Brand, Identity and Reputation: Exploring, Creating New Realities ...

What keeps smokers from quitting?
Uncovering attribute-based loyalty determinants in cigarette brands
Athanasios Krystallis, Aarhus School of Business and Social Sciences, Denmark
Purpose
Recently, a number of studies have examined the possibility of better explaining the loyalty structure of brands based on
various product attributes or ―variants‖ (Trinh et al, 2009). From this perspective, it is considered that a product
category consists of subcategories according to product variants, and that each of these subcategories performs
differently in terms of loyalty, obtaining its own attribute-level loyalty (Chrysochou, Krystallis, & Rungie, 2008;
Krystallis & Chrysochou, 2011). The present work aims to uncover product variants that possibly contribute to brand
performance and loyalty creation in the tobacco category. More specifically, based on four differentiated product
variants (i.e. nicotine/tar content, length, flavour and thickness) the category is divided into a number of sub-categories
comprising differentiated or regular cigarette versions (i.e. light vs. regular nicotine/tar content, long vs. normal,
menthol vs. regular, and slim vs. regular). The objective of the work is to provide a deeper understanding of the exact
contribution of each variant in market performance and/or loyalty outcomes.
From a methodological point of view, the present study combines the stochastic and the deterministic approaches to
measuring brand loyalty using the Dirichlet model for describing patterns of attribute-based loyalty behaviour. Data
based on the Juster Probability Scale is collected from a sample of N=155 smokers in Iceland through a questionnaire
completion task. All sample participants are Icelanders who expressed their conscious effort to quit smoking, so this
work intends to bring into light the cigarette product variants that operate as marketing-related loyalty ―hooks‖ that keep
smokers from fulfilling their expressed want to exit the market.
According to the Public Health Institute of Iceland (ATVR, 2010), 21.6 percent of people between 15 and 89 years of
age smoke every day. Icelanders bought at average 50.55 packs per person in 2008, 7.2 percent less than in 2003.
Smoking has been prohibited in Iceland in most public places for several years, while it is the first country in the world
to ban tobacco advertisements in all mass media in the 1970‘s. Tobacco brand prices in the Icelandic market essentially
do not differ across brands and are set at a very high level (more than €5.5 per pack).
Methodology
A well-known model for studying repeat-purchase behaviour from a stochastic approach is the Dirichlet model
(Goodhardt, Ehrenberg, & Chatfield, 1984; Ehrenberg, 1988; Wright, Sharp, & Sharp, 1998). The Dirichlet model
calculates the probability of how many purchases each customer will make in a specific time period, as well as specifies
the probability of each brand being bought on each purchase occasion. The model assumes that each consumer has a
certain probability to buy a specific brand and that this probability is steady over time. Moreover, each consumer is seen
to choose from a repertoire of brands, typically buying one brand more often than another. This steady but divided
brand loyalty differs across heterogeneous consumers and aggregates to brand performance measures (BPMs, i.e. brand
and category penetration, brand and category purchase frequency and brand market share) that follow certain patterns
from brand to brand. The only input needed to calibrate the model is the overall incidence of a brand being chosen. In
order to describe and predict purchase behaviour, the Dirichlet model is used as a benchmark tool by comparing
theoretical BPMs for a given brand as estimated from the Dirichlet model to its observed BPMs derived from the data
(Li, Habel, & Rungie, 2009). Thus, in order to apply the Dirichlet model one has to provide to the model certain inputs
from the initial dataset (i.e. the observed BPMs). Wright, Sharp, & Sharp (2002) explain in details how these inputs can
be generated.
Moreover, the main measure used for assessing loyalty at the category, product attribute or brand levels is the
polarization index φ (Rungie, 2004; Rungie, Laurent, O‘Riley, & Morrison, 2005). This measure captures heterogeneity
in consumers‘ choices and varies from 0 to 1. Zero indicates pure homogeneity in consumer choice, meaning that all
consumers have the same brand choice probabilities; whereas 1 indicates maximal heterogeneity, i.e. when each
consumer always buys only his/hers favourite brand. In a perfect Dirichlet market all brands would have the same
polarization value, identical to the category polarization. However, real markets are rarely perfect Dirichlet and the
brand level polarization differs. This difference represents the degree to which the brand systematically deviates from
perfect Dirichlet in terms of its loyalty metrics (Li et al., 2009). Rungie, Goodman & Lockshin (2006) first outlined the
use of polarisation to examine attribute-based loyalty (i.e. loyalty to price tiers).
An approach to estimate BPMs and polarisation is to use stated preference data. Juster (1966, in Wright et al., 2002)
developed the 11-point Juster Probability Scale (JPS) that can be applied in a questionnaire survey context. Respondents
are asked to state their possibility to buy a certain brand within a product category over a certain future period of time.
The answers are on a scale from 0 to 10, where zero stands for ―(almost) no chance (1 in 100 times)‖, and 10 stands for
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