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Predictive Analytics

From Big Data to Predictive Analytics

Dashboards and reporting systems provide valuable information for managers. However, there is often a lack of connection to business decisions, process optimisations, customer experiences or other actions based on predictive findings. This is usually because companies must look to the future in order to be prepared for the rapidly approaching challenges they face.

Predictive analytics starts where OLAP or reporting ends. Instead of just analysing the existing situation, predictive analytics uses algorithms to find patterns in data to predict similar results in the future.

Winning with predictive analytics: 6 examples from corporate practice

Predictive analytics allows you to gain completely new insights into customers and business processes. New assumptions on customer loyalty and better planning of future challenges lead to increasing sales potential. The company also receives inspiration for completely new products.

Predictive analytics is successfully used in these areas:

  1. Real-time fraud detection
    Algorithms automatically detect irregularities in payment transactions. Suspicious business transactions are automatically stopped and manually checked.
  2. Predictive maintenance
    The goal is to move from downtime-related repairs to preventive maintenance. For this purpose, algorithms perform on-going analyses of machine behaviour and also include historical data. In this way, the appropriate time for the next inspection can be calculated and the required spare parts kept in stock.
  3. Reducing waste (predictive quality)
    By parameterising the production stages, predictive analytics enables faulty products to be identified early and removed from the production process. It is important that suitable criteria based on samples from sensor data indicating a quality defect are known.
  4. Identifying dissatisfied customers (churn management)
    Predicting which customers will choose to migrate away from a business in the near future is becoming increasingly important for companies. For example, telecommunications companies can use customer data such as calls placed, minutes used, number of SMS messages sent, average billing amounts and hundreds of other variables to find models that predict which customers are likely to switch operators.
  5. Improving sales forecasts
    Predictive analytics enables a production company to better estimate the demand for individual products and plan production accordingly. The analysis results are used to optimally adjust production and storage capacities as well as logistics. Problems caused by excessive storage or overproduction are avoided.
  6. Increasing good payment behaviour
    Unpaid invoices from suppliers and business partners are an issue for every company. Experience of the conditions under which customers pay on time (e.g. through discounts) is incorporated into cash forecasting and thus helps to improve the company's liquidity through faster invoice payment.

Our approach to predictive analytics projects

[] Predictive Analytics
  1. Kick-off workshop with experts and decision-makers to discuss what questions are to be answered with predictive analytics and determine what data is required to do this.
  2. Definition of a concrete target with key figures (e.g. increase sales by a total of X, reduce shortfalls) that need to be achieved with the help of the forecasts.
  3. Selection/trimming of data sets and combination with external data.
  4. Creating a first evaluation of the data in corresponding prediction models by means of a pilot project/prototype.
  5. Continuous refinement, combination and evaluation of models and analytical methods to improve forecasting quality.
  6. Finally, the integration of the new analytical methods into the existing systems.

Expert recommendation

Predictive analytics is a continuous, iterative process. The models used continue to improve as use progresses with predictions becoming more and more precise as a result. Companies should start with a smaller project and then expand their solution step by step.

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Index

  • Companies should consider the following when implementing a predictive analytics solution
    • Prerequisite: High-performance database infrastructure
    • Selection of data records
    • Methodology
  • The six steps of predictive analytics
  • Successfully translating insights gained into actual business practice
    Predictive Analytics
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