The purpose of research is to find out what is in a consumer's mind that cannot be directly observed and measured. It is done in order to access and get a rough idea about the person's perspective. It helps the researcher to become oriented to the range and complexity of the consumer activity and concerns.
We are experienced in a wide range of qualitative methodologies and techniques, including..
Market Research Group Discussions or Focus Groups
We organize the market research group discussion or focus group as a qualitative market research technique, carried out with consumers or businesses. They will be open ended in nature and help market research clients understand opinions, perceptions, generate ideas or gauge reactions to concepts. Groups normally include between 5 and 8 respondents and a research moderator who carefully leads the discussion.
Market Research Depth Interviews
We have many years experience of conducting in-depth market research face to face or telephone interviews with consumers or businesses. The in-depth interview is a one-to-one qualitative market research technique. It is used in both business to business and consumer market places to help understand opinions and perceptions. The interview is un-structured, open in nature and allows the market researcher (or executive interviewer) to explore issues in detail.
The simplest completion test involves giving a respondent an incomplete and ambiguous sentence, which is to be completed with a phrase. The respondent is encouraged to respond with the first though that comes to mind. Using this technique the hidden aspects of user profile can be revealed.
The Clinic methodology constitutes a hybrid approach of both qualitative and quantitative, primarily for testing product or service concepts. Concepts are presented to the large audience of respondents; videos may be shown; product prototypes may be passed around; client technical people may make parts of the presentation. Each respondent is provided with a wireless remote answer device and, during the session, they are asked specific questions and reply in real-time through their wireless devices.
Perceptual mapping has been used as a strategic management tool as it offers a unique ability to communicate the complex relationships between marketplace competitors and the criteria used by buyers in making purchase decisions and recommendations. Its powerful graphic simplicity appeals to senior management and can stimulate discussion and strategic thinking at all levels of all types of organizations. Perceptual mapping can be used to plot the interrelationships of consumer products, industrial goods, institutions, as well as populations.
Datamatix Research Centre offers traditional quantitative expertise, as well as the power of the Internet and the latest in online questionnaire design and web based surveys.
Our personal interviewing process is characterized by the interaction of four entities: the researcher, the interviewer, the interviewee, and the interview environment. The type of interview environment is made by the researcher, depending on the type of data to be collected. We use the following personal interview methods according to interview environment;
- Door-to-Door Interviewing
- Executive Interviewing
- Mall Intercept Surveys
- Self- Administered Questionnaires
- Purchase intercept Questionnaires
- Omnibus Surveys
The Centre operates a Computer-Assisted Telephone Interviewing System to meet the most exacting standards of academic, general and government researchers. With this system, each question appears on a computer monitor for the interviewer to read. The interviewer enters the response directly into the computer, thereby bypassing the time-consuming data coding, editing, and entry processes.
In today's competitively intense markets, it is more critical than ever before to keep your finger on the pulse of the customer. Attitudes, plans and behaviors can change rapidly-and the providers that keep up with these changes gain a competitive advantage. Tracking studies are shorter, focused surveys that are conducted on a regular basis to measure the trends that are taking place-trends that may include customer awareness, behaviors, and purchase plans.
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.
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:
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 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).
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.
Presentation & Delivery Tools
We use a variety of technologies to visually portray complex output, accelerate analytic productivity, and highlight significant findings:
- Cross-tabulated data tables
- Key management summary tables
- Color graphing & charting
- Perceptual mapping
- Web-ready formats (HTML encoded)
- Report writing & management presentations
- Decision support software development