Summarization and personalization of big data method and apparatus
Systems and methods for a user interface to summarize and personalize a large amount of price and product information, to identify patterns therein, and to generate recommendations in relation thereto are described herein.
1. A method including:
receiving, via a user computer, over a network, a first user selection including a product, and a brand, vendor and category associated with the product;
accessing a set of product information that includes information regarding the selected product, the set of product information including a set of products, wherein the product information includes for each product in the set: one or more brands under which the product is sold, a set of vendors offering it for sale, and a product category;
generating a set of analysis results based on the obtained product information;
identifying a first statistical pattern in a first analysis result from the set of analysis results for the selected product by performing statistical analysis on the product information and the analysis results,
in which the first statistical pattern is a statistical pattern between two or more types of data included in the first analysis result or in the product information and in which the two or more types of data share a common aspect, and in which the first statistical pattern comprises a cyclic change in the first analysis result,
in which the set of analysis results includes:
a social metric, an identification of which product in a sub-set of products leads or follows other products in the sub-set of products in terms of price changes, a demand metric based at least in part on visitors record generated from webpage traffic to one or more online stores at which the product is available, and in which the demand metric is stored in the database coupled to the computer, and a reach of the product in terms of the number of people who visit an online sales venue of the product, in which the social metric is generated based on a number of followers or a number of likes of the product, and in which the social metric is stored in a database coupled to the computer;
transmitting to the user computer, over the network, for display to the user, the first analysis result and the first statistical pattern;
receiving, via the user computer, over the network, a create alert command for the product, the create alert command including:
alert criteria, including at least one of: absolute or percentage change in price, initiation or termination of sales at a venue, and
a notification window and frequency;
in response to the command, executing the alert and transmit to the user computer, over the network, a notification;
receiving a second user selection and a second analysis result with respect to the second user selection; and
identifying a second statistical pattern in the second analysis result.
2. The method of claim 1 , in which the first user selection further comprises at least one of a user list and a user favorite.
3. The method of claim 2 , in which the user list comprises a custom list or a smart list.
4. The method of claim 3 , in which the smart list is created upon receipt of a selection of at least one of a brand, a vendor or a category in the set of product information and at least one criterion for limiting the first analysis result presented in relation to the user list, and in which the custom list comprises a list of products provided by the user.
5. The method of claim 1 , further including:
comparing the second analysis result relating to the second user selection to the first analysis result, and
making a first recommendation to the user based on the comparison of the first and second analysis results.
6. The method of claim 5 , in which:
the first analysis result comprises, for a product sold by a first vendor: a demand metric for the product sold by the first vendor, a price history for the product sold by the first vendor, and a promotion metric for the product sold by the first vendor;
the second analysis result comprises, for a product sold by a second vendor, a demand metric for the product sold by the second vendor, a price history for the product sold by the second vendor, and a promotion metric for the product sold by the first vendor; and
in which the first recommendation comprises at least one of:
a recommendation to add or remove the product sold by the first vendor or the product sold by the second vendor from an inventory of the first or second vendor,
a recommendation to the first or second vendor to charge a higher or lower price for the product sold by the first vendor or the product sold by the second vendor, and
a recommendation to the first or second vendor to increase or decrease a promotion of the product sold by the first vendor or the product sold by the second vendor.
7. The method of claim 6 further including:
determining if the demand metrics in the first and second analysis results are low;
and in response to a determination that they are low, recommending to the user that the product sold by the first vendor or the product sold by the second vendor be removed from inventory.
8. The method of claim 6 , further including:
when:
the price histories of the product sold by the first and second vendors indicate a price difference that exceeds a pre-defined threshold, and
there is a difference in the promotion metrics between the product sold by the first and second vendors, at least one of:
recommending an increase in the price of the product sold by the first vendor or the product sold by the second vendor to a vendor associated with a higher demand metric, a lower promotion metric and a lower current price,
recommending an increase in the price of the product sold by the first vendor or the product sold by the second vendor or a lower promotion to a vendor associated with the higher demand metric, a higher promotion metric, and the lower current price, and
recommending that the product sold by the first vendor or the product sold by the second vendor be discontinued by a vendor associated with a lower demand metric, the lower promotion metric, and the lower current price.
9. The method of claim 1 , further including:
receiving from the user, over the network, an instruction to link two or more different products in the set of product information; and
in response to a number of times the two or more different products in the set of product information are linked by any user exceeding a threshold, identifying the two or more products as substitutes for each other.
10. The method of claim 1 , further including:
receiving an instruction to send a notice to the user when the first analysis result corresponds to a user-specified alarm setting.
11. The method of claim 10 , in which the user-specified alarm setting comprises at least a first target in the first user selection and one or more alarm criteria.
12. The method of claim 11 , in which the one or more alarm criteria comprise at least one of:
an absolute or percentage change in price of the first target,
a promotion of the first target, and
an inception of availability or discontinuation of availability of the first target.
13. The method of claim 11 ,
in which the one or more alarm criteria comprise a second target comprising at least one of: a product, a category in a product categorization schema, a brand, and a vendor, and
in which the one or more alarm criteria comprise at least one of an absolute or percentage change in price between the first and second targets and a change in the promotion status of one of the first and second targets relative to either the first or the second target.
14. The method of claim 1 , further including:
associating the user with a competitor of the user selection and in which presenting the analysis result comprises presenting the first analysis result in relation to the user selection and the competitor.
15. The method of claim 1 , further including:
receiving a re-price command from the user, and, in response, reprice a product offered by the user in response to a condition of the first analysis result, and
in which the re-price command comprises at least one of an instruction to re-price the product offered by the user to a price higher or lower than a price of a product in the first analysis result.