Businesses face an array of challenges these days, but UK SMEs and accountants can gain an enormous advantage over their competitors by spotting trends early. However, financial data doesn’t usually tell a singular story. Most of the time, a variety of factors work simultaneously and impact outcomes. That’s when multivariate regression analysis (MRA) makes an enormous distinction. 

When you conduct a simple regression analysis, usually only one variable is considered at a time. In practice, issues like cash flow, profit margins, and revenue growth are impacted by multiple variables. A mix of market factors, internal activities, and external economic situations influences outcomes. 

This blog digs into how MRA can help accountants and SMEs not just understand but anticipate financial trends with more accuracy and confidence.  

Why Multivariate Regression is Utilised for Analysing Financial Trends

Financial performance is layered and complex. Take revenue, for example. It’s influenced by pricing, sales volume, marketing spend etc. MRA gives you the ability to isolate each of these factors and discover the true driver behind changes. 

For accountants and SME leaders, this means:

Identifying Drivers: What elements are really affecting your bottom line? 

Measuring Impact: How much does each factor contribute? 

Controlling Variables: Getting a clearer view by taking into account overlapping impacts. 

Improving Predictions: Better forecasts are achieved when you consider more than one variable at a time. 

Building Your Multivariate Regression Model

Step 1: Pick Your Target Metric

Start with a clear dependent variable: the financial outcome you want to understand or forecast. For many UK SMEs, this might be: 

• Net profit margin 

• Operating cash flow 

• Revenue growth rate 

• Days sales outstanding (DSO) 

• Inventory turnover 

Make sure it’s a continuous, numeric value for regression to work properly. 

Step 2: Choose the Influencers

Next, gather variables that might influence your target. These could include: 

• Marketing spend 

• Sales volume and pricing changes 

• Staff costs 

• Payment terms with suppliers  

• Economic indicators like inflation or interest rates 

Use your own accounting data and information from reliable external sources such as ONS and the Bank of England. 

Step 3: Prepare Your Data Carefully

Good data prep is key to solid results: 

Watch for multicollinearity: 
If two predictors are too closely related, it can confuse your model. For example, sales and marketing spend. Either choose one or combine them to ensure clarity. 

Standardise Data
Financial data can be uneven or have large outliers. Standardisation is essential to ensure optimal results. 

Match the timing
Make sure your data lines up correctly. For example, if you’re looking at sales this quarter, use the marketing spend data from the previous quarter, not the same one. 

Step 4: Run and Check Your Model

Use the appropriate tools to simplify the process with automation, AI and real-time data. Then: 

• Interpret coefficients carefully: Positive or negative impacts? Statistically significant or just noise? 

• Look at adjusted R-squared: How well do your predictors explain the variance in your target? 

• Test predictive power: Try cross-validation or holdout samples to avoid overfitting. 

Going Deeper: Advanced Tips for Financial Analysis

Include Interaction Terms

Sometimes variables don’t add up; they interact. For example, marketing’s effect on revenue might depend on economic growth. Adding interaction terms helps capture such subtleties. 

Account for Time Lags

Financial effects often have delays. Maybe marketing spend today boosts sales next quarter. Including lagged variables helps you model these delayed impacts, especially in time-series data. 

Consider Non-Linear Relationships

Financial data isn’t always linear. Sometimes effects accelerate or taper off. Polynomial terms or other flexible models can capture these patterns better than straight lines, but ensure you avoid complicating interpretation. 

How MRA Can be Leveraged By SMEs and Accountants

Better Cash Flow Forecasts

MRA gives SMEs a clearer view of sales trends, payment patterns, and other factors, enabling them to plan for the future and manage liquidity effectively. 

More Insightful Profitability Analysis

Want to know what’s actually cutting into your profits? MRA lets you see how costs, prices, competition, and changes in operations affect each product, sector, or client. This helps you make better decisions about prices and costs.  

Sharpen Credit Risk Evaluations

For accountants advising on lending or credit, combining financial ratios with payment histories and economic data through MRA provides a richer risk profile than traditional credit scores. 

Stress-Test Scenarios

Want to know what happens if interest rates rise while marketing spend increases? MRA encourages situational scenario planning, helping you prepare for different outcomes backed by factual data. 

Making Multivariate Regression Work in Your Practice

Many modern accounting and BI platforms integrate tools for regression analysis—no advanced coding required. For instance: 

• Cloud ERP systems provide clean, real-time data streams for modelling. 

• Open-source packages like scikit-learn and statsmodels enable custom analyses. 

Platforms like Pulse can also prove useful to accountants. With robust cash-flow forecasting and scenario planning, accountants can focus on embracing advisory roles rather than traditional number crunching.

To learn more about Pulse, book a demo today.

Conclusion 

Multivariate regression isn’t simply an academic exercise for SMEs and accountants in the UK; it’s a useful tool for making sense of complicated finances and discovering useful data. Examining multiple factors simultaneously makes identifying financial trends more accurate. If you’re ready to take your financial analysis to the next level, both single and multivariate regression are worth exploring.