Why is internal data considered more reliable and easier to collect than external data?
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Simply explained: Why internal data is better for predictive analytics in B2B.
Companies use sales forecast to make business decisions. They also employ them to predict future developments better than their competitors. However, reliable predictions are rare, and sales teams try to play a safe card by applying external forecasts. Companies are nevertheless better off using their in-house data – with predictive analytics.
“There are three types of lies: lies, damn lies, and statistics.” This quote from Benjamin Disraeli, a British statesman and 19th-century novelist, fits the situation in companies very well.
They collect figures on sales, profits, losses, customer growth and decreases and much more – and generate statistics from them. A lot of economic data is collected and processed to flow into forecasts for the near or distant future.
Sales forecasts, for example, serve to adjust your individual investment decisions. In general, predictions are also used to foretell (possible) future developments better than the market.
Forecasts using predictive analytics models are generally superior to decisions based on gut instinct or human-intuition, even when it is clear that forecasts cannot be 100 per cent correct.
Business Intelligence: Why General Forecasts Alone Are Not Enough
Business decisions are often made based on general economic and market forecasts. International companies, for example, monitor economic developments in the countries and industries in which their customers operate.
Such forecasts can provide valuable context. They help companies better understand economic trends, changes in demand, or developments in specific markets. However, they are often not sufficient for concrete sales decisions.
General economic forecasts do not, for example, indicate which customer has an increased risk of churn, which products offer cross-selling potential, or where unusual price developments are occurring. For this, companies need to take a closer look at their own customer, order, and product data.
In addition, economic forecasts are always based on assumptions. Economic data is continuously updated and sometimes revised retrospectively. As a result, forecasts can primarily reflect short-term developments and broader trends, but they cannot serve as a reliable basis for every individual operational sales decision.
Why External Factors Make Forecasting More Difficult
The further a forecast looks into the future, the more uncertain it becomes. Economic developments depend on many factors that influence one another. These include political decisions, changes in supply chains, raw material prices, new legal requirements, or unexpected events in important sales markets.
Such developments cannot be predicted completely. They can change assumptions about demand, prices, or growth within a short period of time. This is why general market and economic forecasts are helpful, but they are only one part of the decision-making process.
For companies, it is therefore particularly important to analyse developments within their own business in detail. This is where they can see how changes actually affect customers, products, prices, and sales.
Using Internal Data for Predictive Analytics
External market information can help companies better understand the economic environment. However, the basis for operational sales decisions is often found in the company’s own ERP data.
Predictive Analytics uses this data to identify patterns in past customer behaviour and purchasing activity. Unlike Business Intelligence, which mainly describes what has happened in the past, Predictive Analytics aims to assess possible future developments based on probabilities.
The goal is not to predict the future with absolute certainty. Instead, Predictive Analytics helps companies set sales priorities on a more informed basis. For example, companies can identify customers with a potential risk of churn, uncover cross-selling opportunities, detect unusual pricing developments, or recognise customers with a higher probability of making a purchase.
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Conclusion: Internal Data Is the Foundation for Better Sales Decisions
General market and economic forecasts can help companies put economic developments into context. However, they are rarely sufficient for making specific sales decisions.
A company’s own ERP data shows what is actually happening within the business: which customers are changing, which products are frequently purchased together, where prices stand out, and which potential opportunities remain undiscovered.
Predictive Analytics makes this data actionable and helps sales teams identify opportunities and risks at an earlier stage. External information can be a useful addition. However, the operational basis for well-informed sales decisions usually already exists within the company itself.
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Predictive analytics is the technology that enables a look into the future. What data do you need? How do you get started with predictive analytics? What methods can you use?
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