Multivariate Analysis (MVA) examines multiple variables (more than 2) to determine whether there are relationships between them.
Multivariate analysis examines all the independent variables and their relationships. It provides a holistic view of the data.
MVA helps organizations predict the future, become more efficient, make informed decisions about policies and processes, correct mistakes, and uncover new insights that can drive strategy.
This builds on univariate analysis (one variable) and bivariate analysis (two variables). By examining multiple variables, MVA gives a deeper view of the data.
The quality of multivariate analysis is directly related to the quality of the data. The more a company invests in good data, the more reliable and useful the results will be.
In an interview with John Bates, Director of Product Management for Adobe Marketing Cloud, we discussed the importance and application of multivariate analysis in today’s data-driven world.
What is Multivariate Analysis?
Multivariate analysis is a statistical technique for gathering and analyzing multiple data sets simultaneously so researchers and analysts can draw cause-and-effect conclusions about the relationships between the different components. It’s essential to understand complex data structures and find patterns that may not be visible when looking at individual variables in isolation.
In today’s data-driven world, companies need to collect all the data to make decisions. Often, this means looking at three or more data sets at the same time, which is where multivariate analysis (MVA) comes in. By looking at multiple variables together, organizations can uncover insights that help them understand how different factors interact and impact each other and ultimately drive better strategies and outcomes.
What are the Different Types of Multivariate Analysis?
To classify the different types of multivariate analysis, you need to understand the nature of the variables involved – specifically, are they independent or dependent? Data scientists use different techniques depending on the relationships between the variables. By knowing how many variables you’re testing – distinguishing between dependent and independent – you can categorize the analysis into two main families of techniques.
Dependence Methods
Dependence methods look at how dependent variables are affected by one or more independent variables. These are often used in predictive analytics, where the goal is to model and predict outcomes based on input data. Key techniques in this family are:
Multiple Regression Analysis: This looks at the relationship between one dependent variable and multiple independent variables. It helps you see how changes in the independent variables affect the dependent variable and allows you to predict based on new data. I find this useful in a business where you want to understand the impact of different factors on sales or customer satisfaction and driving strategy.
Logistic Regression: Used when the dependent variable is categorical (e.g., yes/no, success/failure), logistic regression models the probability of a particular outcome based on one or more independent variables. This is useful in fields like healthcare, where predicting patient outcomes can inform treatment plans.
Path Analysis: This looks at the direct and indirect relationships between variables so researchers can understand the causal pathways to a particular outcome. I like how path analysis simplifies complex relationships so you can communicate findings to stakeholders.
Structural Equation Modeling (SEM): SEM combines factor analysis and multiple regression so researchers can analyze complex relationships between observed and latent variables. It’s useful for testing theoretical models. I think SEM is a great tool for researchers to validate their hypotheses in social sciences and psychology.
Interdependence Methods
Interdependence methods look at the relationships between multiple variables without distinguishing between dependent and independent variables. These are useful for data mining and finding patterns. Key techniques in this family are:
Factor Analysis: This reduces the data to its underlying factors that explain the correlations between observed variables. It’s commonly used in survey research to identify latent constructs. I like factor analysis because it can reveal insights you wouldn’t have seen otherwise and help organizations understand the underlying drivers of customer behavior.
Cluster Analysis: Cluster analysis groups similar observations based on their characteristics so researchers can identify segments in the data. This is widely used in market segmentation and customer profiling. I think effective clustering can lead to more targeted marketing and higher customer engagement and satisfaction.
Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms a large set of variables into a smaller set of uncorrelated variables (principal components) while retaining most of the original variance. It’s useful for simplifying data without losing information. I like PCA because it can take complex data and distill it into actionable insights so decision-makers can focus on the most important factors.
Discriminant Analysis: This classifies observations into pre-defined categories based on their characteristics. It helps you see which variables differentiate between groups, so it’s useful for classification. I find this useful in fields like finance, where you want to distinguish between high-risk and low-risk clients and inform lending decisions.
Assumptions in Multivariate Analysis
With all these techniques, you need to make strong assumptions about both independent and dependent variables upfront. For example, regression assumes a linear relationship between variables, and factor analysis assumes the observed variables are influenced by underlying latent factors. Knowing these assumptions is key to interpreting the results correctly and ensuring the analysis is valid. I think being aware of these assumptions can help analysts avoid common mistakes and improve their results.
Why Do Companies Use Multivariate Analysis?
Multivariate analysis can benefit companies by helping them forecast future opportunities, assess risks, and assess product demand. This is key to developing investment strategies, making business decisions, and setting realistic growth and performance expectations.
Forecasting Future Opportunities and Risks
One of the main reasons companies use multivariate analysis is to forecast future opportunities and risks. By examining multiple variables at once, organizations can see trends and patterns that wouldn’t be visible when examining individual data points. This allows businesses to make proactive decisions, allocate resources, and mitigate risks before they get out of hand.
Data-Driven Decision Making (DDDM)
The insights from multivariate analysis support data-driven decision-making (DDDM), which is key in today’s competitive world. By using statistical significance rather than gut feeling, companies can eliminate speculation around corporate policies and processes. By using data, they can ensure decisions are evidence-based, more effective, and produce better outcomes.
Risk Management
By using multivariate analysis, businesses can reduce their overall risk and chance of failure. Being able to look at multiple variables at once means organizations can see the interplay between different variables and make more informed risk assessments. For example, a company can look at how changes in pricing, marketing, and customer demographics impact sales and make adjustments to minimize risk.
New Insights
Multivariate analysis can also uncover new insights that drive business growth. For example, it can help you find new customer segments or target markets you may have missed. Also, MVA can reveal market patterns that occur at specific times of the year or hours of the day so that you can adjust your marketing and product offerings accordingly.
Organizing and Analyzing Large Data Sets
In a world where businesses have access to vast amounts of financial, operational, customer, and purchase data, multivariate analysis (MVA) is a powerful tool for organizing and analyzing this data. Without MVA, valuable opportunities can get lost in an avalanche of unorganized data. By using multivariate techniques, you can take complex data and distill it into actionable insights so you can see trends and make informed decisions.
Multivariate Analysis with Regression Models
Multivariate analysis is a powerful tool for data scientists, and one of the most popular methods is using regression models. These models allow analysts to explore and quantify relationships between multiple variables and gain valuable insights to inform decisions.
What are Regression Models?
Regression models are statistical techniques for understanding the relationship between a dependent variable and one or more independent variables. By examining these relationships, data scientists can draw conclusions about how changes in independent variables affect the dependent variable. This is useful in many business scenarios.
Practical Example: Call Centre
For example, a data scientist wants to examine the relationship between call wait time and the number of complaints at a call center. Using regression modeling, the analyst can quantify how changes in wait time impact customer satisfaction (as measured by the number of complaints).
Modeling the Relationship: The model may show that as wait time increases, the number of complaints increases significantly. This would prompt management to implement strategies to reduce wait time, such as increasing staffing during peak hours or improving call routing systems.
What are the Downsides of Multivariate Analysis?
While multivariate analysis (MVA) has many benefits, it also has some disadvantages that businesses should consider before implementing it. Knowing these limitations will help you make informed decisions on when and how to use multivariate analysis. Here are some of the downsides:
Complexity
Multivariate analysis requires more complex computations than simpler analytical methods. The models are mathematical and require more statistical knowledge and expertise, which can be a challenge for teams that lack the skills or resources to do the analysis properly.
Data
MVA requires a large amount of data to get accurate and reliable results. Each variable being analyzed requires a sufficient number of data points to get meaningful results. If the dataset is too small or not diverse, the analysis will give misleading results. Businesses need to invest time and resources in data collection and preparation to meet these requirements.
Governance and Preparation Complexity
Governance and preparation for multivariate analysis are much more complex, time-consuming, and expensive than for simpler analysis. Businesses need to have robust data management practices, including data cleaning, validation, and integration, to ensure the quality of the data being analyzed. This preparation phase can be resource-intensive and may require specialist tools and people.
Interpretation
Interpreting the results of multivariate analysis can be tricky, especially for non-statisticians. The relationships between variables are complex, and understanding their implications requires careful thought. Misinterpretation of results can lead to bad decisions and strategic mistakes.
Overfitting
When analyzing multiple variables, there is a risk of overfitting the model to the data. Overfitting occurs when a model becomes too complex and captures noise rather than the underlying relationship between variables. This can result in a model that performs well on the training data but poorly on new unseen data. Businesses need to be careful when choosing the right model complexity to avoid this trap.
Misleading Conclusions
While MVA gives a more realistic view of the relationships between variables, it can also lead to misleading conclusions if not done properly. For example, a simple bivariate correlation model might show that increasing marketing spending will result in a proportional increase in sales. However, multivariate analysis may show that other factors, such as the quality of the marketing spend, the channels used, or seasonal variations, impact the outcome. If these factors are not accounted for, businesses may make decisions based on incomplete or inaccurate information.
When is Multivariate Analysis Not Needed?
While multivariate analysis (MVA) is a powerful tool for understanding the relationships between multiple variables, there are scenarios where it’s not needed or even counterproductive. Here are the situations where MVA might not be the best approach:
Simple Insights or Forecasts
If you just want a simple insight or a basic forecast of a single metric, MVA might not be required. For example, if you want to forecast future revenue based on historical revenue data, a simple time series analysis or trend analysis will do. In these cases, the added complexity of MVA doesn’t add value.
Limited Data
When data is limited, MVA might not be practical. MVA requires a large amount of data to get accurate and reliable results. If you only have a small dataset or not enough data points for the variables, you want to analyze MVA, which might not be the way to go. Univariate or bivariate analysis might be more suitable.
Initial Exploration
Before you get into MVA, it’s good to do univariate or bivariate analysis as an initial exploration. For example, you can start by doing univariate analysis to calculate basic statistics such as mean and median. This foundational analysis will give you valuable insights into the data distribution and characteristics and help you identify areas to dig deeper.
Basic Relationships
If you just want to understand the basic relationships between two variables, bivariate analysis might be enough. For example, if you want to see the correlation between marketing spend and sales revenue, a simple correlation analysis will do the job without the complexity of MVA.
Resource Constraints
MVA often requires significant resources, including time, expertise, and computational power. If your business has resource constraints or the insights don’t justify the investment, it’s better to go for simpler analytical methods.
Causation or Direct Relationships
Where the relationships between variables are clear and established, MVA might not be needed. For example, if you know that increasing prices directly affects sales volume, a simple analysis of price changes and sales data will do the job of informing your decisions.
Best Practices for Better MVA Results
Companies can follow these best practices to get more accurate and actionable results from multivariate analysis (MVA). These best practices are based on data quality, technique selection, and communication of insights. Here are some of them:
Invest in Data Collection
The foundation of MVA is high-quality and consistent data collection. By having accurate, complete, and systematic data, organizations can significantly reduce the need to check the quality of underlying data or reinvent the wheel with each analysis. Implementing good data governance practices will help maintain data integrity over time.
Standardise Data Management
Standardizing data management practices across the organization will enhance the consistency and reliability of the data used in MVA. This includes defining data formats, naming conventions, and data entry protocols. By having a single approach to data management, companies can reduce discrepancies and ensure all departments are working with the same high-quality data.
Select the Right Technique
As you have more data, you might be tempted to use more complex techniques like neural networks or deep learning. These methods can provide great insights but often require significant computational power and time to get results. Companies should evaluate if the complexity of these techniques is justified for their use case. In many cases, simpler models will give you the same insights with less resource investment.
Know the Sweet Spot for Each Department
Different departments or use cases will have different needs and capabilities when it comes to MVA. You need to know the “sweet spot” for each department and what level of complexity and sophistication fits their objectives and resources. For example, marketing teams will benefit from simple regression models, and data science teams can handle more complex analyses. Adapting to the context will give you better results.
Communicate Insights Clearly
Once the analysis is complete, you need to communicate the insights across the organization. Given the complexity of MVAs, organizations should try to present the findings clearly and simply. This might mean using visualizations, dashboards, or summary reports that highlight the key takeaways and recommendations. Make sure stakeholders can easily understand the results to enable data-driven decision-making.
Iterate and Refine Models
MVA is not a one-time exercise; it should be an iterative process. Organizations should refine their models with new data and insights. Regularly revisiting and updating the analysis will ensure the findings stay relevant and accurate over time. This iterative approach will also help you to adapt to changing market conditions and business needs.
Train and Enable Staff
Investing in training and development for staff involved in data analysis is crucial. By giving team members the skills and knowledge they need, organizations can improve their analytical capabilities and the quality of their MVA. Empowering staff to own data analysis will create a data-driven culture across the organization.
What will Change in MVA in the Future?
MVA is set to undergo significant changes in the next few years. Historically, MVA has been the domain of actuaries, statisticians, and data scientists. As technology advances, the model-building process will become more automated, and MVA will be more accessible and efficient for a wider range of users. Here are some of the trends and changes we will see:
Automation
One of the biggest changes will be the automation of the MVA process. We are already seeing software tools that allow you to input your objectives or metrics and the variables you want to optimize. These tools can compute multiple models at once and give you immediate results for different scenarios. Automation will reduce the manual effort in model building and allow analysts to focus on interpreting results and making strategic decisions rather than getting bogged down in complex calculations. Personally, I think this will enable more people to get involved in data analysis regardless of their statistical background.
Simple Interfaces
As MVA tools become more automated, we will see a move towards simpler interfaces. This will allow non-experts to get involved in MVA without needing extensive statistical training. Intuitive dashboards and visualizations will make it easier for users to see the relationships between variables and the impact of different models and democratize access to advanced analytics. I love this as it will bring more diverse perspectives to data analysis, insights, and innovation.
Machine Learning
The combination of MVA with machine learning will take it to another level. As machine learning algorithms get more advanced, they can find patterns and relationships in large datasets that MVA might miss. This will allow organizations to leverage the best of both worlds and get more accurate predictions and insights. I think this is a game changer, especially in areas like healthcare and finance, where understanding complex interactions will lead to better outcomes and more informed decisions.
Real-Time
With advances in computing power and data processing, MVA will become real-time. Organizations will be able to analyze data as it’s generated, get immediate insights, and make quicker decisions. This will be especially valuable in fast-moving industries like finance, marketing, and e-commerce, where time is of the essence. I think being able to act on real-time data will change how businesses operate and allow them to respond better to market changes and customer needs.
Better Visualisation
Better visualization will also be a part of the future of MVA. As the complexity of the analysis increases, visualization will be key to communicating insights clearly. Advanced visualization tools will help users interpret the results of MVA and make it easier to see trends, correlations, and outliers. I think investing in better visualization tools will not only help us understand more but also create a data-driven culture across the organization.
Ethics
As MVA becomes more automated and combined with machine learning, there will be more focus on ethics. Organizations will need to ensure their analysis is done responsibly, with no bias in data interpretation and model selection. Transparency in the analytical process will be key to trust and accountability, as decisions made by MVA can have a significant impact on individuals and communities. Personally, I think ethics in data analysis is key to a sustainable future where data is used for the good of all.