We are dedicated to bridging the gap between academic advances and practical applications to apply the latest, cutting-edge methodologies and procedures for our clients' benefit. We combine in-depth theoretical understanding of econometrics, statistics and market research techniques with a perspective of practical business/management application to endow our research with applicable insights.
While we truly customize methodologies depending on business needs, some advanced methods that we often use include:
- Discrete Choice Modeling / Conjoint
- Best-Worst / MaxDiff
- Latent Class Cluster Analysis
- Structural Equation Modeling
These methods, many other methods, and combinations of these methods are used in addressing different types of research depending on business needs. Some examples of research we do include:
provides a framework for aligning all aspects of the business, from early phase planning and strategy development through in-market performance.
Through segmentation, cross functional teams are targeting, building for, and marketing ta the same target segments resulting in higher business success.
Brand Engagement Research
Archetypes, initially classified based on the work of scientists Jung and Heylen, have evolved to be more actionable for business needs. The brand engagement analysis provides a holistic view of how consumers experience a brand.
Brands are positioned within the archetypes to identify emotional connections with the brand as well as differentiation opportunities.
Location on the emotional archetype wheel is based on a model that considers two separate analyses to understand best fit.
Strength within an archetype
Is the brand more or less associated with each set of imagery attributes when compared to other brands?
Relative strength across all archetypes
How strongly do consumers associate the brand with the imagery attributes in one archetype vs. another?
Consumer Purchase Journey
companies look for those “moments of truth”, touch points in a consumer’s experience when they are open to influence or are vulnerable.
Using observational research and quantitative survey, Strategy Analytics’ approach uncovers and quantifies motivations, trigger points, factors that lead to decisions, sources of information, and more.
This holistic approach encompasses a deep understanding of the triggers, influencers, decision points, and deal breakers associated with consumer behavior in today’s consumer journey with brands and experiences.
Identify the path that consumers go through as they make purchase decisions
Determine similarities and differences across segments in the consumers’ journey – triggers, influencers, decision points, including emotions that they go through during the process.
User Behavior and Profiling
This research describes current habits and practices and how these vary by audience and segments. It provides deeper profiling information to cover lifestyle, social networks, media usage, attitudes and behavior, etc.
Insights help uncover specific consumer groups’ latent consumer desires, needs, identify the problems they are trying to solve to inform strategy and communication efforts.
User behavior and profiling research provides foundational knowledge of end consumers' desired experiences, including their must-have needs, perceptions, and attitudes.
Enables the business to identify drivers of consumer preference and behavior.
Identifies key metrics that measure and track the health of the business, the brand, and its sub-brands
Drivers of Preference
allows the business to focus on the most critical factors that drive consumer behavior such as purchase intent, preference, or satisfaction.
There are several different methods that can be used to identify drivers of choice. We often recommend Partial Least Squares Path Modeling because of its strong ability to identify complex relationships.
Using Partial Least Squares Path Modeling (PLS-PM) to identify drivers of brand attraction or choice
It is a statistical approach for modeling complex multivariable relationships (structural equation models) among different sets of variables
It identifies relationships between variables, uncovering which (independent) variables have a predictive impact on the outcome of other (dependent) variables. It also provides very strong statistical testing of these relationships.
The “factor loadings” that are a result of this method indicate the strength of the impact between the two types of variables.
Satisfaction, Loyality & Advocacy
research enables the business to have a deep understanding of its customers' experiences with its products. Measuring the standard metrics of satisfaction are no longer sufficient to gain customers' trust.
This program provides a comprehensive, early and continuous read-out on who’s buying the product, reasons for buying, consideration set, influencers, sources of awareness, and gauges the effectiveness of marketing investments.
The analysis provides insights on the drivers of choice, satisfaction, and advocacy – critical information that should be fed back into product and strategy development.
For more information please contact us