Data Analysis Services from AbsolutData
At AbsolutData, one of the top Data Analysis Companies in India, a pool of experienced and skilled analysts carries out rigorous statistical and exploratory analyses on survey data, to extract meaningful and actionable information for the clients. Our analysts are not only well versed in diverse analytical techniques, but are also well aware of consumer and business trends. They are involved from an early stage of research and have a thorough understanding of the business objectives as well as research goals. This enables them to apply the right techniques and come up with rich marketing insights that guide better decision making at the clients end. At AbsolutData, we see analytics as a tool to add incremental value, by putting results in perspective and offering custom recommendations.
We utilize various techniques, depending on the specific questions we are looking to address. Some of these are:
CHAID (Chi Square Automatic Interaction Detector): This involves creating a decision tree model that tells us relative degrees of association between a dependent variable and a set of independent variables.
Cluster Analysis: Used to classify respondents into market segments, i.e. set of consumers who are similar to each other but markedly different from their counterparts in other segments. The segment characteristics determine marketing strategy for that segment, as different from other segments.
Conjoint Analysis: An advanced technique used to identify the optimal bundle of product features that have maximum influence on consumer purchase or preference decisions. We study the trade offs between alternate combinations of attributes to gauge purchase motivations. This can have various forms like Adaptive (ACA) or Choice Based (CBC).
Key Driver Analysis: Uses regression/logistic regression to identify the most important factors among multiple factors influencing the target/dependent variable.
Factor Analysis: Helps to explain a large set of interrelated variables in terms of their underlying dimensions or factors. Essentially used to bring down a large number of attributes to a more manageable smaller set of factors.
Hierarchical Bayes: A powerful tool employed to evaluate choice data and calculate respondent level utility values for conjoint analyses.
Latent Class Analysis: Used to relate observed variables to a set of unobserved (latent) variables.
Perceptual Mapping: Graphical technique used to depict relationship between brands and their ratings on various attributes. The closer a brand appears to an attribute, more is its association with that attribute. Brands placed closer to each other have similar images.
Regression Analysis: Technique used to find relative contributions made by different attributes in predicting a dependent variable.
TURF Analysis: Used to determine the combination of products in a line that can achieve the highest level of consumer interest.
Max Diff Analysis: A variation of conjoint analysis used to generate importance or preference scores for brands/attributes/concepts. It gives a clearer idea of what matters more to the consumer.
Funnel Analysis: Looks at the different stages through which a customer builds a relationship with the product/brand/company. Investigates the choke points and tells us how to ensure transition to the next stage.
Price Sensitivity Meter: A technique that helps to understand consumer reactions to movements in price and helps determine the optimal pricing strategy.
