Forecasting Models

What are Forecasting Models?

Forecasting models are statistical tools designed to make predictions about future events or outcomes based on historical data and trends. They assess historical patterns and trends to project future scenarios, which individuals and businesses use to plan for otherwise unpredictable events like stock prices, sales, demand, or inventory levels.

Businesses can choose between dozens of forecasting models, each of which is either qualitative or quantitative.

• Qualitative models rely on subjective inputs to make predictions, such as expert opinions, surveys, or market research.
• Quantitative models use statistical techniques to analyze numerical data and predict future trends and results.

The best forecasting model to use depends on the specific business scenario. For instance, a qualitative model may be more suitable for predicting fashion trends or consumer preferences, while a quantitative model would work better when forecasting sales or inventory levels.

A forecasting model is different from a forecasting method. Forecasting methods use mathematical calculations to create predictions with or without a forecasting model. The model, on the other hand, is a framework that guides the use of forecasting methods to make useful predictions.

Synonyms

• Forecasting methods
• Sales forecasting models
• Revenue forecasting models
• Financial forecasting models

The Purpose of Forecasting Models

Forecasting models are essential for businesses to navigate the complexities of today’s competitive, often inexplicable markets. Their significance is paramount in a time of rapid technological advancements, evolving consumer preferences, and a dynamic global economy.

Briefly, predictive modeling offers businesses the following benefits:

1. Predictive insights. At its core, forecasting offers a glimpse into the future. Especially given the improvements in data collection, storage, and availability, forecasting models are now a reliable source of insights into the future of markets, demand, and consumer behavior.
2. Informed decision-making. Whether it’s determining inventory levels, setting budgets, or launching a new product, accurate forecasts provide a robust foundation for high-level business decisions.
3. Risk management. Organizations can eliminate some of the uncertainty that comes with doing business by using forecasting models to run simulations, test risks and vulnerabilities, and develop contingency plans or safety nets.
4. Resource allocation: Capital, manpower, and time are all limited and, as such, are bound to the same constraints of unpredictability. Business leaders use forecasts to understand future supply and demand fluctuations and plan their finances, workforce, and inventory ahead of time.
5. Setting realistic goals. Before you can set reasonable revenue and sales targets, you have to know what your company has done in the past and the trajectory it’s headed. With the aid of forecasting, you can set benchmarks rooted in data-driven realities rather than mere aspirations.
6. Understanding customer behavior. Some customers will have a certain degree of similarity to one another, which you can use to market and sell to them more effectively. The ability to predict certain behaviors is a critical component of customer segmentation, lead scoring, sales/marketing strategies, product/feature development, and ongoing customer relationship management.

6 Common Types of Forecasting Models

As mentioned above, there are two basic types of forecasting models — qualitative and quantitative. Within those two categories, there are dozens of specific models that businesses can use depending on their needs.

Here are six predictive forecasting models businesses use most often:

Econometric

The econometric forecasting model is a blend of economics, mathematics, and statistical methods used to test hypotheses or theories concerning economic phenomena (e.g., a country’s GDP or inflation rate). Econometrics forecast economic trends by analyzing the relationship between key indicators and their mutual impacts.

Thanks to their ability to predict economic indicators like GDP, inflation rates, and stock market trends while considering external factors, they’re widely used in economics, finance, and high-level business planning.

In business, econometric modeling is most often used for price optimization and revenue forecasting. Organizations use it to understand how well a certain product might perform at various prices and analyze the specific factors that drive ROI.

Let’s say you’re considering using penetration pricing to launch a new product and drive faster adoption. An econometric forecasting model would help you…

• examine how different pricing and distribution channels will impact your revenue performance.
• calculate optimal resource allocation across all your sales and marketing activities.
• forecast future demand at different price points.
• zero in on your customer segments and their price sensitivity.
• evaluate competitive factors that might change customer behavior.

Complex econometric modeling is what you’ll use to answer the high-level questions about what drives your company’s bottom line — sales volume, perceived value, market share, and gross margin.

Time-Series Modeling

Time-series modeling is one of the most popular quantitative methods. It examines past company behavior over a specific time period to predict future sales outcomes and consumer behavior.

Common examples of time-series forecasting models include:

• Straight-line method — uses a statistical formula to plot past trends against current and future periods. For example, an asset generally depreciates at a constant rate of X% over time.
• Moving average model — smoothens out short-term fluctuations by averaging sales data over several successive time periods (months, quarters, or years).
• Exponential smoothing method — a statistical technique (similar to the moving average model) that assigns more weight to recent data points in the series. This model’s strength is that it takes into account the most recent changes, making it ideal for short-term forecasting.
• Trend projection — examines long-term trends to identify patterns in data more reliably and account for seasonality or cyclical behavior.

The first step in any time-series model is a time-series analysis. You use it to find patterns in the data, define variables’ values, consider seasonality, and adjust for external factors that might influence future sales. Time-series modeling predicts changes in the time series based on previous values.

The “key assumption” of this kind of predictive modeling is that the future will look like the past. You can use it to pinpoint business problems you need to solve. Let’s say you want to know whether your website traffic will increase/decrease in the coming month. If your model predicts a sharp decrease (based on consistent past behavior), you’d want to increase your ad spend to compensate.

Delphi Method

The Delphi method is a qualitative predictive forecasting model. It relies on expert opinions and best practices to predict future events, performance, or trends.

It’s an iterative process that involves asking the same set of questions over multiple rounds (3+). Its primary focus is to seek consensus and agree on a specific result or foreseen event.

Delphi has several advantages compared to other qualitative methods:

• Expert opinions. The model pools the wisdom of many experts, making it reliable.
• Anonymity. It enables participants to share their forecasts without fear of judgment.
• Controlled feedback loops. Each round offers another opportunity to review prior predictions and revise them based on group feedback.

This type of forecasting model is best suited for long-range forecasting without historical data, like estimating the market size for a new product or entry into a new market. Companies in innovative spaces without much research — e.g., health sciences, AI/ML, or renewable energy — benefit most from Delphi modeling.

Associative Models (Causal Models)

Associative models use regression and correlation analysis to identify relationships between independent and dependent variables. In business terminology, the variable you’re forecasting (e.g., sales figures) is interconnected with several other variables in the business system.

So, the forecasting process considers external changes and how they’ll affect the ultimate result. Sales figures might be connected to marketing spend, economic growth rates, efficient business operations, or even weather conditions.

Here are a few associative models commonly used in business forecasting:

• Linear regression — uses a linear equation to explain the relationship between two or more variables. It’s most popular for explanatory purposes, such as estimating demand elasticity.
• Multiple regressions — extends the concept of linear regression by considering several independent variables, not just one. The goal is to isolate the effect of each variable and understand how it contributes to the dependent variable.
• Logistic regression — a type of regression used when the dependent variable is binary (e.g., yes/no). Logistic models are often used in marketing strategies to predict consumer behavior.

Associative models assist businesses in evaluating how external factors impact the desired prediction and uncovering patterns in large datasets. For instance, your model would analyze past ad spend and sales data to predict sales from advertising expenditure to establish the relationship between the two. Let’s say a $1 increase in ad spend yields a $5 increase in sales — that’s the correlation you’d use to plan future campaigns.

Market Research

Market research is one of the best qualitative forecasting methods because it’s directly tied to customer behavior. You can use it to estimate the impact of new product launches, consumer preference changes, or market trend shifts.

We don’t often think of market research as a ‘forecasting model’ because it doesn’t use complex mathematical formulas. But, it still involves predicting future outcomes based on current data and insights.

There are several ways market research can inform your forecasting:

• Customer surveys
• Focus groups
• Competitor analyses
• Social media monitoring
• Sentiment analysis
• Product testing

Market research helps identify potential roadblocks and opportunities for your business (e.g., “If 75% of users have issues with Feature X, our retention rate will drop if we don’t fix it.”). It can also provide valuable context for other forecasting models, such as associative models or Delphi.

Sales Force Composite

The sales force composite is a special type of forecasting model where an organization uses insights from its sales staff to develop a forecast. The idea behind this method is the sales team is in tune with current market dynamics because they’re the ones talking to customers on the daily. Since it’s based on feedback rather than numbers, it’s qualitative.

In a sales force composite, the process typically looks like this:

1. Each sales team member submits their forecast for the upcoming period.
2. The forecasts are reviewed and adjusted by management based on external factors.
3. A final forecast is developed based on a combination of individual forecasts and expert judgment.

This method can provide valuable insights into customer demand, market trends, and sales strategies. Nevertheless, it’s important to note its accuracy heavily relies on the competence and honesty of the sales team.

Challenges of Forecasting Models

Of course, there’s no way to predict the future with 100% accuracy. Forecasting models are based on historical performance and assumptions. With enough data and accurate assumptions, they can be quite accurate. But overreliance on something that isn’t guaranteed poses significant challenges for businesses.

Data Accuracy

Garbage in, garbage out. Forecasting models rely on accurate data to make accurate predictions. If the data is flawed or incomplete, the forecast will be as well.

Consider an ecommerce store that uses associative forecasting to predict sales based on advertising spend, seasonal factors, and average customer spending. Now, let’s say there’s a small error in the data input for advertising spend — perhaps due to a manual entry error or a system glitch. Ad spend for one month was recorded as $100,000 instead of the actual $10,000.

One zero might seem harmless. But, in this instance, it’s created a $90,000 discrepancy. Even if your ROAS was 1,000%, your prediction would show you’re barely breaking even. A discrepancy this large would be easy to catch (you’d go into the analysis with an idea of whether ads are profitable or not), but the numbers are often smaller and harder to catch.

Assumption Bias

Forecasting models also rely on assumptions about future events. These assumptions can be influenced by personal biases or external factors, ultimately impacting the accuracy of the forecast.

For example, a company might assume that demand for their product will continue to increase at a steady rate. But, if there’s an economic downturn or a competitor introduces a similar product, this assumption could be proven wrong, leading to inaccurate forecasts.

Forecast Reliability

Several factors impact whether your data is actually reliable or not:

• Context. Without data enrichment, there’s no guarantee you’re considering all the variables or even comparing the right ones.
• Trends and anomalies. While models can account for past trends and patterns, they may not always be able to predict future anomalies (e.g., COVID-19’s impact on consumer behavior).
• Human error. As mentioned, incorrect data input or assumptions can lead to unreliable forecasts.
• Data volume and quality. Larger datasets can provide more accurate forecasts, but only if they’re complete and consistent.

To verify data readiness, look at completeness, timeliness, and validity. In other words, is the data complete, current, and accurate? If not, your forecasts may be unreliable.

Unforeseen Market Changes

Forecast errors are entirely possible when market dynamics shift at just the right time. At the current rate of technological innovation and globalization, there’s no way to predict the future trends in most industries more than a few years out.

Economic, political, and social changes can all impact consumer behavior and market trends in ways that are impossible to predict. Even the most sophisticated forecasting models may not be able to account for these unforeseen shifts.

Importance of Sales Forecasting Models

In the startup world, it’s nearly impossible to make a sales forecast. There just isn’t enough data (or product validation, for that matter). But, for companies with some established infrastructure, sales forecasting is the bedrock of intelligent business planning.

It helps organizations:

• predict sales trends and improve inventory management
• identify roadblocks and sales growth opportunities
• refine the company’s sales motion
• set sales quotas and hold reps accountable
• plan headcount increases and budget for expansion
• refine their pricing strategy
• secure funding and make strategic decisions

Particularly at more mature companies with well-defined sales infrastructure, the sales process becomes a lot more cyclical. With sales forecasting models, companies can take a methodical and outcome-driven approach to sales planning, goal setting, and stakeholder communication.

Importance of Revenue Forecasting Models

Revenue forecasts are valuable for a lot of the same reasons as sales forecasts. The main difference is they focus on all a company’s revenue drivers (which includes sales).

With forecasting models, revenue teams can make educated guesses about:

• future cash flow and budget decisions
• expansion opportunities and investments
• market trends and potential shifts

They can also zero in on specific sources of revenue, like one marketing channel or product line. They could also break down revenue by customer segment and make predictions for enterprise customer growth vs. SMB market growth. That’s just the tip of the iceberg, though; there are countless ways to break down and use revenue forecasts, assuming access to the right data.

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