WO2014195761A1

WO2014195761A1 - Buyer-driven online push advertising platform for e-commerce

Info

Publication number: WO2014195761A1
Application number: PCT/IB2013/054589
Authority: WO
Inventors: Paul Pearson
Original assignee: Paul Pearson
Priority date: 2013-06-04
Filing date: 2013-06-04
Publication date: 2014-12-11

Abstract

A computer system having a non-transitory computer readible storage medium having computer program logic embodied therein for buyer-driven online push advertising in e-commerce. The computer program logic including a search module ( 2 ) that receives product data relating to products in response to searches for the products by users; a data module (4) that maps the product data to a data store that is accessible by the users and advertisers of the products; a wish list module (3) that generates wish lists of the products that the users indicate they wish to buy if user-defined buying criteria are satisfied; a functions module (5) that pushes offers relating to the products from the advertisers to the users based on matching product inventories of the advertisers to the product data and the wish lists of the users; a user management module (6) that allows the users to manage the wish lists and the pushed offers; an advertiser management module (7) that allows the advertisers to manage the pushed; a monetisation module ( 8 ) that allows the advertisers to buy push offers based on cost- per-push; a sale module (9) that determines if the pushed offers are accepted or rejected by the users; and a reporting module (10) that provides reports relating to the pushed offers to the users and the advertisers.

Description

BUYER-DRIVEN ONLINE PUSH ADVERTISING PLATFORM FOR E-COMMERCE

Field

[0001 ] The present invention relates to a buyer-driven online push advertising platform for e- commerce.

Background

[0002] Online "push" advertising of goods and services (collectively referred to as "products") in e- commerce generally involves advertisers sending electronic advertising (such as email, text messaging or display advertising on websites) to a user of online services and content based on the user's subscription or registration profile, or web history.

[0003] Existing online push advertising models are typically based on general fact-based user profiles resulting in push advertising that is broad and not targeted to an individual user's needs or desires to buy products. Current push advertising also intrudes upon a user's attention without providing an incentive for the user's consideration, and is typically regarded as spam. In addition, existing online push advertising methods use "pay-per-click" monetisation where the advertiser only pays if a user clicks on the push advertising (i.e., cost-per-click), and so the advertiser often lacks incentive to target their push advertising effectively, since poorly targeted push advertising will not be clicked and therefore will not require payment.

[0004] As a result of the problems described above, no one has yet deployed a successful model of online push advertising that allows users to shape or influence the push advertising they see and provide an incentive for considering the push advertising. The failure to develop and deploy a user/buyer-driven online push advertising model is a key barrier to monetising online content and the large user base of social media networks such as Facebook.

[0005] Accordingly, a need exists for a new model of online push advertising that addresses or alleviates at least some of the problems described above. Summary

[0006] According to the present invention, there is provided a computer system having a non- transitory computer-readable storage medium having computer program logic embodied therein for buyer-driven online push advertising in e-commerce, the computer program logic including: a search module that receives product data relating to products in response to searches for the products by users;

a data module that maps the product data to a data store that is accessible by the users and advertisers of the products;

a wish list module that generates wish lists of the products that the users indicate they wish to buy if user-defined buying criteria are satisfied;

a functions module that pushes offers relating to the products from the advertisers to the users based on matching product inventories of the advertisers to the product data and the wish lists of the users;

a user management module that allows the users to manage the wish lists and the pushed offers;

an advertiser management module that allows the advertisers to manage the pushed offers;

a monetisation module that allows the advertisers to buy push offers based on cost-per- push;

a sale module that determines if the pushed offers are accepted or rejected by the users; and

a reporting module that provides reports relating to the pushed offers to the users and the advertisers.

[0007] The products can be selected from new products, second hand or used products, in-store products and combinations thereof.

[0008] The users can be users of online services or content including social media networks.

[0009] The advertisers can be sellers of the products or advertisers for the sellers.

[0010] The product data can be received from user input, webpages, search engines, social media shares or likes, online product catalogues, QR codes, bar codes, object recognition from product images, online product information and combinations thereof. [001 1 ] The user-defined buying criteria can be selected from price, delivery time, discount, location, product category, product line, product condition, product specification, warranty and combinations thereof.

[0012] The pushed offers can include electronic messages or data communicated to network- capable mobile or desktop computer devices of the users.

[0013] The monetisation module can further perform:

segmenting the users by at least price into price segments each having a number of users therein;

determining a conversion rate for pushed offers to the users in each price segment, wherein a conversion is a user buying the product;

offering the advertisers a cost-per-push in each price segment; and

allowing the advertisers to buy push offers in an optimal price segment having a minimal cost-per-conversion calculated by dividing a total cost of advertising by a number of conversions, wherein the total advertising cost is calculated by multiplying the number of users by the cost-per- push, and wherein the number of conversions is calculated by multiplying the number of users by the conversion rate.

[0014] The conversion rate can be an average conversion rate determined from historic and/or current data of the users' buying habits for the products in a location after and/or during a push advertising campaign.

[0015] The present invention also provides a computer program product including:

a non-transitory computer-readable medium having computer program logic embodied therein for buyer-driven online push advertising in e-commerce, the computer program logic comprising:

a search module that receives product data relating to products in response to searches for the products by users;

a functions module that pushes offers relating to the products from the advertisers to the users based on matching product inventories of the advertisers to the product data and the wish lists of the users; a user management module that allows the users to manage the wish lists and the pushed offers;

a monetisation module that allows the advertisers to buy push offers based on cost-per-push;

[0016] The present invention further provides a computer-implemented method for buyer-driven online push advertising in e-commerce, the method including:

receiving product data relating to products in response to searches for the products by users;

mapping the product data to a data store that is accessible by the users and advertisers of the products;

generating wish lists of the products that the users indicate they wish to buy if user- defined buying criteria are satisfied;

pushing offers relating to the products from the advertisers to the users based on matching product inventories of the advertisers to the product data and the wish lists of the users;

allowing the users to manage the wish lists and the pushed offers; allowing the advertisers to manage the pushed offers;

allowing the advertisers to buy the push offers based on cost-per-push;

determining if the pushed offers are accepted or rejected by the users; and providing reports relating to the pushed offers to the users and the advertisers.

Brief Description of Drawings

[0017] Embodiments of the invention will now be described by way of example only with reference to the accompanying drawing, in which:

Figure 1 is a high-level block diagram illustrating a computer system for buyer-driven online push advertising in e-commerce according to an embodiment of the invention;

Figure 2 is a high-level block diagram illustrating a search module;

Figure 3 is a high-level block diagram illustrating a wish list module; Figure 4 is a high-level block diagram illustrating a data module;

Figure 5 is a high-level block diagram illustrating a functions module;

Figure 6 is a high-level block diagram illustrating a user management module;

Figure 7 is a high-level block diagram illustrating an advertiser management module;

Figure 8 is a high-level block diagram illustrating a monetisation module;

Figure 9 is a high-level block diagram illustrating a sale module;

Figure 10 is a high-level block diagram illustrating a reporting module;

Figures 1 1 A and 1 1 B illustrate examples of mobile terminal interfaces for new products;

Figures 12A and 12B illustrate examples of mobile terminal interfaces for second hand products; and

Figures 13A and 13B illustrate examples of mobile terminal interfaces for store discounted products.

Detailed Description

[0018] Figure 1 is a high-level block diagram of a computer system 10 for buyer-driven online push advertising in e-commerce according to an embodiment of the present invention. The computer system 10 is adapted to execute computer program modules. As used herein, the term "module" refers to computer program logic and/or data for providing the specified functionality. A module can be implemented in hardware, firmware, and/or software. In one embodiment, the modules are stored on a storage device, loaded into a memory, and executed by a processor. The modules executed by the computer system 10 include a search module 12, a wish list module 14, a data module 16, a functions module 18, a user management module 20, an advertiser management module 22, a monetisation module 24, a sale module 26 and a reporting module 28.

[0019] Referring to Figure 2, the search module 12 receives product data relating to products in response to searches for the products by users. The users are users of online services or content, including users of social media networks, such as Facebook, Twitter, Linked In, MySpace and Google+. The products are selected from new products, second hand or used products, in-store products and combinations thereof. The product data is received, for example, from user input, webpages, search engines, social media shares and likes, online product catalogues, QR codes, bar codes, object recognition from product images, online product information and combinations thereof. Other equivalent methods of searching for, capturing and receiving data related to products may also be used. [0020] As illustrated in Figure 3, the wish list module 14 enables the users to generate wish lists of the products found by the search module 12 that they wish to buy if their own user-defined buying criteria are satisfied. The user-defined buying criteria are, for example, selected from price, delivery time, discount, location, product category, product line, product condition, product specification, warranty and combinations thereof. The wish list module 16 also allows the users to enter and manipulate local data relating to their buying criteria data via user interfaces presented on displays of network-capable mobile or desktop computer devices.

[0021 ] Referring to Figure 4, the data module 16 maps product data relating to new, second hand and in-store products from sources of product data such as the search module 12, and assigns unique identifier codes to the mapped product data. The product data is then associatively stored with the unique identifier codes in a data store that is accessible by both the users and advertisers of the products.

[0022] The functions module 18 illustrated in Figure 5 pushes offers relating to the products from the advertisers to the users based on matching product inventories of the advertisers to the product data and the wish lists of the users. The pushed offers are, for example, electronic messages (such as SMS, text messages, etc.) or data (such as RSS feeds, Tweets, etc.) communicated to mobile or desktop computer devices of the users notifying them that their selected products are available to buy based on the user-selected buying criteria. The functions module 18 allows advertisers or sellers of products to manage offers relating to new, second hand and in-store discounted products. They are also able to set maximum budgets for pushing offers, formulate instant pushed offers that price match user-selected buying criteria based on prices or discount levels.

[0023] The user management module 20 illustrated in Figure 6 provides a user portal that allows the users to manage their wish lists, and reject or accept the pushed offers they receive from advertisers, via user interfaces on displays of mobile or desktop computer devices.

[0024] Referring to Figure 7, the advertiser management module 22 provides a user portal via mobile or desktop computer devices that enables the advertisers to manage the offers they push to users. In addition, the advertisers are able to process payments, such as point-of-sale (POS) payments, from completed sale transactions. The advertiser management module 22 also allows the advertisers to manage banks of product and discount data relating to push offers. [0025] The monetisation module 24 illustrated in Figure 8 allows the advertisers to buy, via mobile or desktop computer devices, for push offers based on cost-per-push. The cost-per-push is determined, for example, based on a demand algorithm that relates demand for the products from the users to the available supply of products from the advertisers or sellers.

[0026] As illustrated in Figure 9, the sales module 26 determines whether pushed offers are accepted or rejected by the users. The users are able to accept the pushed offers by, for example, scanning a unique bar code on their mobile computer device, or by pre-selecting instant acceptance of a pushed offer at a specific price. Scenarios in which pushed offers are rejected include, for example, if the pushed offer has a time period for acceptance that is missed, if the product in the pushed offer is out of stock, or the user manually rejects the pushed offer.

[0027] Referring to Figure 10, the reporting module 28 provides reports relating to the pushed offers to the users and the advertisers. The reports available to users include, for example, reports on completed buying transactions and reports on historic information about pushed offers and products. The reports available to advertisers include, for example, reports on completed transactions, rejected push offers, user uptake conversion, online enquiries, call tracking, and POS payments.

[0028] The invention will now be described in more detail by reference to the following examples, which are illustrative only and do not limit the scope of the invention.

Example 1 - New products

[0029] An example use case involves a user who needs a new toaster. The user walks past a shop and sees a new toaster in a shop window. The user points a smartphone executing software of the system at the toaster and takes a photo. The system software searches for the toaster in the data store using object recognition. Referring to Figure 1 1A, the system finds the toaster and presents a product image and description of the toaster in a window 30 on the smartphone screen. The system also shows the location of the toaster in a map window 32. The recommended retail price of $128 is presented by the system on a slide toggle 34. The user then considers the price they are willing to pay for the toaster. Referring to Figure 1 1 B, the user slides the toggle 34 to the left to indicate a willingness to buy at a price of $68. The user then decides that they will buy the toaster if their price of $68 is offered in future, so they decide to enter the toaster in their wish list and use the "instant buy" option of the system under which a future pushed offer of $68 will be automatically accepted by the system on behalf of the user. To activate the "instant buy" option, the user enters credit card payment details. Once these are verified, the system illuminates an ® icon 36 on the smartphone screen to signify that the "instant buy" option is activated. At this stage, the user's credit card has not been charged - it will only be charged if and when a pushed offer (or notification) matching the user's buying criteria is received in future.

[0030] Two weeks pass before an advertiser of the toasters wishes to sell some stock of the toasters in the same location as the user described above. The system indicates to the advertiser that there are 100 matching users in the location that are willing to pay $68 for the toaster. The advertiser enters credit card payment details into the system and buys 100 push offers to sell at $68 at a cost-per-push of $2. The system then pushes offers at $68 to the 100 users, including the user described above. That user receives notification on the smartphone of receipt of a push offer to buy the toaster at $68. The pushed offer is automatically accepted due to the prior selection of the instant acceptance option, and payment for the toaster is processed using the pre- entered credit card details. One day later the toaster is delivered to the user.

Example 2 - Second hand products

[0031 ] This example use case is similar to Example 1 described above except that the product is a second hand product. A user searches for a second hand TV on a smartphone using a keyword search executed by the system. Referring to Figure 12A, the system finds a matching second hand TV near the user's location in the product data store, and an image and description of the second hand TV are presented to the user in a window 30 on the smartphone screen. The location of the toaster is shown in a map window 32. The price of the second hand TV is to be determined by the user so the toggle slide is initially set at $0. Referring to Figure 12B, the user is interested in buying the TV by adding it to their wish list and inputting a buying price of $390 by sliding the toggle 34 to the right. The use case for second hand products then proceeds to the seller pushing offers to matching users as described above in Example 1 . However, an additional feature provided by the system for second hand products is that pushed offers include detailed photos and description of the condition of the actual second hand product being offered for sale. This extra detail provided in the pushed offer allows users to scrutinise the condition of a second hand product before deciding to accept the pushed offer.

Example 3 - Store discounted products

[0032] This example use case involves a user that is interested in receiving push offers when a store discounts by a percentage discount selected by the user. The user enters the store of interest and the system finds the store in the system database. Referring to Figure 13A, the system presents a description of the store on the user's smartphone in window 30, and the location of the store is presented in map window 32. The slide toggle 34 is initially presented as 0% because the user has not yet selected a discount level of interest. Referring to Figure 13B, the user then considers that a 55% discount would be of interest, and decides to input a discount of 55% by sliding the toggle 34 to the right.

[0033] The next day the manager of the store decides to a hold a storewide discount sale. The system indicates to the store that there are 1000 matching users in the location that are interested in a storewide discount of 55%. The store manager enters credit card payment details into the system and buys 1000 push offer at a cost-per-push of $5. The system then pushes offers to the 1000 users, including the user described above. That user receives notification on the smartphone of receipt of a push offer of a 55% storewide discount. The user then uses the smartphone to navigate through the store's product categories and product lines to select individual products to buy at the 55% discount.

Example 4 - Optimising return on investment in online push advertising using cost-per-push

[0034] In this example, the system segments users in Sydney that are interested in buying a new set of golf clubs by price into price segments. The system determines the number of users in each price segment. A conversion rate for pushed offers to the users in each price segment is also determined by the system, where a "conversion" is a user buying the product. The conversion rate is calculated as an average conversion rate determined from historic data from a previous push advertising campaign of the same golf clubs to users in Sydney. The system then offers an advertiser that has 100 sets of the golf clubs to sell a cost-per-push in each price segment. The demand metrics presented to the advertiser in this example are set out in the following table.

Table 1

(price) $845 (location) Sydney (quantity) 100 > (users) 63 (CR) 21.4% (cost-per-push) $6.75 (total cost) $425.25 (price) $810 (location) Sydney (quantity) 100 > (users) 170 (CR) 33.2% (cost-per-push) $6.22 (total cost) $1057.40 (price) $790 (location) Sydney (quantity) 100 > (users) 220 (CR) 35.1 % (cost-per-push) $5.82 (total cost) $1280.40 (price) $778 (location) Sydney (quantity) 100 > (users) 300 (CR) 37.2% (cost-per-push) $5.72 (total cost) $1716.00 (price) $750 (location) Sydney (quantity) 100 > (users) 800 (CR) 40.2% (cost-per-push) $4.72 (total cost) $3776.00 (price) $730 (location) Sydney (quantity) 100 > (users) 2000 (CR) 68.2% (cost-per-push) $4.22 (total cost) $8440.00 [0035] The demand and supply metrics provided by the system enable the advertiser to find a "sweet spot" for push offers in an optimal price segment having a minimal cost-per-conversion which is calculated by dividing the total cost of advertising by the number of conversions. The total advertising cost is calculated by multiplying the number of users by the cost-per-push, and the number of conversions is calculated by multiplying the number of users by the conversion rate. The system performs these calculations and indicates to the advertiser that the minimum cost-per- conversion is achieved by push offers in the price segment shown in bold in Table 1 above with a cost-per-push of $5.72. The advertiser then buys these indicated push offers in order to optimise the return on investment in the push advertising.

[0036] Embodiments of the present invention provide a new model of online push advertising that enables a user or buyer to shape and influence the push advertising they see. The users are incentivised to consider the pushed offers because they set their own offer terms to attract matching push offers. Advertisers or sellers of products are incentivised to pay for the buyer- driven push offers because they are closely targeted to the buyer's demand for products and price sensitivity. This enables the advertisers and sellers to closely match their product inventory to buyer demand. It will be appreciated that the buyer-driven online push advertising model of embodiments of the present invention unlocks a key barrier to monetising online content and the large user base of social media networks.

[0037] The above embodiments have been described by way of example only and modifications are possible within the scope of the claims that follow.

Claims

1 . A computer system having a non-transitory computer-readable storage medium having computer program logic embodied therein for buyer-driven online push advertising in e-commerce, the computer program logic including:

2. A computer system according to claim 1 , wherein the products are selected from new products, second hand or used products, in-store products and combinations thereof.

3. A computer system according to claim 1 or 2, wherein the users are users of online services or content including social media networks.

4. A computer system according to any preceding claim, wherein the advertisers are sellers of the products or advertisers for the sellers.

5. A computer system according to any preceding claim, wherein the product data is received from user input, webpages, search engines, social media shares or likes, online product catalogues, QR codes, bar codes, object recognition from product images, online product information and combinations thereof.

6. A computer system according to any preceding claim, wherein the user-defined buying criteria are selected from price, delivery time, discount, location, product category, product line, product condition, product specification, warranty and combinations thereof.

7. A computer system according to any preceding claim, wherein the pushed offers include electronic messages or data communicated to network-capable mobile or desktop computer devices of the users.

8. A computer system according to any preceding claim, wherein the monetisation module further performs:

offering the advertisers a cost-per-push in each price segment; and

9. A computer system according to any preceding claim, wherein the conversion rate is an average conversion rate determined from historic and/or current data of the users' buying habits for the products in a location after and/or during a push advertising campaign.

10. A computer program product including:

a data module that maps the product data to a data store that is accessible by the users and advertisers of the products; a wish list module that generates wish lists of the products that the users indicate they wish to buy if user-defined buying criteria are satisfied;

1 1 . A computer-implemented method for buyer-driven online push advertising in e-commerce, the method including:

allowing the advertisers to buy push offers based on cost-per-push; determining if the pushed offers are accepted or rejected by the users; and providing reports relating to the pushed offers to the users and the advertisers.