SYSTEM AND METHOD FOR ANALYZING AND PREDICTING CONSUMER BEHAVIOR

1 . A method of analyzing and predicting consumer behavior, comprising:

receiving a plurality of consumer answers to a corresponding plurality of questions, each answer of the plurality of consumer answers input by a consumer having a unique consumer identity, each question of the plurality of questions corresponding to at least one of the following sectors:

a lifestyle attitude sector comprising a plurality of lifestyle attitude segments, each of which correspond to a different consumer attitude toward a lifestyle;

a consumer mindset sector comprising a plurality of consumer mindset segments, each of which corresponds to a different manner in which the consumer prefers to receive product information;

a product preference sector comprising a plurality of product preference segments, each of which corresponds to a different product quality desired to be experienced by the consumer;

an influencer sector comprising a plurality of influencer segments, each of which corresponds to a different factor that influences the consumer's behavior; and

a need state sector comprising a plurality of need state segments, each of which corresponds to a different consumer emotional need on a consuming occasion;

assigning a value to each user answer;

creating, based on the assigned values of each user answer and using a computer processor, a composite value associating the consumer identity with a particular lifestyle attitude segment, consumer mindset segment, product preference segment, influencer segment, and need state segment; and

comparing, via a comparator, the composite value with a plurality of product values each associated with a respective plurality of products in a product database.

2 . The method according to claim 1 , wherein the product is a food product.

3 . The method according to claim 1 , further comprising identifying, based on the compared composite value and product value, a corresponding product of the product database.

4 . The method according to claim 3 , further comprising presenting the consumer with the identified corresponding product.

5 . The method according to claim 4 , further comprising recording the consumer's judgment regarding the identified corresponding product.

6 . The method according to claim 1 , wherein the plurality of lifestyle attitude segments comprises five lifestyle attitude segments.

7 . The method according to claim 1 , wherein the plurality of consumer mindset sectors comprises five consumer mindset segments.

8 . The method according to claim 2 , wherein the plurality of product preference sectors comprises four product preference segments, each segment comprising a food olfactory strength value and a food mechanical value.

9 . The method according to claim 8 , wherein the plurality of product preference sectors further comprises a product size preference value.

10 . The method according to claim 1 , wherein the plurality of influencer segments comprises twenty influencer segments, each influencer segment comprising one of an internal influence and an external influence.

11 . The method according to claim 1 , wherein the plurality of need state segments comprises eight need state segments, each need state segment corresponding to a personal dimension in a range between pleasure and control, and further corresponding to a social dimension in a range between individuality and conformity.

12 . The method according to claim 1 , wherein the product is one of a product, marketing message, a service, a brand, one or more groups of products, and a package.

13 . At least one processor for analyzing and predicting consumer behavior, the processor configured to:

receive a plurality of lifestyle attitude segment values, each of which correspond to a different consumer attitude toward a lifestyle;

receive a plurality of consumer mindset segment values, each of which corresponds to a different manner in which the consumer prefers to receive product information;

receive a plurality of product preference segment values, each of which corresponds to a different product quality desired to be experienced by the consumer;

receive a plurality of influencer segment values, each of which corresponds to a different factor that influences the consumer's behavior; and

receive a plurality of need state segment values, each of which corresponds to a different consumer emotional need on a consuming occasion.

14 . At least one computer that executes an application for generating a composite consumer behavior image, comprising:

a memory that stores the application; and

a processor that executes the application, wherein the application, when executed by the processor, causes the computer at least to:

generate one of a plurality of lifestyle attitude sub-images, each of which represents a different consumer attitude toward a lifestyle;

generate one of a plurality of consumer mindset sub-images, each of which represents a different manner in which the consumer prefers to receive product information;

generate one of a plurality of product preference sub-images, each of which represents a different product quality desired to be experienced by the consumer;

generate at least one of a plurality of influencer sub-images, each of which represents a different factor that influences the consumer's consuming behavior; and

generate one of a plurality of need state sub-images, each of which represents a different consumer emotional need on a consuming occasion, wherein:

the generated lifestyle attitude sub-image, consumer mindset sub-image, product preference sub-image, influencer sub-image, and need state sub-image together form the composite consumer behavior image.

15 . At least one computer that executes an application for analyzing and predicting consumer behavior, comprising:

at least one memory that stores the application; and

at least one processor that executes the application, wherein the application, when executed by the at least one processor, causes the computer at least to:

receive a plurality of consumer answers to a corresponding plurality of questions, each answer of the plurality of consumer answers input by a consumer having a unique consumer identity, each question of the plurality of questions corresponding to at least one of the following sectors:

a lifestyle attitude sector comprising a plurality of lifestyle attitude segments, each of which correspond to a different consumer attitude toward a lifestyle;

a consumer mindset sector comprising a plurality of consumer mindset segments, each of which corresponds to a different manner in which the consumer prefers to receive product information;

a product preference sector comprising a plurality of product preference segments, each of which corresponds to a different product quality desired to be experienced by the consumer;

an influencer sector comprising a plurality of influencer segments, each of which corresponds to a different factor that influences the consumer's consuming behavior; and

a need state sector comprising a plurality of need state segments, each of which corresponds to a different consumer emotional need on a consuming occasion;

assign a value to each user answer;

create, based on the assigned values of each user answer and using a computer processor, a composite value associating the consumer identity with a particular lifestyle attitude segment, consumer mindset segment, product preference segment, influencer segment, and need state segment; and

compare the composite value with a plurality of product values each associated with a respective plurality of products in a product database.

16 . At least one non-transitory computer readable medium for analyzing and predicting consumer behavior, the medium comprising:

a receiving code segment which, when executed by the computer, receives a plurality of consumer answers to a corresponding plurality of questions, each answer of the plurality of consumer answers input by a consumer having a unique consumer identity, each question of the plurality of questions corresponding to at least one of the following sectors:

a lifestyle attitude sector comprising a plurality of lifestyle attitude segments, each of which correspond to a different consumer attitude toward a lifestyle;

a consumer mindset sector comprising a plurality of consumer mindset segments, each of which corresponds to a different manner in which the consumer prefers to receive product information;

a product preference sector comprising a plurality of product preference segments, each of which corresponds to a different product quality desired to be experienced by the consumer;

an influencer sector comprising a plurality of influencer segments, each of which corresponds to a different factor that influences the consumer's consuming behavior; and

a need state sector comprising a plurality of need state segments, each of which corresponds to a different consumer emotional need on a consuming occasion;

an assigning code segment that assigns a value to each user answer;

a creating code segment which, when executed by the computer, creates, based on the assigned values of each user answer and using a computer processor, a composite value associating the consumer identity with a particular lifestyle attitude segment, consumer mindset segment, product preference segment, influencer segment, and need state segment; and

a comparing code segment which, upon executed by the computer, compares, using a comparator, the composite value with a plurality of product values each associated with a respective plurality of products in a product database.

17 . The method according to claim 2 , wherein each lifestyle attitude segment of the plurality of lifestyle attitude segments comprise a different taste percentage value, convenience percentage value and health percentage value, wherein the taste, convenience and health percentage values total 100%.