SYSTEM AND METHOD FOR OPTIMIZING MARKETING EFFECTIVENESS

• US 20120116868A1
• Filed: 11/01/2011
• Published: 05/10/2012
• Est. Priority Date: 11/10/2010
• Status: Abandoned Application

• Current Assignee: Optimal Effect, Inc.
• Original Assignee: Optimal Effect, Inc.
• Inventors: Chin, Wendy Tsyr-Wen, Heard, Andrew Michael, Hager, Paul Richard, Roth, Donald Lloyd
• Application Number:
  US13/286,348
• Publication Number:
  US 20120116868A1
• Time in Patent Office: Days
• US Class Current: 705/14.43
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0244 Optimization

Abstract

The claimed invention relates to a system and computer implemented method for quantitatively measuring effectiveness of a marketing campaign or marketing strategy execution. The client device accesses the server over a communications network to define targeted customer segments based on the marketing campaign or a specific set of marketing objectives and to define a quantifiable outcome or payoff for each targeted customer segment of the client, thereby linking the performance of client'"'"'s marketing campaign or objectives to the quantifiable outcome for each targeted customer segment. The server generates behavior paths for each targeted customer segment on a client website for effective measurement of adherence of a website visitor/customer to the behavior paths leading to the quantifiable outcome. The rules engine calculates a path score to determine whether the customer adheres to or diverges from a preferred behavior path established for the customer'"'"'s targeted customer segment.

Claims

1. A computer implemented method for quantitatively measuring effectiveness of a marketing campaign or marketing strategy execution, comprising the steps of:

accessing a processor based server over a communications network to define targeted customer segments based on the marketing campaign or a specific set of marketing objectives by a client using a client device;

defining a quantifiable outcome or payoff for each targeted customer segment of the client on the server by invoking one or more component tools of the server by the client device over the communications network, thereby linking the performance of client'"'"'s marketing campaign or objectives to said quantifiable outcome for each targeted customer segment;

generating behavior paths for each targeted customer segment on a client website using the server for effective measurement of adherence of a website visitor/customer to the behavior paths associated with said visitor/customer'"'"'s targeted customer segment leading to said quantifiable outcome;

assigning a weight to each step of each behavior path for each targeted customer segment by the client using said one or more component tools of the server;

categorizing the customer visiting the client website into one of said targeted customer segments by a rules engine of the server based on segmentation rules;

determining a customer path traversed on the client website;

calculating a path score by the rules engine of the server in accordance with rules associated with the targeted customer segments established by the client and the weight assigned to each step of the customer path to determine whether the customer adheres to or diverges from a preferred behavior path established for the customer'"'"'s targeted customer segment; and

storing the path score and the customer path in a database.

2. The method of claim 1, further comprising the step of analyzing the client website to import sitemaps and to imbed tags to various pages of the client website to facilitate data collection and analysis by the server.

3. The method of claim 1, further comprising the step of determining whether the customer was correctly placed into the targeted customer segment based on the segmentation rules established by the client and imposed by the rules engine of the server.

4. The method of claim 3, further comprising the step of categorizing the customer by the rules engine based on at least the following segmentation rules:

a first webpage of the client website accessed by the customer or a landing page rule and a website visited by the customer before accessing the client website or a referral website rule.

5. The method of claim 4, wherein the rules engine is a self-learning rules engine and further comprising the step of adding, deleting or modifying the segmentation rules to correctly place misplaced customers into a correct targeted customer segment in the future by the self-learning rules engine of the server;

and storing the added or modified segmentation rules in the database.

6. The method of claim 4, further comprising the step of assigning a priority rank to each segmentation rule to prioritize the segmentation rules.

7. The method of claim 1, further comprising the step of filtering web data to exclude immaterial visits to client website.

8. The method of claim 1, further comprising the step of generating the preferred behavior path for each targeted customer segment to shape customer interactions on the client website and guide the customer to the preferred behavior path, thereby maximizing said quantifiable outcome consistent with the client'"'"'s marketing campaign or objectives.

9. The method of claim 1, further comprising the step of simulating changes to the client websites by a simulation engine of the server to determine its effectiveness before actually deploying the changes to the client website.

10. The method of claim 1, further comprising the step of generating a standard or custom data visualization report based on a data range selected by the user.

11. A system for quantitatively measuring effectiveness of a marketing campaign or marketing strategy execution, comprising:

a processor based server comprising rules engine and one or more component tools;

a client device accessing the server over a communications network to define targeted customer segments based on the marketing campaign or a specific set of marketing objectives;

to define a quantifiable outcome or payoff for each targeted customer segment of the client on the server by invoking one or more component tools of the server, thereby linking the performance of client'"'"'s marketing campaign or objectives to said quantifiable outcome for each targeted customer segment; and

wherein the server generates behavior paths for each targeted customer segment on a client website for effective measurement of adherence of a website visitor/customer to the behavior paths associated with the visitor/customer'"'"'s targeted customer segment leading to said quantifiable outcome; and

assigns a weight to each step of each behavior path for each targeted customer segment based on a client input from said one or more component tools accessed by the client device over said communications network;

wherein the rules engine categorizes the visitor/customer visiting the client website into one of said targeted customer segments based on segmentation rules;

determines a customer path traversed on the client website; and

calculates a path score in accordance with rules associated with the targeted customer segment established by the client and the weight assigned to each step of the customer path to determine whether the customer adheres to or diverges from a preferred behavior path established for the customer'"'"'s targeted customer segment; and

a database for storing the path score and the customer path.

12. The system of claim 11, wherein said one or more component tools of the server analyzes the client website to import sitemaps and imbed tags to various pages of the client website to facilitate data collection and analysis by the server.

13. The system of claim 11, wherein the rules engine of the server determines whether the customer was correctly placed into the targeted customer segment based on the segmentation rules established by the client.

14. The system of claim 13, wherein the rules engine categorizes the customer based on at least one of the following segmentation rules:

a first webpage of the client website accessed by the customer or a landing page rule and a website visited by the customer before accessing the client website or a referral website rule.

15. The system of claim 14, wherein the rules engine is a self-learning rules engine, and adds, deletes and modifies the segmentation rules to correctly place misplaced customers into a correct targeted customer segment in the future;

and stores the added or modified segmentation rules in the database.

16. The system of claim 14, wherein the segmentation rules are ranked or prioritized based on a client input and wherein the rules engine favors the segmentation rules with a higher rank or priority.

18. The system of claim 11, wherein the server generates the preferred behavior path for each targeted customer segment to shape customer interactions on the client website and guides the customer to the preferred behavior path, thereby maximizing said quantifiable outcome consistent with the client'"'"'s marketing campaign or objectives.

19. The system of claim 11, wherein the server further comprises a simulation engine to simulate changes to the client websites to determine its effectiveness before actually deploying the changes to the client website.

20. A non-transitory computer readable medium comprising computer executable code for quantitatively measuring effectiveness of a marketing campaign or marketing strategy execution, said computer executable code comprising instructions:

accessing a processor based server over a communications network to define targeted customer segments based on the marketing campaign or a specific set of marketing objectives by a client using a client device;

defining a quantifiable outcome or payoff for each targeted customer segment of the client on the server by invoking one or more component tools of the server by the client device over the communications network, thereby linking the performance of client'"'"'s marketing campaign or objectives to said quantifiable outcome for each targeted customer segment;

generating behavior paths for each targeted customer segment on a client website using the server for effective measurement of adherence of a website visitor/customer to the behavior paths associated with said visitor/customer'"'"'s targeted customer segment leading to said quantifiable outcome;

assigning a weight to each step of each behavior path for each targeted customer segment by the client using said one or more component tools of the server;

categorizing the customer visiting the client website into one of said targeted customer segments by a rules engine of the server based on segmentation rules;

determining a customer path traversed on the client website;

calculating a path score by the rules engine of the server in accordance with rules associated with the targeted customer segments established by the client and the weight assigned to each step of the customer path to determine whether the customer adheres to or diverges from a preferred behavior path established for the customer'"'"'s targeted customer segment; and

storing the path score and the customer path in a database.