Market Segmentation – Multivariate Solutions

Small big data is consumer information held by a company, which can be leveraged with analysis, as in this Quirk’s article: Using Self Organizing Maps to Segment Small Big Data.

Using Open Source Software to Make Sense of Small Big Data explains how to apply small big data methodology and AI to produce an actionable deliverable.

The Misrepresentation of Statistical Accuracy, published in GreenBook, examines where to draw the line with fraud using a segmentation as an example.

Assessing a Brand Funnel Using Comparative Discriminant Analysis explores how a brand can improve commitment to brands by using multiple sections of a survey or existing data silo input.

Canonical Multiphase Segmentation, published in ADMAP, discusses segmentation as a vital tool in determining the effectiveness of ad spend.

The Segmentation Toolbox looks at four types of segmentation and Tandem segmentation.

Read a discussion of data analysis and permissions hypothesis in this article, Why Do Google Apps Want My Sensitive Data?

Using open-source software to make sense of small big data, published in Quirk’s Media, describes how to use new capabilities with traditional marketing research deliverables.

Tandem Segmentation – Getting Your Product to Sell Itself describes how conducting a factor analysis followed by a cluster analysis improves market ROI.

Graphic – Key Measures of Market Segments is a visual which examines how to speak to each key segment.

The premium market research statistical software offers and excellent introduction to segmentation, SPSS Tutorial-Cluster Analysis.

Coke Brand Differentiation Example shows how to differentiate between coca-cola consumption groups in order to plan targeting which will move Weekly drinkers into the Daily category and Monthly drinkers into the Weekly category.

After creating an add-value presentation with existing multi-wave data, Multivariate Solutions presented Mining Traffic Opportunities jointly with McCann-Erickson, New York.

Two-Step Segmentation utilizes first a traditional cluster analysis across several dimensions of attitudes or behaviors, then binds them in a second step based on a business rule, such as money spent or propensity to purchase a product.

Four A’s Model Segment Presentation examines four key measures in the study to determine key segments, Satisfaction with The Company, Would Recommend The Company, Likelihood to Remain with The Company, Magazine Purchase Intent and to determine key discriminators for each.

Green? – Attitudes, Behaviors and the New Green examines environmental issues in politics.

A Walk Through Discriminant Analysis explores choosing predictor variables.

Helping Wrangle Big Data: Monte Carlo for Marketing Research explains the usefulness of Monte Carlo simulations for ROI, loyalty, product development, customer satisfaction and regression modeling.

Small Data Visualization by Michael Lieberman presents two examples for examining primary research – a two-step cluster analysis and a data-mining example that takes existing consumer purchase data and reports the items in which the client should specialize to connect to the greatest number of other products.

Enhanced Customer Segmentation – Targeting the Golden Egg is an application of multi-dimensional cluster analysis and water-fall direct marketing techniques.

The Power of Segmentation examines the price differences various segments of drivers are willing to pay for gasoline.

Segmenting the Burger Market is a key communication strategy for the fast-food industry.

Sign-up to receive marketing research data analysis updates!