DETERMINING PURCHASE SUGGESTIONS FOR AN ONLINE SHOPPING CONCIERGE PLATFORM

The present disclosure is directed to determining purchase suggestions for an online shopping concierge platform. In particular, the methods and systems of the present disclosure may receive, from a computing device associated with a customer of an online shopping concierge platform, data indicating one or more interactions of the customer with the online shopping concierge platform; determine, based at least in part on one or more machine learning (ML) models and the data indicating the interaction(s), a likelihood that the customer will purchase a particular item if presented, at a specific time, with a suggestion to purchase the particular item; and generate and communicate data describing a graphical user interface (GUI) comprising at least a portion of a listing of one or more purchase suggestions including the suggestion to purchase the particular item.

BACKGROUND

Online shopping concierge platforms may link shoppers or pickers with customers, enabling customers to request and receive items located at various remote geographic locations. To increase the efficiency, profitability, and/or feasibility of such platforms, one or more purchase suggestions may be presented to customers, and/or the like.

SUMMARY

In accordance with one or more aspects of the disclosure, a method may be performed at a computer system comprising a processor and a computer-readable medium. The method may include receiving, via a communication interface of the computer system and from a computing device associated with a customer of an online shopping concierge platform, data indicating one or more interactions of the customer with the online shopping concierge platform. The method may also include determining, by the computer system and based at least in part on one or more machine learning (ML) models and the data indicating the interaction(s), a likelihood that the customer will purchase a particular item if presented, at a specific time, with a suggestion to purchase the particular item. The method may further include determining, by the computer system and based at least in part on the likelihood that the customer will purchase the particular item, to include the suggestion to purchase the particular item in a listing of one or more purchase suggestions to be presented to the customer at the specific time. The method may further include generating, by the computer system, data describing a graphical user interface (GUI) comprising at least a portion of the listing of the purchase suggestion(s) including the suggestion to purchase the particular item. The method may further include communicating, via the communication interface and to the computing device associated with the customer, the data describing the GUI such that the computing device associated with the customer renders and displays, at the specific time, the at least a portion of the listing of the purchase suggestion(s) including the suggestion to purchase the particular item.

In accordance with one or more additional aspects of the disclosure, a system may comprise one or more processors, and a memory storing instructions that when executed by the processor(s) cause the system to perform operations. The operations may include determining, based at least in part on one or more ML models and data indicating one or more interactions of a customer with an online shopping concierge platform, a likelihood that the customer will purchase a particular item if presented, at a specific time, with a suggestion to purchase the particular item. The operations may also include, responsive to determining, based at least in part on the likelihood that the customer will purchase the particular item, to include the suggestion to purchase the particular item in a listing of one or more purchase suggestions to be presented to the customer at the specific time, generating and communicating data describing a GUI comprising at least a portion of the listing of the purchase suggestion(s) including the suggestion to purchase the particular item, such that a computing device associated with the customer renders and displays, at the specific time, the at least a portion of the listing of the purchase suggestion(s) including the suggestion to purchase the particular item.

In accordance with one or more further aspects of the disclosure, one or more non-transitory computer-readable media may comprise instructions that when executed by one or more computing devices cause the computing device(s) to perform operations. The operations may include determining, based at least in part on one or more ML models and data indicating one or more interactions of a customer with an online shopping concierge platform, a likelihood that the customer will purchase a particular item if presented, at a specific time, with a suggestion to purchase the particular item. The operations may also include updating at least one of the ML model(s) based at least in part on data indicating whether the customer invoked the suggestion to purchase the particular item at the specific time.

DETAILED DESCRIPTION

FIG.1illustrates an example system environment for an online concierge system140, in accordance with one or more embodiments. The system environment illustrated inFIG.1includes a customer client device100, a picker client device110, a retailer computing system120, a network130, and an online concierge system140. Alternative embodiments may include more, fewer, or different components from those illustrated inFIG.1, and the functionality of each component may be divided between the components differently from the description below. Additionally, each component may perform their respective functionalities in response to a request from a human, or automatically without human intervention.

As used herein, customers, pickers, and retailers may be generically referred to as “users” of the online concierge system140. Additionally, while one customer client device100, picker client device110, and retailer computing system120are illustrated inFIG.1, any number of customers, pickers, and retailers may interact with the online concierge system140. As such, there may be more than one customer client device100, picker client device110, or retailer computing system120.

The customer client device100is a client device through which a customer may interact with the picker client device110, the retailer computing system120, or the online concierge system140. The customer client device100can be a personal or mobile computing device, such as a smartphone, a tablet, a laptop computer, or desktop computer. In some embodiments, the customer client device100executes a client application that uses an application programming interface (API) to communicate with the online concierge system140.

A customer uses the customer client device100to place an order with the online concierge system140. An order specifies a set of items to be delivered to the customer. An “item,” as used herein, means a good or product that can be provided to the customer through the online concierge system140. The order may include item identifiers (e.g., a stock keeping unit or a price look-up code) for items to be delivered to the user and may include quantities of the items to be delivered. Additionally, an order may further include a delivery location to which the ordered items are to be delivered and a timeframe during which the items should be delivered. In some embodiments, the order also specifies one or more retailers from which the ordered items should be collected.

The customer client device100presents an ordering interface to the customer. The ordering interface is a user interface that the customer can use to place an order with the online concierge system140. The ordering interface may be part of a client application operating on the customer client device100. The ordering interface allows the customer to search for items that are available through the online concierge system140and the customer can select which items to add to a “shopping list.” A “shopping list,” as used herein, is a tentative set of items that the user has selected for an order but that has not yet been finalized for an order. The ordering interface allows a customer to update the shopping list, e.g., by changing the quantity of items, adding or removing items, or adding instructions for items that specify how the item should be collected.

The customer client device100may receive additional content from the online concierge system140to present to a customer. For example, the customer client device100may receive coupons, recipes, or item suggestions. The customer client device100may present the received additional content to the customer as the customer uses the customer client device100to place an order (e.g., as part of the ordering interface).

Additionally, the customer client device100includes a communication interface that allows the customer to communicate with a picker that is servicing the customer's order. This communication interface allows the user to input a text-based message to transmit to the picker client device110via the network130. The picker client device110receives the message from the customer client device100and presents the message to the picker. The picker client device110also includes a communication interface that allows the picker to communicate with the customer. The picker client device110transmits a message provided by the picker to the customer client device100via the network130. In some embodiments, messages sent between the customer client device100and the picker client device110are transmitted through the online concierge system140. In addition to text messages, the communication interfaces of the customer client device100and the picker client device110may allow the customer and the picker to communicate through audio or video communications, such as a phone call, a voice-over-IP call, or a video call.

The picker client device110is a client device through which a picker may interact with the customer client device100, the retailer computing system120, or the online concierge system140. The picker client device110can be a personal or mobile computing device, such as a smartphone, a tablet, a laptop computer, or desktop computer. In some embodiments, the picker client device110executes a client application that uses an application programming interface (API) to communicate with the online concierge system140.

The picker client device110receives orders from the online concierge system140for the picker to service. A picker services an order by collecting the items listed in the order from a retailer. The picker client device110presents the items that are included in the customer's order to the picker in a collection interface. The collection interface is a user interface that provides information to the picker on which items to collect for a customer's order and the quantities of the items. In some embodiments, the collection interface provides multiple orders from multiple customers for the picker to service at the same time from the same retailer location. The collection interface further presents instructions that the customer may have included related to the collection of items in the order. Additionally, the collection interface may present a location of each item in the retailer location and may even specify a sequence in which the picker should collect the items for improved efficiency in collecting items. In some embodiments, the picker client device110transmits to the online concierge system140or the customer client device100which items the picker has collected in real time as the picker collects the items.

The picker can use the picker client device110to keep track of the items that the picker has collected to ensure that the picker collects all of the items for an order. The picker client device110may include a barcode scanner that can determine an item identifier encoded in a barcode coupled to an item. The picker client device110compares this item identifier to items in the order that the picker is servicing, and if the item identifier corresponds to an item in the order, the picker client device110identifies the item as collected. In some embodiments, rather than or in addition to using a barcode scanner, the picker client device110captures one or more images of the item and determines the item identifier for the item based on the images. The picker client device110may determine the item identifier directly or by transmitting the images to the online concierge system140. Furthermore, the picker client device110determines a weight for items that are priced by weight. The picker client device110may prompt the picker to manually input the weight of an item or may communicate with a weighing system in the retailer location to receive the weight of an item.

When the picker has collected all of the items for an order, the picker client device110instructs a picker on where to deliver the items for a customer's order. For example, the picker client device110displays a delivery location from the order to the picker. The picker client device110also provides navigation instructions for the picker to travel from the retailer location to the delivery location. Where a picker is servicing more than one order, the picker client device110identifies which items should be delivered to which delivery location. The picker client device110may provide navigation instructions from the retailer location to each of the delivery locations. The picker client device110may receive one or more delivery locations from the online concierge system140and may provide the delivery locations to the picker so that the picker can deliver the corresponding one or more orders to those locations. The picker client device110may also provide navigation instructions for the picker from the retailer location from which the picker collected the items to the one or more delivery locations.

In some embodiments, the picker client device110tracks the location of the picker as the picker delivers orders to delivery locations. The picker client device110collects location data and transmits the location data to the online concierge system140. The online concierge system140may transmit the location data to the customer client device100for display to the customer such that the customer can keep track of when their order will be delivered. Additionally, the online concierge system140may generate updated navigation instructions for the picker based on the picker's location. For example, if the picker takes a wrong turn while traveling to a delivery location, the online concierge system140determines the picker's updated location based on location data from the picker client device110and generates updated navigation instructions for the picker based on the updated location.

In one or more embodiments, the picker is a single person who collects items for an order from a retailer location and delivers the order to the delivery location for the order.

Alternatively, more than one person may serve the role as a picker for an order. For example, multiple people may collect the items at the retailer location for a single order. Similarly, the person who delivers an order to its delivery location may be different from the person or people who collected the items from the retailer location. In these embodiments, each person may have a picker client device110that they can use to interact with the online concierge system140.

Additionally, while the description herein may primarily refer to pickers as humans, in some embodiments, some or all of the steps taken by the picker may be automated. For example, a semi- or fully autonomous robot may collect items in a retailer location for an order and an autonomous vehicle may deliver an order to a customer from a retailer location.

The retailer computing system120is a computing system operated by a retailer that interacts with the online concierge system140. As used herein, a “retailer” is an entity that operates a “retailer location,” which is a store, warehouse, or other building from which a picker can collect items. The retailer computing system120stores and provides item data to the online concierge system140and may regularly update the online concierge system140with updated item data. For example, the retailer computing system120provides item data indicating which items are available at retailer location and the quantities of those items. Additionally, the retailer computing system120may transmit updated item data to the online concierge system140when an item is no longer available at the retailer location. Additionally, the retailer computing system120may provide the online concierge system140with updated item prices, sales, or availabilities. Additionally, the retailer computing system120may receive payment information from the online concierge system140for orders serviced by the online concierge system140. Alternatively, the retailer computing system120may provide payment to the online concierge system140for some portion of the overall cost of a user's order (e.g., as a commission).

The customer client device100, the picker client device110, the retailer computing system120, and the online concierge system140can communicate with each other via the network130. The network130is a collection of computing devices that communicate via wired or wireless connections. The network130may include one or more local area networks (LANs) or one or more wide area networks (WANs). The network130, as referred to herein, is an inclusive term that may refer to any or all of standard layers used to describe a physical or virtual network, such as the physical layer, the data link layer, the network layer, the transport layer, the session layer, the presentation layer, and the application layer. The network130may include physical media for communicating data from one computing device to another computing device, such as MPLS lines, fiber optic cables, cellular connections (e.g., 3G, 4G, or 5G spectra), or satellites. The network130also may use networking protocols, such as TCP/IP, HTTP, SSH, SMS, or FTP, to transmit data between computing devices. In some embodiments, the network130may include Bluetooth or near-field communication (NFC) technologies or protocols for local communications between computing devices. The network130may transmit encrypted or unencrypted data.

The online concierge system140is an online system by which customers can order items to be provided to them by a picker from a retailer. The online concierge system140receives orders from a customer client device100through the network130. The online concierge system140selects a picker to service the customer's order and transmits the order to a picker client device110associated with the picker. The picker collects the ordered items from a retailer location and delivers the ordered items to the customer. The online concierge system140may charge a customer for the order and provides portions of the payment from the customer to the picker and the retailer.

As an example, the online concierge system140may allow a customer to order groceries from a grocery store retailer. The customer's order may specify which groceries they want delivered from the grocery store and the quantities of each of the groceries. The customer's client device100transmits the customer's order to the online concierge system140and the online concierge system140selects a picker to travel to the grocery store retailer location to collect the groceries ordered by the customer. Once the picker has collected the groceries ordered by the customer, the picker delivers the groceries to a location transmitted to the picker client device110by the online concierge system140. The online concierge system140is described in further detail below with regards toFIG.2.

FIG.2illustrates an example system architecture for an online concierge system140, in accordance with some embodiments. The system architecture illustrated inFIG.2includes a data collection module200, a content presentation module210, an order management module220, a machine learning training module230, and a data store240. Alternative embodiments may include more, fewer, or different components from those illustrated inFIG.2, and the functionality of each component may be divided between the components differently from the description below. Additionally, each component may perform their respective functionalities in response to a request from a human, or automatically without human intervention.

The data collection module200collects data used by the online concierge system140and stores the data in the data store240. The data collection module200may only collect data describing a user if the user has previously explicitly consented to the online concierge system140collecting data describing the user. Additionally, the data collection module200may encrypt all data, including sensitive or personal data, describing users.

For example, the data collection module200collects customer data, which is information or data that describe characteristics of a customer. Customer data may include a customer's name, address, shopping preferences, favorite items, or stored payment instruments. The customer data also may include default settings established by the customer, such as a default retailer/retailer location, payment instrument, delivery location, or delivery timeframe. The data collection module200may collect the customer data from sensors on the customer client device100or based on the customer's interactions with the online concierge system140.

The data collection module200also collects item data, which is information or data that identifies and describes items that are available at a retailer location. The item data may include item identifiers for items that are available and may include quantities of items associated with each item identifier. Additionally, item data may also include attributes of items such as the size, color, weight, stock keeping unit (SKU), or serial number for the item. The item data may further include purchasing rules associated with each item, if they exist. For example, age-restricted items such as alcohol and tobacco are flagged accordingly in the item data. Item data may also include information that is useful for predicting the availability of items in retailer locations. For example, for each item-retailer combination (a particular item at a particular warehouse), the item data may include a time that the item was last found, a time that the item was last not found (a picker looked for the item but could not find it), the rate at which the item is found, or the popularity of the item. The data collection module200may collect item data from a retailer computing system120, a picker client device110, or the customer client device100.

An item category is a set of items that are a similar type of item. Items in an item category may be considered to be equivalent to each other or that may be replacements for each other in an order. For example, different brands of sourdough bread may be different items, but these items may be in a “sourdough bread” item category. The item categories may be human-generated and human-populated with items. The item categories also may be generated automatically by the online concierge system140(e.g., using a clustering algorithm).

The data collection module200also collects picker data, which is information or data that describes characteristics of pickers. For example, the picker data for a picker may include the picker's name, the picker's location, how often the picker has services orders for the online concierge system140, a customer rating for the picker, which retailers the picker has collected items at, or the picker's previous shopping history. Additionally, the picker data may include preferences expressed by the picker, such as their preferred retailers to collect items at, how far they are willing to travel to deliver items to a customer, how many items they are willing to collect at a time, timeframes within which the picker is willing to service orders, or payment information by which the picker is to be paid for servicing orders (e.g., a bank account). The data collection module200collects picker data from sensors of the picker client device110or from the picker's interactions with the online concierge system140.

Additionally, the data collection module200collects order data, which is information or data that describes characteristics of an order. For example, order data may include item data for items that are included in the order, a delivery location for the order, a customer associated with the order, a retailer location from which the customer wants the ordered items collected, or a timeframe within which the customer wants the order delivered. Order data may further include information describing how the order was serviced, such as which picker serviced the order, when the order was delivered, or a rating that the customer gave the delivery of the order.

The content presentation module210selects content for presentation to a customer. For example, the content presentation module210selects which items to present to a customer while the customer is placing an order. The content presentation module210generates and transmits the ordering interface for the customer to order items. The content presentation module210populates the ordering interface with items that the customer may select for adding to their order. In some embodiments, the content presentation module210presents a catalog of all items that are available to the customer, which the customer can browse to select items to order. The content presentation module210also may identify items that the customer is most likely to order and present those items to the customer. For example, the content presentation module210may score items and rank the items based on their scores. The content presentation module210displays the items with scores that exceed some threshold (e.g., the top n items or the p percentile of items).

The content presentation module210may use an item selection model to score items for presentation to a customer. An item selection model is a machine learning model that is trained to score items for a customer based on item data for the items and customer data for the customer. For example, the item selection model may be trained to determine a likelihood that the customer will order the item. In some embodiments, the item selection model uses item embeddings describing items and customer embeddings describing customers to score items. These item embeddings and customer embeddings may be generated by separate machine learning models and may be stored in the data store240.

In some embodiments, the content presentation module210scores items based on a search query received from the customer client device100. A search query is text for a word or set of words that indicate items of interest to the customer. The content presentation module210scores items based on a relatedness of the items to the search query. For example, the content presentation module210may apply natural language processing (NLP) techniques to the text in the search query to generate a search query representation (e.g., an embedding) that represents characteristics of the search query. The content presentation module210may use the search query representation to score candidate items for presentation to a customer (e.g., by comparing a search query embedding to an item embedding).

In some embodiments, the content presentation module210scores items based on a predicted availability of an item. The content presentation module210may use an availability model to predict the availability of an item. An availability model is a machine learning model that is trained to predict the availability of an item at a retailer location. For example, the availability model may be trained to predict a likelihood that an item is available at a retailer location or may predict an estimated number of items that are available at a retailer location. The content presentation module210may weight the score for an item based on the predicted availability of the item. Alternatively, the content presentation module210may filter out items from presentation to a customer based on whether the predicted availability of the item exceeds a threshold.

The order management module220that manages orders for items from customers. The order management module220receives orders from a customer client device100and assigns the orders to pickers for service based on picker data. For example, the order management module220assigns an order to a picker based on the picker's location and the location of the retailer location from which the ordered items are to be collected. The order management module220may also assign an order to a picker based on how many items are in the order, a vehicle operated by the picker, the delivery location, the picker's preferences on how far to travel to deliver an order, the picker's ratings by customers, or how often a picker agrees to service an order.

In some embodiments, the order management module220determines when to assign an order to a picker based on a delivery timeframe requested by the customer with the order. The order management module220computes an estimated amount of time that it would take for a picker to collect the items for an order and deliver the ordered item to the delivery location for the order. The order management module220assigns the order to a picker at a time such that, if the picker immediately services the order, the picker is likely to deliver the order at a time within the timeframe. Thus, when the order management module220receives an order, the order management module220may delay in assigning the order to a picker if the timeframe is far enough in the future.

When the order management module220assigns an order to a picker, the order management module220transmits the order to the picker client device110associated with the picker. The order management module220may also transmit navigation instructions from the picker's current location to the retailer location associated with the order. If the order includes items to collect from multiple retailer locations, the order management module220identifies the retailer locations to the picker and may also specify a sequence in which the picker should visit the retailer locations.

The order management module220may track the location of the picker through the picker client device110to determine when the picker arrives at the retailer location. When the picker arrives at the retailer location, the order management module220transmits the order to the picker client device110for display to the picker. As the picker uses the picker client device110to collect items at the retailer location, the order management module220receives item identifiers for items that the picker has collected for the order. In some embodiments, the order management module220receives images of items from the picker client device110and applies computer-vision techniques to the images to identify the items depicted by the images. The order management module220may track the progress of the picker as the picker collects items for an order and may transmit progress updates to the customer client device100that describe which items have been collected for the customer's order.

In some embodiments, the order management module220tracks the location of the picker within the retailer location. The order management module220uses sensor data from the picker client device110or from sensors in the retailer location to determine the location of the picker in the retailer location. The order management module220may transmit to the picker client device110instructions to display a map of the retailer location indicating where in the retailer location the picker is located. Additionally, the order management module220may instruct the picker client device110to display the locations of items for the picker to collect and may further display navigation instructions for how the picker can travel from their current location to the location of a next item to collect for an order.

The order management module220determines when the picker has collected all of the items for an order. For example, the order management module220may receive a message from the picker client device110indicating that all of the items for an order have been collected. Alternatively, the order management module220may receive item identifiers for items collected by the picker and determine when all of the items in an order have been collected. When the order management module220determines that the picker has completed an order, the order management module220transmits the delivery location for the order to the picker client device110. The order management module220may also transmit navigation instructions to the picker client device110that specify how to travel from the retailer location to the delivery location, or to a subsequent retailer location for further item collection. The order management module220tracks the location of the picker as the picker travels to the delivery location for an order and updates the customer with the location of the picker so that the customer can track the progress of their order. In some embodiments, the order management module220computes an estimated time of arrival for the picker at the delivery location and provides the estimated time of arrival to the customer.

In some embodiments, the order management module220facilitates communication between the customer client device100and the picker client device110. As noted above, a customer may use a customer client device100to send a message to the picker client device110. The order management module220receives the message from the customer client device100and transmits the message to the picker client device110for presentation to the picker. The picker may use the picker client device110to send a message to the customer client device100in a similar manner.

The order management module220coordinates payment by the customer for the order. The order management module220uses payment information provided by the customer (e.g., a credit card number or a bank account) to receive payment for the order. In some embodiments, the order management module220stores the payment information for use in subsequent orders by the customer. The order management module220computes a total cost for the order and charges the customer that cost. The order management module220may provide a portion of the total cost to the picker for servicing the order, and another portion of the total cost to the retailer.

The machine learning training module230trains machine learning models used by the online concierge system140. The online concierge system140may use machine learning models to perform functionalities described herein. Example machine learning models include regression models, support vector machines, naïve bayes, decision trees, k nearest neighbors, random forest, boosting algorithms, k-means, and hierarchical clustering. The machine learning models may also include neural networks, such as perceptrons, multilayer perceptrons, convolutional neural networks, recurrent neural networks, sequence-to-sequence models, generative adversarial networks, or transformers.

Each machine learning model includes a set of parameters. A set of parameters for a machine learning model are parameters that the machine learning model uses to process an input. For example, a set of parameters for a linear regression model may include weights that are applied to each input variable in the linear combination that comprises the linear regression model. Similarly, the set of parameters for a neural network may include weights and biases that are applied at each neuron in the neural network. The machine learning training module230generates the set of parameters for a machine learning model by “training” the machine learning model. Once trained, the machine learning model uses the set of parameters to transform inputs into outputs.

The machine learning training module230trains a machine learning model based on a set of training examples. Each training example includes input data to which the machine learning model is applied to generate an output. For example, each training example may include customer data, picker data, item data, or order data. In some cases, the training examples also include a label which represents an expected output of the machine learning model. In these cases, the machine learning model is trained by comparing its output from input data of a training example to the label for the training example.

The machine learning training module230may apply an iterative process to train a machine learning model whereby the machine learning training module230trains the machine learning model on each of the set of training examples. To train a machine learning model based on a training example, the machine learning training module230applies the machine learning model to the input data in the training example to generate an output. The machine learning training module230scores the output from the machine learning model using a loss function. A loss function is a function that generates a score for the output of the machine learning model such that the score is higher when the machine learning model performs poorly and lower when the machine learning model performs well. In cases where the training example includes a label, the loss function is also based on the label for the training example. Some example loss functions include the mean square error function, the mean absolute error, hinge loss function, and the cross-entropy loss function. The machine learning training module230updates the set of parameters for the machine learning model based on the score generated by the loss function. For example, the machine learning training module230may apply gradient descent to update the set of parameters.

The data store240stores data used by the online concierge system140. For example, the data store240stores customer data, item data, order data, and picker data for use by the online concierge system140. The data store240also stores trained machine learning models trained by the machine learning training module230. For example, the data store240may store the set of parameters for a trained machine learning model on one or more non-transitory, computer-readable media. The data store240uses computer-readable media to store data and may use databases to organize the stored data.

FIG.3depicts one or more example methods for an online concierge system, in accordance with one or more embodiments. Alternative embodiments may include more, fewer, or different steps from those illustrated inFIG.3, and the steps may be performed in a different order from that illustrated inFIG.3. These steps may be performed by an online concierge system (e.g., online concierge system140). Additionally, each of these steps may be performed automatically by the online concierge system without human intervention.

At (302), at a computer system comprising a processor and a computer-readable medium (e.g., online concierge system140, and/or the like), one or more ML models configured to determine a likelihood that a customer will purchase an item, whether to include such item in a listing of suggestions, and/or the like may be generated, updated, trained, and/or the like, for example, based at least in part on historical sales, interaction, and/or the like data associated with a customer, customer base, cohort of related customers, retailer, related group of retails, and/or the like.

At (304), at the computer system, data indicating one or more interactions of a customer with an online shopping concierge platform may be received (e.g., via a communication interface of the computer system, and/or the like) from a computing device associated with the customer of the online shopping concierge platform (e.g., customer client device100, and/or the like). For example, online concierge system140may receive, from customer client device100, data indicating a customer has viewed, purchased, indicated an interest in, and/or the like one or more products associated with a newborn baby (e.g., baby strollers, baby formula, baby food, and/or the like).

At (306), the computer system may determine, based at least in part on one or more ML models and the data indicating the interaction(s), a likelihood that the customer will purchase a particular item if presented, at a specific time, with a suggestion to purchase the particular item. For example, online concierge system140may determine, based at least in part on one or more ML models and the data indicating the customer has viewed, purchased, indicated an interest in, and/or the like the product(s) associated with a newborn baby, a likelihood that the customer will purchase a particular item (e.g., baby diapers, and/or the like) if presented, at a specific time (e.g., next month, and/or the like), with a suggestion to purchase the particular item, and/or the like.

In some embodiments, the computer system may determine the likelihood that the customer will purchase the particular item based at least in part on one or more of an expiration date determined by the computer system for an item, previously purchased by the customer via the online shopping concierge platform, of a same item type as the particular item; an inventory level determined by the computer system for the particular item; data received, by the computer system, from one or smart appliances associated with the customer; a frequency with which the customer shops via the online shopping concierge platform; and/or the like.

In some embodiments, the computer system may determine, based at least in part on the ML model(s) and for each item type of a plurality of different and distinct item types available via the online shopping concierge platform, a likelihood that one or more items of the item type will be repurchased a given amount of time after a previous purchase. For example, online concierge system140may determine (e.g., based at least in part on the ML model(s), and/or the like) a likelihood that the particular baby diapers of the general item type diapers will be repurchased a week, month, and/or the like after a previous purchase, a likelihood that bananas of the general item type fruit will be repurchased a week, month, and/or the like after a previous purchase, and/or the like. In some of such embodiments, the computer system may rank the listing of the purchase suggestions including the suggestion to purchase the particular item and one or more suggestions to purchase one or more items of the plurality of different and distinct item types based at least in part on their determined likelihoods of being repurchased at the specific time. For example, online concierge system140may rank (e.g., based at least in part on their determined respective likelihoods, and/or the like) the listing of the purchase suggestions such that a suggestion to purchase bananas appears before, more prominently, and/or the like the suggestion to purchase the particular baby diapers, and/or the like.

In some embodiments, the computer system may determine the likelihood that the item(s) of the item type will be repurchased based at least in part on data indicating a plurality of different and distinct interactions with the online shopping concierge platform by a plurality of different and distinct customers of the online shopping concierge platform. In some of such embodiments, the computer system may identify the plurality of different and distinct customers based at least in part on a determination that the plurality of different and distinct customers are customers of one or more retailers associated with the online shopping concierge platform that offer the particular item. Additionally or alternatively, the computer system may identify the plurality of different and distinct customers based at least in part on a determination that the plurality of different and distinct customers are associated with a customer cohort sharing one or more characteristics with the customer. For example, online concierge system140may identify a plurality of different and distinct customers based at least in part on a determination that the plurality of different and distinct customers are associated with a customer cohort (e.g., new parents, and/or the like) sharing one or more characteristics with the customer, and/or the like. Similarly, in some embodiments, the computer system may weight the determined likelihoods based at least in part on a determination of a degree of similarity between the customer and the plurality of different and distinct customers (e.g., demographic similarity, geographic similarity, one or more similarities in shopping patterns, and/or the like).

In some embodiments, the computer system may determine the likelihood that the items of the item type will be repurchased based at least in part on one or more of a maximum time between purchases of the item type by the plurality of different and distinct customers, a minimum time between purchases of the item type by the plurality of different and distinct customers, an average time between purchases of the item type by the plurality of different and distinct customers, a median time between purchases of the item type by the plurality of different and distinct customers, a standard deviation of time between purchases of the item type by the plurality of different and distinct customers, and/or the like.

At (308), the computer system may determine, based at least in part on the likelihood that the customer will purchase the particular item, to include the suggestion to purchase the particular item in a listing of one or more purchase suggestions to be presented to the customer at the specific time. For example, online concierge system140may determine, based at least in part on the likelihood that the customer will purchase the particular item (e.g., the baby diapers, and/or the like), to include the suggestion to purchase the particular item in a listing of one or more purchase suggestions to be presented to the customer at the specific time (e.g., next month, and/or the like).

In some embodiments, the computer system may determine, based at least in part on the ML model(s), the specific time, and/or the like, one or more features of the particular item to modify in the suggestion from a previous purchase by the customer of the particular item via the online shopping concierge platform. For example, online concierge system140may determine (e.g., based at least in part on the ML model(s), time since the previous purchase, and/or the like) one or more features (e.g., a size, and/or the like) of the particular baby diapers suggested, and/or the like.

At (310), the computer system may generate data describing a GUI comprising at least a portion of the listing of the purchase suggestion(s) including the suggestion to purchase the particular item. For example, online concierge system140may generate data describing a GUI comprising at least a portion of the listing of the purchase suggestion(s) including the suggestion to purchase the particular item (e.g., the baby diapers, and/or the like).

At (312), the computer system may communicate (e.g., via the communication interface, and/or the like) the data describing the GUI to the computing device associated with the customer (e.g., customer client device100, and/or the like), such that the computing device associated with the customer renders and displays, at the specific time, the at least a portion of the listing of the purchase suggestion(s) including the suggestion to purchase the particular item. For example, online concierge system140may communicate, to customer client device100, the data describing the GUI, such that customer client device100renders and displays, at the specific time (e.g., the next month, and/or the like), the at least a portion of the listing of the purchase suggestion(s) including the suggestion to purchase the particular item (e.g., the baby diapers, and/or the like).

In some embodiments, the computer system may update at least one of the ML model(s) based at least in part on data indicating whether the customer invoked the suggestion to purchase the particular item. For example, if the customer invokes the suggestion to purchase the particular baby diapers, and/or the like, online concierge system140may update at least one of the ML model(s), e.g., such that the ML model(s) will return a higher likelihood with respect to the particular baby diapers for the customer going forward, and/or the like.

ADDITIONAL CONSIDERATIONS

The foregoing description of the embodiments has been presented for the purpose of illustration; many modifications and variations are possible while remaining within the principles and teachings of the above description.

Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In some embodiments, a software module is implemented with a computer program product comprising one or more computer-readable media storing computer program code or instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described. In some embodiments, a computer-readable medium comprises one or more computer-readable media that, individually or together, comprise instructions that, when executed by one or more processors, cause the one or more processors to perform, individually or together, the steps of the instructions stored on the one or more computer-readable media. Similarly, a processor comprises one or more processors or processing units that, individually or together, perform the steps of instructions stored on a computer-readable medium.

The description herein may describe processes and systems that use machine learning models in the performance of their described functionalities. A “machine learning model,” as used herein, comprises one or more machine learning models that perform the described functionality. Machine learning models may be stored on one or more computer-readable media with a set of weights. These weights are parameters used by the machine learning model to transform input data received by the model into output data. The weights may be generated through a training process, whereby the machine learning model is trained based on a set of training examples and labels associated with the training examples. The training process may include: applying the machine learning model to a training example, comparing an output of the machine learning model to the label associated with the training example, and updating weights associated for the machine learning model through a back-propagation process. The weights may be stored on one or more computer-readable media and are used by a system when applying the machine learning model to new data.