Data collection, analysis and modeling are critical to revenue management
ÂÜÀòÉç¹ÙÍørs need to anticipate demand so they can make important resource allocations, issue pricing decisions and manage revenue. Unfortunately, many companies lack the data or analytics skills they need to peer into the future.
If you’re considering growing your tech stack, consider prioritizing processes and tools that affect revenue management.
What is revenue management?
Revenue management is an analytical process used to predict customer behavior and optimize inventory, production and pricing to maximize revenue. It’s about understanding supply and demand trends and identifying what customers value most. This insight helps businesses anticipate demand changes and other factors that influence revenue.
Revenue management helps leaders understand how to manage resources when demand fluctuates so they can make the best situational decisions possible. It also informs the planning process to maximize future revenue.
Revenue management practices are supported by accounting software for:
- Demand management: Leaders need to predict when demand changes will occur and how they may affect inventory by price and willingness to pay.
- Resource management: On the supply side, leaders need to configure products and processes for targeted customers.
- Data modeling: Systematic analyses let management see customer segments and identify demand patterns.
- Data collection: ÂÜÀòÉç¹ÙÍørs need to understand habits that accompany transactions to predict volume over time.
Data practices that support revenue management
Once a company has identified revenue generators, cost drivers and the relationships between both, they should focus on data collection. Three data practices support revenue management:
- Analytical approach: This method focuses on how a business analyzes data. It’s common for some analyses to be based on a manager’s judgment and experience. As companies become more advanced, they should rely on more formulaic techniques than intuition. Some analytics are purely computational in nature and use casualty modeling to incorporate revenue drivers.
- Data type: The type of data collected influences what type of analysis can be performed. In the early stages, many businesses have limited recordkeeping or data may be aggregated for compliance purposes. As complexity increases, customer and product data are combined with external factors to identify trends. In mature organizations, companies capture extensive internal and external information along with transactions to provide maximum insight.
- Data collection method: Many businesses rely on experience-based collection practices, such as on-the-job feedback and casual observations that are manually documented. As data collection matures, it becomes continuous and features fully automated systems that capture both internal and external data.
Revenue management relies on detailed records of historical trends. It’s important to document transaction details, operational performance, and customer information to support effective analyses.
How Wipfli can help
To manage revenue effectively — and proactively — you need the right set of data collection, analyses and modeling tools. Combined, they’ll give you the insight you need to carefully plan and execute revenue-generating strategies. To learn more about or , contact the professionals at Wipfli today.
Related reading:
