ABSTRACT :-
A system and method of combining group buying with regular selling is provided. A group-buying company advertises a first product for sale from a group-buying website. The first product is a group-buying deal with deep discount offered for a short period of time. The group-buying websites also provides recommendation information on one or more products based on the first product. The one or more recommended products are supplied by another non-group-buying company. Consumers are able to purchase the one or more recommended products from the group-buying website
DESCRIPTION :-
The present invention relates generally to e-commerce and, more particularly, to system and method for cross-promotion of group-buying products with non-group-buying products.
There are several models in B2C (business to consumer) commerce. A first model is the traditional brick and mortar stores and shops. In this model, consumers visit the stores to buy products or services (e.g., in a restaurant). Usually, most of the products or services are sold at regular price, with a small number of items sold at lower (promotional) price and for a limited time. This form of commerce has been in existence for thousands of years.
A second model is e-commerce via online websites and stores. A typical example of this is amazon.com, where consumers buy products online and receive the products through shipment. In general, this model does not cover local services (e.g., restaurants). The pricing structure is similar to brick and mortar stores in that most of the products are sold at regular price, with a small number of items sold at lower (promotional) price and for a limited time. This form of commerce has been in existence for the past two decades or so.
A third model is online group-buying. This is a model got popularized in the recent 2-3 years. It started by offering coupons for local services online, and more recently, has evolved to also cover delivered products at scale. Different from the above two models, all the products sold in a typical group-buying website are priced at deep discount, but a minimum number of buyers is required for a deal (the product or service being sold) to be valid. Another limitation of the group-buying model is that product selection at any particular time is limited. Compared with (possibly) hundreds of thousands of products on a regular e-commerce site, a group-buying website usually just has hundreds of products or less at any particular time.
In B2C commerce, recommendation is a very common technique. For example, when a customer buys a laptop, the merchant may recommend the customer to buy a mouse. In the online world, recommendations often appears as “Customer who bought this product also bought”, or “You may also like”. These recommendations are often computed base on sophisticated data mining algorithms, such as association rules, collaborative filtering, and/or personalization algorithms. Because of the importance of this technique, recommending systems has become a focused research area in recent years. Currently, however, recommendations on B2C websites are limited to products offered by the same website, and under the same B2C model. For example, a group-buying company may recommend other group-buying deals from the same company, and an online store may recommend other products from the same site..
A system and method of combining group buying with regular selling is provided. A group-buying company advertises a first product for sale from a group-buying website. The first product is a group-buying deal with deep discount offered for a short period of time. The group-buying websites also provides recommendation information on one or more products based on the first product. The one or more recommended products are supplied by another non-group-buying company. Consumers are able to purchase the one or more recommended products from the group-buying website.
In one embodiment, the non-group-buying company as a supplier opens up its inventory database to the group-buying company. The group-buying company determines the recommendation information based on data mining algorithms using the following information: relationship to the currently advertised group-buying product, personal information of the current buyer, collective buying behavior of users, and what is currently available in the inventory database. In addition, extended product selection is achieved by searching capability. Users can search the entire inventory database of the supplier from the same group-buying website and make payments from the same group-buying website.
In another embodiment, the non-group-buying company receives relevant information from the group-buying company and determines the recommendation information, which is sent back to the group-buying company. The non-group-buying company may have more control on the cross-promotion process including the payment process and even the display format of the recommendation.
In another preferred embodiment, the non-group-buying company is a local store that provides a large selection of products and services to local consumers. The recommended products/services may be determined based on buyer's location. After the buyer purchases a product or service from the group-buying website, the buyer gets a voucher or coupon code. The buyer then goes to the nearby local store to redeem the coupon (e.g., pick up the ordered product or enjoy the ordered service). This type of commerce model is a type of O2O (online-to-offline) transaction that has certain advantages by combining online and offline business, especially when location-based recommendation can be targeted to consumers located near the local store.
Other embodiments and advantages are described in the detailed description below. This summary does not purport to define the invention. The invention is defined by the claims.
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