From insights to action: Enhancing revenue management with data analytics

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 to make accurate demand predictions.

When it comes to revenue management, there are a number of key considerations to incorporate into your overall data analytics strategy. A thorough understanding of your organization’s essential data and metrics can help you optimize your operations, make critical business decisions more confidently and ultimately boost your business’s revenue.

What is revenue management?

Revenue management is a strategic business process that leverages analytics to anticipate customer behavior. Those predictions are then used to optimize inventory, production and pricing, resulting in maximized revenue.

In other words, it’s 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.

Understanding your key data points is critical to successful revenue management. As variables change and fluctuate, you should be continually evaluating and analyzing what your data is telling you to stay up to date with changing market forces.

Key revenue management practices

Revenue management helps businesses understand how to manage resources during periods of fluctuating demand. This allows management to make informed decisions to maximize revenue. To accomplish this goal, it is necessary to have technology in place that supports the following:

• Demand management: Understanding demand and predicting when favorable changes will occur, resulting in the ability to allocate inventory by price and willingness to pay.
• Resource management: Anticipating supply constraints, allowing the configuration of products and processes for targeted “A+” customers.
• Data collection and analysis: Analyzing your sales and forecast data to determine customer segments and identify demand patterns.

Data practices

Once a company has advanced through the process of identifying revenue generators, cost drivers and the relationship between both, it should focus on data collection and analysis to support the revenue management process. This includes the data analysis approach, data types and collection methods.

• Data analysis approach: Focusing on how a business analyzes data ranging from low to high intensity. A less intense approach is often characterized by individual judgment and intuition. Under these circumstances, it’s common for the analysis to be based on a manager’s judgment and experience. It progresses to an approach that reduces intuition while increasing the use of data in the decision-making process. A more intense approach is computational in nature and relies on using data to identify correlations and trends, and isolate revenue drivers.
• Types of data: The types of data collected play 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 integrated 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 observations that are often manually documented. As complexity increases, it is common to use more automated data collection systems 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 increase the accuracy and effectiveness of the analysis.

How Wipfli can help

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. Our team of dedicated professionals has the industry experience and technical experience to help you collect relevant data and turn it into actionable insights, boosting your revenue management strategy. Contact us today to learn more.