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Discriminating between behaviour using market data from panels
Hsiu-Yuan Tsao, Leyland Pitt and Colin Campbell, International Journal of Market Research, Digital First, August 2013
Considerable research exists on stochastic models of switching behaviour that uses sequences of individual-level purchase data.
Considerable research exists on stochastic models of switching behaviour that uses sequences of individual-level purchase data. While at the individual level, sample size and sequence length are limiting factors, at the aggregate level, heterogeneity with respect to purchase sequences may assist in interpreting results. The authors propose an approach to discriminate between the switching behaviour of variety seeking, indifference and reinforcement. Only the proportion of 100% loyal customers, market share data and an estimation of the promotional effect - information all available from consumer panel data - are necessary to fit the model.
The effect of engagement with social media on purchase behaviors
Edward Malthouse, Mark Vandenbosch, Su Jung Kim and Bobby Calder, ESOMAR, 3D Digital Dimensions, Boston, June 2013
This paper investigates whether the contribution of user generated content (UGC) in social media competitions affects consumer purchase behaviour, and then attempts to calculate the return on investment.
This paper investigates whether the contribution of user generated content (UGC) in social media competitions affects consumer purchase behaviour, and then attempts to calculate the return on investment. The paper also attempts to identify the key components of an effective social media competition, and investigates if there are any long-term effects on consumer buying behaviour. Data is analysed from two social media contests for Canada's Air Miles Reward Program (AMRP), one of the largest loyalty programs in the world.
Big Data, Better Decisions: How does business intelligence drive change in the Chinese automotive market?
Tiger Lee Weihan, ESOMAR, Automotive Research Forum, Wolfsburg, May 2013
This paper shows the process of generating an automotive customer repurchase model of customer relationship management in China, based on data warehousing after data integration.
This paper shows the process of generating an automotive customer repurchase model of customer relationship management in China, based on data warehousing after data integration. China has become the world's largest automotive market, but only in the new sales market. For most Original Equipment Manufacturers (OEMs) and dealers, new vehicle sales lead to a better market share and greater revenue, while after-sales deliver the main source of profit: after-sales profit margins are five to ten times higher than sales profit margins. Customer loyalty is key to avoiding customer churn and the model proposed in the paper offers a new solution for marketers to attract more customer loyalty in China's rapidly growing after-sales market.
Towards a better measure of customer experience
Philipp Klaus and Stan Maklan, International Journal of Market Research, Vol. 55, No. 2, 2013, pp. 227-246
Defining and improving customer experience is a growing priority for market research because experience is replacing quality as the competitive battleground for marketing.
Defining and improving customer experience is a growing priority for market research because experience is replacing quality as the competitive battleground for marketing. Service quality is an outgrowth of the total quality management (TQM) movement of the 1980s and suffers from that movement’s focus on the provider rather than the value derived by customers. Researchers today state that customer experience is generated through a longer process of company–customer interaction across multiple channels, generated through both functional and emotional clues. Our research with practitioners indicates that most firms use customer satisfaction, or its derivative the Net Promoter Score, to assess their customers’ experiences. We question this practice based on the conceptual gap between these measures and the customer experience. In IJMR 53, 6 (2011), we introduce a new measure appropriate for the modern conceptualisation of customer experience: the customer experience quality (EXQ) scale. In this article we extend that work and compare EXQ’s predictive power with that of customer satisfaction. We establish that EXQ better explains and predicts both, loyalty and recommendations, than customer satisfaction.
From customer loyalty to social advocacy: Leveraging loyalty data and shopper insights to optimize social media engagement and drive in-store sales
Matthew Keylock and Malcolm Faulds, ARF Experiential Learning, Audience Measurement 7.0, 2012
This paper from dunnhumby and BzzAgent highlights how shopper insights can improve the effectiveness of social marketing programs.
This paper from dunnhumby and BzzAgent highlights how shopper insights can improve the effectiveness of social marketing programs. The study utilised anonymous household-level shopping data from retailer loyalty cards by inviting shoppers to link their social media profiles to their loyalty card purchase profiles. These consumers received product samples, pass-along offers and other things designed to spread recommendations both online and in person. The study found that advocacy programs that leverage both shopper and social data increase in-store sales of a brand by an average of 8% and the sales lift sustains at around 4% for six months after the end of the program. The authors also recommend that advocacy programs should target consumers based on a combination of shopper data, level of social activity and demographics.
Using supermarket loyalty card data to analyse the impact of promotions
Melanie Felgate, Andrew Fearne, Salvatore DiFalco and Marian Garcia Martinez, International Journal of Market Research, Vol. 54, No. 2, 2012, pp. 221-240
The aim of this paper is to show how supermarket loyalty card data from a panel of over 1.4 million shoppers can be used to analyse the effect of price promotions in a way which can bring significant advantages to retailers and manufacturers when making promotional decisions.
The aim of this paper is to show how supermarket loyalty card data from a panel of over 1.4 million shoppers can be used to analyse the effect of price promotions in a way which can bring significant advantages to retailers and manufacturers when making promotional decisions. The paper demonstrates the significant advantages that loyalty card data can bring to enhance our understanding of promotions, compared to traditional scanner and panel datasets. Regression analysis is used to compare the effects of different promotional mechanics upon different tiers of product across the fresh beef category in Tesco, using both scanner data and loyalty card data. The results show that using loyalty card data, which enables us to moderate for specific shopper characteristics, produces more statistically significant results and provides a more detailed picture of how promotions influence sales.
Marrying CRM Analytics With Research Insights to Formulate the Best in Class Customer Loyalty Strategy
Andy Kung, James Wong and King Fai, ESOMAR, Congress Odyssey, Athens, September 2010
This presentation intends to examine the analytics and research approach that are being used in customer loyalty.
This presentation intends to examine the analytics and research approach that are being used in customer loyalty. It will evaluate the pros and cons of each method and recommend a new methodology to help business leaders and loyalty marketers to better formulate customer loyalty strategy. The presentation will also review the latest development in Customer Loyalty / CRM programs. It will examine the strategic issues that are confronting business leaders / loyalty marketers and most importantly it will identify enhancements in customer research and CRM analytics for building an effective loyalty programs. In addition, business cases will be discussed to showcase how the new analytical approach can be applied in the commercial world and what impacts it could bring in formulating Customer Loyalty strategy.
Loyalty card databases - revolutionary analysis in shopper behaviour
Matthieu Jolly and Laurent Battais, ESOMAR, Retail Conference, Valencia, February 2007
The paper describes how the development of store loyalty programmes enabled the emergence of a new kind of data allowing analysis of consumer behaviour at the point of sale.
The paper describes how the development of store loyalty programmes enabled the emergence of a new kind of data allowing analysis of consumer behaviour at the point of sale. This information is of course first used to develop purchases of the store's loyalty card holders, then to describe behavioural characteristics of these clients. But a third way of analysis now exists now, thanks to the size of FMCG stores' client databases; the analysis of consumer behaviour on product categories and consumer reaction before marketing stimuli.
Leveraging customer panels for business success
Jon Mamela and John Morton, ESOMAR, Leisure Conference, Rome, November 2006
Fairmont Hotels & Resorts is embarking on the fifth year of the management of its online FPC Guest Advisory Panel.
Fairmont Hotels & Resorts is embarking on the fifth year of the management of its online FPC Guest Advisory Panel. Gathering a diverse range of guest feedback, this Panel has proven to be an invaluable source of both strategic and operational information for Fairmont's business. This paper provides real-life examples of how a world-class hotel brand effectively leverages its guest panel, not only to measure the success of its loyalty programme, but also to drive the brand and manage the guest experience.
Predictive segmentation in action - using CHAID to segment loyalty card holders
Laura Galguera, David Luna and M. Paz Méndez, International Journal of Market Research, Vol. 48, No. 4, 2006, pp. 459-479
This paper illustrates the use of a post hoc predictive segmentation procedure to segment the loyalty card market.
This paper illustrates the use of a post hoc predictive segmentation procedure to segment the loyalty card market. The specific procedure used is CHAID, an algorithm that has been gaining in acceptance because it fits the needs of marketing researchers regarding segmentation: it provides non-binary classification trees; the resulting categories/segments are mutually exclusive; it permits prediction of whether certain segments are more likely to engage in the target behaviour, and it is relatively easy to use and interpret. We conducted two parallel studies in two different countries. In both studies, we first modelled the data using logistic regression and then used the significant variables in that model for the CHAID analysis. The results show that CHAID is a reliable segmentation procedure.
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