Multidimensional Analysis: Definition

Summary

Multidimensional analysis is a statistical method that examines data from multiple angles simultaneously via an OLAP cube, exceeding the limitations of two-dimensional databases.

The five main techniques are:

• ACP: groups similar statistical units together to reduce variables
• AFC: studies distribution according to qualitative criteria
• ACM: simplifies the interpretation of numerous qualitative variables
• AD: predicts category membership
• AC: explores the links between groups of variables

Marketing applications: customer segmentation, campaign optimization, cross-channel performance measurement.

Process: data collection → cleaning → distance calculation → graphical representations → interpretation of axes.

Benefits: improved performance, intuitive visualization, enhanced decision support.

What is multidimensional analysis and why use it?

Multidimensional analysis is a statistical method of data exploration. It examines data from multiple angles by crossing different dimensions. This approach goes beyond the limitations of traditional two-dimensional databases.

Traditional databases organize information in rows and columns. Multidimensional analysis uses an OLAP cube to structure data. This cube allows users to navigate between multiple dimensions simultaneously. Users select combinations of dimensions according to their analysis needs.

The cube structure incorporates hierarchies in each dimension. For example, the time dimension is organized into days, weeks, months, and years. This organization facilitates data exploration at different levels of detail.

The main areas of application include:

• Marketing for customer segmentation and campaign analysis
• Business intelligence for performance monitoring
• Scientific research for the study of complex phenomena

There are many concrete advantages to this statistical method. Performance improves thanks to pre-aggregated data. Visualization becomes intuitive with clear graphical representations. Decision-making is enhanced by accurate and relevant analyses.

This approach also has technical limitations. Scalability decreases as data volumes increase. Flexibility remains limited when it comes to integrating new types of analysis. Specialized languages complement standard SQL for complex queries.

What are the main techniques of multidimensional analysis?

Multidimensional analysis encompasses five major techniques. Each method meets specific objectives depending on the data being analyzed.

• Principal component analysis (PCA): This technique explores quantitative data tables. It groups similar statistical units and highlights their differences. PCA reduces the number of variables while retaining as much information as possible. Marketers use it to analyze customer satisfaction scores.
• Correspondence factor analysis (CFA): CFA studies the distribution of a population according to qualitative criteria. It also deals with positive quantitative variables. This method reveals the associations between rows and columns in a table. It helps to understand the preferences of market segments.
• Multiple correspondence analysis (MCA): MCA simplifies the interpretation of numerous qualitative variables. It summarizes complex correlations into a few main axes. Analysts apply it to surveys with multiple closed questions.
• Discriminant analysis (DA): This method establishes relationships between predefined groups and quantitative variables. It predicts which categories individuals belong to. DA optimizes the classification of customers according to their purchasing behavior.
• Canonical analysis (CA): CA explores the relationships between two sets of quantitative variables. It maximizes correlations between groups of variables.

The choice depends on the type of data and the analysis objectives. Detailed statistical methods guide this selection. Specialized training courses provide in-depth coverage of these techniques.

How can you implement effective multidimensional analysis?

Data collection is the first crucial step. Gather all your data from multiple sources. Then convert it into a uniform format to facilitate processing.

Data cleansing allows you to correct errors and fill in gaps. This phase ensures the reliability of your analysis. Enrichment integrates external data with your company’s internal information.

The calculation of distances between statistical units is performed using reduced centered variables. This mathematical method measures the deviations between your data points. It reveals hidden relationships in your dataset.

Creating graphical representations transforms your calculations into understandable visuals. Correlation circles show the relationships between variables. Factorial designs position your observations in a small space.

The interpretation of the principal axes reveals the major trends in your data. Each axis represents a combination of original variables. Analyzing the identified groups allows you to understand the natural segments.

Modern multidimensional analysis tools simplify these complex calculations.

Practical applications in digital marketing include:

• Customer segmentation based on multiple behaviors
• Campaign optimization through cross-analysis of performance
• Performance measurement integrating several indicators simultaneously
• Automatic audience classification for targeting
• Detecting anomalies in customer journeys

Multidimensional analysis transforms complex data into actionable insights. This statistical method reveals hidden patterns in your marketing data. PCA, LCA, and MCA offer unique perspectives on your audiences. These techniques improve your strategic decisions and optimize your performance. Integrate these methods into your analytical arsenal for more effective campaigns.