A practical guide to quantitative data analysis

Quantitative data analysis powers decision-making across teams. Marketers track campaign performance. Product teams study user behavior. Everyone’s after the same thing: insights that improve results.

But analysis breaks down when data is hard to access. Waiting on engineering, exporting CSVs, stitching spreadsheets — these workarounds waste time and introduce errors.

Modern data integration solves this. By automating data movement and centralizing sources, it gives teams fast, reliable access to the numbers they need.

What is quantitative analysis?

Quantitative analysis uses numerical data to measure outcomes and uncover patterns. For example, to evaluate a product launch, you might track how many users activated a feature and how behavior changed over time.

Statistical techniques help identify relationships between variables, like whether more notifications (independent variable) drove higher activation rates (dependent variable).

Unlike qualitative analysis, which focuses on user feedback or sentiment, quantitative analysis delivers measurable, objective insights. Together, both methods provide a fuller picture of what’s happening and why.

What’s quantitative data analysis used for?

Teams use quantitative data analysis to:

• Test hypotheses: By measuring how behavior changes over time, teams can confirm or challenge their assumptions. For example, product teams might test whether offering a free trial increases sign-ups.
• Compare groups: Quantitative analysis makes it possible to spot differences in performance across different segments. This could involve analyzing average order value for new customers versus returning ones to see where the strongest revenue comes from.
• Examine relationships: Data reveals how variables interact, showing whether one factor affects another. An example might be exploring whether higher email frequency leads to more website visits. 

How does quantitative analysis work? 4 methods

The quantitative analysis method you choose depends on the question you’re trying to answer. Here are four common approaches.

1. Descriptive analysis 

Descriptive analysis involves calculating averages, percentages, ranges, and standard deviations. These metrics reveal the shape and structure of your dataset. 

Once you’ve run these checks, patterns start to emerge. You might spot anomalies or differences that weren’t obvious at a first glance. This first check often raises new questions, helping you decide which areas to explore next. 

2. Diagnostic analysis 

Diagnostic analysis investigates the reasons behind a result. After you’ve identified a change or pattern, this approach helps you find out what’s driving it by examining relationships — correlations — between variables to understand what might be driving it. From there, analysts can test whether those correlations point to true causal factors or are simply coincidental. 

Imagine you notice a drop in weekly active users. You compare the data by customer type or platform and find that the drop only occurred for mobile users. That correlation narrows the scope of investigation, helping you explore potential causes and prioritize the right fixes. 

3. Predictive analysis 

Predictive analysis looks at past data to estimate what might happen next. You might use regression techniques to see how different factors correlate with an outcome or a time-series model to understand how a metric has changed over time — without necessarily proving causation. 

Churn forecasting is a common example. When you look at customer behavior from previous quarters, patterns tend to appear before customers leave. For example, reduced activity or slower engagement with new features can signal that a customer is about to drop off. If those same signs show up in active accounts, you know where to focus to retain customers. 

4. Prescriptive analysis

Prescriptive analysis focuses on what to do once you understand the situation. It builds on descriptive, diagnostic, and predictive insights and turns them into actions. Teams might use optimization models, scenario analysis, or simulation models to compare possible actions and see which options are going to get the best results.

How to analyze quantitative data

Here’s a straightforward, five-step process for analyzing quantitative data:

1. Data collection: Gather the numerical data you need from various sources, like website analytics and surveys.
2. Data cleaning: Review your dataset for missing values and inconsistencies. Remove any outliers — data points that are far outside the normal range — so they don’t skew your results. 
3. Statistical analysis: Once the data is in good shape, apply the above methods to find patterns and relationships in your data. Use descriptive analysis to learn what happened, diagnostic methods to understand why, and predictive or prescriptive methods to look ahead. At this stage, it’s important to distinguish between correlation and causation — identifying relationships is often the first step, but additional analysis is needed to confirm true drivers.
4. Visualization: Turn your results into charts and tables that make trends easy to spot — clear visuals help stakeholders view your insights without having to dig through the data. 
5. Interpretation: Connect the results back to the original question. Simply explain what the numbers show, why they matter, and which actions to take next. 

Key benefits of quantitative data analysis

For data analysts, marketing ops leaders, and business intelligence teams, quantitative data analysis supports better reporting and more reliable insights that can guide day-to-day and long-term decisions. The key benefits include: 

• Objective decision-making: Quantitative analysis grounds decisions in data rather than assumptions, making it easier to justify recommendations. 
• Scalability across large datasets: Statistical methods work the same way at any scale, providing consistent results, whether you’re reviewing thousands or millions of numbers. 
• Statistical validation: Statistical methods confirm whether patterns and differences are real, adding credibility to your insights. 
• Predictive modeling capabilities: Looking at historical data makes it possible to estimate future trends, helping you spot risks and confidently plan ahead. 

Quantitative data analysis examples across industries

Here are a few ways teams apply quantitative analysis in their everyday work.

Marketing campaign performance

Marketing teams often start by reviewing how an audience responded to a campaign. They track open rates, clicks, conversions, and the cost of acquiring those results. From there, they dig into the data to understand why certain messages resonated. 

Regression models reveal which elements of a campaign, such as the creative or targeting, had the strongest influence on performance, while time-series analysis shows how performance changes over the course of a campaign. These insights help marketers decide how to adjust the strategy and where to invest more budget. 

Financial forecasting

Financial forecasting is central to business planning. Finance teams analyze historical revenue, spending patterns, seasonal effects, and pipeline performance to predict future results. 

Predictive techniques estimate cash flow and revenue growth under different conditions, and scenario analysis helps teams understand the impact of changes, like adjusting pricing. This gives leaders a view of risk and a more accurate basis for resource planning. 

Healthcare outcomes analysis

Healthcare teams rely on quantitative analysis to understand how well treatments work across different patient groups. Metrics like treatment adherence, demographic patterns, and readmission rates form the baseline. Statistical tests help identify meaningful differences in outcomes, while predictive models highlight high-risk patients early, which can help save lives. 

UX behavior tracking

Looking into task completion rates, click paths, and drop-off points shows product teams how people interact with the experience. Once the data is segmented, differences between groups often stand out. You might see desktop users moving through a flow with ease, but notice that mobile users stall at a specific point. 

Funnel analysis highlights where users get stuck, and regression techniques show which interactions are most closely related to completing the task. With these insights, teams ship better experiences and make design decisions grounded in real behavior rather than assumptions. 

How Fivetran facilitates quantitative data analysis

Accurate analysis starts with reliable, timely data. Fivetran automates the entire data movement process — so teams spend less time fixing pipelines and more time interpreting results. Here’s how:

• Automated data pipelines: Fivetran pulls data from SaaS apps, databases, and marketing platforms into your warehouse — no engineering tickets required.
• Clean, consistent schemas: Fivetran’s transformations organize data into an analysis-ready structure. You don’t have to fix mismatched fields before running models or building reports — the data arrives in a format you can use right away.
• Real-time data syncs: Pipelines regularly refresh, giving you access to up-to-date numbers that help you stay on top no matter how quickly your business moves.
• Reduced ETL overhead: Fivetran manages schema drift, API changes, and pipeline maintenance automatically, cutting down on time spent troubleshooting pipelines so both analysts and engineers can focus on higher-value work.

Start analyzing with trusted data — get started with Fivetran today.

FAQs 

What’s the best way to analyze quantitative data?

The best data analysis technique depends on your question. Use regression to find relationships between variables. Use time-series analysis to track changes over time. Start with what you want to know, then pick the method that answers it fastest.

What are examples of quantitative statistics in business?

Metrics like conversion rate, revenue, average order value, daily active users, churn, and cost per acquisition. These numbers track performance, highlight trends, and guide decisions.

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