Volumetric forecasting: What it is and why you need to do it

Businesses can’t predict the future, can they? Well, if they’re using volumetric forecasting, that may be fairly close to being true. 

From minimizing the risks of overstocking to ensuring products are available when consumers need them most, volumetric forecasting enables businesses to navigate the complexities of consumer behavior and market trends with greater confidence. And according to a report by Statista, the global demand forecasting market is projected to reach $5.5 billion by 2025, underscoring the growing reliance on accurate forecasting methods. 

Consider the retail sector, where a 10% improvement in forecast accuracy can lead to a 5% reduction in inventory costs and a 2-3% increase in revenue, as highlighted by a McKinsey study. These statistics reveal the impact that precise volumetric forecasting can have on a company’s bottom line. 

In this blog, I’ll dive into what volumetric forecasting is, how it works, its benefits and some tools that will help to implement it effectively in your business strategy.

Volumetrics refers to the measurement and analysis of the volume of objects or substances and plays a crucial role in many fields such as engineering, geology, biology, medicine, computer graphics and of course, consumer insights. 

In the business world, volumetric forecasting can be used to systematically predict the future demand for products by analyzing past sales volumes, trends and consumer behavior — which also helps inform product design, packaging and marketing strategies for future volume forecasts.

It’s essentially what we're all doing all the time — making decisions based on what we think will happen as a result, with a more systematic approach to your "What if?" questions.   

It helps you get closer to your consumers by allowing you to learn more about their wants and needs over time. This is achieved by getting their reactions to your new product ideas, and asking them your "What if?" questions. For example: If this new product were available, would they buy it? When and how would they use it? Does this provide a better solution for them? 

All of this helps businesses predict the future more accurately, which is a huge advantage. 

Volumetric forecasting benefits

Here’s some of the top benefits to volumetric forecasting:

1. Better inventory management: Volumetric forecasting helps businesses keep just the right amount of stock. By predicting product demand accurately, companies can avoid the costs of having too much or too little inventory, ensuring products are available when customers need them without having excess that ties up resources.

2. Smarter production planning: Knowing what the demand will be allows businesses to plan their production schedules more efficiently. This means they can allocate resources better, cut down on production costs and meet market demand quickly, especially during busy seasons or special promotions.

3. More effective marketing strategies: With a clear picture of demand trends and consumer behavior, businesses can fine-tune their marketing efforts. This leads to more targeted promotions, efficient use of marketing budgets and the ability to seize growth opportunities, driving higher sales and keeping customers happy.

Now that we’re on the same page in terms of what volumetric forecasting is and its benefits, let's dive deeper into how it works. 

1. Gather your data: Start by collecting historical sales or new consumer insights data, depending on what you’re trying to forecast. To predict sales trends, this could include how many products were sold over different periods. To predict consumer response, this could include insights into whether or not they’d use a product to predict potential. 

2. Clean the data: Make sure the data you’ve gathered is accurate, unbiased and consistent. Remove any errors or outliers that could mess up your analysis.

3. Identify trends: To identify sales trends, look at the historical data to spot patterns and trends. Check for seasonal variations, recurring peaks, dips in sales and other factors that might affect demand. To identify consumer insights trends, look at how they respond to your survey questions, especially their verbatim responses.   

4. Segment the data: Break down the data into relevant segments. This could be by product type, region, customer group, or time period (weekly, monthly, etc.).

5. Choose a forecasting model: Pick a forecasting model that suits the patterns you’ve identified. 

6. Generate forecasts: Apply the chosen model to your segmented data to predict future sales volumes or customer needs.

7. Validate the forecast: Compare your forecast with a portion of historical data that wasn't used to build the model. This helps check the accuracy of your predictions and fine-tune the model if needed.

8. Adjust for external factors: Factor in external elements that could influence demand but aren't reflected in historical data. This could be things like economic changes, market trends, promotions or shifts in consumer behavior.

9. Review and refine: Regularly review your forecasts and compare them to actual sales. Use this feedback to make adjustments and improve future forecasts.

10. Implement and monitor: Use the forecasts to guide your inventory management, production planning and marketing strategies. Keep an eye on sales and consumer data and tweak forecasts as needed to stay responsive to any changes in demand.

Following these steps will help you create reliable forecasts that can guide your business decisions and optimize operations.

Here are some typical volumetric forecasting techniques for both sales and consumer behavior analysis:

1. Moving averages: By calculating the average sales volume over a specific period, moving averages can help smooth out short-term shifts and reveal underlying trends in consumer demand.

2. Exponential smoothing: This technique gives more weight to recent data, making it more responsive to changes in consumer behavior. This can also account for seasonality and trends, which is helpful for products with cyclical demand.

3. Time series decomposition: This method breaks down historical sales data into trend, seasonal and residual components. By analyzing these components, businesses can gain insights into long-term trends, seasonal variations and irregular patterns, helping them predict future consumer demand more accurately.

4. ARIMA (AutoRegressive Integrated Moving Average): ARIMA models are sophisticated tools that use past sales data to predict future demand. ARIMA can capture complex patterns in consumer purchasing behavior, making it suitable for more dynamic markets.

5. Regression analysis: This technique explores the relationship between sales volume and various other variables, such as price, marketing efforts and economic indicators. By understanding these relationships, businesses can predict how changes in these factors will influence consumer demand.

6. Machine learning algorithms: Advanced machine learning techniques can handle large datasets and complex relationships. These algorithms can learn from past sales data and other relevant factors (like social media trends, weather patterns and economic conditions) to provide highly accurate demand forecasts.

7. Market basket analysis: This technique examines the combinations of products that consumers frequently purchase together. By understanding these associations, businesses can predict demand for related products and optimize cross-selling and upselling strategies.

8. Customer segmentation analysis: By segmenting customers based on demographics, purchase behavior and preferences, businesses can tailor their forecasting models to different customer groups. This helps in predicting demand more accurately for each segment.

9. Sentiment analysis: Analyzing consumer sentiment from social media, reviews and surveys can provide insights into future demand. Positive or negative sentiment trends can be leading indicators of changes in consumer behavior.

10. Scenario analysis: This involves creating different scenarios based on potential changes in market conditions, such as economic shifts or competitive actions. By forecasting demand under various scenarios, businesses can prepare for different outcomes and remain agile.

These techniques can provide valuable insights into consumer behavior, helping businesses make informed decisions about inventory management, marketing strategies and production planning.

Zappi is thrilled to announce our partnership with Woxi, a modern, agile predictive analytics platform for today's innovative decision-makers!

With this partnership, we’ll amplify our always-on insights and forecasting solutions, to help you better optimize your product innovations with richer consumer insights and more accurate revenue impact, while continuously learning from past consumer research. 

You can learn more about our partnership with Woxi here.