What Data is Necessary for AI in Wholesale?

July 23, 2024|In Artificial Intelligence, b2b sales, future of sales, Machine Learning, Predictive Analytics|By Svenja Szillat

What data is needed for precise AI-based sales forecasts in wholesale?

Wholesale companies today face a multitude of challenges. Rising customer expectations, complex supply chains, increasing competitive pressure, and the need to cut costs and improve efficiency put the industry under considerable pressure.

Against this backdrop, using artificial intelligence (AI) is becoming increasingly important. Many wholesalers see AI as an opportunity to optimize their processes, make better decisions, and gain competitive advantages.

AI can be used in various wholesale areas, from warehouse management to sales planning to customer analysis.

In this article, we focus on the use of AI in wholesale sales. This area of AI-based sales forecasting in wholesale offers enormous potential to improve the sales process, strengthen customer loyalty, and ultimately increase sales.

To successfully use AI in wholesale sales, comprehensive and high-quality data is required. Historical sales data from ERP systems play a central role here, as they provide current, valid, and easily accessible information.

Forecasting or Predictive Analytics Explained Simply

What does AI have to do with it? Artificial intelligence, specifically machine learning models, accelerates and improves classic predictive analytics. Forecasts created with the help of AI have been proven to be more accurate than manual, rule-based forecasts.

The basic principle, however, remains the same: The goal of predictive analytics is to calculate the most accurate probability estimates for future events. These calculations are based on historical data.

Another prerequisite must also be met: The events to be predicted must contain patterns. That means purely random events, such as predicting lottery numbers, cannot be forecasted.

The good news is that almost all actions and dynamics in our lives follow certain patterns. In the context of predictive analytics, this is good news. Some philosophers recognized this early on, such as George Santayana, who warned humanity: “Those who cannot remember the past are condemned to repeat it.”

Enough philosophizing; back to forecasts. This section’s moral is that we need historical data for forecasts.

Which Data is Best for AI? A Rule of Thumb

The term “historical data” is very broad, especially in times of big data. In wholesale alone, many historical data could play a role: competitive data, CRM data, ERP data, social media data, market data, and much more.

Which of these are relevant for forecasts? Here is a simple rule: Use data close to the forecast target.

Do you want to predict when a machine needs maintenance? Then, you need historical data containing information about previous maintenance.

Weather forecasts? Historical weather data. Inventory forecasts? Historical sales and inventory data.

Accordingly, historical sales data from ERP systems are also the best data for sales forecasts. The next section explains the additional advantages of this data.

Advantages of High-Quality Data for Predictive Analytics

High-quality data are the foundation of successful AI applications in predictive sales analytics for wholesale companies. Such data is characterized by being easy to obtain and having low error rates. A classic example of this is own sales data (ERP data). This data is usually current, valid, and easily accessible, making it ideal for use in AI systems.

In contrast, external market data is often delayed and can contain errors, as it comes from various sources and uses different collection methods. While this data is useful, it is less reliable for accurate sales forecasts than internal sales data.

For AI-based predictive sales software, standardized data is particularly important. Standardized data is organized in a consistent format, allowing for smooth processing by the AI system. Examples of standardized data are structured databases with clearly defined fields such as product ID, sales date, sales quantity and price.

Conversely, contextual data, such as email messages and CRM reports, are less suitable. These data often contain unstructured information that is difficult to analyze. To use such data, an additional AI system like natural language processing (NLP) would be required to structure and standardize it.

The process of preparing contextual data for AI is very time and resource-intensive. Moreover, sales forecasts usually do not improve significantly when such data is included. Therefore, the cost-benefit ratio is often not favourable.

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