Seller Value Outliers
Identify and determine how much value sellers provide your company.
Being able to identify underpaid and overpaid sellers can help improve your compensation plan design. This blueprint helps you to determine by period, how much value sellers provide your company.
This blueprint works by looking at seller's credited revenue versus their payout. Using this information, you can better understand the effectiveness of your sellers. You can also answer questions like who performs at the most efficient rate.
This blueprint can also help catch and reduce mistakes in your data that could lead to to future costs. For example, you can avoid future adjustments to your sellers' pay or in your projected results.
Legend
The tools in this blueprint are color-coded based on their function:
Yellow: These tools contain the input data or results that are ready for export. These are what you’ll be mapping to in Incentives for the Varicent ELT Calc object. The Input Payout Data (Data) tool contains the incoming payout data that you want to find outliers in.
Turquoise These tools remap input data to fit the pipe or remap the data for export.
Pink These tools contain the core of the outlier analysis.
Red: This tool contains the business logic that interprets the result of the outlier analysis.
Blue: These tools manipulate and transform data for visualizations.
Lime Green: These are the output tools which contain results that are ready for export.
Pipe inputs
This blueprint uses the Adapt tool to map the columns in your data. This blueprint expects data describing payouts to your sellers for each transaction they're a part of.
You can look at the sample data to see how the tool maps columns. Whatever column names you use, you can use the Adapt tool to change them to names the pipe recognizes.
The columns required for this blueprint are: FinalPayout: The commission amount paid to the seller for the transaction.Periods: The time period in which this transaction occured.
PayeeID: The unique identifier for each seller.Credit: The amount of the sales transaction credited to the seller.FinalPayout: The commission amount paid to the seller for the transaction.Periods: The time period in which this transaction occurred.
How to interpret the results
There are two different types of exports in this blueprint. One set of exports are for visualizations. The other type of export provides different versions of the outlier analysis. These results can help you dive deeper into your results or help figure out actionable next steps.
The most important export tool is the Export List of Outliers by Period (Export) tool. This export tool shows you any outliers in what a seller's pay versus the revenue they brought in. The tool organizes the information by seller and period.
Within those results, the Outlier_Type column tells you why a period might be an outlier. For example, a row with an Outlier_Type value of Passed means that row is not an outlier. But if the row has an Outlier_Type value of Credit Outlier, this means the credited value is higher than normal. In this case, you'd want to investigate the reason for the outlier. It could be the result of a data entry error or a genuine large deal.
How the blueprint works
This blueprint works by calculating the ratio between a seller's credited amount versus their payout. This is done for each transaction they're a part of during a given period. You can see how the blueprint does this in the Credit Payout Ratio Formula (Formula) tool. The blueprint then does an outlier analysis on this calculated ratio, the credited amount, and the payout for each period.
Varicent ELT analyzes each of these attributes using three outlier tools. The results are then fed back into the Outlier Type (Case) tool. This tool contains the business rules needed to interpret the outlier results.
Tools labeled as pink are where the blueprint analyzes the outliers. If you look at these tools, you'll find three focused on analyzing the credit to payout ratio: Ratio Outlier - Z Score (Outlier), Ratio Outlier - PCA (Outlier), and Ratio Outlier - kNN (Outlier). Each of these tools find outliers in data sets with different distributions. For example, Ratio Outlier - Z Score (Outlier) is great at finding outliers in normally-distributed data. It doesn't perform as well when the data is randomly distributed. That’s where the other tools come in, each tool can find outliers with different types of data distribution.
The Outlier Type (Case) tool contains the business rules that convert the results of the outlier analysis into a single column: Outlier_Type. This column not only indicates if a row is an outlier, but it also shows the reason why.