Introduction:
In the ever-changing world of sales, organizations face the challenge of not only accurately planning their sales volume and revenue forecasts, but also considering the impact of price changes on those forecasts. In this blog post, I will talk about how companies can optimize their sales volume price planning using SAP Analytics Cloud and the "Content for Corp FP&A" Rapid Sales Planning package included in their SAP Analytics Cloud license.
In addition to the planning options in the rapid package, we have also built in reports and analyses to better understand the price-volume mix and incorporate the findings into our planning. If you are interested, I will be happy to discuss these options in a future blog.
Today we will focus in particular on the use of predictive scenarios made possible by machine learning and the integration of prices as an important influencing factor in the forecasts.
Using this example, I will show how existing content in the SAP Analytics Cloud can be easily adapted to benefit from machine learning forecasts (predictive scenario).
Sales volume price planning with SAP Analytics Cloud:
SAP Analytics Cloud offers companies a powerful platform for analyzing and planning their sales activities. With the free "Content for Corp FP&A" Rapid Sales Planning package, companies receive ready-made dashboards and models that help them to create their sales forecasts quickly and accurately, taking price changes into account. In the standard package, the planner can plan very flexibly for volume, price, discounts and other variables. I would like to expand these options in the package with the functions of the SAP Analytics Cloud to include machine-generated forecasts. This reduces the manual effort involved in planning, as a large part of the forecasts can be generated from the automatic forecast.
Predictive scenarios and machine learning:
A crucial aspect of sales volume price planning is the use of predictive scenarios enabled by machine learning. These predictive scenarios analyze historical sales data and other relevant factors to automatically generate future sales forecasts taking price changes into account. The price changes are the future variables that the planner enters as influencing factors on the machine forecast in the forecast period.
Integration of prices as an influencing factor:
An important component of sales volume price planning is the consideration of prices as an influencing factor on sales forecasts. By integrating price data into the analysis, companies can better understand how price changes will affect their future sales figures. This information enables companies to make informed decisions about their pricing strategy and adjust their sales forecasts accordingly. Here too, manual effort can be significantly reduced as the system automatically includes prices as an influencing factor in the forecasts.
A look into the future:
To demonstrate the effect of price changes on future sales figures, in our example company we plan to set an extreme price for bicycles ("Mountain Bike") in the month of March (actual data up to Sep/2023 is available in the example). By including this extreme price in our forecasts, we can see how this change will affect sales figures and what impact it will have on the overall sales target.
Structure of the sceanrio in SAP Analytics Cloud:
We need the following versions (scenarios):
- Actual (actual data until Septmber 2023)
- Plan (plan scenario, in which the future prices are also planned)
- Predictive_ML (scenario in which actual data and future prices are transferred).
The "Predictive Forecast Scenario" is executed on a private copy of the Predictive_ML version. After the forecast run, the forecast values are transferred to the private version and can then be copied back to the Predictive_ML version. The execution of these actions (data actions) is triggered by the embedded button in my SAP Analytics Cloud story.
Conclusion:
Sales volume price planning is a crucial aspect of many companies' sales success. By using SAP Analytics Cloud and Predictive Scenarios, companies can improve their sales forecasts and make informed decisions about their sales strategy. The integration of prices as an influencing factor enables companies to better understand the impact of price changes on their sales figures and adapt their strategy accordingly. With a clear view into the future and a reduction in manual effort, companies can achieve their sales targets more effectively and ensure long-term success in sales. SAP Analytics Cloud and content packages such as Rapid Sales Planning and Analysis help with this.
I hope you found this blog post insightful and informative. If you have any questions, feedback, or ideas to share, please don't hesitate to reach out. Your input is valuable in shaping future discussions and content. Thank you for reading, and may your sales planning endeavors with SAP Anayltics Cloud be prosperous. Live long and prosper!
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