Table of Contents:

• The Importance of Historical Data in Demand Forecasting

• How Historical Data Strengthens RFP Responses

• Turning Historical Data into a Competitive Advantage

• Partner with Circana to Turn Data into Smarter Decisions

What happened in the past has always been a basis of predictions for the future. In the consumer packaged goods (CPG) and retailer world, if things don’t turn out as forecast, the result is waste, stockouts, and thinner margins, along with dissatisfied customers. If a projection is on target, however, businesses can optimize inventory, boost revenue, and keep customers happy.

Accurate predictions hinge on a data-driven approach that includes historical insights. Historical data has been used for decades and remains a solid foundation on which to layer other tools and technologies. Analyzing previous performances allows you to uncover patterns, refine predictions, and strengthen business proposals.

The Importance of Historical Data in Demand Forecasting

Historical data is more than just a record of products sold — it reveals how a business has performed and helps benchmark goals. Keeping overall objectives in mind helps you fine-tune forecasts that drive results.  Are you driving traffic? Are you raising margins? Are you looking for new customers?

Such insights also help assess the competition. For example, historical data can determine the effects of price elasticity and understand what competitors did at the same time. 

Ultimately, historical information, spanning past sales figures, promotional impacts, and market trends, is the basis of reliable demand forecasts that sync with goals. This kind of data includes a wealth of useful information. Internal sales numbers reveal product performance, based on what items sold, when, and where. Promotional data indicates how tactics like discounts and marketing campaigns influenced consumer behavior. These and other insights affirm existing strategies or highlight the need for new ones.

Finding seasonal and repeat patterns

Brands and retailers rely on historical data to identify seasonal and other recurring trends. It’s understood that ice cream sales will rise in the summer, chocolate sales will peak around Valentine's Day, and turkey gravy spikes before Thanksgiving. Comprehensive historical data, combined with other analytics, allows a retailer or manufacturer to move beyond general assumptions and quantify upticks with greater precision.

Whether tied to a particular holiday or time of year, patterns become evident within a year or two, depending on the industry and product lifecycle. Reviewing data before that time, especially gauging trends during unusual years like the COVID-defined 2020 and 2021, can introduce outdated consumer behaviors and market conditions that potentially skew a forecast. The goal is to distinguish between a genuine seasonal trend and a short-term surge attributed to an event or even a viral social media trend, and respond to those movements accordingly.

Measuring and increasing forecast accuracy

Measuring the accuracy of predictions is critical for continuous improvement. Instead of focusing on a single, rigid forecast in today’s dynamic supply chain, companies are better served by creating a range with upper and lower thresholds and “most likely” scenarios. 

This kind of scenario planning provides the flexibility to plan for different outcomes and understand the levers to pull if demand shifts. For example, if you know the potential range of demand for a promotion, you can align your capacity planning more effectively. This prevents the costly consequences of either overproducing and facing spoilage or underproducing and losing sales due to stockouts. In a broader sense, effective scenario planning supports a responsive and agile supply chain.

Optimizing inventory and resource allocation

Optimizing historical insights for improved forecast accuracy directly translates to better inventory management with more interconnectivity across the supply chain. Having a clearer picture of future demand enables you to align inventory levels without tying up unnecessary capital in excess stock. 

This is especially crucial for new item introductions. Launching a new product, of course, comes with some inherent risks, but historical data from similar product launches can provide a realistic benchmark for success. Analyzing the performance of similar items leads to a more grounded estimate and reduces the risk of over-ordering. A data-driven approach to launches ensures that resource allocation, from manufacturing to logistics, is based on evidence.

Layering AI and ML with historical data for smarter predictions

Emerging technologies can be used in tandem with historical insights to create even more accurate predictions. Artificial intelligence (AI) and machine learning (ML), for example, analyze vast and complex datasets faster, incorporating a wider range of inputs into predictions.

Indeed, there are many variables that can influence demands that a human analyst can’t track all at once, including weather patterns, social media trends, competitor advertising, and even the price of gas. AI can ingest and process this information in real time and in a tailored way, identifying subtle correlations and adjusting forecasts automatically.  Brands and retailers can add these capabilities to their proverbial toolbox that includes historical insights. 

How Historical Data Strengthens RFP Responses

Beyond internal planning, historical data is a powerful asset in the procurement process. When working on an RFP, this kind of information helps build a proposal that is both credible and compelling. It moves an RFP from a simple price quote to a strategic plan rooted in proven performance.

Using data to justify pricing and costs

Historical data also helps explain how and why pricing is set in an RFP.  The data demonstrates supply chain efficiency, fill rates, and the ability to manage fluctuations in demand. Sharing these insights also builds trust and underscores that costs are tied to delivering reliable value. 

Demonstrating a data-driven approach to clients

Prospective clients want partners who make informed decisions. Sharing how historical data is used for forecasting and resource planning helps prove that your organization is committed to leveraging data and analytics for both operational efficiencies and retailer and customer satisfaction. It also signals that you are well equipped to navigate supply chain challenges and are hence a more reliable, lower-risk partner.

Turning Historical Data into a Competitive Advantage

Ultimately, analyzing and acting on historical data remains an important, if basic, competitive play. It allows you to connect your forecast directly to your capacity planning, ensuring you can meet demand without incurring excessive costs from overtime, temporary labor, or expedited shipping.

Companies that master this integration and build on it with new capabilities such as AI can maintain high fill rates while protecting their margins. They can commit to promotions, knowing they have the inventory and resources to support them. In a landscape where supply chain disruptions are common, a data-driven forecasting process maintains competitiveness and can unlock growth.

Partner with Circana to Turn Data into Smarter Decisions

Getting the full value of historical data and pairing it with other tools, requires expert guidance and advanced analytics tools. Many companies struggle to translate raw numbers into actionable strategies that drive real business results.

Circana bridges the gap between insight and action. We help organizations integrate their data, enhance their forecasting capabilities with tools like our Liquid Supply Chain™ suite, and solve complex challenges like ensuring on-shelf availability. Our expertise in data analytics and supply chain management transforms historical information into a measurable advantage, empowering smarter decisions in forecasting, procurement, and RFP success.