Patent Application
20240346537
Today's consumer has the option to shop in brick and mortar retail outlets, shop online, or patronize retail outlets that have brick and mortar and an online presence. The convenience of shopping through a retail outlets' online presence provides the consumer additional cost and time saving advantages, making the option of purchasing products over the Internet an increasingly popular choice.
The advent of the auto-replenishment system is an evolution to the online shopping phenomenon that increases efficiencies for the consumer by allowing the customer to make purchases, at regular intervals, of consumable items, so that the customer may never run out of these products. The auto-replenishment system eliminates the need for the weekly and monthly purchase of household consumables, and items regularly bought by the consumer household are refilled without the intervention of the consumer. Additionally, the consumer may have the ability to adjust the frequency and volume of the auto-replenishment cart thereby updating the delivery volume and schedule as needed.
The auto-replenishment platform removes the mundane task of re-ordering of consumable household products, freeing up time for the consumer to focus on more enjoyable tasks.
The detailed description is depicted with reference to the accompanying figures, in which the left most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different figures indicates similar or identical items.
The disclosure of application attorney docket REP.P0004US entitled “Automated Replenishment Shopping Platform” to Tom Furphy, Justin Leigh, and Umair Bashir is hereby incorporated by reference.
This disclosure is directed to techniques for an auto-replenishment platform that exchanges information with retailers and manufacturers to analyze shopping data from multiple sources to determine or predict consumer demand for selected products.
The auto-replenishment platform may be extended to seamlessly integrate into the e-commerce platform of multiple retailer/manufacturers. Specifically, auto-replenishment shopping platform may collect consumer shopping data, analyze it to generate models of the customer product/service demands and share it with retailers/manufacturers to enable retailers/manufacturers to efficiently satisfy consumer demands and expectations. This may be achieved by integrating the consumer and product data from retailers, direct to consumer manufacturers, and other 3rd parties and analyzing the data to determine the consumer demand for the product and factors that influence a consumer's perception of convenience or ease in purchasing that product.
Data from different retailer/manufacturer e-commerce platforms can be collected and then combined into a coherent data set. The greater the number of retailers and manufacturers that provide information to the data set, the greater the ability of the auto-replenishment shopping platform to provide accuracy in the shopping data analysis. The data set is then mapped and organized, so that disparate data can be turned into a format suitable for analysis. In turn, a set or collection of consumer data, such as data collected by the auto-replenishment platform from retailers/manufacturers, can also be mined, clustered, and harmonized. This can facilitate integration of the auto-replenishment platform with the e-commerce platform(s), and may enable the auto-replenishment platform to distribute targeted ads, identify and track consumers' delivery/curbside pickup preferences, aggregate customer orders from multiple e-commerce platforms for delivery or pickup at specified intervals, and provide last mile delivery visibility.
Example embodiments variously provide technical advantages, including for example, reducing product waste by enabling consumers to efficiently purchase products in appropriate amounts for delivery/receipt at appropriate times, to avoid wastage of excess product, and to avoid wastage of resources that can result when alternate, less appropriate substitute products are used. In addition, planned and recurring orders of consumer products can enable retailers and manufacturers to use logistical and financial resources more efficiently, and reduce usage of both materials and energy. For example, where products are grouped and shipped together to a consumer, less fuel, packing material, and transportation resources can be used to transport the products to the consumer. Sharing of information allows retailers and manufactures to have greater knowledge and more advance notice of orders and consumer demand, resulting, for example, in longer lead times and more precise information to enable greater planning and efficient use of resources to achieve a more steady or measured manufacture and flow of goods, which means fewer resources can be used to accomplish logistical objectives-smaller, efficient transportation vehicles and manufacturing machines operating sustainably at higher duty cycles, more modest inventory and correspondingly lower warehousing requirements, and so forth. Thus, example embodiments provide technical advantages of conserving and enabling more efficient use of manufacturing, transportation, and computing and network communication resources and machinery
The servers 104, of the auto-replenishment platform 102, may also interact with one or more manufacturers' e-commerce platforms, such as a manufacturer platform 110. The manufacturer e-commerce platform 110 may include the online sales presence of a manufacturer that provides goods directly to the consumer, with which a customer has established one or more user accounts. A user account for a customer may include account access information that enables the customer to conduct a sales transaction with the manufacturer e-commerce platform 110. The account access information may include bank account numbers, routing numbers, security codes, passwords, payment instrument expiration dates, and/or so forth. Additionally, the manufacturer e-commerce platform 110 may provide for the customer to select products and services for individual purchase or may provide for repeated purchases at regular time intervals, such as for auto-replenishment. The manufacturer e-commerce platform 110 may store the customer's shopping history data for a specified regular time interval. The shopping data may include a customer identifier, such as a customer account number, a list of purchased products, a quantity of purchased products, a purchased product price, etc. The customer's shopping history data for the regular time interval may be stored in a file, such as a manufacturer data file 112. The manufacturer data file 112 may include the shopping data for at least one customer for the regular time interval.
Additionally, the servers 104 of the auto-replenishment platform 102 may also interact with one or more 3rd party e-commerce platforms, such as a 3rd party e-commerce platform 114. The 3rd party e-commerce platform 114 may include an online sales presence that resells goods of third-party retailers and manufacturers, and with which a customer has established one or more user accounts. A user account for a customer may include account access information that enables the customer to conduct a sales transaction with the 3rd party e-commerce platform 114. The account access information may include bank account numbers, routing numbers, security codes, passwords, payment instrument expiration dates, and/or so forth. Additionally, the 3rd party e-commerce platform 114 may provide for the customer to select products and services for individual purchase or may provide for repeated purchases at regular time intervals, such as for auto-replenishment. The 3rd party e-commerce platform 114 may store the customer's shopping history data for a specified regular time interval. The shopping history data may include a customer identifier, such as a customer account number, a list of purchased products, a quantity of purchased products, a purchased product price, etc. The customer shopping data for the regular time interval may be stored in a file, such as a 3rd party data file 116. The 3rd party data file 116, may include shopping data for at least one customer for the regular time interval.
In additional embodiments, the servers 104 of the auto-replenishment platform 102 may communicate with servers 120 of the retailer e-commerce platform 106, may communicate with servers 122 of the manufacturer e-commerce platform 110, and may communicate with servers 124 of the 3rd party e-commerce platform 114, via a network 126. The network 126 may be or include one or more of a local area network (“LAN”), a larger network such as a wide area network (“WAN”), a mobile telephone network, and/or a collection of networks, such as the Internet. The network 126 may be a wired network, a wireless network, or both.
The servers may include general purpose computers or other electronic devices that can receive inputs, process the inputs, and generate output data. In other embodiments, the servers may be virtual computing devices in the form of virtual machines or software containers that are hosted in a cloud.
The auto-replenishment platform 102 may initially receive the retailer data file 108 from a retailer, such as retailer e-commerce platform 106. The retailer e-commerce platform 106 may send the retailer data file 108 at regular time intervals or at the request of the auto-replenishment platform 102, via the network 126. The auto-replenishment platform 102 may receive the retailer data file 108 from at least one retailer for the regular time interval. In this case, the auto-replenishment platform 102 may route the retailer data file 108, from at least one retailer, to a harmonization engine 128 for processing. The harmonization engine 128 will be described in greater detail further below.
Additionally, the auto-replenishment platform 102 may receive the manufacturer data file 112 from the manufacturer e-commerce platform 110. The manufacturer e-commerce platform 110 may send the manufacturer data file 112 at the regular time interval, or the request of the auto-replenishment platform 102, via the network 126. The auto-replenishment platform 102 may receive the manufacturer data file 110 from at least one manufacturer for the regular time interval. In this case, the auto-replenishment platform 102 may route the manufacturer data file 110, from at least one manufacturer, to the harmonization engine 128 for processing.
Furthermore, the auto-replenishment platform 102 may receive the manufacturer data file 116 from the 3rd party e-commerce platform 114. The 3rd party e-commerce platform 114 may send the 3rd party data file 116 at the regular time interval, or at the request of the auto-replenishment platform 102, via the network 126. The auto-replenishment platform 102 may receive the 3rd party data file 116 from at least one 3rd party retailer for the regular time interval. In this case, the auto-replenishment platform 102 may route the 3rd party data file 116, from at least one manufacturer, to the harmonization engine 128 for processing.
The harmonization engine 128 may be implemented by the computing devices 104 of the auto-replenishment platform 102. The harmonization engine 128 may collect the retailer data, the manufacturer data, and the 3rd party data across all retailers, manufacturers and 3rd parties and combine the collected data into a data set. The cohesive data set may then be mapped and organized, via a machine learning algorithm, so that all disparate data is turned into a format that can be used for analysis. For example, such data may be organized in an array format or any other format which can enable the harmonization engine 128 to readily correlate the extracted data from the cohesive data set and correlate with the remaining data from the data set. The harmonization engine 128 may categorize and analyze the cohesive data set to compute the consumer demand for the product and a consumer's perception of convenience or ease in purchasing the product, in real time. As a result, the customer demand and convenience are conditions for the probability of a customer's intent to purchase a specific product, at a particular volume and with a particular frequency over the regular time interval. Based on the harmonization engine 128 analysis, the auto-replenishment platform 102 may distribute targeted ads to customers and may enable the auto-replenishment platform 102 to identify and track customers' last mile delivery preferences, e.g., for different products. In additional embodiments, the harmonization engine 128 may group products ordered by a customer from multiple e-commerce platforms into one bundle for delivery or pickup at specified intervals. As a result, products with disparate shipping intervals are harmonized into one delivery that satisfies the replenishment interval and/or the customer fulfillment options. The grouping of products for shipment or pickup may provide additional efficiencies for the customer in the form of time savings and/or shipping cost savings. Grouping of products for shipment or pick/up can additionally or alternatively provide financial efficiencies for the retailer, for example in the form of reduced shipping and handling costs and related logistical advantages.
The harmonization engine 128 analysis may be stored by the auto-replenishment platform 102 in the consumer model file 130 and routed to the retailer e-commerce platform 106, the manufacturer e-commerce platform 110, and the 3rd party e-commerce platform or any other party that may or may not have a commercial relationship with the auto-replenishment platform 102. In additional embodiments, data contained with the consumer model file 130 may be anonymized prior to being routed, so that the original data source may not be identifiable.
The memory 206 may be implemented using computer-readable media, such as computer storage media. Computer-readable media includes, at least, two types of computer-readable media, namely computer storage media and communications media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, Random-Access Memory (RAM), Dynamic Random-Access Memory (DRAM), Read-Only Memory (ROM), Electrically Erasable Programable Read-Only Memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by a computing device. Computer readable storage media do not consist of, and are not formed exclusively by, modulated data signals, such as a carrier wave. In contrast, communication media may embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transmission mechanism.
The processors 204 and the memory 206 of the computing devices 104 may implement an operating system 210 and the harmonization engine 128. The operating system 210 may include components that enable the computing devices 104 to receive and transmit data via various interfaces (e.g., user controls, communication interface, and/or memory input/output devices), as well as process data using the processors 204 to generate output. The operating system 210 may include a presentation component that presents the output (e.g., display the data on an electronic display, store the data in memory, transmit the data to another electronic device, etc.). Additionally, the operating system 210 may include other components that perform various additional functions generally associated with an operating system.
The harmonization engine 128 may include a data input module 212, a data aggregation module 214, and data mining module 216, a sort/cluster data module 218, and a trend data module 220. The auto-replenishment engine 124 may also interact with a data store 222. These modules may include routines, program instructions, objects, and/or data structures that perform particular tasks or implement particular abstract data types.
The data input module 212 may receive retailer data 108, manufacturer data 112, and 3rd party data 116 via the network 126. The retailer data 108 may include the consumer shopping data or the products that are in the consumer shopping cart of the retailer. Example consumer shopping data may include a retailer customer identifier, such as a customer account number, a list of purchased products, a volume of purchased products, a suggested retail price, a product purchase price, and any retailer discounts. Additionally, this may include a list of products the consumer ordered repeatedly for a set time period through the e-commerce platform or via the auto-replenishment platform of the retailer e-commerce platform.
The manufacturer data files 112 may include the consumer shopping data or the products that are in the consumer shopping cart of a manufacturer that retails directly to the consumer. Example consumer shopping data may include a manufacturer customer identifier, such as a customer account number, a list of purchased products, a volume of purchased products, a suggested retail price, a product purchase price, and any manufacturer discounts. Additionally, this may include a list of products the consumer ordered repeatedly from the manufacturer for a set time period through the e-commerce platform or via the auto-replenishment platform of the manufacturer e-commerce platform.
The 3rd party data files 116 may include the consumer shopping data or the products that are in the consumer shopping cart of a 3rd party that retails the products of other retailers or manufacturers. Example consumer shopping data in the data files 116 may include a 3rd party customer identifier, such as a customer account number, a list of purchased products, a volume of purchased products, a suggested retail price, a product purchase price, and any manufacturer and retailer discounts. Additionally, the data may include a list of products the consumer ordered repeatedly from the 3rd party for a set time period through the e-commerce platform or via the auto-replenishment platform of the 3rd party e-commerce platform.
The data aggregation module 214 may combine the retailer data files, the manufacturer data files and the 3rd party data files, for the regular time interval, into a combined data set and prepare the combined data set for processing and further analysis. Preparation of the data set may include sorting the combined data by specific categories or values. Data in the set may be sorted by one category or multiple categories. In cases where the data is sorted by multiple categories, the first category may be given a higher priority than a second category, the second category may be higher priority than a third category, and so on. For example, in example embodiments the data may be sorted by categories of products, then in each product category; the products may be sorted by manufacturer. Furthermore, the products listed in each manufacturer category may be ranked by volume or price. In additional embodiments, the data aggregation module 214 may anonymize the data so that the original source for the data may not be identifiable.
The data mining module 216 may apply a decision tree algorithm to the combined data to identify critical features of one or more conditions that lead to the determination of anomalies, patterns, and correlations within the data sets to predict outcomes.
Accordingly, the data mining module 216 uses the aggregated combined data from the data aggregation module 214 as an input to decision tree learning in order to discover data trends from an output decision tree. Starting from the categories of the combined data, a decision tree learning algorithm may find a first data point for the data trend, corresponding to a first-time value. The data mining module may define the first-time value as a datum point for establishing the data trend in real time. From an updated aggregated combined data set, the decision tree algorithm may find a second data point for the data trend and a corresponding second time value. For example, the updated aggregated combined data results in a second time value, and if the second time value is current or very recent, then the second data point can indicate a real-time status of the data trend. A relationship between the first data point and a second data point is represented by a tree leaf in the decision tree. An update in the aggregated combined data set and subsequent or consequent change in the relationship between data sets may create new leaf nodes for the decision tree and updates to the data trend.
The sort/cluster data module 218 may extract and group decision tree leaves based on relationships of data points. The data points may be sub-divided into clusters such that items in a cluster may be similar to one another, but different from items in another cluster. The clusters may reveal associations, patterns, and relationships between the data and may be grouped by a category of products, a specific geographic area, by consumer or any other set of data points that determine both consumer demand for the product and factors that influence a consumer's perception of convenience or ease in purchasing the product. For example, a cluster may establish that a segment of society may prefer a specific auto-replenishment product at a specific regular time interval. The clustering of decision tree leaves of the decision tree may determine a model constrained by consumer/manufacturer data for the consumer model in real time.
The trend data module 220 may establish the relative consumer model 130 for a set time period. The consumer model 130 may be comprised of a grouping of clusters and/or data that model product/service consumer demand and product/service factors that influence a consumer's perception of convenience or ease in purchasing the product/service. For example, the clusters may show a consumer's likelihood of selecting a product/service from available alternatives, a list of products that exhibit a consumer inclination for auto-replenishment, or any other criteria that reflect a consumer preference. The clusters may be categorized by geographic area, by consumer demographics, by consumer replenishment interval preferences, by consumer shipping preferences, by product category, and so forth. The trend data module 220 may update the consumer model 130 as tree leaves from the tree learning algorithms change in real time, and data clusters are revised and updated. In additional embodiments, the trend data module 220 may determine the preferred consumer fulfillment options and may aggregate products with disparate fulfillment intervals into one shipment, or pickup, that satisfies the replenishment interval and/or the customer fulfillment options.
The data store module 222 may store data that is used by the various modules of the auto-replenishment engine 128. The data store module 222 may include one or more databases, such as relational databases, object databases, object-relational databases, and/or key-value databases. In various embodiments, the data store module 222 may store the retailer data file 108 and updates, the manufacturer data file 112 and updates, the 3rd party data file 116 and updates, and the consumer model 130.
At block 304, an auto-replenishment platform receives, via at least one manufacturer e-commerce platform, at least one manufacturer data file for a regular time interval. From block 304, control proceeds to block 306.
At block 306, an auto-replenishment platform receives, via at least one 3rd party e-commerce platform, at least one 3rd party data file for a regular time interval. From block 306, control proceeds to block 308.
At block 308, the harmonization engine compiles the retailer data files, the manufacturer data files and the 3rd party data files into one large data file for analysis. From block 308, control proceeds to block 310.
At block 310, the harmonization engine applies a machine learning analysis tool to examine data trends, make comparisons, reveal information and insights, or discover hidden patterns that would not be observable when the data elements are viewed in isolation. The analysis tool objective is to identify auto-replenishment product demand and consumer preferences. From block 310, control proceeds to block 312.
At block 312, the harmonization engine clusters the data by the relationship of data points. Data points of similar qualities or with interrelations are organized into groups that define product demand and consumer preferences. From block 312, control proceeds to block 314.
At block 314, the harmonization engine develops a consumer model for the regular time interval. The consumer model contains clusters and/or data that model product/service demand and customer preferences. From block 314, control proceeds to block 316.
At block 316, the auto-replenishment platform sends the consumer model to at least one retailer for the integration of the retailer e-commerce platform and the auto-replenishment platform. From block 316, control proceeds to block 318.
At block 318, the auto-replenishment platform sends the consumer model to at least one manufacturer for the integration of the manufacturer e-commerce platform and the auto-replenishment platform. From block 318, control proceeds to block 320.
At block 320, the auto-replenishment platform sends the consumer model to at least one 3rd party retailer for the integration of the 3rd party retailer e-commerce platform and the auto-replenishment platform.
In accordance with example embodiments, process steps shown in
At decision block 404, if the harmonization engine establishes that the new retailer, manufacturer, and 3rd party data files for the regular time interval are not identical to the existing retailer, manufacturer and 3rd party data files (“no” at decision block 404), then the process 400 may proceed to block 406. If the harmonization engine establishes that new retailer, manufacturer, and 3rd party data files for the regular time interval are identical to the existing retailer, manufacturer and 3rd party data files (“yes” at decision block 404), then the process 400 may proceed to block 408.
At block 406, the harmonization engine updates the consumer model 130.
At block 408, the harmonization model does not update the consumer model 130.
The consumer model 130 may be used by the auto-replenishment platform 102 to group products ordered by a customer from multiple e-commerce platforms into one bundle for delivery or pickup at specified intervals, so that products with disparate shipping intervals may be harmonized into one delivery that satisfies the replenishment interval and/or the customer fulfillment options. The grouping of products for shipment or pickup may provide additional efficiencies for the customer in the form of time savings and/or shipping cost savings. Grouping of products for shipment or pick/up can additionally or alternatively provide financial efficiencies for the retailer, for example in the form of reduced shipping and handling costs and related logistical advantages.
Although the subject matter has been described in language specific to the structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as exemplary forms of implementing the claims.
This application is a continuation of U.S. application Ser. No. 17/126,916, filed on Dec. 18, 2020, and titled “AUTOMATED REPLENISHMENT SHOPPING HARMONIZATION,” which is herein incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 17126916 | Dec 2020 | US |
| Child | 18412391 | US |
