METHODS AND APPARATUS TO PREDICT ATTITUDES OF CONSUMERS

• US 20160189173A1
• Filed: 12/30/2014
• Published: 06/30/2016
• Est. Priority Date: 12/30/2014
• Status: Abandoned Application

• Current Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Original Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Inventors: King, Michael, Bell, Paul, Baden, Brett Morgner, Hurwitz, Joshua
• Application Number:
  US14/586,434
• Publication Number:
  US 20160189173A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

Methods, apparatus, systems and articles of manufacture to predict attitudes of consumers are disclosed. An example method includes obtaining purchasing behavior data associated with a consumer and obtaining product review data associated with a plurality of reviewers. The example method also includes identifying a set of reviewers from the plurality of reviewers based on a strength of relationship between each of the plurality of reviewers and the consumer. The example method further includes predicting, using a processor, an attitude of the consumer based on the product review data associated with the set of reviewers.

Claims

1. A method, comprising:

obtaining purchasing behavior data associated with a consumer;

obtaining product review data associated with a plurality of reviewers;

identifying a set of reviewers from the plurality of reviewers based on a strength of relationship between each of the plurality of reviewers and the consumer; and

predicting, using a processor, an attitude of the consumer based on the product review data associated with the set of reviewers.

2. The method of claim 1, further comprising assigning a weight to each reviewer of the set of reviewers based on the strength of relationship between each of the plurality of reviewers and the consumer.

3. The method of claim 1, further comprising:

identifying product ratings assigned by the plurality of reviewers to reviewed products based on the product review data;

identifying at least one of a quantity or a price of products purchased by the consumer based on the purchasing behavior data; and

determining the strength of relationship between each of the plurality of reviewers and the consumer based on the product ratings and the at least one of the quantity or the price.

4. The method of claim 1, further comprising:

identifying feature ratings assigned by the plurality of reviewers to features of reviewed products based on the product review data; and

determining the strength of relationship between each of the plurality of reviewers and the consumer based on the feature ratings.

5. The method of claim 4, wherein the set of reviewers corresponds to a first set of reviewers when the strength of relationship is determined relative to a first one of the features of the reviewed products, the set of reviewers corresponding to a second set of reviewers different than the first set of reviewers when the strength of relationship is determined relative to a second one of the features of the reviewed products.

6. The method of claim 4, wherein the features correspond to concepts associated with the reviewed products as identified by the plurality of reviewers.

7. The method of claim 4, wherein the attitude of the consumer is predicted based on the features of the reviewed products as identified by the plurality of reviewers.

8. The method of claim 1, further comprising predicting the attitude of the consumer with respect to a product previously purchased by the consumer.

9. The method of claim 1, further comprising predicting the attitude of the consumer with respect to a reviewed product not previously purchased by the consumer.

10. The method of claim 1, further comprising predicting the attitude of the consumer with respect to a product not previously purchased by the consumer and not previously reviewed by the set of reviewers.

11. The method of claim 1, further comprising identifying a marketing segment for at least one of a product or a product feature based on the attitude of the consumer.

12. An apparatus comprising

a purchasing behavior data collector to obtain purchasing behavior data associated with a consumer;

a product review data collector to obtain product review data associated with a plurality of reviewers;

a predictive reviewer set identifier to identify a set of reviewers from the plurality of reviewers based on a strength of relationship between each of the plurality of reviewers and the consumer; and

an attitude predictor, implemented via a processor, to predict an attitude of the consumer based on the product review data associated with the set of reviewers.

13. The apparatus of claim 12, wherein the predictive reviewer set identifier is to assign a weight to each reviewer of the set of reviewers based on the strength of relationship between each of the plurality of reviewers and the consumer.

14. The apparatus of claim 12, further comprising:

a product review data analyzer to identify product ratings assigned by the plurality of reviewers to reviewed products based on the product review data;

a purchasing behavior data analyzer to identify at least one of a quantity or a price of products purchased by the consumer based on the purchasing behavior data; and

a relationship analyzer to determine the strength of relationship between each of the plurality of reviewers and the consumer based on the product ratings and the at least one of the quantity or the price.

15. The apparatus of claim 12, further comprising:

a product review data analyzer to identify feature ratings assigned by the plurality of reviewers to features of reviewed products based on the product review data; and

a relationship analyzer to determine the strength of relationship between each of the plurality of reviewers and the consumer based on the feature ratings.

16. The apparatus of claim 15, wherein the set of reviewers corresponds to a first set of reviewers when the strength of relationship is determined relative to a first one of the features of the reviewed products, the set of reviewers corresponding to a second set of reviewers different than the first set of reviewers when the strength of relationship is determined relative to a second one of the features of the reviewed products.

17. The apparatus of claim 15, wherein the features correspond to concepts associated with the reviewed products as identified by the plurality of reviewers.

18. The apparatus of claim 15, wherein the attitude predictor is to predict the attitude of the consumer based on the features of the reviewed products as identified by the plurality of reviewers.

19. The apparatus of claim 12, wherein the attitude predictor is to predict the attitude of the consumer with respect to a product previously purchased by the consumer.

20. The apparatus of claim 12, wherein the attitude predictor is to predict the attitude of the consumer with respect to a reviewed product not previously purchased by the consumer.

21. The apparatus of claim 12, wherein the attitude predictor is to predict the attitude of the consumer with respect to a product not previously purchased by the consumer and not previously reviewed by the set of reviewers.

22. The apparatus of claim 12, further comprising a market analyzer to identify a marketing segment for at least one of a product or a product feature based on the attitude of the consumer.

23. A tangible computer readable storage medium comprising instructions that, when executed, cause a machine to at least:

obtain purchasing behavior data associated with a consumer;

obtain product review data associated with a plurality of reviewers;

identify a set of reviewers from the plurality of reviewers based on a strength of relationship between each of the plurality of reviewers and the consumer; and

predict an attitude of the consumer based on the product review data associated with the set of reviewers.

24. The storage medium of claim 23, wherein the instructions further cause the machine to assign a weight to each reviewer of the set of reviewers based on the strength of relationship between each of the plurality of reviewers and the consumer.

25. The storage medium of claim 23, wherein the instructions further cause the machine to:

identify product ratings assigned by the plurality of reviewers to reviewed products based on the product review data;

identify at least one of a quantity or a price of products purchased by the consumer based on the purchasing behavior data; and

determine the strength of relationship between each of the plurality of reviewers and the consumer based on the product ratings and the at least one of the quantity or the price.

26. The storage medium of claim 23, wherein the instructions further cause the machine to:

identify feature ratings assigned by the plurality of reviewers to features of reviewed products based on the product review data; and

determine the strength of relationship between each of the plurality of reviewers and the consumer based on the feature ratings.

27. The storage medium of claim 26, wherein the set of reviewers corresponds to a first set of reviewers when the strength of relationship is determined relative to a first one of the features of the reviewed products, the set of reviewers corresponding to a second set of reviewers different than the first set of reviewers when the strength of relationship is determined relative to a second one of the features of the reviewed products feature.

28. The storage medium of claim 26, wherein the features correspond to concepts associated with the reviewed products as identified by the plurality of reviewers.

29. The storage medium of claim 26, wherein the instructions further cause the machine to predict the attitude of the consumer based on the features of the reviewed products as identified by the plurality of reviewers.

30. The storage medium of claim 23, wherein the instructions further cause the machine to predict the attitude of the consumer with respect to a product previously purchased by the consumer.

31. The storage medium of claim 23, wherein the instructions further cause the machine to predict the attitude of the consumer with respect to a reviewed product not previously purchased by the consumer.

32. The storage medium of claim 23, wherein the instructions further cause the machine to predict the attitude of the consumer with respect to a product not previously purchased by the consumer and not previously reviewed by the set of reviewers.

33. The storage medium of claim 23, wherein the instructions further cause the machine to identify a marketing segment for at least one of a product or a product feature based on the attitude of the consumer.