Attribute Performance & Derived Importance Mapping
Our analytic solutions encompassing Attribute Performance and Derived Importance Mapping can be applied to a number of differently themed studies, such as Attitude & Usage studies, customer satisfaction studies, customer loyalty studies, employee satisfaction studies 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 overall satisfaction, loyalty or “intent to recommend” the product/service.
Analytically, we compute the Derived Importance of these attributes, correlating attribute performance values with the respondents overall product favorability. These correlations are indexed, and then mapped against corresponding indices of performance, depicted in an x/y axis graph with four distinct quadrants. The quadrants in which the mapped attribute points lay helps initially determine actions our clients should address first, to positively impact the desired outcome.
An actual rank ordering of attributes requiring attention is not readily discernible through the depicted 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. A hypothetical example of a Performance/Derived Importance attribute map appears below, with attribute labels and their corresponding Leverage Index value accompanying each mapped point:
Exhibit A – Performance/Derived Importance Map for Home Developer
It is also quite common for us to group product attributes along a common theme for our clients (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, and typically base in this on some percentile rank threshold.
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.
Finally, for Attitude & Usage type studies, no analysis would be deemed sufficient without consideration of appropriate Subgroup 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 subgroup 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 a split of analyses, based on the respondent’s assigned customer segment. For example, value-based customer segments may be described as “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).
To learn more about MWI’S Attribute Performance & Derived Importance Mapping capabilities here to request more information.
