Market Research Analysis
Once we’ve conducted the market research fieldwork, the data will need to
be analysed. There are various methods that can be adopted to do this
depending on whether the market research project is qualitative,
quantitative or both. For quantitative market research we are able to
utilise various statistical packages that enable us to produce detailed
market research data tabulations – these allow in-depth integration of the
market research data. For qualitative market research, summary transcripts
of all interviews or group discussions are produced - analysis is then
conducted using these market research summaries.
Analytical Tools
We process, analyze, synthesize & summarize questionnaire data — and
prepare actionable reports focused on the salient research objectives.
Multivariate statistical techniques are utilized in exploring, discovering,
summarizing & modelling relationships in the data. The analytic tools
include, but are not limited to:
- Nonparametric Tests
- T and Z Tests
- Correlation Analysis (r)
- Analysis of Variance (ANOVA)
- Multivariate Analysis of Variance (MANOVA)
- Analysis of Covariance (ANCOVA)
- Multivariate Analysis of Covariance (MANCOVA)
- Repeated Measures ANOVA, ANCOVA, MANOVA, MANCOVA
- Conjoint and Discrete Choice
- CHAID, Exhaustive CHAID, C & RT, QUEST
- Discriminant Analysis
- Factor Analysis, Principal Components, and Cluster Analysis
- Perceptual Mapping Analysis
- Cluster analysis
Nonparametric Tests
If distributions are not normal; i.e., non-parametric, such as those
that are flat, peaked, or strongly skewed, non-parametric statistics are
recommended. These statistics are particularly relevant in the IT realm
where data frequently does not fit into a normal distribution.
T and Z Tests
T-tests are typically used for determining whether or not one group
significantly differs from another on some type of metric. For instance, we
may discover that females, on average, spend significantly more hours than
males at health-related Internet sites. When conducting numerous t-tests the
probability of reporting that a result is significant when it actually is
not, dramatically increases. In such cases, one should use Anova to help
control for chance findings.
One of the most useful z-test applications in market research is
determining whether or not one proportion significantly differs from
another. For example, we may discover that the proportion of Internet users
in one geographic region exceeds that of another.
Correlation Analysis (r)
Correlation measures the degree of relationship between one variable and
another. There may be, for instance, a high correlation between those who
have two or more phone lines in their household and time spent on the
Internet. One must note, however, that correlation is a measure of linear
(i.e., straight line) relationships. If the two variables of interest have a
non-linear relationship such as an inverted U, the correlation coefficient
(r) will fail to detect a relationship when one is actually present.
Analysis of Variance (ANOVA)
This procedure is useful for detecting mean differences among three or
more groups. ANOVA is a viable alternative to conducting numerous t-tests
because the analysis controls for chance findings (Type I error). To assess
differences in the average number of hours spent on the Internet among PC
owners in four countries, ANOVA would be an appropriate tool. Similar to
other statistical techniques, ANOVA is not immune from Type I error when
used repeatedly with a data set. To address this problem, MANOVA (explained
below) should be employed.
Multivariate Analysis of Variance (MANOVA)
This analysis can detect mean differences among a number of different groups
on several different measures while protecting for chance findings. The
method is an efficient and powerful analysis for large research studies in
which there are a variety of segments being assessed on a number of
different measures.
Analysis of Covariance (ANCOVA)
This procedure is useful for detecting mean differences among three or
more groups while holding one variable constant. To assess differences in
the average number of hours spent on the Internet among PC owners in four
countries, while controlling for access speed, ANCOVA would be an
appropriate analytic tool. ANCOVA is particularly useful in research
situations where a variable, such as income, gender, education, or age, can
potentially obscure or bias the results.
Multivariate Analysis of Covariance (MANCOVA)
This analysis can detect mean differences among a number of different
groups on several different measures, while holding one or more variables
constant. The method is useful for research studies where there are a
variety of segments being assessed on a number of different measures, where
one or more variables needs to be controlled for that may potentially bias
the results.
Repeated Measures ANOVA, ANCOVA, MANOVA, MANCOVA
In ANOVA, ANCOVA, MANOVA, MANCOVA, the respondent is assessed once for each
measure. In repeated measures ANOVA-based designs, the respondent is
measured several times. For instance, data collected through measuring the
number of online purchase transactions made by different buying segments per
quarter would be appropriate for this analysis. Accuracy is increased when
measuring a respondent on several occasions as opposed to one, thus making a
repeated measures approach one of the more powerful analytic techniques.
Conjoint and Discrete Choice
These techniques identify buyer preferences for product features, the
most desired set of features for a product, and what tradeoffs buyers are
willing to make for their desired product. The techniques are thus effective
tools for developing a successful product design and bundling of product or
service offerings.
CHAID, Exhaustive CHAID, C & RT, QUEST
These are all tree-based tools that segment groups of respondents that
share similar characteristics. CHAID (Chi-squared Automatic Interaction
Detector) and Exhaustive CHAID are ideal for visualizing large data sets for
consumer profiles and segments. C&RT (Classification and Regression Tree)
and QUEST (Quick Unbiased Efficient Statistical Tree) provide similar
results but, unlike CHAID techniques, produce trees with binary splits which
are more appropriate for some types of research. All four techniques are
effective variable reduction tools and precursors to other types of
analyses, such as regression and higher-order predictive models.
Discriminant Analysis
Discriminant analysis is useful for finding a group of variables (i.e.,
a discriminant function) that distinguishes one group from another. Although
it works well for group membership situations, it is not as robust to
statistical violations as, for example, logistic regression that will
provide similar information.
Factor Analysis, Principal Components, and Cluster Analysis
In the realm of market research, these variable reduction schemes
identify underlying dimensions of what respondents may be thinking when, for
example, evaluating a product or service. Please note that these analyses do
not test whether the dimensions that surface relate to a specified outcome
(e.g., an online purchase). Regression or higher-order predictive models,
such as RPM, are required to assess whether the dimensions have any
predictive value.
Perceptual Mapping Analysis
This technique is particularly effective for exploring branding issues.
Several brands can be compared and contrasted, on a number of different
attributes, in one comprehensive picture. A perceptual map may indicate that
several brands of laptops are perceived similarly in terms of price,
performance, and wireless capabilities, but not in terms of reliability and
warranty coverage. Another advantage of perceptual maps is that the data
required to construct them is straightforward and typically not difficult to
collect – consumers usually rate the product/service attributes on simple
Likert-type scales (e.g., ranging from strongly agree to strongly disagree).
Cluster analysis
Through Cluster analysis, you receive exact information about your
target audience. Their objective is to identify transparent representation
of the information contained within the data. From this, it is possible to
develop target group typologies. The process is based on similarity
categories of the survey elements within homogenous groups. The objective of
cluster analysis is to identify groups that resemble each other. Generally,
several variables that appear to be similar are selected to ascertain
"Similarity" and establish the measure of similarity of the respective
characteristics. As there is not only one process of cluster analysis, a
process for the individual survey objective is constructed.
