One group of studies we support analytically is Attitude & Usage studies, a general grouping of studies that includes customer satisfaction, customer loyalty, employee satisfaction and brand equity studies. Respondents of these studies evaluate a set of attributes individually, as well as provide an overall outcome or favorability response, such as level of satisfaction, loyalty or “promotability” of the product or service.
Analytically, we compute the Derived Importance of attributes, correlating attribute performance values with the respondents overall product favorability. These correlations are indexed, and then mapped against:
- Stated Importance Index – providing an “Attitudinal Motivation and Perceptions map (AMAP)”, depicting the true motivational factors driving customers’ differentiation of competitive product/service offerings (click here for further “AMAP” details).
- Attribute Performance Index – providing a “Brand Marketing and Performance (BMAP)”, depicting attributes across four graph quadrants that vary in terms of actions to be undertaken. (click here for further “BMAP” details).
We typically focus on the results of our “BMAP” analysis as a means for our clients to prioritize the attributes that they should address first, to positively impact the desired outcome. An actual rank ordering of attributes requiring attention is not readily discernible through the BMAP graph; therefore, we augment the analysis by rank ordering attributes via Leverage Indices, computed from map coordinates. Addressing top ranked attributes, as ordered by this index, will allow our clients to leverage proportionally higher impact on the desired outcome.
It is also quite common for our clients to group attributes along a common theme (i.e., product quality, service experience, etc.). When this occurs, we can provide a Classification Summary, which provides a rank ordering of these more general classifications. In doing this, we look at the percentage of attributes within the classification having a high leverage index. We draw on our analytic experience to set the appropriate threshold qualifying an attribute as a “high leverage” attribute.
In those cases where clients do not designate attribute groups, yet desire to pinpoint underlying summary factors within the attribute data, MWI can conduct Factor Analysis to achieve these results.
The Classification Summary is another example of how MWI doesn’t just throw numbers back at our clients – we strive to summarize and interpret the results, both at the individual and classification attribute level.
Appearing below is a theoretical Classification Summary:
Your current Market Research vendor may or may not already provide Derived Importance and Leverage analyses for individual attributes and their association to desired outcomes. But ask your vendor this question: “Do you consider the interaction of brand name and price on product satisfaction?” Brand and Price commonly have a unique, interactive relationship on important outcomes such as demand, market share and overall satisfaction. At MWI, we explore this and other joint attribute relationships through Interaction Effect Estimation. Through the use of CHAID statistical algorithms, we identify which interactions amongst your attributes contribute the greatest positive impact on your outcomes, so you can leverage this information to create your own positive impact.
For circumstances where brand or product positioning is key to our clients, we provide a “Competitive Marketing Analysis and Positioning Map (CMAP)” These maps provide a visual representation of not only where the brand/product lies within the marketplace with respect to its competitors, but how closely attributes are associated with brands/products. Such a representation is invaluable to our clients when setting brand positioning and competitive strategy. (click here for further “CMAP” details)
Finally, for Attitude & Usage type studies, no analysis would be deemed sufficient without consideration of appropriate “Split Analyses”. For each study, we look at the potential for subsetting the study sample by criteria relevant to the study objectives. This is a unique, customized level of analytic support we provide our clients. Examples of the types of split analyses we have previously conducted include:
- Customer vs. non-customer – Analyzing data on the subset of respondents representing the client’s customers facilitates the formulation of customer retention strategy; analyzing non-customers contributes to customer acquisition strategy.
- Customer Segments – With its origins within the Database Marketing arena, customer segmentation strategy can easily be formulated or augmented through split analysis, based on the respondent’s assigned customer segment. Typical customer segments include “Loyal”, “Active”, ”Marginal” and “Inactive”, and are assigned based on their current level of patronage with both the client and its competitors.
- Geographic – Different strategies are commonly pursued when clients look to expand and/or contract within certain geographic markets. Analyses can be split by either actual geographic divisions (country/state/MSA’s), or by clients own defined geographic groupings (region/district).
Click here to open a full description all the most common StatWork™ offerings. (.pdf file)
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