Chhokar, J.S.; Brodbeck, F.C.; House, R.J. (2007)] which influence employee commitment strongly. Coming back to the international regions comparison, employees in Europe show a very steady mean index over the past 7 years, meaning almost no change can be seen in how committed employees are in general. The movement of the top 10% and bottom 10% as well is very slight and shows only little diminution. Similar Top 10% Bottom 10% Base: TRI*M Benchmarking Database Employee Commitment 2002 – 2008 Europe North America Asia 39 39 40 46 52 43 51 38 40 80 78 79 77 73 71 73 73 72 Mean Example of our Benchmarking Database (Employee Commitment) 60 61 59 61 55 59 63 57 55 TRI*M Index Employee Commitment by Region 2002 - 2005 2003 - 2006 2004 - 2007 2005 - 2008 41 78 59 51 73 62 41 66 53 Dr. Susanne O’Gorman | Director | TRI*M Centre of Excellence | Germany Anne Uekermann | Project Manager | TRI*M Centre of Excellence | Germany
  14. 14. |17 in North America, where a definite change occurred in the early 2000s but then levels of employee commitment stabilised as well and show little change. Asian companies on the other hand show a definite pattern of becoming more homogenous over the past couple of years with movements of both the top10% and bottom10% closer to the mean levels of commitment. Still, levels of employee commitment are much lower than in western markets. Apart from the above mentioned cultural and economic variables, this might be an indication that many companies still do not focus strongly on understanding and developing of human resources. As with Customer Retention values, understanding Employee Commitment benchmarks better, it helps to have a look at the values of the singular index questions: With employee commitment as well as customer retention, overall satisfaction is slightly higher in North America than in Europe. North American employees are also slightly more likely to recommend their employer and join the company again. On the other hand, European employees perceive a slightly greater market strength of their employers. It can also clearly be seen that employees in Asia are less satisfied with their employment, compared to employees in the other regions. Overall satisfaction with the employment is very often influenced by working conditions such as health and safety regulations, quantity and variety of work, but also by pay and benefits. Negative experiences of employees with these “basic” aspects of employee satisfaction very often cause overall engagement levels to go down. Top 10% Bottom 10% Base: TRI*M Benchmarking Database Employee Commitment 2002 – 2008 Mean Comparison of employee commitment values in different geographical regions TRI*M Index Employee Commitment by region 2002 - 2005 2003 - 2006 2004 - 2007 2005 - 2008 Western Europe Northern Europe Southern Europe Central Europe Austria, Belgium, France, Germany, Liechtenstein, Luxembourg, Netherlands, Switzerland Denmark, Finland, Ireland Norway, Sweden, UK Bulgaria, Croatia, Czech Rep., Hungary, Kazakhstan, Macedonia, Moldova, Poland, Romania, Russia, Slovakia, Slovenia, Ukraine Andorra, Cyprus, Greece, Italy, Portugal, Spain 38 39 41 39 76 77 78 78 59 5859 59 41 78 58 41 79 60 40 77 60 40 79 59 38 73 55 38 70 52 40 73 53 38 76 54 38 81 61 39 80 59 40 81 59 39 82 59 Dr. Susanne O’Gorman | Director | TRI*M Centre of Excellence | Germany Anne Uekermann | Project Manager | TRI*M Centre of Excellence | Germany
  15. 15. 18 | For organisations entering the global market and expanding their workforce into different cultures, it can be very helpful to be aware of commitment levels of employees and to tailor their employer branding, their employee initiatives and human resource management towards the requirements of this specific culture. Overall it is evident that cultural influences can have an impact on stakeholder behaviour and therefore should be considered when making business decisions regarding Stakeholder Management. Fairly often in large international projects, differences are being found when comparing results for the same company from different countries/regions. Our experience has shown that these differences very often don’t have a technical reason (such as different use of answering scales), but reflect differences in the overall experiences of Stakeholder Groups as well as performances of individual companies: The overall level of Customer Retention in a country is also influenced by features such as structure of economies, maturity of the markets, competitive landscape, service cultures in countries and so on. The overall level of Employee Commitment in a country is for example influenced by working conditions, social security legislation, unemployment rates, organisational and leadership cultures – just to name a few. Furthermore, differences in results might be due to a different strategic positioning of companies in certain countries. It is the task of each company to adapt to the “framework” they are operating in. If a company is working in a very service-oriented culture, they have to adapt their operations to this high-standard, otherwise customers will not be satisfied. If a company has branches in regions with very low unemployment-rates and a very employee- oriented market, they might have to take extraordinary measures to ensure Employee Commitment at a high level. Benchmarking helps companies to place their own results in a specific cultural context, thereby showing whether survey results are in line with the overall market situation in a certain country or whether a company is outperforming or underperforming in a given economic environment. Europe North America Asia Example of our Benchmarking Database (Employee Commitment) TRI*M Index Employee Commitment by Region 3,5 3,4 3,8 3,3 3,2 3,7 3,4 3,5 3,9 3,2 3,13,2 Mean Index 59 5362 Overall satisfaction Motivation of colleagues Rejoining Recommendation Market Strength/ Success 3,7 3,73,8 Base: TRI*M Benchmarking Database Employee Commitment 2006 – 2008 Footnote: The figures show the means of the single Index questions on a verbalised 5-point scale with “5” being the best answer, “1” being the worst answer Dr. Susanne O’Gorman | Director | TRI*M Centre of Excellence | Germany Anne Uekermann | Project Manager | TRI*M Centre of Excellence | Germany
  16. 16. |19 References Chhokar, J.S.; Brodbeck, F.C.; House, R.J. (2007). Culture and Leadership across the world. Mahhaw, New Jersey: Lawrence Erlbaum Associates, Inc. Freeman, R. E. (1984): Strategic Management. A Stakeholder Approach. Boston: Pitman. Hermann, S. (2006): “Stakeholder Management - Long term business success through sustainable stakeholder relationships”, Excellence One, published 05/2006 on www.excellenceone.org. Hofstede, G. (1983). Dimensions of national culture in fifty countries and three regions. In J.B. Deregowski, S. Dziurawiec & R.C. Annis (Eds.) Explanations in cross-cultural psychology (pp.335-355). Lisse, The Netherlands: Swets & Zeitlinger. Dr. Susanne O’Gorman | Director | TRI*M Centre of Excellence | Germany Anne Uekermann | Project Manager | TRI*M Centre of Excellence | Germany
  17. 17. 20 | About the authors Susanne joined TNS in 1998, and worked in the German Technology Sector for a number of years, where she had the account responsibility for a large Fixed Line Provider. In 2004 she joined the Global TRI*M Centre in Munich where she currently holds the position of Director. She is in charge of Knowledge Management (TRI*M Wiki, the Global Knowledge Observer, the TRI*M Benchmarks, etc.) as well as the Global TRI*M Network. Susanne also coordinates the Cooperation with the University of Münster and the Center for Customer Management (CCM). Susanne did a degree in Sociology at the University of Regensburg with her main focus being on organisational sociology and corporate culture for her M.A. After a stay at the London School of Economics (United Kingdom) she finalised her PhD at the University of Regensburg. In her PhD thesis she conducted an empirical research project, comparing family structures and life-styles in Germany and the United Kingdom. Dr. Susanne O’Gorman | Director | TRI*M Centre of Excellence | Germany Anne Uekermann | Project Manager | TRI*M Centre of Excellence | Germany
  18. 18. |21 Anne works as a project manager at the Global TRI*M Centre in Munich, Germany. She coordinates the international TRI*M Apostle Network and organises international functions for the TRI*M Centre. In our daily work at the TRI*M Centre, we are constantly facing questions about cultural aspects in research and how it may or may not affect the results of a survey. Especially when we get involved in major studies spanning different countries and different culture groups, it is extremely interesting to have a look at the results and compare different regions with each other, trying to figure out, why some have different outcomes than others. After graduating from University with a business degree, Anne took some time to travel the world and learn more about different cultures. She started working at the TNS Global TRI*M Centre in 2005 where she’s coordinated and worked on client projects as well as internal projects like the TNS Look Inside survey. She now fulfils an internal role in the Knowledge Management Team. Anne has studied, worked and lived in Germany, Denmark, the United States and New Zealand. She’s been around the world three times and has visited all continents (except Antarctica – it’s too cold there). Dr. Susanne O’Gorman | Director | TRI*M Centre of Excellence | Germany Anne Uekermann | Project Manager | TRI*M Centre of Excellence | Germany
  19. 19. |23 How consumer engagement can help develop sales forecasting models It is relatively easy to develop a volumetric forecasting model in developed countries if you have access to the right data. Calibration of these models requires accurate in-market data to be linked with attitudinal data collected via a standardised survey methodology. The necessary in-market data to calibrate a Simulated Test Market (STM) model includes consumer purchase panel data, distribution and awareness build data. This type of data is available in developed countries, such as the United States, UK, Germany or France through large consumer panels, retail panels and tracking studies. Typically, a concept product test is conducted among a representative sample of the target group to gather attitudinal data about the new product prior to the launch, which can then be compared to real in-market data in conjunction with the launch marketing plans. From this data we can ‘correct’(or calibrate) the overstatement of purchase interest. However in emerging markets, much of the data necessary for the calibration is inadequate or non-existent. And this is not the only problem to overcome. The level of overstatement is dependent on social conditions that in many developing markets change quickly. In particular level of disposable income, which affects the ability to buy branded products, is a moving target. Because the standard of living is improving, the way people overclaim is changing as well. Therefore, for these developing markets, any calibrations must be regularly updated. This paper gives an overview of the research funded by TNS to produce a calibration method that can be applied globally and dynamically. The primary objective was to understand and measure the influences that drive claimed choice and the ultimate behaviour that occurs in the marketplace. First we will explain the foundations of the volumetric forecasting process. Then we will focus on the research and development (R&D) work conducted by TNS on consumer engagement. Lastly, we will link these results to our forecasting experience. Volumetric forecast estimation process The core principle of volumetric forecasting models is to mimic consumer purchase behaviour. The consumer must first try a new product, and then after use can re-purchase it (hopefully on multiple occasions). Therefore the volume to be estimated can be decomposed into two parts: trial volume and repeat volume. The number of buyers in a given period can be calculated by multiplying the target universe by the cumulative trial rate and the trial volume is calculated by multiplying the number of buyers by their average purchase units at trial. David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  20. 20. 24 | The number of repeaters in a given period can be calculated by multiplying the number of triallists by the cumulative first repeat rate among all triallists. We calculate the repeat volume by multiplying the numbers of repeaters by the average repeat purchase occasions per repeater within the given period and by the average units at each repurchase occasion. Test Methodology Here we will define briefly the TNS methodology named InnoSuite Launch Maximiser in relation to other methodologies using trial and repeat models. The TNS methodology is based on a concept product test (when the new product is available for consumer in-home testing), otherwise it is based on a concept only test and assumptions are made about the repeat components to produce sales volumes. Concept stimulus is presented to all potential consumers. We use a broad sample definition by including all potential buyers of the new product regardless of whether they are current buyers of the product category or not. Most STM systems available on the market today were developed in the 1970s/ early 80s when television advertising still dominated in creating awareness for new products in the developed markets. Typically they use a monadic concept board exposure as the exclusive stimulus. However, in the modern marketing environment a significant amount of awareness is created in-store (see following chart). Concept boards communicate the sort of positioning information that (television) advertisements convey. This is much more information than can be communicated by in-store activities. Consumer trial is higher when persuasive information is conveyed. If an STM communicates too much positioning and persuasive information, there is a risk it will overestimate the likely sales potential of the new product. This is particularly the case when limited advertising support is part of the launch plan. The instore environment is increasingly important for new product success The solution proposed by TNS via InnoSuite Launch Maximiser consists of a split-cell design. In the first split- cell, the respondent is exposed to a concept board as per the traditional methods. In the second split-cell, the respondent is exposed first to a shopping experience through VISIT – a virtual shelf. This methodology makes sales forecasting relevant for today’s marketing environment, especially for new product launches with very little above the line support. David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  21. 21. |25 Earlier R&D has shown a high correlation between share of choice from a VISIT exercise and market share across a range of categories, validating this approach for sales forecasting. In the two split cells, purchase intention and other key measures are collected. The product is placed among favourable (definitely or probably would buy) respondents. An after-use stage is conducted to measure the product performance. Purchase intention, satisfaction versus expectations and other key measures are collected. Estimation process Consumer attitudinal responses and observed choice behaviour (i.e. collected through virtual in-store environments such as VISIT) are calibrated and then combined with marketing plan information to provide estimates of the consumer purchase behaviour components. The main components are the trial rate, the first repeat rate, the average purchase cycle and the average purchase units. Validations are used to define the calibration factors used in the models for each of the components. In developed markets, the calibration from claimed purchase intention to estimated trial rate is calculated from empirical comparison of survey data and in-market data. The in-market data is gathered from representative 18 18 26 30 34 38 Recommendation New and existing idea On promotion From a brand I like Advertising It caught my eye in store Source: UK OnLineBus Reasons why bought new product David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  22. 22. 26 | consumer panels – a purchase diary of products bought in the tested product categories for at least a period of one year. Based on this empirical data, it is possible to calibrate precisely the probability of behavioural conversion from the purchase intent question for each point on the purchase intention scale, i.e. we can determine the proportion of those who claim they “definitely would buy” who actually would buy the new product if they are aware of it, and so on for the other answers of the purchase intent question. What we observe when calibrating the purchase intent is that consumers overstate, meaning that the proportion of “definitely would buy” respondents really buying the product is far lower than 100%, even after adjusting for awareness and availability. Claimed purchase intention and actual purchase behaviour (Claimed intention corrected for over-claim, distribution and awareness leads to realistic purchase probabilities) The magnitude of this calibration for overstatement differs according to several factors: Nationality or cultural background àà In Europe, the Mediterranean countries e.g. Italy, Spain, exhibit higher levels of overstatement àà In Asia, the Japanese tend to overstate very little. Standard of living àà The higher the standard of living, the lower the overstatement. Claimed purchase intention and actual purchase behaviour (Claimed intention corrected for over-claim, distribution and awareness leads to realistic purchase probabilities) Journey to Trial 0 10 20 30 40 50 60 claimed aware available actual David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  23. 23. |27 Product category àà The overstatement varies by product category. We observe high conversion rates (i.e. a greater proportion of those claiming to want to buy do actually go on to purchase) for snacks and low rates for durables, which is linked to the importance of the choice or potential risk for the consumer. Data collection mode àà The data collection mode also has an impact. The conversion rates used with online surveys need to be different, and should be updated regularly in countries where the internet penetration is growing quickly. Generally respondents over-claim more in face-to-face interviews – the usual explanation being the presence of the interviewer leading to more positive response. Some general rules have been observed on conversion rates from purchase intent to trial rate: àà There are systematic differences from one general category to another (food products, household care products, personal care products, etc.) within a country àà This product category difference is fairly consistent from one country to another àà Within a product category, the higher the unit price, the lower the conversion rate (linked with risk of the purchase decision for more expensive products) àà Within a general category (e.g. household care), the longer the purchase cycle, the lower the conversion rate, as long cycle products tend to be a higher risk decision (choices are made less frequently, therefore wrong choices need more time to be corrected) These rules are important, because they allow the minimisation of calibration efforts for new countries and new categories. For instance, as soon as we have calibrated the model for food products in a new country, we could in principle derive an approximate calibration for the other categories, such as household care. Nevertheless, these rules need to be applied very carefully, because some product categories can be country specific. Repeat components, average purchase cycle and average units at trial and repeat have to be estimated as well – but are less difficult to calibrate (using panel data). The first repeat rate is calculated mainly from the purchase intention score at the after-use stage, with adjustments based on other measures such as the price value. We observe the same factors affect the magnitude of the calibration for repeat as influence trial, and in the same directions. But the variations observed in conversion probability for re-purchase are far less than for the trial rate calibration. This greater consistency is because the David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  24. 24. 28 | evaluation is made after usage experience of the new product which leads to more realistic claimed behaviour. Therefore, this calibration is less difficult than the calibration of the trial rate. The estimation of Average Purchase Cycle is based on after-use claimed purchase frequency of the test product among those likely to re-purchase. Again, it has to be calibrated with the purchase cycle observed in consumer panels. This adjustment is most critical because the level of overstatement is usually high for this measure. The level of overstatement can be controlled at the survey level by also measuring the claimed purchase frequency of the product category and comparing this with the purchase cycle of the category observed in consumer panels. The average purchase units bought at trial and repeat are estimated from their claimed purchase units respectively at concept and after-use stages, again using consumer panel data. Consumer Engagement R&D Our previous research has shown the level of overstatement in response to the purchase intention question varies depending on the country and the product category. For example, we have observed higher levels of overstatement in Italy than in Germany. Regarding product categories, we have observed high conversion rates for snacks and low conversion rates for durable goods. In other words, if a respondent tells us that they would definitely buy a new snack, there is a higher probability that this converts into actual purchase behaviour than if they were to tell us that they would definitely buy a television set. We know that consumer engagement (involvement) also varies across product categories. We have observed the more important the choice of a branded product, the lower the likelihood that intention to purchase actually converts into real behaviour. This insight leads to our collaboration with the TNS Conversion Model Centre of Excellence, which has a wealth of experience in consumer engagement research. The importance of a product category to a consumer’s purchase decision is a key dimension in the Conversion Model’s measurement of a consumer’s commitment (attitudinal loyalty) to an existing brand. The Conversion Model Centre have found that people who are highly involved in a product category have a stronger relationship with the brands that they use and are less likely to leave those brands to try a new product. This ultimately leads to higher levels of overstatement in their intention to purchase the new product. They have also found that the importance of product categories to people’s lives varies from country to country, e.g. product categories in the technology sector are more important in China than they are in Russia. Knowing that the Conversion Model category importance question captured product category and country variation similar to the purchase intention question, we sought to analyse this variation and determine its correlation with the David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  25. 25. |29 variation observed in the purchase intention question. Furthermore, the Conversion Model Centre of Excellence has the largest global category importance database in the world. Thus, we hoped to find a good correlation between these variables in order to be able to find a way to calibrate purchase intention in countries where we have not yet conducted STMs. Firstly, we observed a strong relationship between the variation in category importance across countries and the overstatement factors used for those countries where we have a validated model: they are positively correlated. Secondly, we observed a strong relationship between the variation in category importance across product categories and the overstatement factors validated for those product categories: they are inversely correlated. To provide further data, TNS conducted a large R&D survey to collect the importance of the choice of brand across range of product categories, in different countries, using a standardised methodology: àà Samples representative of each national shopper population, based on quota of gender, sex àà Online data collection mode (except in Russia) àà Standard Conversion Model importance of choice of brand question The survey was fielded in 13 countries, including both developed countries and emerging markets: US, Australia, China, Japan, Korea, Thailand, France, Germany, Italy, Netherlands, Spain, UK and Russia. Product categories covered both consumer goods and durables. The specific categories were: carbonated soft drinks, coffee, yoghurts, shampoos, laundry detergents, mobile phones, television sets and credit cards. The results confirmed our assumptions, with the following findings: 1. The importance of choice of brand differs significantly by category. A lower importance was observed for carbonated soft drink (2.82 on a scale from 1 to 5) and a higher importance for credit cards, mobile phones, television sets and shampoos (higher score 3.58). 2. The relative importance of choice of brand per category is fairly consistent across country and region. David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  26. 26. 30 | The table below shows an example of the results (slightly modified for confidentiality reasons) observed in some countries for three typical product categories of consumer goods: yoghurts, shampoos and laundry detergents. Average importance of choice of brand across different countries and categories (results slightly modified for confidentiality reasons) Yoghurts Shampoos Laundry Detergents France 2.93 3.29 3.17 Spain 3.49 3.76 3.51 Russia 2.79 3,38 3,18 Japan 2.59 3.33 3.08 Thailand 3.35 4.18 3.95 Australia 3.12 3.66 3.37 3. There are differences corresponding to cultural differences As a first example, the choice of a brand of soft drink is more important in the US than in all the other countries – after all it is the birthplace of Coca Cola. As a second example, the choice of a brand of television set and of mobile phone is more important in Korea and in Japan, which fits with local knowledge of these markets being technology-driven. David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  27. 27. |31 Linking these results with our STM experience The next step was to link the results of this R&D survey to our STM experience. The two main findings were: 1. The average importance of the choice of the brand differs by country and is in line with the level of overstatement observed for STMs per country. Firstly, we calculated for each country the mean of the average importance of choice of brand observed for the different product categories. We then defined an index of the overstatement by country. Secondly, we analysed the relationship between these measures and found a high correlation (R2=0.75). Using this output we were able to build regression models to derive the index of overstatement for one country from the Conversion Model importance measures asked for the standard set of eight products categories. 2. The average Conversion Model importance of choice of brand category is inversely proportional to the conversion factor of the product category in the TNS STM model. For countries with established forecasting models we have a set of conversion factors to be applied to the purchase intent to calculate the theoretic trial for different product categories. These sets of coefficients are relatively consistent across countries, except when we have clear cultural or economical differences, or like for the over the counter (OTC) category, where local legislation is different. So it is possible to summarise the set of conversion factors to be used for a given product category (e.g. personal care) within all the countries by a single index. From the R&D data we calculated for each product category the mean of the average importance of choice of brand observed across the different countries. By comparing the calculated mean to the index of purchase intent calibration, we observed a strong correlation (R2=0.91). Therefore it was possible to create a model to derive the calibration coefficients for a given category from its Conversion Model importance. What are the implications of these findings? Firstly, it provides TNS with the knowledge to deliver sales forecasts worldwide. The model is validated in the largest countries of the STM industry, such as the US, Canada, France, Germany, Italy and UK because the calibration factors have been proven against real launches of new products and line extensions. In the short term, it will not be a validated model in the other countries. It should rather be called a “calibrated” David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  28. 28. 32 | model, meaning that we have set up calibration factors for these countries by using the input of the Conversion Model importance measure for standardised product categories. Through this research, TNS has discovered the drivers of over-claim and therefore can quantify them on a case-by-case basis and importantly have a more reliable future-proofed model (future development will update the models regularly for fast developing countries). What should be the conditions of use of calibrating models? Firstly, it will be required to ask the Conversion Model importance of choice of brand questions for the eight standardised product categories and the category of the test product. We should systematically ask the question for the eight categories, because the drivers of overstatement are dynamic and not static. They should be monitored in each project rather than passive estimates based on historical knowledge which will be out of date in dynamic markets. This is very important in the emerging markets because, with their fast-moving economies and evolving cultures, a model based on validations conducted only a few years ago will have questionable accuracy. In addition, the product category of the test is used to fine tune the model: instead of using a unique set of calibration factors for a broad product category such as household care, we can use a more precise set of calibrations tailored to a specific product category such as laundry detergents, softeners or dishwasher products. Secondly, it is recommended that benchmark cells are included in the research wherever possible. This allows the use of the Conversion Model importance of choice of brand measure to calibrate the model as a starting point, and then to fine tune the model by using the benchmark cell. It is recognised that volumetric forecasting is both a science and an art: a science because it is based on sophisticated models and large databases, and an art because in every STM project the forecaster has to use judgement, forecasting experience, and knowledge of the local market to fine tune the forecasting. STM companies try to minimise the human intervention of the forecasters by taking into account as much information as possible such as in-market data. Here we have a good example where the CM importance measure for the product category of the test product brings objective information improving the accuracy of the forecasting in a significant and meaningful way. With this calibration technique, it is also possible to broaden the spectrum of the categories where STMs are used and where validated models are not always available. This is particularly true in sectors outside of the FMCG industry where marketers are increasingly demanding volumetric forecasting capabilities, as in finance, automotive or technology. By ‘calibrating’ the drivers of choice TNS has successfully discovered quantifiable inputs that make for more informed model parameters, a higher degree of forecasting accuracy, along with a deeper understanding of choice that will lead to more insightful analysis to guide new product launches. David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  29. 29. |33 Conclusion The benefits of a better knowledge of consumer engagement are clear in the area of volumetric forecasting. From a better understanding of the market, we can offer: àà greater forecasting accuracy àà volumetric services in more countries, including emerging markets, and across a broader range of products and services including finance and technology àà And we can future-proof our models by using this efficient approach to update calibrations so that we remain in-tune with the rapidly changing global marketplace. David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  30. 30. 34 | About the authors David Soulsby is responsible for managing TNS’ suite of business solutions which cover the product development process from idea generation, early stage screening, concept and product optimisation through to volumetric forecasting and post-launch evaluation. He has 25 years research experience and has worked for the last 20 years for TNS in London. Prior to his current role he was Managing Director of TNS Consumer in the UK. David has extensive experience in sales forecasting having helped develop the InnoSuite Launch Maximiser model plus an approach to predict on-premise sales for alcoholic drinks. He has worked on over 250 new product launches and many more ideas, which (thankfully) never made it to the shelves. David has an MA in Mathematics from Cambridge University, and a Marketing Diploma. When he is not being passionate about innovation, he is passionate about his family, Sunderland Football Club and James Bond films. David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  31. 31. |35 Jean-Pascal Martin is Global Director of TNS’ Innovation and Product Development area of expertise. He manages TNS’ benchmarking database, which enables the strengths and weaknesses of new concepts and products to be tested against global norms. Jean-Pascal is passionate about cultural research, having taken an active role in several international groups for the past twenty years. He is a keen traveller and enjoys discovering new cultures and meeting foreign colleagues. Jean-Pascal has been highly involved in developing models to forecast the sales of new products in new countries, which requires in-depth understanding of the marketing environment and the cultural background of these countries. With a background in statistics and economics, Jean-Pascal has worked in marketing research for more than 20 years in different areas, such as data processing, marketing science, operations and new product development. Jean-Pascal brings his solid experience in innovation and volumetric forecasting to develop and harmonise TNS’ portfolio of solutions for Innovation and Product Development. As a typical Frenchman Jean-Pascal enjoys wine, particularly, but not only, Bordeaux and Bourgogne wines. He is very interested in tasting wines from foreign countries and in visiting wineries, for instance in California or South Africa. David Soulsby | Global Head of Product Development and Innovation | UK Jean-Pascal Martin | Global Director of TNS’ Innovation and Product Development | France
  32. 32. |37 Should we adjust for cultural response bias in questionnaire surveys? When conducting cross-cultural research, market researchers are constantly faced with the conundrum of whether or not to adjust their analysis to cater for cultural response bias. Cultural variability in response style also challenges the way in which market research findings are interpreted, particularly when samples contain differing cultural demographics. This paper analyses responses to importance and satisfaction survey questions, and compares them across regions, countries, ethnic groups and markets. The paper’s aim is to provide a basis for shaping thoughts around the field of cross-cultural research. The magnitude of response bias across the above mentioned groupings is assessed. Consistent and statistically significant differences are found between developed and developing regions and countries, but these differences become inconsistent at the product/service category level. Differences are also found between ethnic groups with differing economic status within a country, though these differences become exceedingly small, inconsistent and not statistically significant. We propose that the differences found are real, can be explained by wealth and education effects and should not be adjusted for but used as-is in any measurement tool. Introduction As multinational companies continue to grow their business the world over and engage in global trade, international marketing is playing an increasingly important role (van Herk, Poortinga and Verhallen, 2005; Malhotra and Peterson, 2001). When conducting a multi-country study, one should ideally use a consistent methodology across all countries to allow for cross-country comparisons of the results. This means that marketing research methodologies must be well-adapted to provide accurate and comparable cross-country information. When conducting research we should consider the possibility that people from different cultures and backgrounds are inherently different, and as a result, can answer questions differently. The purpose of this paper is to add to the body of knowledge relating to the issue of cultural response bias, and in particular, scalar response bias. This will be achieved through an investigation of the magnitude and nature of response bias across a variety of countries, regions, product categories and ethnic groups. We hoped that this paper will aid researchers worldwide in making the decision of whether or not to adjust their results when doing cross-country and cross-cultural research, and to bring more clarity to the topic. Many recent studies in marketing research journals argue that in order to ensure the integrity of the results, it is of pivotal importance to take into account cultural, economic, legal and geographic nuances when doing cross- country research (van Herk, Poortinga and Verhallen, 2005). Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  33. 33. 38 | Data equivalence The broad subject matter in question is that of data ‘equivalence’ – a term referring to the extent to which one can compare data across cultures and countries (van Herk, Poortinga and Verhallen, 2005; Clarke III, 2001; Smith and Reynolds, 2001; Reynolds, 2000). It encompasses a large variety of key themes and is often dealt with in the context of specific stages in the research process (Van Herk, Poortinga and Verhallen, 2005; Malhotra, Agarwal and Peterson, 1996). For data to be equivalent it needs to have the same meaning, interpretation and accuracy in all countries and cultures (van Herk, Poortinga and Verhallen, 2005). Equivalence has a wide range of ingredients, which one can broadly group into two main themes: construct equivalence and measurement equivalence (Craig and Douglas, 2000). Construct equivalence deals with three aspects to the construct in question that determine whether it has the same significance and importance across different cultures. The interpretation of a concept, such as a bicycle, should be consistent across countries (concept equivalence). The category to which the concept belongs, i.e. sporting goods, should also be consistent (categorical equivalence) as well as its function, i.e. fitness/recreation (functional equivalence). Measurement equivalence deals with three aspects of the measurement instrument that determine whether it provides accurate, comparable results across countries. Questionnaires should be translated such that their meaning doesn’t change (translation equivalence). The units of measurement should be adapted for each country (calibration equivalence) and the scale or scoring procedure should be held constant (scalar equivalence). Within the multi-dimensional topic of equivalence, scalar equivalence is considered by some to be the highest level of equivalence that can be achieved – although this type of equivalence is less widely researched (Van de Vijver and Leung, 1997). Therefore, our paper aims to add to the relatively small body of knowledge relating to scalar equivalence. According to Van Herk, Poortinga and Verhallen (2005), comparisons of scores on a single variable across countries are only meaningful if scalar equivalence has been established. If there is scalar equivalence, it can be concluded that “cross-national differences in score distributions on a variable correspond to actual differences in the underlying constructs.” According to Malhotra and Peterson (2001), the importance of choosing the correct scale when measuring attitudes cannot be understated, largely due to the effect of differing education levels in developed countries vs. developing countries. Opinion formation is generally not well-crystallised in some developing countries, while respondents in developed countries are skilled in providing responses to more complex ratio scales (mainly due to generally higher education levels in such countries). Respondents in Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  34. 34. |39 developing countries may find these scales difficult to respond to and interpret. Ordinal scales have proved to be more effective in developing countries where education levels are fairly low as they don’t require respondents to express their opinion on a gradation. Specifically, binary scales such as “preferred/not preferred” have been recommended as the best option available (Malhotra and Peterson, 2001). Malhotra, Agarwal and Peterson (1996) indicate that one should also take cognizance of the different distributions that one might find on a scalar question – as the top-two-boxes score on a specific variable may not reflect similar meanings. They draw attention to the fact that a specific scale can only be equivalent to the extent that it delivers homogenous indices across cultures. We recognise this as an important point and we have therefore avoided the utilisation of boxing in this paper to prevent potential misinterpretations of the true relationships within the data. Instead, we’ve made use of means, which incorporate more sensitivity towards spread within the data. Data standardisation Some adjustments may be necessary before analysing multi-country data to ensure that the data is truly comparable across countries or cultural contexts. Normalisation (or ‘standardisation’) techniques are well researched and can be used to achieve just that (Malhotra and Peterson, 2001). According to Fischer (2002), standardisation is increasingly viewed as a key issue in cross-cultural research, and is widely believed to be a true indicator of cross- cultural differences. When analysing cross-country data, one needs to take into account both the emic and etic approaches that may need to be followed. When taking the emic approach to examining the data, one would focus one’s analysis within the specific country, or culture – whereas the etic approach allows one to examine a phenomenon across countries or cultures (Malhotra, Agarwal and Peterson, 2001). There are a number of ways in which one can standardise data in order to be able to analyse within and across countries. An important step in the standardisation process is to investigate the differences in means and distribution of data across countries, as pointed out by Malhotra and Peterson (2001). Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  35. 35. 40 | There is, however, also a case for not standardising data to the degree that researchers often propose it be done. This argumentrevolvesaroundnotartificiallymakingpeopleappearthesame,wheninactualfactthereareveryrealdifferences between them and the markets in which they exist, which should under no circumstances be diluted. Fischer (2002) suggestsjustthatwhenhepointsoutthat“thesedifferencesmightbetterreflectdifferentcommunicationstylesacross cultures rather than bias that need to be controlled for… [and] more research is needed to explore this possibility”. It may also be important to recognise and mention the important role that focus group interviews could play in understanding the intricate differences between domestic and foreign markets/cultures (Malhotra and Peterson, 2001). The insight gained through this type of qualitative research can help to understand whether standardisation should in fact be employed, and if so, how it should be carried out. When discussing data equivalence in the context of cultural response bias it is important to note the existence of differing ‘response styles’: the extent to which an individual displays a response bias consistently across time and situations. The two most common response styles are Extreme Response Style (ERS) and Acquiescence Response Style (ARS). ERS refers to the tendency of a group to use the top end of a scale, which results in ‘extreme’ results and differences in means across cultural (or other) groupings (Clarke III 2001, Dolnicar and Grün 2007). ARS is the tendency of respondents to agree with measurement items (to use the positive end of the response scales) despite having a range of possibilities (Dolnicar and Grün 2007, Smith 2004). These response styles have been explained in terms of the distinction between individualist and collectivist cultures. Stronger endorsement of individualism is accompanied by the more frequent use of extreme values on a scale, rather than midpoints (Chen et al. 1995). A phenomenon that could also influence the distribution of answers on a scalar question is Courtesy Bias – this refers to the fact that in certain cultures, it is accepted that one should answer questions in the appropriate (or ‘right’) way. One’s answer should therefore be in line with what is thought to be courteous and acceptable. This is commonly found in Asian cultures (Malhotra, Agarwal and Peterson, 1996). Issues relating to equivalence are highly relevant when doing cross-cultural research and certainly when one attempts to understand cultural nuances that might influence data comparability. Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  36. 36. |41 The issue of cultural response bias in scalar data is one that is not widely researched. It appears as though the idea that respondents from different countries could answer scalar data differently is not viewed as a possibility but rather taken to be fact. Hardly any research has been conducted that attempts to understand the actual magnitude of these differences. If the differences in scalar bias are smaller than what is commonly accepted to be the case, then we could in fact be overemphasising the relevance of this issue within the field of marketing research. Due to the wide range of multi-country data that we have access to, we are uniquely placed to add insight into this subject matter and to investigate the emphasis we should be placing on cultural response bias as pertaining to scalar measurement tools. Methodology In order to better understand cultural response bias at the region, country and category level we analysed 449 multi-country data sets in 48 countries across 14 different product/service categories. A total of 317,636 respondents were surveyed yielding 8,980,232 observations. Each data set contained two metrics which we used for our analysis: 1. Brand satisfaction/needs-fit rating (satisfaction) àà Respondents were asked to rate each brand that they were aware of on a 10-point scale – with 10 being the highest rating possible (perfect in every way), and 1 being the lowest (terrible). 2. Importance of brand choice (importance) àà This question was asked at a category level (answered once by every respondent in each data set) using a 5-point scale, where 1 was the highest rating possible (extremely important) and 5 was the lowest rating possible (not at all important). àà For the purposes of this paper we flipped the scale for ease of analysis – i.e. 1 = not at all important and 5 = extremely important. The questions for each metric were phrased exactly the same way in each country in which research was conducted. In non-English speaking countries, translations that have proven (across a multitude of studies) to have the same meaning, were used. In this way, we ensured that translation equivalence was achieved and that the metrics were truly comparable across the different countries. Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  37. 37. 42 | We classified the countries included in the research according to their development status using the World Bank’s classification system (World Bank List of Economies, July 2005). According to this system, middle and lower income economies are referred to as the developing economies, while high income countries are classified as being developed, unless otherwise stipulated. We then analysed the two metrics in relation to the development status of the countries, the type of market being researched and the category in question to gain insight into how, why, and indeed if, the magnitude of cultural response bias differs across markets and countries. In order to compare cultural response bias among ethnic groups we analysed data from the USA and South Africa. With respect to the USA, only the importance question was asked to a sample of 329 respondents as well as two booster samples of 243 Hispanics and 226 African Americans. Respondents were questioned about 8 different product/service categories. With respect to South Africa, we used data from 6 studies (32,875 respondents in total) with representative samples surveying Black, White, Coloured and Indian/Asian ethnic groups. Each study covered a different product category and included measures of satisfaction and importance. T-tests (parametric and nonparametric) and ANOVAs were carried out on the data where appropriate, testing at a 5% level of significance which is applicable to all significant differences reported below. Results To gain an initial understanding of what, if any differences existed across the countries, we compiled a basic table of the studies comparing the responses on each metric across the 47 countries included in our analysis (Table 1). Table 1 – Country information Country Importance Satisfaction Status* Studies Categories Sample Argentina 3.7 7.6 ND 8 4 3315 Australia 3.7 6.8 D 21 9 7502 Austria 3.2 6.7 D 2 1 384 Belgium 3.0 7.6 D 1 1 500 Brazil 3.9 7.9 ND 21 8 14840 Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  38. 38. |43 Country Importance Satisfaction Status* Studies Categories Sample Canada 3.5 7.1 D 17 8 10544 Chile 4.0 7.5 ND 3 2 428 China 3.6 7.6 ND 38 8 23558 Colombia 3.8 8.2 ND 5 2 1506 Czech 3.7 7.0 ND 8 4 2836 Denmark 2.9 8.1 D 1 1 1001 Egypt 4.7 8.4 ND 5 2 2247 France 3.4 6.9 D 29 9 13950 Germany 3.1 6.9 D 28 9 16296 Hong Kong 3.3 6.6 D 8 4 1956 Hungary 3.5 7.0 ND 4 1 800 India 4.2 7.7 ND 18 6 8404 Indonesia 3.6 7.5 ND 4 1 1207 Italy 3.4 7.6 D 8 3 4842 Japan 3.2 6.6 D 13 5 11613 Korea 4.0 7.2 ND 9 3 4154 Malaysia 3.4 7.1 ND 7 3 2473 Mexico 3.7 8.4 ND 14 4 6635 Netherlands 3.2 7.3 D 10 4 3311 New Zealand 3.5 6.9 D 3 2 1426 Norway 2.8 8.2 D 1 1 300 Pakistan 4.4 7.9 ND 4 1 1215 Peru 3.8 7.1 ND 2 2 419 Philippines 3.1 8.1 ND 2 2 1400 Poland 3.6 7.4 ND 12 5 5192 Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  39. 39. 44 | Country Importance Satisfaction Status* Studies Categories Sample Puerto Rico 4.1 7.5 ND 2 2 1605 Romania 3.6 8.9 ND 2 1 9665 Russia 3.4 7.5 ND 14 5 4917 Saudi Arabia 4.3 8.0 ND** 5 2 1787 Slovakia 2.7 7.5 ND 1 1 1020 South Africa 4.0 7.3 ND 11 6 32350 Spain 3.2 7.3 D 9 5 5127 Sweden 4.4 7.2 D 4 1 396 Switzerland 3.9 7.3 D 4 1 800 Taiwan 3.4 7.1 ND 6 3 3756 Thailand 3.5 7.5 ND 4 1 1200 Turkey 4.1 7.8 ND 6 3 2138 UK 3.5 7.0 D 29 9 12967 Ukraine 3.4 7.8 ND 4 1 1200 USA 3.6 7.1 D 36 12 81330 Venezuela 4.5 7.1 ND 1 1 1900 Vietnam 3.9 7.9 ND 4 1 1222 Zambia 4.3 7.1 ND 1 1 12 *Note: D = Developed, ND = Developing (according to the World Bank’s classification of countries). **Saudi Arabia is classified as developed by the World Bank. We disagree with this classification, and hence, treated it as a developing country in our analyses. Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  40. 40. |45 As can be seen more easily from figure 1, at a basic level, it is clear that respondents across countries (and cultures) do respond differently. Fig. 1 – Country comparison In order to determine what factors could be influencing thesedifferences,wedividedthe responses according to region and development status (using the World Bank’s classification system), which showed a distinct pattern (figure 2). Fig. 2 – Regional Comparison (By Development Status) Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  41. 41. 46 | In developing regions the choice of brand is more important than in developed regions. Although the overall mean difference between the two is not very large (0.3 or 8% greater in developing regions), the difference is significant. Looking at the mean satisfaction scores, a similar significant difference is evident with developing regions tending to give higher responses (0.6 or 8% higher in developing regions). Thus, within regions where different income levels are apparent; these differences do translate into different responses. For example, Asia is a region which includes countries that can be classified as both developing and developed. In developing Asian countries, the responses given tend to be consistently higher than in the developed countries. The mean importance for developing countries is 0.5 (or 16%) greater and the mean satisfaction is 0.9 (or 14%) greater than for the developed countries. Fig. 3 – Regional comparison: B2B vs. B2C This highlights the importance of looking at the data not only by region/geography, but perhaps more importantly, by economic factors such as development status. To determine which other factors had an influence on the responses given, we split the data out according to the type of market being researched: business-to- business (B2B) or business-to- consumer (B2C). At an overall level, the difference between the means for each type of market by development status was minimal (though significant): a mean difference of 0.2 for importance and only 0.1 for satisfaction was observed. Looking at these two types of markets across developed and developing regions, however, showed a greater variation in the results (figure 3). Although the differences in means between B2B and B2C in the developing regions are minimal (not significant), in the developed regions they are more obvious (significant for all except importance in Asia and satisfaction in Australasia).In the developed regions, there is a tendency to view brand choice as more important in B2B decisions than in B2C decisions. The mean importance is 0.4 (or 12%) greater Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  42. 42. |47 for B2B in the developed regions combined. The greatest difference observed is in North America, where the B2B mean importance is 0.5 (or 13%) greater than the B2C mean score. Satisfaction also tends to be marginally higher for B2B in the developed regions, with the overall mean rating 0.1 (or 2%) greater than for B2C. This difference between B2B and B2C is exceedingly similar in Asia, Australasia and North America where the gaps are ca. 0.3 (or 4-5% greater). Fig. 4 – Category comparison We then split out the responses according to the category in question (figure 4). Differences in the responses given by developing and developed countries can still be seen at this level. Differences between mean importance scores were only significant for the financial, FMCG, IT, media and telecommunications categories. Differences between mean satisfaction scores were significant for most categories except automotive, mining and tooling. Looking at the mean importance scores, although there is a tendency for developing countries to give higher scores than developed countries, this is not always the case. Financial and QSR (Quick Service Restaurant) categories are slightly more important among developed countries, whilst tooling and mining categories are (near) identical. The greatest difference between developed and developing countries is observed in the FMCG category, where the developing country mean importance is greater by 0.7 (20%). Looking at the mean satisfaction ratings, the tendency for developing countries to give brands higher overall ratings across categories is clearer. Tooling is the only category where there is no difference between developed and developing countries. The greatest difference/gap observed for this metric is in the automotive category where the mean satisfaction rating for developing countries exceeds that for developed countries by 0.9 (or 14%). Since the gap for automotive is among the smallest with respect to mean importance scores, the impact of category is entirely dependent on the metric being measured. Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  43. 43. 48 | The key take-out from this is that one should not generalise about the tendency for developed economies to always give higher scores. One should rather take into account the individual metric that is being analysed. Within developed and developing countries, different ethnic groups may also differ in economic status, thus, in a bid to better understand response bias at this level we analysed ethnic response data in a developed (USA) and developing (South African) country. Limited by the data available to us, we looked at mean importance scores for ethnic groups in the US and mean importance and satisfaction scores for ethnic groups in South Africa. From the data we see clear differences in response patterns. In the USA, we found that Hispanics, who have a low economic status relative to other ethnic groups in the US (Amador, 2006), generally rate higher on the importance scale than other ethnicities. However, the difference is extremely small with the mean for Hispanics being just 0.1 or 4% greater than the mean for the total sample. Other authors have also found Hispanics to rate higher than other ethnicities in the US, though on different rating scales to the ones analysed in this paper (Smith 2004, Weech-Maldonado et al. 2008). Hispanics tend to rate higher than African Americans across most of the product categories we analysed, although the difference was only found to be significant for the coffee, yoghurt and laundry detergent categories (Figure 5). Fig. 5 – Ethnic group comparison (USA) In South Africa, we found a significant difference between the average responses given by various ethnic groups to measures of importance and satisfaction. Similar to what we found in the US, the differences were extremely small. Blacks tended to rate higher than other ethnicities on importance by just 0.1 or 2% more than the mean for the total sample. Coloureds tend to rate higher than other ethnicities on satisfaction by just 0.3 or 5% more than the total sample. Both Blacks and Coloureds have historically represented groups with low economic status relative to other ethnicities in South Africa. Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  44. 44. |49 At a product category level, the choice of brand tends to be more important to Blacks than other ethnic groups in South Africa, though not in all categories (figures 6; only scores for Blacks, Whites and Coloureds are shown since they were included in all the studies analysed). Fig. 6 – Ethnic group comparison - Importance (South Africa) Looking at mean satisfaction scores by product category, Coloured people tend to rate higher than other ethnic groups in South Africa, but not in all categories (figure 7). To reiterate, at an aggregate/ category level, differences between the responses of various ethnic groups were consistently significant. When we analysed the differences between product-linked satisfaction scores by ethnic groups within each of these studies, differences were significant for some products but not for others. Only the beer/cider study yielded significant differences across all products in the competitive set. It is likely that product targeting and usership characteristics confound the affect of culture on scale usage at the product level. Fig. 7 – Ethnic group comparison - Satisfaction (South Africa) Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  45. 45. 50 | Summary of results a) Differences exist, but they may be small We’ve shown evidence within this paper that a difference exists between the responses of people in developing countries compared to developed countries. However, it should be noted that this difference tends to be small, i.e. less than a full point on the scale. When we look within countries at the variation in responses of people belonging to ethnic groups with differing economic statuses, these differences in responses become even smaller, i.e. less than half a point on the scale. b) Across product categories, differences vary greatly We’ve shown that the differences we see are not consistent across product categories. The inconsistency and significance of these differences worsens as one moves from high level comparisons (developed vs. developing) to low level comparisons (product-linked ethnic comparisons), probably due to the confounding effects of product targeting and usership characteristics. c) B2B and B2C markets are remarkably similar The data also suggests that relationships in business-to-business situations may not be as different from business- to-consumer situations as some marketers would like to believe. In developing markets, the difference is negligible, while in developed markets, a difference could be said to exist, but is by no means large. d) Differences vary depending on the metric used The final major finding in our results suggests that the differences that may exist between responses from developed and developing countries vary by metric. A large difference on one metric does not automatically imply a large difference on another metric, holding all other variables constant. Implications of results The most important consideration that these results bring to light is that response bias is not as simple as a blanket increase in response by a constant. The differences between responses are variable, and cannot be summarised by a constant. We cannot explain or remove the difference between responses from developed and less developed economies using a fixed value. There seem to be multiple variables at work. Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  46. 46. |51 Since the 80’s, the prevailing viewpoint among cross-cultural researchers has been that response bias is a source of error when making comparisons between cultures and should be discounted, as it is a result of the ability of different cultures to use likert-type scales (Smith 2004). Given what we’ve shown, rather than discount response bias, perhaps we should be asking the question: “Are these differences real?”, a viewpoint similarly expressed by Smith (2004). If the differences are in fact real, then perhaps people in less developed countries genuinely do feel more strongly about brands, and maybe their range of emotion really is bigger. There are two concepts that we propose to help explain these differences, a ‘wealth effect’ and an ‘education effect’, which our team has observed in more than 10,000 studies across over 80 countries. Our proposed ‘wealth effect’ suggests that developed country consumers can afford to make a mistake in their choice, since the cost of this mistake is lower for them than for consumers in less developed economies. Poorer consumers cannot simply throw a low quality purchase away, so they rely on brands as a symbol of quality. For example, ‘Western’ brands in Africa or even China can cost 2,5 to 5 times as much as local brands, but these brands still enjoy widespread support. Our proposed ‘education effect’ suggests that uneducated consumers will be less confident of their own ability to judge quality and value. Brand names, by definition, signify quality, and reduce the need for consumers to make up their own minds. Consumers in less developed countries tend to be less well educated than developed country consumers, which impacts on their brand choices, as they seem to need outside validation of their purchases. These effects also help explain why certain metrics give bigger differences than others. While the importance of the brand choice might not be seen to be any different, the perception of the specific brands can differ quite a lot. Conclusion Marketing researchers may be overestimating the effect that cultural response bias has on their data and the comparability thereof. We suggest that the difference in response between developed and less developed economies, as well as high income and low income status ethnic groups, may well be the result of real differences in perception and emotion, and should not be adjusted for, but used as-is in any measurement tools. More research and investigation may well be required to establish definitively if the differences are real or not. Qualitative research coupled with quantitative methods may be needed to ascertain whether or not the same mindsets in different situations lead to the same scalar responses, and hence suggest true construct equivalence. Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  47. 47. 52 | Acknowledgements This viewpoint expressed in this paper and some of the data was originally presented in a paper by Alice Louw, Alida Jansen and Adhil Patel at the South African Market Research Association (SAMRA) conference in 2006 in South Africa. This paper is a further development and improvement of this earlier work. The authors also wish to acknowledge Susanna Wessels, Anna Retief, Greg Classen and Charmaine Du Plessis for their assistance in compiling and analysing the data. All the people acknowledged here are current employees of TNS and WPP. References Amador, J. 2006. Hispanic economic status. Monitor Hispano, August 31st, 3 pp. Chen, C., Lee, S. and Stevenson, H. 1995. Response style and cross-cultural comparisons of rating scales among East Asian and North American students. Psychological Science 6 (3): 170-175. Clarke III, I. 2001. Extreme response style in cross-cultural research. International Marketing Review 18 (3): 301-324. Craig, C.S. and Douglas, S.P. (2000), International Marketing Research, 2nd ed., Wiley, New York, NY. 524 pp. Dolnicar, S. and Grün, B. 2007. Cross-cultural differences in survey response patterns. International Marketing Review 24 (2): 127-143 Fischer, R.(2004), “Standardization to Account for Cross-Cultural Response Bias”, Journal of Cross-Cultural Psychology 35 (3): 263-282. Malhotra, N.K., Agarwal, J. and Peterson, M. (1996), “Methodological Issues in Cross-Cultural Marketing Research”, International Marketing Review 13 (5): 7-43. Malhotra, N.K. and Peterson, M. (2001), “Marketing Research in the New Millennium: Emerging Issues and Trends”, Marketing Intelligence and Planning 19 (4): 216-325. Smith, P.B. 2004. Acquiescent response bias as an aspect of cultural communication style. Journal of cross-cultural psychology 35 (1): 50-61 Van Herk, H., Poortinga, Y.P. and Verhallen, T.M.M. (2005), “Equivalence of Survey Data: Relevence for International Marketing”, European Journal of Marketing 39 (3/4): 351-364. Van de Vijver, F.J.R. and Leung, K. (1997), Methods and Data Analysis for Cross-cultural Research, Sage, Beverly Hills, CA. 200 pp. Weech-Maldonado, R., Elliot, M., Oluwole, A., Schiller, K.C. and Hayes, R.D. 2008 Survey response style and differential use of CAHPS rating scales by Hispanics. Medical Care 46 (9): 963-968. “World Bank List of Economies (July 2005)”, The World Bank, URL: http://www.worldbank.org/. Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  48. 48. |53 Appendix 1. Questionnaire Metrics Satisfaction (needs-fit rating)*: “Taking into account everything you look for in a (insert category), please rate each brand listed in the grid below on a 10-point scale where “10” means you think it’s perfect and “1” means you think it’s terrible. It doesn’t matter if you’ve used the brand or not, we’d like your opinion of all the brands you are aware of.” Importance of brand choice*: “Some things are extremely important, for example, for many people, choosing where to live is extremely important. On the other hand, there are many things that people consider to be less important, for example, what brand of paper plates to take on a picnic. Thinking about brands of (insert category), how important to you is the choice of which brand to use? *These questions are the property and copyright of The Customer Equity Company (SA) (Pty) Ltd, and may not be duplicated without the written permission of The Customer Equity Company (SA) (Pty) Ltd. 2. Regional comparison (by development status) Region Importance Satisfaction Studies Categories Sample developed Asia 3.2 6.6 21 7 13569 Australasia 3.7 6.9 24 9 8928 North America 3.6 7.1 53 12 91874 Western Europe 3.4 7.1 126 11 59864 developing Africa/Middle East 3.8 7.7 22 6 36396 Asia 3.8 7.5 96 10 48589 Eastern Europe 3.6 7.5 51 9 27768 South/Central America 3.8 8.0 55 9 29448 Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  49. 49. 54 | 3. Category comparison (by development status) CATEGORY IMPORTANCE SATISFACTION STUDIES SAMPLE DEVELOPED Automotive 3.4 6.4 2 1007 Construction 3.6 7.2 5 505 Consumer Electronics 3.2 7.2 28 12812 Financial 4.0 7.0 43 11222 FMCG 3.2 7.5 15 11774 IT 3.3 7.3 62 27868 Media 3.5 6.6 21 8402 Mining 3.9 7.2 4 315 QSR 3.3 6.4 20 60831 Telecommunications 3.3 6.9 23 13502 Tooling 3.6 7.2 1 901 CATEGORY IMPORTANCE SATISFACTION STUDIES SAMPLE DEVELOPING Automotive 3.6 7.3 1 502 Construction 4.1 7.9 4 200 Consumer Electronics 3.5 7.8 24 14034 Financial 3.8 7.3 24 5652 FMCG 3.8 8.2 15 46129 IT 3.8 7.7 104 39552 Media 3.8 7.0 10 2048 Mining 3.9 7.7 10 155 QSR 3.1 7.2 8 21166 Telecommunications 3.8 7.6 17 10276 Tooling 3.6 7.2 1 400 Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  50. 50. |55 About the authors Enrico Tronchin is currently the Head of Research and Development at the Global Brand Equity Centre of Excellence, in Cape Town, South Africa. The evolution of mankind over the ages has yielded a world rich with cultural diversity. Cultural nuances abound but are so poorly understood, posing a massive challenge to market researchers. Enrico is passionate about how cultures affect people’s behaviour and how modern society along with consumerism and globalisation is changing that. Before joining TNS in January 2007, Enrico was a post-doctoral fellow at the University of Cape Town. He is a multi-disciplinarian at heart, having conducted scientific research in various branches of the natural sciences. During his term in academia he became intrigued by why some products/businesses succeed while others fail and why consumers often trade a sustainable alternative for an unsustainable one. Together with a passion for business, this lead Enrico to transition into market research in order to immerse himself in a field driven to understand why people do what they do. Enrico was born in South Africa, a country with 11 official languages and a multitude of cultures. As the son of Italian immigrants, his upbringing had strong multicultural influences. Consequently, he has always sought to understand cultural differences. Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa
  51. 51. 56 | Adhil Patel is currently Head of Thought Leadership at the Global Brand Equity Centre of Excellence, in Cape Town, South Africa. Discovering and understanding what makes people do the things they do is at the heart of Adhil’s curiosity. To that end, cultural research is an interesting nuance that is often talked about superficially, but never truly understood. Adhil is a graduate of the University of Cape Town, where he majored in Statistics and Economics. He has been working with the Conversion ModelTM since 1999, when he joined Research Surveys (now part of the TNS family), and has been heavily involved in the training of researchers and data processors all over the world in the use and interpretation of the model. He has worked with researchers and marketers from across the globe, and consulted on multi-country projects for some of TNS’ biggest clients. Although project work still takes up a fair proportion of his time, his division is responsible for research into research, amongst other things, allowing him to focus on the things that he’s passionate about. After growing up in South Africa, during the era of apartheid, and having borne witness to the birth of the Rainbow Nation (as dubbed by Nelson Mandela), cultural research has special relevance for him. Enrico Tronchin | Head of Research & Development | TNS Customer Equity Company | South Africa Adhil Patel | Head of Thought Leadership | TNS Customer Equity Company | South Africa

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