Systems and methods for a revenue causality analyzer

• US 7,904,355 B1
• Filed: 02/06/2008
• Issued: 03/08/2011
• Est. Priority Date: 02/20/2007
• Status: Active Grant

• Current Assignee: Vendavo, Inc.
• Original Assignee: Vendavo, Inc.
• Inventors: Johnson, Jeffrey D.
• Primary Examiner(s): Gart; Matthew S
• Assistant Examiner(s): CRAWLEY, TALIA F
• Application Number:
  US12/027,193
• Time in Patent Office: 1,126 Days
• Field of Search: 705/30, 705/36, 709/223
• US Class Current: 705/30
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

A revenue causality analyzer that provides attribution of causality effects for changes in revenue. The analyzer includes a selector for selecting a reference time period and a comparison time period, a receiver configured to receive transaction data including pricing data and volume data about the products at reference and comparison times, a preparer that includes a missing data exchanger and data error corrector, an attributor for attributing causality effects including a price effect, a volume effect, a mix effect an exchange effect, a cost effect, a dividend effect, a loss effect, and an inventory appreciation effect, by analyzing transaction data through a causality equation; and an output. Price effect sums the product of change in price and volume of the products across the products and currencies. Volume effect multiplies change in volume by the revenue per product sold. Mix effect multiplies percent revenue change by the volume of products sold.

Claims

1. A revenue causality analyzer, useful in association with price management systems, the revenue causality analyzer comprising:

a time period selector configured to select a reference time period and a comparison time period;

a data preparer configured to prepare transaction data including pricing data and volume data about products;

a causality attributor configured to attribute at least one causality effect by analyzing the transaction data utilizing a causality equation; and

wherein the causality equation is;

2. The revenue causality analyzer as recited in claim 1, wherein the transaction data includes reference revenue and comparison revenue, wherein reference revenue is revenue at the reference time period and comparison revenue is revenue at the comparison time period, and wherein by subtracting the reference revenue from the comparison revenue a change in revenue is calculated.

3. The revenue causality analyzer as recited in claim 2, wherein the at least one attributed causality effect includes at least one of a price effect, a volume effect, a mix effect an exchange effect, a cost effect, a dividend effect, a loss effect, and an inventory appreciation effect.

4. The revenue causality analyzer as recited in claim 3, wherein the causality equation is defined by the change in revenue equal to a summation of the at least one attributed causality effect.

5. The revenue causality analyzer as recited in claim 1, wherein the data preparer includes at least one of a missing data exchanger and data error corrector.

6. The revenue causality analyzer as recited in claim 5, wherein the missing data exchanger is configured to identify missing data and to determine if the missing data is price data or volume data;

and

if the missing data is volume data, then replacing the missing volume data with a value of zero;

else, if the missing data is price data, then determining if the missing price data is from the reference time period or the comparison time period; and

if the missing price data is from the reference time period, then replacing the missing price data with price data from the comparison time period; and

else, if the missing price data is from the comparison time period, then replacing the missing price data with price data from the reference time period.

7. The revenue causality analyzer as recited in claim 3, wherein the transaction data includes at least one of an exchange rate at the reference time period, an exchange rate at the comparison time period, prices of the products at the reference time period in the currencies, prices of the products at the comparison time period in the currencies, volume of the products sold in the reference time period, volume of the products sold in the comparison time period, volume of each of the products sold in the reference time period in a currency, and volume of each of the products sold in the comparison time period in a currency.

8. The revenue causality analyzer as recited in claim 7, wherein the price effect comprises:

subtracting the prices of products at the reference time period in the currencies from the prices of the products at the comparison time period in the currencies to calculate a change in price for the products at the currencies;

dividing the change in price for the products at the currencies by the exchange rate at the reference time period to calculate a relative change in price for the products at the currencies;

multiplying the relative change in price for the products at the currencies by the volume of the products sold in the comparison time period to calculate revenues for the products at the currencies;

summing the revenues for the products at the currencies across the products to calculate revenues at the currencies; and

summing the revenues at the currencies across the currencies.

9. The revenue causality analyzer as recited in claim 7, wherein the volume effect comprises:

multiplying the prices of the products at the reference time period in the currencies by the volume of the products sold in the reference time period to calculate revenue of the products for the reference time period in the currencies;

dividing the revenue of the products for the reference time period in the currencies by the exchange rate at the reference time period to calculate relative revenue of the products for the reference time period in the currencies;

summing the relative revenue of the products for the reference time period in the currencies across the products to calculate relative revenue for the reference time period in the currencies;

summing the relative revenue for the reference time period in the currencies across the currencies to calculate relative revenue for the reference time period;

dividing the relative revenue for the reference time period by the volume of the products sold in the reference time period to calculate revenue per product sold in the reference time period;

subtracting the volume of the products sold in the reference time period by the volume of the products sold in the comparison time period to calculate change in volume; and

multiplying the change in volume by revenue per product sold in the reference time period.

10. The revenue causality analyzer as recited in claim 7, wherein the mix effect comprises:

dividing the volume of each product sold in the reference time period in a currency by the volume of the products sold in the reference time period to calculate a percent of sales volume for the products in the reference time period;

dividing the volume of each product sold in the comparison time period in a currency by the volume of the products sold in the comparison time period to calculate a percent of sales volume for the products in the comparison time period;

subtracting the percent of sales volume for the products in the reference time period from the percent of sales volume for the products in the comparison time period to calculate a change in percent of sales volume for the products;

dividing the price of the products in the reference time period in the currencies by the exchange rate at the reference time period to calculate a relative price of the products for the reference time in the currencies;

multiplying the relative price of the products for the reference time in the currencies by the change in percent of sales volume for the products to calculate a percent revenue change of the products in the currencies;

summing the percent revenue change of the products in the currencies across the products to calculate a percent revenue change in the currencies;

summing the percent revenue change in the currencies across the currencies to calculate a percent revenue change; and

multiplying the percent revenue change by the volume of products sold in the comparison time period.

11. The revenue causality analyzer as recited in claim 7, wherein the exchange effect comprises:

dividing the price of the products in the comparison time period in the currencies by the exchange rate at the comparison time period to calculate relative prices of the products at the comparison time periods in the currency;

dividing the price of the products in the comparison time period in the currencies by the exchange rate at the reference time period to calculate prices of the products in the comparison time in the currencies at the exchange rate in the reference time period;

subtracting the price of the product in the comparison time in the currencies at the exchange rate in the reference time period from the relative prices of the products at the comparison time in the currencies to calculate changes in price for the products at the comparison time in the currencies due to exchange rate difference;

multiplying the changes in price for the products at the comparison time in the currencies due to exchange rate difference by the volume of sales of the products at the comparison time period to calculate changes in revenue for the products at the comparison time in the currencies due to exchange rate difference;

summing changes in revenue for the products at the comparison time in the currencies due to exchange rate difference across the products to calculate changes in revenue at the comparison time in the currencies due to exchange rate difference; and

summing changes in revenue at the comparison time in the currencies due to exchange rate difference across the currencies.

12. A computer implemented method for analyzing revenue causality, useful in association with price management systems, the method comprising:

selecting, by a processor, time periods, wherein the selection of the time periods includes selecting a reference time period and a comparison time period;

receiving, by a processor, transaction data, wherein transaction data includes pricing data and volume data about products;

preparing, by a processor, the transaction data;

attributing, by a processor, at least one causality effect by analyzing the transaction data utilizing a causality equation;

outputting, by a processor, the at least one causality effect, and wherein the causality equation is;

13. The method, as recited in claim 12, wherein the transaction data includes reference revenue and comparison revenue, wherein reference revenue is revenue at the reference time period and comparison revenue is revenue at the comparison time period, and wherein by subtracting the reference revenue from the comparison revenue a change in revenue is calculated.

14. The method, as recited in claim 13, wherein the at least one attributed causality effect includes at least one of a price effect, a volume effect, a mix effect an exchange effect, a cost effect, a dividend effect, a loss effect, and an inventory appreciation effect.

15. The method, as recited in claim 14, wherein the causality equation is defined by the change in revenue equal to a summation of the at least one attributed causality effect.

16. The method, as recited in claim 12, wherein the preparation of the transaction data includes at least one of replacing missing data and correcting data errors.

17. The method, as recited in claim 16, wherein the replacement of the missing data comprises:

identifying missing data;

determining if the missing data is price data or volume data; and

if the missing data is volume data, then replacing the missing volume data with a value of zero;

else, if the missing data is price data, then determining if the missing price data is from the reference time period or the comparison time period; and

if the missing price data is from the reference time period, then replacing the missing price data with price data from the comparison time period; and

else, if the missing price data is from the comparison time period, then replacing the missing price data with price data from the reference time period.

18. The method, as recited in claim 12, further comprising interacting with the outputted at least one causality effect, wherein the interacting with the outputted at least one causality effect includes modifying at least one of the transaction data and the selected time periods, and iteratively analyzing revenue causality including the modified at least one transaction data and the selected time periods.