Understanding Multivariate Analysis: A Practical Example

Introduction

Data can only yield successful results if you extract actionable insights. Businesses often generate data in the form of customer demographics, website clicks, sales figures, and more. The insights help businesses make informed decisions.

For example, a marketing team uses data to create campaigns and the sales team leverages it to customize their pitch. Similarly, different teams collaborate to create an ideal customer profile (IDP).

Data shows patterns, unfolds a story, and helps understand how different variables like age, gender, purchase history, and countless others drive collaborative intelligence. And this fuels decision-making.

Multivariate analysis (MVA) is a robust statistical method that helps in analyzing multiple variables at once. This helps identify patterns and relationships that may not be immediately apparent through other types of analysis.

Let’s explore it in detail.

Multivariate Analysis: Understanding It with an Example

Multivariate analysis (MVA) is a statistical method for examining the relationships between three or more variables simultaneously. MVA considers how numerous variables intertwine to help dig for consumer insights and patterns within the data.

This is different from univariate and bivariate analysis in many ways. The table below helps us understand how multivariate analysis differs from the other two.

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Businesses can use this method to identify patterns and trends that are not apparent when looking at variables in isolation. Leveraging MVA allows optimizing different aspects of business operations, from marketing strategies to product development, which improves overall performance.

Example of Multivariate Analysis in a SaaS Setup

Let's take an example of a SaaS business whose users subscribe to its services.

Here, they can use any of the MVA techniques (discussed later) for their customer data to explore the correlation between different factors, such as usage patterns, order history, and customer support interactions, and their impact on retention.

The result of such analysis can be something like, “Users from the UK aged 26-45 who are billed annually are less likely to churn than those billed monthly.” It means that for this demographic, an annual billing model enhances retention compared to a monthly billing model.

A SaaS business can then use this insight to tailor their strategies for recurring billing in the following ways:

• Target Marketing: Focus marketing efforts on users in the UK aged 26-45, emphasizing the benefits of annual billing.
• Pricing Strategy: Incentivize annual subscriptions like discounts or additional features.
• Customer Engagement: Customizing communication and support for a specific set of audiences to level up their product usage experience, which boosts their likelihood of staying subscribed.

This way, with MVA, you'll unlock CX insights that help increase SaaS customer success by enhancing customer retention.

Four Types of Multivariate Analysis

Multivariate analysis opens up opportunities to explore the correlation between different functions within the organization. Each of the types explained below provides a unique view of your business data to reveal hidden patterns and relationships for better decision-making.

#1. Multiple Regression Analysis

Regression analysis helps to understand the relationship between one dependent variable and two or more independent variables. It is useful in predicting and investigating the influence of independent variables on the dependent variable. The change is analyzed by quantifying the relationship between multiple predictor variables and a single outcome variable.

The major types of Multiple Regression Analysis are as follows:

• Linear Multiple Regression: Analyzes the linear relationship between the dependent variable and multiple independent variables.
• Polynomial Regression: Measures the relationship between variables as an nth-degree polynomial, which is useful for curvilinear relationships.
• Lasso Regression: Performs a variable selection and regularization that shrinks less important variable coefficients to zero.

Practical Usage

A retail company leverages multiple regression analysis to predict its sales based on advertising spend, seasonality, and economic indicators. This helps optimize its marketing budget to maximize sales.

Businesses in eCommerce can apply multiple regression analyses to predict customer satisfaction. They factor in parameters like delivery speed, pricing, product quality, etc., to gauge the efficiency of their order fulfillment system and build a regression model for eCommerce business.

Then, this model is further used to assess the impact of each factor on customer satisfaction. For example, inspecting how faster delivery times can be enabled through a seamless order fulfillment system can positively affect customer satisfaction.

#2. Factor Analysis

This is a data reduction technique to simplify complex datasets by identifying underlying factors that explain the correlations between variables. It involves grouping different variables based on shared characteristics to narrow the correlation. The end goal is to identify structure within data sets.

Some of the commonly used factor analysis tactics are as follows:

• Exploratory Factor Analysis (EFA): Helps uncover the underlying structure of a dataset when there are no preconceived notions about the number or nature of factors.
• Maximum Likelihood Factor Analysis: Employs a statistical approach (maximum likelihood estimation) to identify the most likely factors explaining the observed correlations in the data.
• Confirmatory Factor Analysis (CFA): Useful in testing a specific hypothesis about the factor structure of a dataset, often based on prior theory or research.

Practical Usage

Market research firms often grapple with many data points, such as price sensitivity, brand loyalty, product features, and countless others. All these require thorough attention to understand consumer behavior better.

Here, factor analysis helps simplify this complexity by finding the underlying factors that drive purchasing decisions.

No single variable functions in isolation as factor analysis groups them based on their interrelationships. This reveals a smaller set of fundamental factors, each representing a distinct dimension of consumer motivation.

Factor analysis may then refine several key consumer behavior variables into different groups like — 

• Value-conscious consumers who prioritize affordability.
• Brand advocates who demonstrate strong loyalty to specific brands.
• Feature seekers who demand specific product attributes.

These distinct groups are further helpful in tailoring marketing campaigns, providing discounts for value seekers, promoting brand legacy for brand advocates, and offering innovative features for quality seekers.

#3. Principal Component Analysis (PCA)

This dimensionality reduction technique simplifies complex datasets by reducing the number of variables while preserving most of the variance. It transforms a large set of correlated variables into a smaller set of uncorrelated components, known as principal components, so you get simplified data without losing significant information.

Analysts can simplify a dataset by focusing on the top principal components that capture the most variance. This preserves the dataset's essential patterns and relationships, making it easier to visualize and interpret.

Some of the widely used PCAs are as follows:

• Sparse PCA: A variation that constructs components comprising fewer variables to enhance interpretability.
• Kernel PCA: Extends PCA to handle non-linear relationships in data by leveraging kernel functions.
• Robust PCA: Designed to be less sensitive to outliers to provide reliable results when data contains noise or anomalies.
• Incremental PCA: Enables online learning and adaptation to new data, which helps analyze large or streaming datasets.

Practical Usage

Businesses often use PCA in finance, marketing, biology, and engineering to uncover hidden patterns, reduce noise, and simplify complex data structures.

Let’s understand through the example of an eCommerce business that receives customer feedback from different channels (social, website, customer support, etc.). This renders data on customer pain points and preferences to discover areas for improvement.

But how can you identify the most critical issues amid the volume and variety of feedback?

Here, PCA comes into play by analyzing customer feedback data to identify the key factors presenting high friction in customer sentiment. These components could be website usability (UI-related, technical errors, etc.), product quality, delivery speed, or pricing.

If PCA points at website usability for customer dissatisfaction, you will have to allocate the resources for corrective actions. This means directing teams for ecommerce error resolution, website navigation improvements, checkout processes alignment, bugs or glitches management, and other aspects that hinder the user experience.

Ultimately, you’ll cut through the noise, pinpoint the most impactful areas for improvement, and prioritize resource allocation to improve customer satisfaction.

#4. Cluster Analysis

Cluster analysis is a segmentation technique for grouping similar observations based on their characteristics. It's about grouping a set of objects so that objects in the same group (or cluster) are more similar to each other than to those in other groups.

For grouping, the similarity is based on different attributes like purchase history, behavioral data on a website (clicks, time spent on page, downloads, etc.), response to marketing campaigns, etc.

The goal is to identify specific segments within a dataset that share some common characteristics, making it a valuable tool for market segmentation, customer profiling, product development, and more.

Below are some of the common types of cluster analysis:

• Partitioning Clustering (K-Means): Divides data into a fixed number of clusters by minimizing the distance between data points and their assigned cluster centers.
• Hierarchical Clustering: Builds a tree-like hierarchy of clusters wherein each cluster remains a subset of a larger cluster.
• Density-Based Clustering (DBSCAN): Groups various data points based on their density, identifying clusters of arbitrary shapes and handling outliers.

Practical Usage

A product development team in a SaaS setup can leverage cluster analysis to segment its user base according to usage patterns, feature adoption, and support ticket history.

The analysis can reveal distinct clusters, such as power users, occasional users, and users who frequently encounter issues. Knowing these clusters helps customize product development efforts to optimize bug management strategies and provide targeted support resources to different user segments.

Such a proactive approach enhances user satisfaction through better product performance, ultimately driving customer loyalty and retention.

Wrapping Up

Multivariate analysis is a highly useful statistical method businesses across industries can use to simultaneously examine relationships between three or more variables. When implemented correctly, different teams will have data providing deeper insights and patterns within complex datasets to make decisions.

As mentioned above, the data unlocked through different MVA methods can further be used to optimize operations across various functions, from marketing to product development, enhancing overall performance.

The practical use cases shared here will guide you to using actionable insights in the right manner to drive growth and customer satisfaction.