What is MECanalyst?
MECanalysist is a versatile software developed to identify consumers’
motives for buying products and/or services. It has been designed to learn
and understand how consumers ascribe to a product/service a meaning, a
self-relevance, which may encourage, or hamper, their use.
MECanalyst is a tool to analyze the link between products and consumers
and to gather information on consumers goal structures starting from the
analysis of their decision-making processes. It allows to uncover/analyze
consumer cognitive structures and decision maps (also referred as Hierarchical
Value Maps), by the utilization of an innovative research method –
means-end chain analysis (Grunert et al., 1995; Olson and Reynolds, 1983;
Reynolds and Gutman, 1988) – coupled with a in-depth interviewing
technique called laddering.
Who can use MECanalyst?
The software is aimed at:
Companies:
carrying out market surveys;
doing consulting (work) specifically targeted to marketing;
Researchers:
in the field of consumer research;
in the field of qualitative and quantitative research.
Universities:
teaching staff and marketing students interested in further information
on, and the application of, means-end chain analysis.
Developers
MECanalyst was developed in 2002 by Prof. Raffaele Zanoli and Dr. Simona
Naspetti of the University of Ancona (IT) in cooperation with Leonardo Cigolini Gulesu and Antonio Ruccia of SKYMAX DG
(IT).
Objectives
MECanalyst aims at providing users with the opportunity to apply means-end
chain analysis to consumer research in order to shape, where possible,
appropriate strategic marketing decisions.
The results of the application of this method can provide marketers and
researchers with an essential starting point in the understanding of consumer
decision-making processes as well as with a basis to develop communication
strategies and improve product positioning.
Main applications
Suitable areas for application of means-end analysis, hence of MECanalyst,
may be (Gutman, 1982):
- product positioning and segmentation analysis;
- design of new products or improvement of existing ones;
- design of communications strategies.
As regards to product positioning, means-end chain analysis is a useful
tool to identify opportunities and threats from competitors. MECAnalyst
allows to process the information obtained from laddering interviews (strengths
as well as weaknesses) in order to decide whether to focus on promoting
favourable product features or on highlighting the weaknesses of competing
products (Reynolds and Whitlark, 1995), by exploting perceived product
differences.
With reference to segmentation, MECAnalyst can be used to determine the
influence of values among different consumer segments and to link the
possible differences detected with as many product variants.
Design of new products and improvement of existing ones begins from the
attributes, but the significance of the latter becomes apparent as a function
of their associated consequences. When introducing a new product or an
innovation, knowledge of the attributes-consequences-values links allows
those responsible for Company marketing to recognize consumers’
desires and communicate them, after translating them into physical characteristics,
to product managers (Gutman, 1982).
Finally, the cognitive maps that can be obtained with MECAnalyst, though
obviously not aspiring to be the sole basis on which more effective communication
strategies or advertising can be developed, can however be valuable supports
to direct those choices (Reynolds and Whitlark, 1995). Knowledge of the
key factors (values) influencing consumers, as well as of the leverage
points (psycho-social consequences) connecting multiple values and multiple
lower-level constructs, enables companies to develop original communication
strategies, reduce advertising expenditure (e.g. by standardisation of
advertising programs) and increase its efficiency (Reynolds and Whitlark,
1995).
Basic concepts:
means-end analysis and laddering
MECanalyst uses the conceptual model of means-end chain analysis(1) to
study the motivations underlying consumer purchasing decisions. By analyzing
the link between consumers and products/services, the means-end approach
attempts to pinpoint consumer preferences, in order to reveal the often
hidden motives behind choices. Indeed, the concept that consumer choices
are not solely directed by material product features but, to a considerable
extent, also by a psychological component to which these appeal, is relatively
recent.
To a consumer, a good/service can have diverse meanings. For instance,
when buying coffee, the product could be chosen based on attributes related
to: type (e.g. in grains, soluble); origin (e.g. Brazil); package (e.g.
jar or pouch); brand, etc. However, consumers are not only interested
in the bundle of characteristics – attributes – that distinguish
one type of coffee from another; rather, they are attracted/discouraged
by the desirable (benefits) or undesirable (risks) consequences deriving
from the use/purchase of the relevant good and/or service. In the case
of coffee, the decision to buy may thus result to be connected with benefits
such as “less strong than espresso coffee”, “easy to
make”, “suitable for elderly people” (Quelch et al.,
1994); however, it is mainly the personal relevance to the eye of the
consumer that constitutes the main purchase motive (fig. 1).
In other words, if one delves into the consumer’s mind, the motivation
of the choice results to be based on psychological states, i.e. reasons
connected with a need/value which is satisfied by the purchase: e.g. the
achievement of a physical sense of well-being, self-esteem, belonging
to a group, etc.
It is thus interesting to study the link between product characteristics
and the consumer in order to achieve a better understanding of the way
consumer choice is influenced by his/her values. Means-end chain analysis
tries to make this relationship emerge.
References
“Measuring subjective meaning structures by laddering method: theoretical
considerations and methodological problems”, in International Journal
of Research in Marketing.
: “Means-end chains and laddering: an inventory
of problems and an agenda for research”, Mapp, The Aarhus School
of Business, working paper n. 34.
“A means-end
chain model based on consumer categorization processes”, Journal
of marketing, 46, 1.
“GPS:
A program that simulates human thought”, in Computer and Thought,
ed. Feigenbaum and Feldman, McGraw-Hill, New York. Olson e Reynolds, 1995;
“Understanding
Consumer’s cognitive structures: Implications for advertising strategy”,
in Percy L., Woodside A. (eds.), Advertising and Consumer psychology,
Lexinton Books, Lexinton, MA.
Consumer Behaviour and marketing strategy, European ed., McGraw-Hill,
London.
“Laddering
Theory, Method, Analysis and Interpretation” Journal of Advertising
Research, 28, 1.
“Applying laddering data to communications strategy and advertising
practice”, Journal of Advertising Research, July/August.
“Improvements in means-end chain analysis: using graph theory and
correspondence analysis”, Journal of Advertising Research, February/March.
