Forecast Demand with Predictive Analytics

Demand forecasting is the process of estimating customer demand throughout a predetermined period. Demand forecasting typically uses a wide range of historical and analytical data to project customer trends. Those projections inform decision-making relating to product pricing, growth initiatives, competitive pressure, and other business strategies.

Businesses of all sizes use demand forecasting strategies to assess market potential and sales opportunities. Without demand forecasting, companies risk supply shortages, lost revenue resulting from customer churn, and dissatisfied shareholders.

Why Is Demand Forecasting Important?

In addition to the reasons stated above, there are numerous reasons why demand forecasting is essential in business today.

  1. Sales Forecasting: Demand planning provides a better understanding of sales trends. You can align budgets, resources, and company goals with demand planning. With the right goals in place, demand and sales forecasting analytics can measure sales pipeline performance, inform cash flow statements, and refine other key metrics that provide visibility into the business’s health.
  2. Staff Planning and Capacity Planning: Knowing trends in customer demand allows you to anticipate staffing resources needed to meet customer expectations. With demand forecasting, improve staff plans and align resources to achieve a more productive workforce and higher margins.
  3. Inventory Management: Demand forecasting helps companies manage inventory levels to meet customer needs better. Companies can increase inventory turnover rates through better management while reducing inventory holding costs.

Factors Influencing Demand Forecasting

Five factors typically influence demand forecasting. These factors may differ by industry, but you should consider all of them when developing a demand forecast.

  1. Type of service or product: The kind of service or good can vary from B2B to B2C, subscription vs. retail, or commodity vs. luxury. The type of products or services you offer impacts purchasing behavior, which is crucial in creating demand forecasts.
  2. Seasonality: As the seasons change, so does demand. For some products, seasonal factors significantly impact demand and buying behavior. For example, the demand for chlorine for swimming pools will be stronger in the summer than in the fall and winter.
  3. Geography: Location is a fundamental principle in business operations and influences how you meet customer demand. Today, with so much online business, it’s vital for companies to understand where demand is located. In addition, understanding your business’s geographic distribution trends can help you plan to offset shipping costs, fulfill orders faster, and build brand presence.
  4. Economy: The economy is one of the most significant impacts on demand forecasts since it influences macro and micro trends. If the economy is in a recession or depression, this situation will impact your top-line profit goals and all bottom-line operational revenue.
  5. Competition: What your competition does will impact the demand for your products and services. If the competition lowers prices or offers a new customer promotional offer, their move will affect how your customers interact and buy from your business.

How Predictive Analytics Supports Demand Forecasting

Predictive analytics is emerging as the preferred forecasting method for modeling and predicting demand. Now, with the help of AI and machine learning, predictive analytics can not only look at historical sales results but also can enrich data sets with current market trend data. That enrichment provides more accurate forecasts incorporating the key factors that influence buyer behavior.

Predictive analytics also helps demand forecasting via:

  • Detecting subtle and granular demand patterns at the SKU and POS level
  • Identifying non-trivial and non-intuitive relationships within the data to unlock greater forecast accuracy
  • Leveraging limitless data to base your models on, e.g., sales, marketing, operations, customer transaction, macroeconomic, and competitor data
  • Answering the “why” from multiple angles, from demographics to geography, allowing for better personalization
  • Connecting supply chains to marketing, sales, and other internal orgs

Examples of using predictive analytics for demand forecasting include:

  1. Cutting overstock: Retailers can cut overstock by as much as 50% using predictive analytics.
  2. Staff planning: This manufacturing company realized a labor cost savings of 15% and an inventory cost savings of over 25% with predictive analytics.
  3. Resource allocation: A major North American insurer uses demand forecasting to assign staff and physical resources to the correct locations to satisfy urgent customer needs.

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