can be used by marketing managers. Critical for marketing and the successful development of
market share are mental and physical availability, which should help increase penetration.
We have considered the phenomenon of double jeopardy and its impact on brand
loyalty and repeat purchasing using defection in the car industry (Consumer Insight 10.1) as
a good example of this. Of great importance to marketers is the argument that increasing
brand loyalty can be a difficult task which may have very little effect on increasing a
company's market share. In the FMCG sector, inertia or random choice sometimes dictates the
purchasing decisions of consumers, In sum, a brand that has higher market penetration will
have more customers who buy it, and that is ultimately what makes the difference to market
share.
Data illustrating the Pareto law in action in the deodorant market (Table 10.4)
highlighted the differences between light and heavy buyers of a brand and the law of
moderation. Research Insight 10.2 introduced the Dirichlet model as a method of evaluation
sales performance of a new brand. Overall, it is important for marketers to understand light
and heavy buyers may change their habits from one year to the next.
Market segmentation and differentiation were covered and their implication for
marketers made clear through the phenomenon of duplication of purchase. Companies to
consider how to make their offering distinctive, but not so distinctive that they do not
naturally fit into a repertoire of brands. Consumer Insight 10.5 presented an interesting debate
around differentiation and buyer attitudes, noting that whilst differentiation important is an
strategic option, being a ' me-too' brand is also a viable option if it can gain penetration and
market share.
Practitioner Insight 10 supplemented these theories by showing how marketers use
empirical buyer behaviour principles in everyday business decisions. We also learnt from the
case of the Tesco Clubcard that there are other methods of collecting huge swathes of
consumer behaviour information in a way tailored to your company's specific needs.
Part 4: Where are we going?
Chapter 11: Future trends in consumer behaviour
Three main areas where consumer behaviour may be susceptible to change: 1. Domain of
technology induced change (i.e. big data); 2. Sustainability (environmental and economic); 3.
Constraints on behaviour and the impact this has on consumers and organizations service
those consumers.
Technology trends impacting consumer behaviour
Quantified self: the idea that tracking metrics can lead to self-improvement in some way
(Wolf, 2010). E.g. fitness/calorie trackers/smart watches.
Research has shown that this move towards increased measurement/quantification of self has
some negative effects. Etkin (2016) demonstrates that consumers who track and quantify their
daily activities may experience decreased enjoyment and engagement in these activities, and a
decline in subjective well-being (i.e. Fitbit).
, They new technological trends are not only manifested in hardware. Software developments
are also significant. One are where the software is key is the adoption of apps by doctors and
physicians to monitor and improve health outcomes.
Responsibility for health may soon devolve more towards the individual and less towards the
state as these new systems, techniques and technologies enable people to monitor their own
health performance. With such information, consumers may be considered accountable for
their own health choices, raising ethical and political questions around responsibilities in
relation to health and well-being. Issues also emerge in relation to ownership of data; this
information is valuable to the business customers of the companies providing the tracking
tools for targeting their marketing to individuals and identifying market trends.
Another technological trend is evident in how consumers share their interests, likes, and
experiences from the real world through social media platforms. Other key technologies
centre on location-based information. The familiar barcode is an early example of this group
of technologies: encoded information, typically about a product, that can be read by an optical
scanner. More recently, QR (quick response) codes, which are optical machine-readable
barcodes that record and store information related to items (Dean, 2013), have been used for a
range of marketing and information dissemination (verspreiding) purposes. QR codes offer a
method of adding web-based content to real-world messages, objects, or locations. QR codes
showed great promise but have failed to dominate as they were expected to a few years ago.
They have been shown to have a number of disadvantages; poor physical placement in
particular has given them a bad reputation among business professionals and a lack of
enthusiasm on the part of consumers. Redundant (overtollig) or wrong information is another
key problem.
Augmented reality techniques combined with geotargeting are used to allow information to be
presented to customers at various points during their journeys. Geotargeting uses GPS
locational data generated by a user's smartphone to provide next alerts or in-app information
as they move around an area. Geotargeting is used where the business has data about the
individual user, and therefore can identify that user, which means that the information
presented can be tailored.
Big data
The new tools described above have the potential to change our sense of self in the world,
acting not only as mirror for self-improvement, discovery, and knowledge, but also in how we
engage with other people (Giesler, 2012).
Big data: vague term that can embrace everything from datasets gathered by large scientific
experiments and surveys to the extremely large datasets that are generated by business digital
processes, media searches, and social media interactions. Of most interest in the context of
consumer behaviour are forms of found data (i.e. credit card transactions, Google search
data, mobile phone transactions).
For consumer researchers there is an important methodological aspect of big data, namely that
rather than using data to support theories, researchers can identify patterns in big data without
forming a hypothesis (Lycett, 2013) and hence develop new theories of how consumers
