Sales Forecast: From Accuracy to Profit.

CPG and Retail industries are hyper competitive. Sales forecast becomes a key tool for making strategic decisions.

CPG and Retail industries are hyper competitive. In this context, sales forecast becomes a key tool for making strategic decisions.

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The Consumer Packaged Goods (CPG) and Retail industries are highly competitive and dynamic. The speed at which consumer trends change, the constant evolution of technology, and the globalization of the market are just some of the complexities faced by those responsible for pricing and promotions in these industries. In this context, the sales forecast becomes a vital tool for making informed and strategic decisions.

What is a Sales Forecast?

The sales forecast is a vital tool for making informed and strategic decisions. It is the process of predicting the future sales of products or services, typically for a specific time period, which involves analyzing past sales data, market trends, and other relevant factors for an accurate estimation. Ultimately, the goal of sales forecasting is not only to predict sales, but also to provide a clear understanding of them, so that companies can plan and allocate resources accordingly.

Sales forecasting vs Demand forecasting?

Before we continue, it is important that we clarify the differences between sales forecasting and demand forecasting. Although similar, these processes have different scopes and objectives. Here are some of the main differences.


A sales forecast predicts the sales that a company expects to make based on its sales targets and goals, while a demand forecast predicts the quantity of products or services that customers are likely to purchase.


A sales forecast typically focuses on a shorter timeframe, usually a few months or a year, while a demand forecast usually considers a longer timeframe, such as 3-5 years.


Sales forecasts are used by companies to plan and allocate resources, set sales targets, and measure performance. Demand forecasts, on the other hand, are used to inform production, inventory management, and supply chain decisions.

As you may imagine, this is not a fixed rule. It is not uncommon to see sales forecasting and demand forecasting used interchangeably, or with one incorporating some characteristics of the other.

What is the importance of sales forecasting?

An accurate sales forecasting allows managers to adjust prices and promotions to maximize profits and minimize costs, which in turn improves the company's profitability. Additionally, an accurate sales forecast helps identify consumption trends and patterns that can be used to develop new marketing strategies and products, thereby enhancing the company's competitiveness in the market.

However, according to Gartner’s State of Sales Operations Survey (2020), “only 45% of sales leaders and sellers have high confidence in their organization’s forecasting accuracy.”

In a similar way, RevOps’s The 2021 State of Sales Forecasting (2021) finds that "only 15 percent of revenue leaders are very satisfied with their forecast process, with 91 percent of participants reporting their predicted forecast is six percent or more off from actual results."

This skepticism may result in actions based on intuition instead of evidence, which often results in reduced commercial outcomes.

Inaccuracies in sales forecast have multiple unintended consequences, such as effects on:

— Short-term spending decisions

— Decisions regarding key deals

— External guidance provided by investor relations

Therefore, selecting the right method and improving accuracy become key tasks in order to leverage all the benefits of a sales forecast.

What are the methods and tools for accurate sales forecasting?

There are various methods and tools for accurate sales forecasting in the CPG and Retail industries. Here are some of the most commonly used:

  • Historical trend analysis

    Examining past data is likely the most conventional and direct method for sales forecast. The information is easily accessible, and it is logical that fluctuations due to variables such as seasonal changes and new product launches would offer indicative guidance.

  • Funnel-based forecasting

    The sales funnel is an accurate predictor of sales outcomes for some companies. As long as sellers provide accurate and frequently updated information, it can be a reliable means for forecasting.

  • Statistical models

    Statistical models use mathematical and statistical techniques to analyze historical sales data and predict future demand. The most commonly used models include linear regression, time series models, and exponential smoothing models.

  • Collaborative forecasting

    This method is based on collaboration between different departments of the company and between the company and its suppliers and customers. It involves collecting information from different sources to make a more accurate and reliable sales forecast.

  • Artificial intelligence tools

    Artificial intelligence tools, such as machine learning and predictive analytics, use algorithms and data to predict future demand with high accuracy. These tools can be especially useful for analyzing large amounts of data and for predicting the demand for new products.

It is important to note that there is no perfect method or tool for accurate sales forecasting. Therefore, it is recommended to use a combination of methods and tools to obtain a more accurate and reliable sales forecast.


Kuona’s Price & Promotion Optimization simplifies sales forecasting thanks to machine learning technology that allows "what-if" scenarios and a high level of accuracy. Contact us to learn more about it.

Tips for improving sales forecasting

To improve sales forecasting in the CPG and Retailers industry, it is important to consider the following tips:

  • Use data from multiple sources

    Collecting information from multiple sources, such as historical sales data, consumer behavior data, economic data, and competition data, can improve the accuracy of sales forecasting.

  • Analyze the impact of external events

    External events, such as weather conditions, changes in laws and regulations, and sports or cultural events, can affect product demand. Therefore, it is important to analyze the impact of these events on sales forecasting.

  • Use data visualization tools

    Data visualization tools can help business owners and pricing and promotion managers analyze data more effectively and identify consumption patterns and trends more easily.

  • Improve interdepartmental collaboration

    Collaboration between different departments of the company, such as sales, marketing, and finance, can improve the accuracy of sales forecasting and help identify new growth opportunities.

  • Regularly update sales forecasts

    Product demand can change rapidly due to factors such as competition, changes in consumer preferences, and external events. Therefore, it is important to regularly update sales forecasts to ensure that the company is prepared to effectively meet future demand.

By following these tips, managers can improve the accuracy of sales forecasting, which will in turn help them make more informed and strategic decisions to improve profitability and competitiveness of the company.

Making sales forecast smarter with Artificial Intelligence

Technology can be a great ally to improve sales forecasting in the CPG and Retailers industry. In this regard, Artificial intelligence (AI) can play a significant role in improving sales forecasting by bringing companies:

  • Data Analysis

    AI algorithms can analyze large amounts of data from various sources such as sales history, market trends, customer behavior, and more. This analysis can help identify patterns and trends that would be difficult for humans to recognize, leading to more accurate sales forecasts.

  • Predictive Analytics

    AI can use predictive analytics to forecast future demand for products. This helps businesses better manage their inventory levels and avoid stock-outs or overstocks. Predictive analytics can also help retailers plan for promotions and optimize pricing strategies.

  • Model and analyze “What if” scenarios

    Create “what-if” scenarios and modeling to analyze the impact to the sales forecast if a specific business, economic, or competitive situation were to occur. Prepare for challenges you might encounter in upcoming deal cycles.

  • Personalized recommendations

    AI algorithms can analyze customer data and shopping behavior to provide personalized recommendations and product suggestions. By understanding customer preferences and purchasing habits, companies can improve their sales forecasting accuracy and predict demand for specific products.

  • Price optimization

    AI algorithms can analyze market trends, competitor pricing, and customer behavior to help companies optimize their pricing strategy. By setting the right price for products, companies can maximize revenue and improve sales forecasting accuracy.

Leverage forecasting to maximize your profit

In conclusion, sales forecasting is an essential tool for any CPG or retail company that wants to stay competitive in the market. An accurate sales forecast can help the company make informed decisions about pricing, promotions, and inventory levels, which can improve its profitability and its ability to meet consumer needs.

In short, an accurate and well-founded sales forecast can be a key factor in the success. By investing in improving sales forecasting, these companies can improve their profitability, increase customer satisfaction, and remain competitive in an increasingly demanding market.

Kuona’s solutions help CPG brands and retailers to optimize their price and promotions, while giving them tools to improve their sales forecast. If you need more information, please contact us.