Why Data Collection, Analysis and Modeling Matter
The ability to anticipate demand can empower management to make important resource allocation and pricing decisions. While such empowerment is attractive, many companies lack the necessary data collection and analytics processes. Here is a summary of key data analytics considerations for revenue management.
What is Revenue Management?
Revenue management is an analytical process that relies on analytics to help predict customer behavior and optimize inventory, production and pricing to maximize revenue. In other words, it is about understanding supply and demand trends and identifying what customers value most. This allows businesses to anticipate demand changes and other factors that should be considered when evaluating revenue opportunities.
Key Revenue Management Practices
Revenue management helps businesses understand how to manage resources during periods of demand fluctuations. This allows management to make the best situational decisions possible. It also informs the planning process to ensure future revenue maximization. To accomplish this goal, it is necessary to have accounting software that supports the following:
- Demand Management – The focus is on demand and predicting when favorable changes will occur. This results in the ability to allocate inventory by price and willingness to pay.
- Resource Management – This approach centers on supply side issues, allowing the configuration of products and processes for targeted “A+” customers.
- Data Modeling – This is a systematic analysis that lets management determine customer segments and identify demand patterns.
- Data Collection – This method allows management to not only track demand but also to understand habits and predicted volume.
Data Practices
Once a company has advanced through the process of identifying revenue generators, cost drivers and the relationship between both, they should focus on data collection. The analytical approach, data types and collection methods are the primary areas of data practice.
- Analytical Approach – This method focuses on how a business analyzes data ranging from low to high intensity. A less intense approach is often characterized by one’s individual judgement and intuition. Under these circumstances, it is common for the analysis to be based on a manager’s judgement and experience. It progresses to an approach that reduces intuition while increasing the use of formulaic techniques. A more intense approach is computational in nature and relies on casualty modeling which incorporates revenue drivers.
- Data Type – The type of data collected plays an important role in what analysis can be performed. In the early stages, many businesses have only limited recordkeeping and data is aggregated primarily for compliance purposes. As complexity increases, customer and product data is maintained with external factors to understand trends. Finally, extensive internal and external information is recorded with transactions, providing maximum insight.
- Data Collection Method – Many businesses initially rely on experience-based collection practices such as on-the-job feedback and casual observations that are often manually documented. As complexity increases, it is common to use a point-of-sale program and conduct varying levels of trend research. At the most complex levels, data collection is continuous and features fully automated systems to capture both internal and external data.
It is important to remember that revenue management relies on detailed records of historical trends. This means it is important to document transaction details, operational performance, and customer information to make the analysis more effective.
Contact Us
Revenue management programs require careful planning and execution to reach desired outcomes. Success is highly dependent on data collection, analysis and modeling to gain access to important insights. If you are interested in learning more about revenue management or data analytics, we can help. For additional information call us at 248.208.8860 or reach out today. We look forward to speaking with you soon.
