As a small business leader, nobody is more responsible for the direction and success of your business than you. Your team can be helpful, but ultimately, decisions about direction and strategy likely fall on your shoulders. Having data and complete information is important to successful decision making, and a good regression model can help you forecast future trends to create strategies that work.
These forecasting tools help you examine the relationship between variables that impact your business, such as economic trends, sales trends, employee satisfaction, customer satisfaction and more.
Regression Model: Definition
If you've ever wondered how businesses predict busy times, slow times, profits, losses and other variables, then learning about the regression model definition of forecasting will probably answer some of your questions. A regression model shows the relationship between two different types of variables:
- Independent variables: These variables are not dependent on any other variable.
- Dependent variables: These variables depend on independent variables.
For instance, a regression model might show a correlation between the independent variable of gross domestic product, or GDP, and the dependent variable of sales rates. When you understand the correlation between different variables, you can better forecast future trends, optimize operations and plan for the ups and downs of your business cycle.
Regression Model: Uses
Regression models are used to answer questions you have about your business so you can address problems proactively instead of reactively. Some of the questions you might use a regression model to answer include:
- How do sales correlate with fluctuations in GDP?
- How does machine manufacturing impact shelf life?
- How does customer satisfaction differ when garments are hand sewn?
- How does customer service wait time impact customer retention?
- How can we budget for a projected recession?
- How can we reduce costs while increasing morale?
Regression models can be used to answer any questions you have about your business that included independent and fixed variables. They can help you understand the relationship between these variables so you can forecast future trends and optimize operations accordingly. For example, if your regression model correlates sales with fluctuations in GDP, you can use it to both predict future sales and optimize production in order to minimize waste.
Regression Model: Purpose
The purpose of a regression model is to help you figure out which variables impact your business the most and how closely they correlate. Your gut might tell you that all kinds of factors are likely to impact sales over the next month. Some of these variables could include:
- Competitor promotions
- Planned sales
- Product changes
- Cultural trends
Regression models help you move beyond this overwhelming plethora of variables in order to discover what really impacts your business the most. You might think GDP impacts sales the most, while your assistant manager is convinced that weather is a driving factor. A regression model can help you see which of those two variables actually correlates closest with fluctuations in sales.
Regression Modeling Strategies for Accuracy
Regression modeling strategies are dependent on gathering accurate data representing the independent and dependent variables you are trying to examine. Your regression model is only as accurate as the data you compile, so great care is needed to ensure you include every bit of information you can find. For instance, if you want to know how rainy days impact sales, you might gather rainfall totals and sales totals for every single day over the past four years. This can be relatively simple if you only have one store location, but if you have three store locations, data will need to be collected and examined for each location individually.
The more complex your regression model, the more likely you are to have some errors in data collection, which can drastically change what your forecast looks like. Some regression modeling strategies to help ensure greater data accuracy include:
- Collect the same data from more than one source.
- Ask more than one person to collect and compile data.
- Compare data and regression models compiled by different parties.
- Double or triple check your numbers before plotting them on a regression model.
- Use computer programs designed to alert you to possible data irregularities.
- Focus on maintaining databases but use statistical analysis software to do the rest.
When data errors and irregularities are discovered, you can choose to omit the data, change the data or accept the data if the irregularity is statistically insignificant. Before you include irregular data, be sure that it actually is statistically insignificant. It is often better to skip one day's data than to include something you know is not accurate.
Regression Model: Example
One regression model example could plot how closely temperatures above 90 degrees and snow cone sales are correlated in the month of July. To create this regression model, you would need the following data:
- Temperatures for every day in July over the past few years
- Snow cone sales for every day in July over the past few years
In order to create a regression model example from this data, you would begin with a dot graph called a scatter plot, where the Y axis represents the amount of snow cone sales (your dependent variable), and the X axis represents the temperature (your independent variable).
Each dot you place on the graph represents sales numbers and temperature. If the two closely correlate, a line running through the middle of all the data points should show an upward trend and will help you predict how much you will sell as temperatures rise. This line is called a regression line and can inform how you plan for supplies, staffing, special promotions and more.
Regression Model: Types
There are over 30 regression model types, but not all of them are frequently used in the business setting. Each type of regression model has its own equations, and the linear regression model, such as in the snow cone example, is very popular in business. Different forms of this model account for varying numbers of independent and dependent variables. It is most appropriate when the line produced through the points on a dot graph is straight in nature.
Some other types of regression models include:
- Logistic regression
- Polynomial regression
- Ridge regression
- Lasso regression
- ElasticNet regression
Keeping track of the complex equations necessary for every type of regression model can seem complicated for the average small business professional who has not had extensive training in statistics. Thankfully, statistical analysis software is designed to do the math for you if you can maintain accurate databases, which help it create a variety of regression models relevant to your business without you needing to memorize a list of equations. Some statistical analysis software options that include regression analysis capabilities include:
- NLREG: Offers linear and nonlinear regression model capabilities
- Analyse-it: Works within Excel to provide multiple regression and other statistical analyses
- GeneXproTools: Assists with logistic regression and function finding
Benefits of Regression Analysis
Most small businesses produce large amounts of unorganized data on a regular basis that could be useful if it was interpreted properly. Think about how regression might empower you to use data about sales, your workforce, new product launches, taxes and more to grow your business. Wise use of regression model applications can yield some of the following five benefits:
- Error identification: What if you think that GDP correlates most closely with sales when it is actually weather or new product launches?
- Increased operational efficiency: When you identify accurate variable correlations, you can save money by planning accordingly.
- Better future forecasting: Good regression analysis gives you a heads up about future opportunities and risks.
- Data-informed decision making: If you've ever wondered whether your gut feeling is accurate, good data analysis can help back it up or refute it.
- Fresh perspectives: Including a regression model in analysis of old data can help you see problems and solutions in new ways.
When you are working with regression models, try using a variety of independent and dependent variables to answer questions you have about your business. If your first attempts do not show correlation, trying other unexpected variables might surprise you by showing strong correlation. This new information can help you think about your business differently and help you grow more quickly than you ever imagined.
Limitations of Regression Analysis
While looking at independent and dependent variables in new ways can help you build your business more quickly and easily, this is only the case when data is accurate. Regression analysis is not without its pitfalls, risks and limitations. Watch out for the following roadblocks as you ask and answer questions using regression forecasting:
- Assumptions: Your assumptions as a business owner will limit the data you see as significant enough to include in a regression model.
- Wrong questions: When your regression model answers a question that doesn't really impact your business, it won't result in needed change.
- Data independence: If independent and dependent variable data overlap in any way, the integrity of your regression model is compromised.
- Poor data: If you gather data that is too generalized, too specific or missing pertinent information, your regression model will be unreliable.
- Software limitations: Statistical analysis software is helpful but can be glitchy or not offer enough variable options to suit the specifics of your situation.
- Human error: If your accountant has a tendency to mistype data or forget to link the database to statistical analysis software, this can create inaccurate regression models.
Because use of a regression model in business planning can be helpful but is not perfect, it is important to combine regression analysis with other methods of forecasting. Consider including some of the following methods in your forecasting efforts:
- Market research: Surveys and focus groups give you insights about customer wants and needs.
- Expert consultation: Consulting with a variety of experts in your field can help you compile reports outlining forecasts.
- Indicator approaches: Operational risk measurements help you avoid risk and find opportunity.
- Trend analysis: Creates future forecasting based on recent trends.
Correlation vs. Causation
One major consideration in working with regression forecasting in business is that your basic regression model shows correlation, not causation. For instance, a regression model showing increased snow cone sales in hot weather shows that hot weather and sales are closely correlated. This model does not show why they are closely correlated. In order to find out the cause for the correlation, further investigation is required.
If you want your marketing efforts to speak to the cause of increased sales, you will need to conduct polls, conversations and focus groups to determine the cause for the correlation in business trends. In the case of snow cone sales, you might discover several common causes for greater snow cone sales on hot days, such as increased thirst, overheating or increased time outdoors with hungry children. Finding out why two variables are closely correlated will help you better serve customer needs and advertise your business in appealing ways.
- Investopedia: Regression Basics for Business Analysis
- Forbes: Three Steps To Get Started On Predictive Analytics Today
- Harvard Business Review: A Refresher on Regression Analysis
- Analytics Vidhya: 7 Regression Techniques You Should Know
- NewGenApps: 5 Applications of Regression Analysis in Business
- PracticalEcommerce: Using Regression Analysis to Drive Ecommerce Sales
- Capterra: Statistical Analysis Software
Anne Kinsey is an entrepreneur and business pioneer, who has ranked in the top 1% of the direct sales industry, growing a large team and earning the title of Senior Team Manager during her time with Jamberry. She is the nonprofit founder and executive director of Love Powered Life, as well as a Certified Trauma Recovery Coach, certified HRV biofeedback practitioner and freelance writer who has written for publications like Working Mother, the San Francisco Chronicle, the Houston Chronicle and Our Everyday Life. Anne works from her home office in rural North Carolina, where she resides with her husband and three children.