METHOD FOR SIMULATION OF HUMAN RESPONSE TO STIMULUS

• US 20090043719A1
• Filed: 03/14/2008
• Published: 02/12/2009
• Est. Priority Date: 01/27/1999
• Status: Abandoned Application

• Current Assignee: Christopher R. Stormann, Douglas B. Hall, Jeffrey A. Stamp
• Original Assignee: Christopher R. Stormann, Douglas B. Hall, Jeffrey A. Stamp
• Inventors: Stormann, Christopher R., Hall, DOUGLAS B., Stamp, Jeffrey A.
• Application Number:
  US12/048,701
• Publication Number:
  US 20090043719A1
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0276 Advertisement creation

Abstract

A method is provided for simulating customer reaction to stimulus based on historical observable customer outcomes. Embodiments of the invention describe a series of steps that when taken together accomplish a predictive outcome of customer simulation from a plurality of source inputs without prior assumptions of relationship between inputs and simulated outcomes. The invention comprises a series of steps that effect the framing of the simulation model from which customer predicted outcomes are made. The various frames required to create the preferred simulation model include: customer database development, stimulus archetype development, model data development, model building, simulation of future customer reaction and suggested courses of action based on the results of the simulation.

Claims

1. A method for predicting reaction to a target concept, said method comprising the steps of:

(a) providing a database comprising subjective reaction data, said subjective reaction data comprising responses of a plurality of individuals to at least one subjective reaction quantifier capable of being used to subjectively evaluate communicable information about one or more source concepts upon exposure of at least some of said one or more source concepts to at least some individuals of said plurality of individuals, said database further comprising responses to at least one common subjective reaction quantifier for a plurality of said one or more source concepts;

(b) selecting one or more archetypes adapted to assist with the objective evaluation of the content of the communicable information of at least some of said one or more source concepts;

(c) generating objective ratings or rule sets of at least some of said source concepts in said database based on one or more of said archetypes;

(d) developing a model defining the relationships between at least some of said subjective reaction data and at least some of said archetypes;

(e) generating objective ratings of said target concept in accordance with one or more of said archetypes defined by said model; and

(f) inputting said objective ratings of said target concept into said model to predict a predetermined population'"'"'s subjective reactions to said target concept.

2. The method of claim 1, wherein said at least one common subjective reaction quantifier is adapted to elicit responses related to consumer likeability.

3. The method of claim 1, wherein said at least one common subjective reaction quantifier is adapted to elicit responses related to consumer interest.

4. The method of claim 1, wherein said at least one common subjective reaction quantifier is adapted to elicit responses related to consumer purchase potential.

5. The method of claim 1, wherein said at least one common subjective reaction quantifier is adapted to elicit responses related to consumer perceptions.

6. The method of claim 1, wherein said at least one common subjective reaction quantifier is adapted to elicit responses related to consumer confidence.

7. The method of claim 1, wherein said at least one common subjective reaction quantifier is adapted to elicit responses related to consumer recall.

8. The method of claim 1, wherein said at least one common subjective reaction quantifier is adapted to elicit responses related to consumer expectation.

9. The method of claim 1, wherein said at least one common subjective reaction quantifier is adapted to elicit responses related to consumer likelihood to purchase tickets.

10. The method of claim 1, wherein said at least one common subjective reaction quantifier is adapted to elicit responses related to voter response to political candidates.

11. The method of claim 1, further comprising the following step after step (b):

(b1) selecting a quantifiable scale for each archetype after said step (b).

12. The method of claim 11, wherein said quantifiable scale is selected from the group consisting of a Likert scale, a Juster scale, a categorical scale, and a continuous scale with anchored descriptors.

13. The method of claim 1, wherein said model is generated using standard univariate, bivariate, and multivariate statistical methods.

14. The method of claim 1, wherein said model is generated using a neural network.

15. The method of claim 1, wherein said model is generated using fuzzy logic.

16. The method of claim 1, wherein said model is generated using genetic algorithms.

17. The method of claim 1, wherein said model is generated using cross tabulations.

18. The method of claim 1, wherein said model is generated using t-tests.

19. The method of claim 1, wherein said model is generated using ANOVA.

20. The method of claim 1, wherein said model is generated using correlation matrix.

21. The method of claim 1, wherein said model is generated using regression.

22. The method of claim 1, wherein said model is generated using Factor Analysis.

23. The method of claim 1, wherein said model is generated using Structural Equation Modeling.

24. The method of claim 1, further comprising the following step after step (d):

(d1) using said model to assist with the selection of archetypes required for evaluation of said target concept.

25. The method of claim 24, further comprising the following step after step (d1):

(d2) testing said model for assumptions of error and fit; and

repeating steps (b)-(d2) as necessary.

26. The method of claim 1, wherein said source concepts are all from substantially the same product class.

27. The method of claim 1, wherein at least some of said source concepts are from substantially distinct product classes.

28. The method of claim 27, wherein said target concept is from substantially the same product class as said source products.

29. The method of claim 1 wherein said one or more archetypes comprise an “

overt benefit”

to a consumer or customer.

30. The method of claim 1, wherein said one or more archetypes comprise a “

real reason to believe”

of a consumer or customer that said target concept will provide a benefit.

31. The method of claim 1, wherein said one or more archetypes comprise the extent to which said target concept represents a unique or “

dramatic difference”

from currently existing concepts.

32. The method of claim 1, further comprising the following step after step (f):

(g) judging the relative potential success of said target concept.

33. The method of claim 20, further comprising the following step after step (g):

(h) developing and applying action criteria based on said archetypes and the relative potential success of said target concept.

34. The method of claim 1, wherein said database of said subjective reaction data comprises data from similar product or service concepts.

35. The method of claim 1, wherein said database of said subjective reaction data comprises data from dissimilar or cross-category product or service concepts.

36. The method of claim 1, wherein said step (a) further comprises the following step:

(a1) creating said common subjective reaction quantifier by normalizing and standardizing two or more separate and distinct databases containing subjective consumer response data and archetype data.

37. The method of claim 1, wherein said step (c) is accomplished by a human evaluator judging against a set of archetype criteria.

38. The method of claim 1, wherein said step (c) is accomplished by machine measure judging against a set of archetype criteria.

39. The method of claim 1, wherein said step (e) is accomplished by a human evaluator judging against a set of archetype criteria.

40. The method of claim 1, wherein said step (e) is accomplished by machine measure judging against a set of archetype criteria.

41. The method of claim 1 further comprising the following step:

(i) providing guidance to developers of said target concept on how to enhance or improve said target concept.

42. A method for predicting reaction to a target concept, said method comprising the steps of:

(a) providing a database comprising subjective reaction data, said subjective reaction data comprising responses of a plurality of individuals to at least one subjective reaction quantifier capable of being used to subjectively evaluate communicable information about one or more source concepts upon exposure of at least some of said one or more source concepts to at least some individuals of said plurality of individuals, said database further comprising responses to at least one common subjective reaction quantifier for a plurality of said one or more source concepts;

(b) selecting one or more archetypes adapted to assist with the objective evaluation of the content of the communicable information of at least some of said one or more source concepts;

(c) generating objective ratings or rule sets of at least some of said source concepts in said database based on one or more of said archetypes;

(d) developing a model defining the relationships between at least some of said subjective reaction data and at least some of said archetypes;

(e) generating objective ratings of said target concept in accordance with one or more of said archetypes defined by said model;

(f) inputting said objective ratings of said target concept into said model to predict a predetermined population'"'"'s subjective reactions to said target concept;

(g) judging the relative potential success of said target concept;

(h) developing and applying action criteria based on said relative potential success of said target concept; and

(i) providing guidance to developers of said target concept on how to enhance or improve said target concept.

43. The method of claim 42, wherein said step (a) further comprises the following step:

(a1) creating said common subjective reaction quantifier by correlating and standardizing two or more separate and distinct databases of subjective reaction data.

44. The method of claim 43, further comprising the following step after step (b):

(b1) selecting a quantifiable scale for each archetype after said step (b).

45. The method of claim 44, wherein said quantifiable scale is selected from the group consisting of a Likert scale, a Juster scale, a categorical scale, and a continuous scale with anchored descriptors.

46. The method of claim 42, further comprising the following step after step (d):

(d1) using said model to assist with the selection of archetypes required for evaluation of said target concept.

47. The method of claim 46, further comprising the following step after step (d1):

(d2) testing said model for assumptions of error and fit; and

repeating steps (b)-(d2) as necessary.

48. The method of claim 42, wherein said source concepts are all from substantially the same product class.

49. The method of claim 42, wherein said step (c) is accomplished by a human evaluator judging against a set of archetype criteria.

50. The method of claim 42, wherein said step (c) is accomplished by machine measure judging against a set of archetype criteria.

51. The method of claim 42, wherein said step (e) is accomplished by a human evaluator judging against a set of archetype criteria.

52. The method of claim 42, wherein said step (e) is accomplished by a machine measure judging against a set of archetype criteria.

53. A method for determining and assigning slotting fees for new product placement in a retail setting, said method comprising the steps of:

(a) providing a database comprising subjective reaction data, said subjective reaction data comprising responses of a plurality of individuals to at least one subjective reaction quantifier capable of being used to subjectively evaluate communicable information about one or more source concepts upon exposure of at least some of said one or more source concepts to at least some individuals of said plurality of individuals, said database further comprising responses to at least one common subjective reaction quantifier for a plurality of said one or more source concepts;

(b) selecting one or more archetypes adapted to assist with the objective evaluation of the content of the communicable information of at least some of said one or more source concepts;

(c) generating objective ratings or rule sets of at least some of said source concepts in said database based on one or more of said archetypes;

(d) developing a model defining the relationships between at least some of said subjective reaction data and at least some of said archetypes;

(e) generating objective ratings of said target concept in accordance with one or more of said archetypes defined by said model;

(f) inputting said objective ratings of said target concept into said model to predict a predetermined population'"'"'s subjective reactions to said target concept;

(g) judging the relative potential success of said target concept; and

(h) assigning an appropriate slotting fee to said target concept corresponding to and based upon said relative potential success of said target concept.

54. A method for validating and testing an organizational cultural rule, said method comprising the steps of:

(a) identifying said organizational cultural rule and characteristics of said organizational cultural rule;

(b) providing a database comprising subjective reaction data, said subjective reaction data comprising responses of a plurality of individuals to at least one subjective reaction quantifier capable of being used to subjectively evaluate communicable information about one or more source concepts upon exposure of at least some of said one or more source concepts to at least some individuals of said plurality of individuals, said database further comprising responses to at least one common subjective reaction quantifier for a plurality of said one or more source concepts;

(c) generating objective ratings or rule sets of at least some of said source concepts in said database based on characteristics of said organizational cultural rule;

(d) developing a model defining the relationships between at least some of said subjective reaction data and characteristics of said organizational cultural rule; and

(e) using said model to evaluate the validity of said organizational cultural rule.