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Adaptive Choice-Based Conjoint Analysis (ACBC)

Although similar in structure and analysis to choice-based conjoint analysis (CBC), adaptive conjoint allows many more attributes and levels to be tested, reducing what would otherwise be unacceptable respondent burden in a survey.  ACBC has much more powerful price testing capabilities than normal CBC, since compounded pricing for specific product configurations can be provided to respondents independent of the price shocking component of the analysis. The deliverable is a simulator that enables the client to manipulate variable values to perform “what-if” analyses.

  • Enables testing of many potential product configurations, brand name testing, and very realistic price testing
  • Predicts the optimum product configuration for various price points based on the attributes measured
  • Measures the impact of brand name on market share in blind studies

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Apriori Association

Apriori association is a powerful data mining tool, mainly used for quantifying which instances (items) occur together.  In retail applications, this technique is often used in basket analysis..

  • Data sources include survey data and behavioral (transactional) data
  • Retail scanner data analysis applications
  • Provides insight into which items are frequently bought at the same time

 

CHAID

CHAID (chi-square automatic interaction detection) is an advanced classification tool that separates individuals into unique groups, based on similarity in the values of a variety of input variables, using a “tree” branching methodology.  CHAID provides probabilities of membership in the available classifications for each individual under consideration.  

  • Data sources include survey data, transactional data, and customer data
  • Classification applications, including predicting attitudinal classifications (i.e. segments) from behavioral or demographic data

 

Choice-Based Conjoint Analysis (CBC)

CBC, also known as Discrete Choice Modeling, is analysis of data where respondents are asked to choose a single alternative among a set of options (i.e. make a discrete choice).  The deliverable is a simulator that enables you to manipulate variable values to perform “what-if” analyses

  • Product configuration, brand name testing, and price testing applications
  • Provides the optimum product configuration and price based on the attributes measured

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Exploratory Data Analysis (EDA)

An EDA provides frequencies and normality distributions of all available data. This analysis involves extensive use of basic graphics (e.g. box charts, histograms) to quickly and easily describe data and focuses on completeness of data, and whether missing data can/should be imputed.

  • Data sources include survey data, transactional data, and customer data
  • Applicable for all research and big data analysis applications
  • Provides data clarity and organization of data
  • Helps narrow down which unique variables will be useful for subsequent modeling

 

Gap Analysis

Gap analysis quantifies the gap between product usage and 1) awareness of the product and/or 2) the market potential of the product.  Various basic analytical techniques are used, including multi-level awareness and barrier tournament methods.

  • Awareness and usage applications
  • Provides insight into whether your sales can be increased by increasing awareness or by solving other barriers

 

Key Drivers Analysis

Key drivers analysis (KDA) articulates in relative terms precisely which product attributes contribute the most to satisfaction, likelihood to purchase again, or likelihood to recommend.  KDA can also be extended through regression to predict the absolute extent of improvement in the dependent variables one might expect through improvements in the drivers themselves.

  • Brand loyalty or customer satisfaction applications
  • Enables your company to focus on items that have the largest impact on loyalty

 

Latent Class Analysis 

Latent class analysis is a clustering technique used to separate individuals into homogeneous groups based on similar behaviors or attitudes.  It is one of several methods that can be used for consumer segmentation research.  Latent class analysis is particularly useful with choice-based conjoint, where consumers can be segmented into groups based on how they make their buying decisions.

  • Conjoint/DCM analysis, attitudinal segmentation, and needs-based segmentation applications
  • Classifies customers and potential customers into unique segments that can be effectively targeted with specific marketing/messaging

 

Logistic Regression

Logistic regression is a predictive modeling technique used to estimate a binary outcome (binomial) or predict one of several potential categorical outcomes (multinomial).  A scoring algorithm can be developed using the output of the regression to score a customer or prospect database based on, for example, their propensity to buy a certain product or service.

  • Churn modeling, adoption modeling, and predictive response modeling applications

 

MaxDiff Scaling

MaxDiff Scaling is a forced-choice exercise that yields the greatest separation (maximum differential) between various product choices.  It is also known as “best – worst” analysis.  MaxDiff is the most effective way to measure and differentiate the importance of product attributes.

  • Gap analysis (barriers), concept screener, planogram analysis (see TURF) applications
  • Provides prioritization of customer barriers to purchase, differences in importance of product attributes, and helps your company determine which of a set of concepts will be most successful
  • While MaxDiff provides the data about appeal of items or features relative to each other, Anchored MaxDiff can be used to assign the absolute variation in that appeal. 

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Ordinary Least Squares (OLS) Regression

OLS, or linear regression, is a basic analytical technique used to predict the absolute scale of a continuous-level variable (e.g. a satisfaction rating, spending level in dollars, etc.) based on changes to the levels of the various independent (predictor) variables.

  • Customer satisfaction, market estimation applications
  • Provides market size estimates, projects customer satisfaction levels, and predicts likelihood to purchase

 

Text Mining

Text mining involves the use of quantitative applications to derive context from unstructured text data, such as tweets, open-ended responses, product reviews, etc.

  • Qualitative research and supplements to quantitative reports applications
  • Shows trending topics of customer interest to the client’s business.
  • Can utilize cluster analysis to determine which words or phrases appear together most frequently

 

TURF Analysis

TURF (total unduplicated reach and frequency) provides the total potential market for a finite group of items offered as options to a consumer, as a subset of a much larger array of choices.

  • Determines which combination of products yields the widest reach, highest frequency, or both