To simulate commissions under different models, you have 4 options:
- Run time-based simulations
- Ex: you are in April and want to forecast potential spend up to year end (using existing models)
- Create new model and calculate
- Clone plans, alter tiers / rates / quotas, calculate payouts using existing sales data
- Leverage Sales Cookie AI
- Ask the Sales Cookie AI to analyze trends or generate forecasts
- Use BI tools
- Connect with BI tools such as Power BI, Excel, Tableau and run forecasts
Here is a detailed description of each option.
Run Time-Based Simulations
Consider the following scenario:
- Your plan has YTD attainment, with increasing rates / tiers
- You are in April, so you have sufficient YTD sales data
- You want to know how you might end the year
You can use time-based simulations to "fill" the remaining timeline (up to December 31) based on past sales data.
Please refer to
this KB for more information.
Create New Model and Calculate
You can perform the following:
- Clone existing plans > new plans in draft mode
- Edit new plan rates / quotas / exclusions / etc.
- Draft plans are not visible to payees
You can then run calculations for draft plans. They will remain hidden from payees.
Even better, you can even run calculations in test mode and create test calculations
Please refer to
this KB for more information.
Leverage Sales Cookie AI
You can use the AI to ask questions like this:
- Which plans had low attainment across many payees?
- Analyze attainment history - any seasonal variations?
- Forecast total payouts for plan X over the next 3 months based on trends
- How would payouts have changed if Tier 3 for plan Y had been paid 5%?
Here is an example where AI produces a forecast using seasonal corrections (ARIMA):
Use BI Tools
You can connect BI tools to Sales Cookie and retrieve your data - for example using Power BI you can:
- Use the Analytics pane for forecasting
- Use DAX to create custom metrics and use linear regression / moving averages
- Use AutoML (Automated Machine Learning) for automated predictive modeling (ex: time-series forecasting)
Here is an example where spend was analyzed under different models with the Analytics pane:
Please refer to
this KB for more information.
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