Driving Successful Revenue Planning for Law Firms with ML Regression Models

A closer look at what Data Models will work for your Law Firm.

Last week, we introduced six data modeling techniques that law firms can use to improve revenue forecasting (see Financial Foresight: Data-Driven Revenue Models for the Modern Law Firm). In the coming weeks, we will take a closer look at each model, examining their pros and cons and offering guidance on how to choose the right one for your planning process. Be sure to check out the EPM resources on our web page (CE Web Site | EPM for Law Firms).

We should begin by clearly defining the objectives of our revenue forecast. Once the goals and selection criteria are established, we can determine the most suitable data model.

Selection Criteria

A well-defined set of selection criteria is essential for a successful revenue forecasting process. Identifying your revenue goals establishes the ‘Why’—the purpose behind the forecast. Once we understand these goals, we can choose data models that align with them—the ‘What.’ The next steps involve building, testing, and validating the models—the ‘How.’ Finally, we assess the results and make adjustments based on performance—the ‘When.’ It is important to recognize that, like your business, your forecasting model is a living system that must evolve with changing conditions.

1. Identify Revenue Goals (the ‘Why’) | Grow revenue by a certain dollar amount. | Increase market share by a certain percent. | Expand into new markets or law practices. | Increase client base for a specific client type or size.

2. Develop and Understand Business/Statistical Method (the ‘What’) | Select a data model that best aligns with the revenue goal. | There may be more than one data model to satisfy different revenue goals

3. Identify Data Sets for Data Models (the ‘What’) | Financial Data from your General Ledger | Operational Data from Sub-Ledgers such as Accounts Receivable | Human Resources Data | Industry Data available to Law Firms describing current market conditions or trends in the industry.

4. Assess and Verify Data Model (the ‘How’) | Unit Testing to validate the build objects. | Integration Testing to validate data flows from source systems to the data model. | User Acceptance Testing to verify business requirements and accuracy of the model.

5. Review and Apply Adjustments (the ‘When’) | Through careful analysis of the model results apply adjustments to your forecast and the model leading to better decision making and strategic adjustment.

Type 1: Regression Models

A regression model is a statistical tool used to quantify the relationship between independent variables—known as predictors—and a single dependent variable, or outcome. In revenue planning for law firms, predictors might include billable hours, client count, and practice areas. Revenue serves as the outcome variable.

Here are some examples of different regression models.

Linear Regression – Assumes a straight-line relationship between a single predictor and the outcome. An example is predicting revenue based on total annual billable hours for the firm.
Multiple Regression – Assumes a relationship between multiple predictors and the outcome. An example is predicting revenue based on billable hours, billing rate, and the number of attorneys the firm employs.
Logistic Regression – The relationship of the variable is categorial such as a yes/no or success/failure outcome. For instance, the outcome of a client’s case.
Polynomial Regression – A more advanced regression model that analyzes non-linear relationships by including powers of the independent variable.

The outcomes of these models are typically visualized on a graph, with the outcome variable on the Y-axis and the predictor on the X-axis. Each data point is represented as a dot. A regression line is then drawn to represent the best-fit prediction of the outcome based on the predictor. This line is calculated to minimize the error—the distance between the actual data points and the predicted values on the line.

Final Thoughts

In the coming weeks, we will explore the remaining five data models and provide examples of how each aligns with specific revenue goals. There is no single ‘right’ model—selecting the best fit requires careful analysis and a clear understanding of the results. This ensures that the business decisions you make are well-informed and aligned with your objectives.

Curious how these methods could work for you?

If you are wondering how to take the first step—or the next one—toward intelligent planning, let us connect. 💬 I would love to hear about your challenges and share what is working for firms like yours. 📩 DM me and watch for future posts on this topic. You can also check out the EPM resources on our web page (CE Web Site | EPM for Law Firms).