UTILIZING MACHINE LEARNING TO DETERMINE WHETHER AN IN-PERSON CUSTOMER FOLLOWS A MERCHANT ON SOCIAL MEDIA

A device may receive, from a point-of-sale (POS) device, transaction data associated with a transaction between a customer and a merchant associated with the POS device. The device may determine a customer email address and other data associated with the transaction, the customer, or the merchant, based on the transaction data and customer data identifying the customer, and may process the customer email address, the other data, and social media data, with a machine learning model, to identify a social media account of the customer. The social media data may include data identifying multiple social media accounts, and the multiple social media accounts may include the social media account of the customer. The device may determine, based on the social media data, whether the social media account of the customer follows the merchant, and may perform actions based on whether the social media account follows the merchant.

BACKGROUND

Merchants invest effort to acquire customers and to retain customers. A retention program is a strategy that focuses on an existing customer base of a merchant. Goals of a retention program include creating repeat customers, increasing frequency of purchase by the customers, increasing an average order volume per purchase by the customers, and/or the like. A retention rate is a popular metric for measuring a success of a retention program campaign.

SUMMARY

According to some implementations, a method may include receiving, from a point-of-sale device, transaction data associated with a transaction between a customer and a merchant associated with the point-of-sale device, and determining a customer email address of the customer and other data associated with the transaction, the customer, or the merchant, based on the transaction data and customer data identifying the customer. The method may include processing the customer email address, the other data, and social media data, with a machine learning model, to identify a social media account of the customer, wherein the social media data may include data identifying a plurality of social media accounts, and wherein the plurality of social media accounts may include the social media account of the customer. The method may include determining, based on the social media data, whether the social media account of the customer follows the merchant (e.g., the customer and the merchant have a connection on social media, such as a like, a friend, a subscribe, and/or the like), and performing one or more actions based on whether the social media account of the customer follows the merchant.

According to some implementations, a device may include one or more memories, and one or more processors, communicatively coupled to the one or more memories, configured to receive, from a point-of-sale device, a name of a customer conducting a transaction with a merchant, an image of the customer, and a geographical location of the merchant. The one or more processors may process the name of the customer, the image of the customer, the geographical location of the merchant, and social media data, with a machine learning model, to identify a social media account of the customer. The one or more processors may determine, based on the social media data, whether the social media account of the customer follows the merchant, and may perform one or more actions based on whether the social media account of the customer follows the merchant.

According to some implementations, a non-transitory computer-readable medium may store one or more instructions that, when executed by one or more processors of a device, may cause the one or more processors to receive, from a point-of-sale device, transaction data associated with a transaction between a customer and a merchant associated with the point-of-sale device, and determine a customer email address of the customer and other data associated with the transaction, the customer, or the merchant, based on the transaction data and customer data identifying the customer. The one or more instructions may cause the one or more processors to process the customer email address, the other data, and social media data, with a machine learning model, to identify a social media account of the customer, wherein the machine learning model may be trained based on historical customer email addresses, historical other data, and historical social media data, wherein the social media data may include data identifying a plurality of social media accounts, and wherein the plurality of social media accounts may include the social media account of the customer. The one or more instructions may cause the one or more processors to determine, based on the social media data, whether the social media account of the customer follows the merchant, and perform one or more actions based on whether the social media account of the customer follows the merchant.

DETAILED DESCRIPTION

Current retention marketing techniques include email marketing, direct mail marketing, social media marketing, and/or the like. Each of these techniques require the merchant to have a reliable contact point with a customer. However, there are few techniques for identifying social media accounts of customers, particularly for in-person customers (e.g., customers shopping within stores of merchants, rather than online). Furthermore, there is a big gap between social media followers (e.g., a customer and a merchant have a connection on social media, such as a like, a friend, a subscribe, and/or the like) of a merchant and actual customers of the merchant (e.g., social media followers of the merchant are not necessarily customers of the merchant). There are also different identities associated with social media accounts and customers (e.g., transaction card numbers, user identifiers, email addresses, and/or the like) even when the different identities are associated with the same people.

Thus, current retention marketing techniques may waste computing resources (e.g., processing resources, memory resources, communication resources, and/or the like), networking resources, and/or the like associated with attempting to accurately provide social media marketing to customers of a merchant, incorrectly identifying social media accounts of the customers, correcting the incorrect identification of the social media accounts if discovered, and/or like.

Some implementations described herein provide a processing platform that utilizes machine learning to determine whether an in-person customer follows a merchant on social media (e.g., the customer and the merchant have a connection on social media, such as a like, a friend, a subscribe, and/or the like). For example, the processing platform may receive, from a point-of-sale device, transaction data associated with a transaction between a customer and a merchant associated with the point-of-sale device, and may determine a customer email address of the customer and other data associated with the transaction, the customer, or the merchant, based on the transaction data and customer data identifying the customer. The processing platform may process the customer email address, the other data, and social media data, with a machine learning model, to identify a social media account of the customer, where the social media data may include data identifying a plurality of social media accounts, and the plurality of social media accounts may include the social media account of the customer. The processing platform may determine, based on the social media data, whether the social media account of the customer follows the merchant, and may perform one or more actions based on whether the social media account of the customer follows the merchant.

In this way, the processing platform utilizes machine learning to determine whether an in-person customer follows a merchant on social media in near-real time (e.g., while the customer is making a purchase at a store of the merchant). The processing platform may enable the merchant to identify if the customer is engaged with the merchant, a product of the merchant, and/or a service of the merchant on social media. Once this is determined, the processing platform may take additional actions, such as promoting merchant products and/or services to the social media account of the customer. This, in turn, conserves computing resources, networking resources, and/or the like that would otherwise have been wasted in attempting to accurately provide social media marketing to customers of a merchant, incorrectly identifying social media accounts of the customers, correcting the incorrect identification of the social media accounts if discovered, and/or like.

FIGS. 1A-1Hare diagrams of one or more example implementations100described herein. As shown inFIG. 1A, a point-of-sale (POS) device (e.g., a client device) may be associated with another client device, a transaction card, and a processing platform. The client devices may include mobile devices, computers, POS devices, and/or the like associated with a merchant and/or a customer. The transaction card may include a credit card, a debit card, a gift card, a payment card, an ATM card, and/or the like that may be used to complete a transaction. The processing platform may include a platform that utilizes machine learning to determine whether an in-person customer follows a merchant on social media, as described herein.

As further shown inFIG. 1A, and by reference number105, the processing platform may receive, from a POS device, transaction data associated with a transaction between a customer and a merchant associated with the POS device. The transaction data may include various information about the customer, payment information associated with the customer, and/or the like. For example, the transaction data may include the customer's bank information, account information, credit card information, and/or the like. This may include a card identifier (e.g., credit card number, debit card number, and/or the like) associated with the customer, a bank associated with the customer, and/or the like.

In some implementations, there may be hundreds, thousands, and/or the like, of client devices that produce thousands, millions, billions, and/or the like, of data points of transaction data and/or customer data. In this way, the processing platform may handle thousands, millions, billions, and/or the like, of data points within a period of time (e.g., daily, weekly, monthly), and thus may provide “big data” capability.

As further shown inFIG. 1A, and by reference number110, the processing platform may receive customer data from a data structure (e.g., a database, a table, a list, and/or the like). Customer data may include various information that identifies the customer. For example, customer data may include a name associated with the customer, an identifier of the customer, a location associated with the customer, a customer account associated with the merchant (e.g., a customer account, a customer rewards account, and/or the like).

As shown inFIG. 1B, and by reference number115, the processing platform may determine a customer email address of the customer and other data associated with the transaction, the customer, and/or the merchant, based on the transaction data and the customer data. For example, the processing platform may interact with a server device of a bank, a financial institution, the merchant, and/or the like, to determine a customer email address associated with a credit card, bank account, customer account, and/or the like associated with the transaction. In some implementations, the processing platform may determine other data associated with the transaction, the customer, and/or the merchant. For example, the processing platform may determine a name, a location of the merchant, a location associated with the customer, an age associated with the customer, a common age group associated with the merchant's clientele, and/or the like. In this way, the processing platform may obtain different metrics that may help identify a customer or distinguish a customer from other customers with similar names or other information. For example, an age of the customer, a location of the customer, and/or the like may distinguish the customer from other customers with similar names.

As shown inFIG. 1C, and by reference number120, the processing platform may process the customer email address, the other data, and social media data, with a machine learning model, to identify a social media account of the customer. The social media data may include various information associated with social media platforms (e.g., a list of accounts associated with the social media platforms, a list of followers associated with an account on the social media platform, and/or the like). For example, the social media data may include information identifying personal accounts (e.g., accounts designed for personal use), business accounts (e.g., accounts designed to represent a business, a merchant, and/or the like), and/or the like. In some implementations, the social media data includes information about a social media account, such as a username associated with the social media account, a picture associated with the social media account, a birthdate of a user of the social media account, an email address associated with the social media account, a location associated with the social media account, and/or the like.

The processing platform may obtain the social media data by scraping a social media website for the social media data, sending a request to a social media platform for the social media data, and/or the like. For example, the processing platform may obtain the social media data by scraping a social media website for publicly available social media data. Additionally, or alternatively, the processing platform may query a social media server for information about social media accounts, perform a search on a social media website, and/or the like. Because a user of a social media platform has full control of what social media data is shared publicly, the processing platform may obtain various amounts of social media data for social media accounts. As one example, the processing platform may obtain, as the social media data, only a name of an owner of a particular social media account. As another example, the processing platform may obtain, as the social media data, a name, a location, a birthdate, a picture, an email address, and/or the like of an owner of another particular social media account. The processing platform may aggregate and store the social media data associated with a plurality of social media accounts associated with one or more social media platforms.

In some implementations, the processing platform may obtain social media data from another device that obtains social media data associated with a plurality of social media accounts associated with one or more social media platforms. For example, the processing platform may obtain social media data from another device, such as a device associated with the merchant that stores social media data associated with a social media account of the merchant. As another example, the processing platform may obtain social media data from a server device that obtains the social media data from a social media platform (e.g., a third party server device that collects data on followers of a social media account, data on social media accounts, and/or the like).

In some implementations, the processing platform may use various information (e.g., the customer email address, the other data, the social media data, and/or the like) to determine potential social media accounts that may be associated with the customer. For example, the processing platform may identify potential social media accounts by searching social media data using the name of the customer, the geographical location of the merchant, and/or the like. Potential social media accounts may have differing amounts of social media data available to help identify a user associated with the social media account. For example, a first potential social media account may have a large amount of social media data available (e.g., social media data indicating an email address associated with the first potential social media account, a first and last name associated with the first potential social media account, and a location associated with the first social media account). In contrast, a second potential social media account may only have social media data available indicating a first name and last initial associated with the second potential social media account. The processing platform may use machine learning with the various information (the customer email address, the other data, and the social media data) to distinguish which potential social media account is likely to be the social media account of the customer based on differing amounts of social media data available.

The processing platform may use machine learning to identify a likelihood that a social media account is associated with the customer. For example, the processing platform may determine a score that indicates the likelihood that a potential social media account is associated with the customer. In some implementations, the processing platform may determine a score for each of a plurality of potential social media accounts that may be associated with the customer.

In some implementations, the processing platform may train the machine learning model with historical input data (e.g., historical transaction data, customer data, names of customers, geographical locations of merchants, and/or the like) and historical output data (e.g., social media accounts associated with customers) to generate a trained machine learning model. The processing platform may be trained to identify weights to be assigned to different types of data (e.g., historical transaction data, customer data, names of customer, geographical locations of merchants, social media data, and/or the like). The weights may be used to identify particular types of data as being more important in a matching operation than other types of data. For example, the processing platform may determine to assign a high weight to the customer email address, such that if the customer email address matches social media data on a potential social media account, then the customer email address is a likely indicator that the potential social media account is the social media account of the customer. In another example, the processing platform may assign a low weight to a customer name, such that if the customer name matches social media data on a potential social media account, the matching customer name is not a likely indicator that the potential social media account is the social media account of the customer. This may be the result of multiple different social media accounts being associated with the customer name, the customer using a different name on the social media account associated with the customer, the customer hiding the name on the social media account associated with the customer, and/or the like.

The processing platform may train the machine learning model in a manner similar to the manner described below in connection withFIG. 2. In some implementations, rather than training the machine learning model, the processing platform may obtain the machine learning model from another system or device that trained the machine learning model. In this case, the processing platform may provide the other system or device with historical data for use in training the machine learning model, and may provide the other system or device with updated historical data to retrain the machine learning model in order to update the machine learning model.

In some implementations, the processing platform may apply the machine learning model to a new observation (e.g., new customer data, transaction data, and/or the like) in a manner similar to the manner described below in connection withFIG. 3to identify a social media account of the customer. For example, the processing platform may analyze the customer email address, the customer name, the geographical location of the customer, and/or the like to determine which is most important in identifying the social media account of the customer. In this way, the processing platform may identify an unknown social media account of the customer based off the training applied to the machine learning model described above.

As shown inFIG. 1D, and by reference number125, the processing platform may determine, based on the social media data, whether the social media account of the customer follows the merchant. In some implementations, the processing platform may determine, through resources associated with a social media account of the merchant, whether the social media account of the customer follows the social media account of the merchant (hereinafter “follows the merchant” is intended to mean follows the social media account of the merchant or that the customer and the merchant have a connection on social media, such as a like, a friend, a subscribe, and/or the like). For example, the social media account of the merchant may have information identifying followers of the merchant, information identifying users who have interacted with the merchant (e.g., left a review on a social media account of the merchant, commented on a merchant's post on a social media account, liked a merchant's post, and/or the like), and/or the like. In some implementations, the processing platform may determine, through information provided by the merchant, whether the social media account of the customer follows the merchant. In some implementations, the processing platform may determine, through resources associated with a social media account of the customer, whether the social media account of the customer follows the merchant. For example, the social media platform may be able to determine that the social media account of the customer follows the merchant if the social media account of the customer is public, publicly lists information that indicates that the social media account of the customer follows the merchant, and/or the like. In some implementations, based on not finding information associating the social media account of the customer with the social media account of the merchant, the processing platform may determine that the social media account of the customer does not follow the social media account of the merchant.

As shown inFIG. 1E, and by reference number130, the processing platform may perform one or more actions based on whether the social media account of the customer follows the merchant. In some implementations, the processing platform may perform one or more actions based on determining that the social media account of the customer does not follow the merchant. For example, the processing platform may provide information designed to encourage the customer to follow the merchant on social media, such as providing, to a client device of the customer, information indicating how to follow the merchant on social media. In some implementations, the processing platform may provide, to the POS device, information identifying an offer when the social media account of the customer follows the merchant, information identifying a discount for the transaction when the social media account of the customer follows the merchant, and/or the like. In some implementations, the processing platform may provide, to the social media account of the customer, a request to follow the merchant when the social media account does not follow the merchant.

In some implementations, the one or more actions may include the processing platform providing, to the client device, instructions for performing the particular action. For example, the processing platform may provide the instructions to the client device, and the client device may display the instructions to the user. In this way, the processing platform may enable the user to receive instructions for performing the particular action, thereby conserving computing resources (e.g., processing resources, memory resources, communication resources, and/or the like), networking resources, and/or the like that would have otherwise been wasted by the user in attempting to determine the particular action.

In some implementations, the processing platform may perform one or more actions based on determining that the social media account of the customer follows the merchant. In some implementations, the processing platform may perform one or more actions based on determining that the social media account of the customer, after originally not having followed the merchant, has changed status to following the merchant. For example, the processing platform may provide a message to the social media account of the customer when the social media account of the customer follows the merchant. Additionally, or alternatively, the processing platform may provide a specialized message with offers, discounts, and/or the like to the customer when the social media account of the customer follows the merchant.

In some implementations, the processing platform may retrain the machine learning model based on whether the social media account of the customer follows the merchant. For example, the one or more actions may include the processing platform retraining the machine learning model based on the particular action and/or feedback associated with performance of the particular action. In this way, the processing platform may improve the accuracy of the machine learning model, which may improve speed and efficiency of the machine learning model and conserve computing resources, networking resources, and/or the like.

FIGS. 1F-1Hrelated to a different example. As shown inFIG. 1F, and by reference number135, the processing platform may receive, from a POS device, a name of a customer conducting a transaction with a merchant associated with the POS device, an image of the customer, and a geographical location of the merchant. In some implementations, the processing platform may obtain the name of the customer from a payment card that the customer uses to conduct the transaction. For example, the processing platform, the POS device, and/or the like may obtain the customer's name from a magstripe, a chip, and/or the like of the payment card to obtain the name of the customer. In some implementations, the processing platform may obtain the image of the customer from a camera used to capture an image associated with the customer. For example, an integrated camera with the POS device may be used to capture an image of the customer. In some implementations, external cameras, such as security cameras, may be used to capture an image of the customer. Additional processing may be performed to associate the image of the customer with the transaction. For example, a time of the transaction may be used with a time associated with the image of the customer conducting a transaction to associate the image of the customer with the transaction, image recognition technology may be used to associate the image of the customer with a name of the customer, and/or the like.

As shown inFIG. 1G, and by reference number140, the processing platform may process the name of the customer, the image of the customer, the geographical location of the merchant, and social media data, with a machine learning model, to identify a social media account of the customer. For example, the processing platform may perform a search of a social media account using the name of the customer, the image of the customer, the geographical location of the merchant, and/or the like to obtain a list of potential social media accounts, and determine which of the potential social media accounts is the social media account of the customer. In some implementations, the processing platform may perform an image search of publicly available images, images that were consented to being released, and/or the like to identify an image similar to the image of the customer. Similar toFIG. 1C, the processing platform may use machine learning to identify which information (e.g., the name of the customer, the image of the customer, the geographic location of the merchant, and/or the like) is most likely to indicate an accurate social media account linked to the customer, which of the information is most useful in applying to a search for the social media account of the customer, which of the various information (if matching information present on a potential social media account) is likely to indicate that the potential social media account is the social media account of the customer, and/or the like.

In some implementations, the processing platform may train the machine learning model with historical data (e.g., historical transaction data, customer data, images of customers, names of customers, geographical locations of merchants, and/or the like) to generate a machine learning model that is trained to identify a social media account of a customer, in a manner similar to that described above with regard toFIG. 1C. In some implementations, the processing platform may train the machine learning model in a manner similar to the manner described below in connection withFIG. 2. In some implementations, rather than training the machine learning model, the processing platform may obtain the machine learning model from another system or device that trained the machine learning model. In this case, the processing platform may provide the other system or device with historical data for use in training the machine learning model, and may provide the other system or device with updated historical data to retrain the machine learning model in order to update the machine learning model.

In some implementations, the processing platform may apply the machine learning model to a new observation in a manner similar to the manner described below in connection withFIG. 3to identify the social media account of the customer. Based on this, the processing platform may identify an unknown social media account of the customer using the trained machine learning model. In this way, the processing platform may identify an unknown social media account of the customer based on the training applied to the machine learning model.

As shown inFIG. 1H, and by reference number145, the processing platform may determine, based on the social media data, whether the social media account of the customer follows the merchant. The processing platform may determine that the social media account of the customer follows the merchant in a similar way to what was described above in relation toFIG. 1D. For example, the processing platform may determine, through resources associated with a social media account of the merchant, whether the social media account of the customer follows the merchant. The social media account of the merchant may have information identifying followers of the merchant, information identifying users who have interacted with the merchant (e.g., left a review on a social media account of the merchant, commented on a merchant's post on a social media account, liked a merchants post, and/or the like), and/or the like. In some implementations, the processing platform may determine, through information provided by the merchant, whether the social media account of the customer follows the merchant. Additionally, or alternatively, the processing platform may determine, through resources associated with a social media account of the customer, whether the social media account of the customer follows the merchant. In some implementations, based on not finding information associating the social media account of the customer with the social media account of the merchant, the processing platform may determine that the social media account of the customer does not follow the social media account of the merchant.

The processing platform may perform one or more actions based on whether the social media account of the customer follows the merchant. Examples of the one or more actions are provided above with regard toFIG. 1E.

In this way, several different stages of the process for determining whether an in-person customer follows a merchant on social media are automated via machine learning, which may remove human subjectivity and waste from the process, and which may improve speed and efficiency of the process and conserve computing resources (e.g., processing resources, memory resources, communication resources, and/or the like), networking resources, and/or the like. Furthermore, implementations described herein use a rigorous, computerized process to perform tasks or roles that were not previously performed or were previously performed using subjective human intuition or input. For example, currently there does not exist a technique that utilizes machine learning to determine whether an in-person customer follows a merchant on social media in the manner described herein. Finally, the process for utilizing machine learning to determine whether an in-person customer follows a merchant on social media conserves computing resources, networking resources, and/or the like that would otherwise have been wasted in attempting to accurately provide social media marketing to customers of a merchant, incorrectly identifying social media accounts of the customers, correcting the incorrect identification of the social media accounts if discovered, and/or like.

As indicated above,FIGS. 1A-1Hare provided merely as examples. Other examples may differ from what is described with regard toFIGS. 1A-1H.

FIG. 2is a diagram illustrating an example200of training a machine learning model. The machine learning model training described herein may be performed using a machine learning system. The machine learning system may include a computing device, a server, a cloud computing environment, and/or the like, such as the client device and/or the processing platform, and/or a device separate from the client device and/or the processing platform.

As shown by reference number205, a machine learning model may be trained using a set of observations. The set of observations may be obtained and/or input from historical data, such as data gathered during one or more processes described herein. For example, the set of observations may include data gathered from user interaction with and/or user input to the processing platform, as described elsewhere herein. In some implementations, the machine learning system may receive the set of observations (e.g., as input) from the client device.

As shown by reference number210, a feature set may be derived from the set of observations. The feature set may include a set of variable types. A variable type may be referred to as a feature. A specific observation may include a set of variable values corresponding to the set of variable types. A set of variable values may be specific to an observation. In some cases, different observations may be associated with different sets of variable values, sometimes referred to as feature values. In some implementations, the machine learning system may determine variable values for a specific observation based on input received from the client device. For example, the machine learning system may identify a feature set (e.g., one or more features and/or corresponding feature values) from structured data input to the machine learning system, such as by extracting data from a particular column of a table, extracting data from a particular field of a form, extracting data from a particular field of a message, extracting data received in a structured data format, and/or the like. In some implementations, the machine learning system may determine features (e.g., variables types) for a feature set based on input received from the client device, such as by extracting or generating a name for a column, extracting or generating a name for a field of a form and/or a message, extracting or generating a name based on a structured data format, and/or the like. Additionally, or alternatively, the machine learning system may receive input from an operator to determine features and/or feature values. In some implementations, the machine learning system may perform natural language processing and/or another feature identification technique to extract features (e.g., variable types) and/or feature values (e.g., variable values) from text (e.g., unstructured data) input to the machine learning system, such as by identifying keywords and/or values associated with those keywords from the text.

As an example, a feature set for a set of observations may include a first feature of an email address, a second feature of other data, a third feature of social media data, and so on. As shown, for a first observation, the first feature may have a value of xxx@yyy.com, the second feature may have an age of a customer, the third feature may have a user name on social medial, and so on. These features and feature values are provided as examples, and may differ in other examples. For example, the feature set may include one or more of the following features: email addresses (e.g., email addresses associated with social media accounts); other data (e.g., names of customers, locations of merchants, locations of customers, ages of customers, sexes of customers, demographics of customers, and/or the like); social media data (e.g., user names, email addresses, demographics, and/or the like); and/or the like. In some implementations, the machine learning system may pre-process and/or perform dimensionality reduction to reduce the feature set and/or combine features of the feature set to a minimum feature set. A machine learning model may be trained on the minimum feature set, thereby conserving resources of the machine learning system (e.g., processing resources, memory resources, and/or the like) used to train the machine learning model.

As shown by reference number215, the set of observations may be associated with a target variable type (e.g., a social media account). The target variable type may represent a variable having a numeric value (e.g., an integer value, a floating point value, and/or the like), may represent a variable having a numeric value that falls within a range of values or has some discrete possible values, may represent a variable that is selectable from one of multiple options (e.g., one of multiples classes, classifications, labels, and/or the like), may represent a variable having a Boolean value (e.g., 0 or 1, True or False, Yes or No), and/or the like. A target variable type may be associated with a target variable value, and a target variable value may be specific to an observation. In some cases, different observations (e.g., different words, phrases, sentences, and/or the like) may be associated with different target variable values.

The target variable may represent a value that a machine learning model is being trained to predict, and the feature set may represent the variables that are input to a trained machine learning model to predict a value for the target variable. The set of observations may include target variable values so that the machine learning model can be trained to recognize patterns in the feature set that lead to a target variable value. A machine learning model that is trained to predict a target variable value may be referred to as a supervised learning model, a predictive model, and/or the like. When the target variable type is associated with continuous target variable values (e.g., a range of numbers and/or the like), the machine learning model may employ a regression technique. When the target variable type is associated with categorical target variable values (e.g., classes, labels, and/or the like), the machine learning model may employ a classification technique.

In some implementations, the machine learning model may be trained on a set of observations that do not include a target variable (or that include a target variable, but the machine learning model is not being executed to predict the target variable). This may be referred to as an unsupervised learning model, an automated data analysis model, an automated signal extraction model, and/or the like. In this case, the machine learning model may learn patterns from the set of observations without labeling or supervision, and may provide output that indicates such patterns, such as by using clustering and/or association to identify related groups of items within the set of observations.

As further shown, the machine learning system may partition the set of observations into a training set220that includes a first subset of observations, of the set of observations, and a test set225that includes a second subset of observations of the set of observations. The training set220may be used to train (e.g., fit, tune, and/or the like) the machine learning model, while the test set225may be used to evaluate a machine learning model that is trained using the training set220. For example, for supervised learning, the test set225may be used for initial model training using the first subset of observations, and the test set225may be used to test whether the trained model accurately predicts target variables in the second subset of observations. In some implementations, the machine learning system may partition the set of observations into the training set220and the test set225by including a first portion or a first percentage of the set of observations in the training set220(e.g., 75%, 80%, or 85%, among other examples) and including a second portion or a second percentage of the set of observations in the test set225(e.g., 25%, 20%, or 15%, among other examples). In some implementations, the machine learning system may randomly select observations to be included in the training set220and/or the test set225.

As shown by reference number230, the machine learning system may train a machine learning model using the training set220. This training may include executing, by the machine learning system, a machine learning algorithm to determine a set of model parameters based on the training set220. In some implementations, the machine learning algorithm may include a regression algorithm (e.g., linear regression, logistic regression, and/or the like), which may include a regularized regression algorithm (e.g., Lasso regression, Ridge regression, Elastic-Net regression, and/or the like). Additionally, or alternatively, the machine learning algorithm may include a decision tree algorithm, which may include a tree ensemble algorithm (e.g., generated using bagging and/or boosting), a random forest algorithm, a boosted trees algorithm, and/or the like. A model parameter may include an attribute of a machine learning model that is learned from data input into the model (e.g., the training set220). For example, for a regression algorithm, a model parameter may include a regression coefficient (e.g., a weight). For a decision tree algorithm, a model parameter may include a decision tree split location, as an example.

As shown by reference number235, the machine learning system may use one or more hyperparameter sets240to tune the machine learning model. A hyperparameter may include a structural parameter that controls execution of a machine learning algorithm by the machine learning system, such as a constraint applied to the machine learning algorithm. Unlike a model parameter, a hyperparameter is not learned from data input into the model. An example hyperparameter for a regularized regression algorithm includes a strength (e.g., a weight) of a penalty applied to a regression coefficient to mitigate overfitting of the machine learning model to the training set220. The penalty may be applied based on a size of a coefficient value (e.g., for Lasso regression, such as to penalize large coefficient values), may be applied based on a squared size of a coefficient value (e.g., for Ridge regression, such as to penalize large squared coefficient values), may be applied based on a ratio of the size and the squared size (e.g., for Elastic-Net regression), may be applied by setting one or more feature values to zero (e.g., for automatic feature selection), and/or the like. Example hyperparameters for a decision tree algorithm include a tree ensemble technique to be applied (e.g., bagging, boosting, a random forest algorithm, a boosted trees algorithm, and/or the like), a number of features to evaluate, a number of observations to use, a maximum depth of each decision tree (e.g., a number of branches permitted for the decision tree), a number of decision trees to include in a random forest algorithm, and/or the like.

To train a machine learning model, the machine learning system may identify a set of machine learning algorithms to be trained (e.g., based on operator input that identifies the one or more machine learning algorithms, based on random selection of a set of machine learning algorithms, and/or the like), and may train the set of machine learning algorithms (e.g., independently for each machine learning algorithm in the set) using the training set220. The machine learning system may tune each machine learning algorithm using one or more hyperparameter sets240(e.g., based on operator input that identifies hyperparameter sets240to be used, based on randomly generating hyperparameter values, and/or the like). The machine learning system may train a particular machine learning model using a specific machine learning algorithm and a corresponding hyperparameter set240. In some implementations, the machine learning system may train multiple machine learning models to generate a set of model parameters for each machine learning model, where each machine learning model corresponds to a different combination of a machine learning algorithm and a hyperparameter set240for that machine learning algorithm.

In some implementations, the machine learning system may perform cross-validation when training a machine learning model. Cross validation can be used to obtain a reliable estimate of machine learning model performance using only the training set220, and without using the test set225, such as by splitting the training set220into a number of groups (e.g., based on operator input that identifies the number of groups, based on randomly selecting a number of groups, and/or the like) and using those groups to estimate model performance. For example, using k-fold cross-validation, observations in the training set220may be split into k groups (e.g., in order or at random). For a training procedure, one group may be marked as a hold-out group, and the remaining groups may be marked as training groups. For the training procedure, the machine learning system may train a machine learning model on the training groups and then test the machine learning model on the hold-out group to generate a cross-validation score. The machine learning system may repeat this training procedure using different hold-out groups and different test groups to generate a cross-validation score for each training procedure. In some implementations, the machine learning system may independently train the machine learning model k times, with each individual group being used as a hold-out group once and being used as a training group k−1 times. The machine learning system may combine the cross-validation scores for each training procedure to generate an overall cross-validation score for the machine learning model. The overall cross-validation score may include, for example, an average cross-validation score (e.g., across all training procedures), a standard deviation across cross-validation scores, a standard error across cross-validation scores, and/or the like.

In some implementations, the machine learning system may perform cross-validation when training a machine learning model by splitting the training set into a number of groups (e.g., based on operator input that identifies the number of groups, based on randomly selecting a number of groups, and/or the like). The machine learning system may perform multiple training procedures and may generate a cross-validation score for each training procedure. The machine learning system may generate an overall cross-validation score for each hyperparameter set240associated with a particular machine learning algorithm. The machine learning system may compare the overall cross-validation scores for different hyperparameter sets240associated with the particular machine learning algorithm, and may select the hyperparameter set240with the best (e.g., highest accuracy, lowest error, closest to a desired threshold, and/or the like) overall cross-validation score for training the machine learning model. The machine learning system may then train the machine learning model using the selected hyperparameter set240, without cross-validation (e.g., using all data in the training set220without any hold-out groups), to generate a single machine learning model for a particular machine learning algorithm. The machine learning system may then test this machine learning model using the test set225to generate a performance score, such as a mean squared error (e.g., for regression), a mean absolute error (e.g., for regression), an area under receiver operating characteristic curve (e.g., for classification), and/or the like. If the machine learning model performs adequately (e.g., with a performance score that satisfies a threshold), then the machine learning system may store that machine learning model as a trained machine learning model245to be used to analyze new observations, as described below in connection withFIG. 3.

In some implementations, the machine learning system may perform cross-validation, as described above, for multiple machine learning algorithms (e.g., independently), such as a regularized regression algorithm, different types of regularized regression algorithms, a decision tree algorithm, different types of decision tree algorithms, and/or the like. Based on performing cross-validation for multiple machine learning algorithms, the machine learning system may generate multiple machine learning models, where each machine learning model has the best overall cross-validation score for a corresponding machine learning algorithm. The machine learning system may then train each machine learning model using the entire training set220(e.g., without cross-validation), and may test each machine learning model using the test set225to generate a corresponding performance score for each machine learning model. The machine learning model may compare the performance scores for each machine learning model, and may select the machine learning model with the best (e.g., highest accuracy, lowest error, closest to a desired threshold, and/or the like) performance score as the trained machine learning model245.

As indicated above,FIG. 2is provided as an example. Other examples may differ from what is described in connection withFIG. 2. For example, the machine learning model may be trained using a different process than what is described in connection withFIG. 2. Additionally, or alternatively, the machine learning model may employ a different machine learning algorithm than what is described in connection withFIG. 2, such as a Bayesian estimation algorithm, a k-nearest neighbor algorithm, an a priori algorithm, a k-means algorithm, a support vector machine algorithm, a neural network algorithm (e.g., a convolutional neural network algorithm), a deep learning algorithm, and/or the like.

FIG. 3is a diagram illustrating an example300of applying a trained machine learning model to a new observation. The new observation may be input to a machine learning system that stores a trained machine learning model305. In some implementations, the trained machine learning model305may be the trained machine learning model245described above in connection withFIG. 2. The machine learning system may include a computing device, a server, a cloud computing environment, and/or the like, such as the processing platform.

As shown by reference number310, the machine learning system may receive a new observation (or a set of new observations), and may input the new observation to the machine learning model305. As shown, the new observation may include a first feature of an email address (e.g., jjj@ttt.com), a second feature of other data (e.g., a customer location), a third feature of social media data (e.g., user locations), and so on, as an example. The machine learning system may apply the trained machine learning model305to the new observation to generate an output (e.g., a result). The type of output may depend on the type of machine learning model and/or the type of machine learning task being performed. For example, the output may include a predicted (e.g., estimated) value of a target variable (e.g., a value within a continuous range of values, a discrete value, a label, a class, a classification, and/or the like), such as when supervised learning is employed. Additionally, or alternatively, the output may include information that identifies a cluster to which the new observation belongs, information that indicates a degree of similarity between the new observations and one or more prior observations (e.g., which may have previously been new observations input to the machine learning model and/or observations used to train the machine learning model), and/or the like, such as when unsupervised learning is employed.

In some implementations, the trained machine learning model305may predict “Account Z” for the target variable of a social media account, as shown by reference number315. Based on this prediction (e.g., based on the value having a particular label/classification, based on the value satisfying or failing to satisfy a threshold, and/or the like), the machine learning system may provide a recommendation, such as the customer is associated with Account Z. Additionally, or alternatively, the machine learning system may perform an automated action and/or may cause an automated action to be performed (e.g., by instructing another device to perform the automated action), such as determining whether Account Z follows the merchant on social media. As another example, if the machine learning system were to predict a value of “Account W” for the target variable of strings of characters, then the machine learning system may provide a different recommendation (e.g., the customer is associated with Account W) and/or may perform or cause performance of a different automated action (e.g., determining whether Account W follows the merchant on social media). In some implementations, the recommendation and/or the automated action may be based on the target variable value having a particular label (e.g., classification, categorization, and/or the like), may be based on whether the target variable value satisfies one or more thresholds (e.g., whether the target variable value is greater than a threshold, is less than a threshold, is equal to a threshold, falls within a range of threshold values, and/or the like), and/or the like.

In some implementations, the trained machine learning model305may classify (e.g., cluster) the new observation in an email address cluster, as shown by reference number320. The observations within a cluster may have a threshold degree of similarity. Based on classifying the new observation in the email address cluster, the machine learning system may provide a recommendation, such as the email address may be used to determine a social media account of the customer. Additionally, or alternatively, the machine learning system may perform an automated action and/or may cause an automated action to be performed (e.g., by instructing another device to perform the automated action), such as using the email address to determine a social media account of the customer. As another example, if the machine learning system were to classify the new observation in other data cluster, then the machine learning system may provide a different recommendation (e.g., a name of the customer may be used to determine a social media account of the customer) and/or may perform or cause performance of a different automated action (e.g., using a name of the customer to determine a social media account of the customer). As still another example, if the machine learning system were to classify the new observation in social media data cluster, then the machine learning system may provide a different recommendation (e.g., a location of a social media user may be used to determine a social media account of the customer) and/or may perform or cause performance of a different automated action (e.g., using a location of a social media user to determine a social media account of the customer).

In this way, the machine learning system may apply a rigorous and automated process to process image-based documents. The machine learning system enables recognition and/or identification of tens, hundreds, thousands, or millions of features and/or feature values for tens, hundreds, thousands, or millions of observations, thereby increasing an accuracy and consistency of processing image-based documents relative to requiring computing resources to be allocated for tens, hundreds, or thousands of operators to manually process image-based documents.

As indicated above,FIG. 3is provided as an example. Other examples may differ from what is described in connection withFIG. 3.

FIG. 4is a diagram of an example environment400in which systems and/or methods described herein may be implemented. As shown inFIG. 4, environment400may include a client device410, a processing platform420, a network430, and a transaction card440. Devices of environment400may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections.

Client device410includes one or more devices capable of receiving, generating, storing, processing, and/or providing information, such as information described herein. For example, client device410may include a mobile phone (e.g., a smart phone, a radiotelephone, and/or the like), a laptop computer, a tablet computer, a desktop computer, a handheld computer, a gaming device, a wearable communication device (e.g., a smart watch, a pair of smart glasses, a heart rate monitor, a fitness tracker, smart clothing, smart jewelry, a head mounted display, and/or the like), a POS device, or a similar type of device. In some implementations, client device410may receive information from and/or transmit information to processing platform420.

Processing platform420includes one or more devices that utilize machine learning to determine whether an in-person customer follows a merchant on social media. In some implementations, processing platform420may be designed to be modular such that certain software components may be swapped in or out depending on a particular need. As such, processing platform420may be easily and/or quickly reconfigured for different uses. In some implementations, processing platform420may receive information from and/or transmit information to one or more client devices410.

In some implementations, as shown, processing platform420may be hosted in a cloud computing environment422. Notably, while implementations described herein describe processing platform420as being hosted in cloud computing environment422, in some implementations, processing platform420may not be cloud-based (i.e., may be implemented outside of a cloud computing environment) or may be partially cloud-based.

Cloud computing environment422includes an environment that hosts processing platform420. Cloud computing environment422may provide computation, software, data access, storage, etc., services that do not require end-user knowledge of a physical location and configuration of system(s) and/or device(s) that hosts processing platform420. As shown, cloud computing environment422may include a group of computing resources424(referred to collectively as “computing resources424” and individually as “computing resource424”).

Computing resource424includes one or more personal computers, workstation computers, mainframe devices, or other types of computation and/or communication devices. In some implementations, computing resource424may host processing platform420. The cloud resources may include compute instances executing in computing resource424, storage devices provided in computing resource424, data transfer devices provided by computing resource424, etc. In some implementations, computing resource424may communicate with other computing resources424via wired connections, wireless connections, or a combination of wired and wireless connections.

As further shown inFIG. 4, computing resource424includes a group of cloud resources, such as one or more applications (“APPs”)424-1, one or more virtual machines (“VMs”)424-2, virtualized storage (“VSs”)424-3, one or more hypervisors (“HYPs”)424-4, and/or the like.

Application424-1includes one or more software applications that may be provided to or accessed by client device410. Application424-1may eliminate a need to install and execute the software applications on client device410. For example, application424-1may include software associated with processing platform420and/or any other software capable of being provided via cloud computing environment422. In some implementations, one application424-1may send/receive information to/from one or more other applications424-1, via virtual machine424-2.

Hypervisor424-4may provide hardware virtualization techniques that allow multiple operating systems (e.g., “guest operating systems”) to execute concurrently on a host computer, such as computing resource424. Hypervisor424-4may present a virtual operating platform to the guest operating systems and may manage the execution of the guest operating systems. Multiple instances of a variety of operating systems may share virtualized hardware resources.

Transaction card440includes a transaction card that can be used to complete a transaction. For example, transaction card440may include a credit card, a debit card, a gift card, a payment card, an automated teller machine (ATM) card, a stored-value card, a fleet card, a room or building access card, a driver's license card, and/or the like. Transaction card440may be capable of storing and/or communicating data for a POS transaction with a transaction terminal. For example, transaction card440may store and/or communicate data, including account information (e.g., an account identifier, a cardholder identifier, etc.), expiration information of transaction card440(e.g., information identifying an expiration month and/or year of transaction card440), banking information (e.g., a routing number of a bank, a bank identifier, etc.), transaction information (e.g., a payment token), and/or the like. For example, to store and/or communicate the data, transaction card440may include a magnetic strip and/or an integrated circuit (IC) chip.

FIG. 5is a diagram of example components of a device500. Device500may correspond to client device410, processing platform420, computing resource424, and/or transaction card440. In some implementations, client device410, processing platform420, computing resource424, and/or transaction card440may include one or more devices500and/or one or more components of device500. As shown inFIG. 5, device500may include a bus510, a processor520, a memory530, a storage component540, an input component550, an output component560, and a communication interface570.

Input component550includes a component that permits device500to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, and/or a microphone). Additionally, or alternatively, input component550may include a sensor for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, and/or an actuator). Output component560includes a component that provides output information from device500(e.g., a display, a speaker, and/or one or more light-emitting diodes (LEDs)).

Communication interface570includes a transceiver-like component (e.g., a transceiver and/or a separate receiver and transmitter) that enables device500to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. Communication interface570may permit device500to receive information from another device and/or provide information to another device. For example, communication interface570may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, and/or the like.

Device500may perform one or more processes described herein. Device500may perform these processes based on processor520executing software instructions stored by a non-transitory computer-readable medium, such as memory530and/or storage component540. A computer-readable medium is defined herein as a non-transitory memory device. A memory device includes memory space within a single physical storage device or memory space spread across multiple physical storage devices.

The number and arrangement of components shown inFIG. 5are provided as an example. In practice, device500may include additional components, fewer components, different components, or differently arranged components than those shown inFIG. 5. Additionally, or alternatively, a set of components (e.g., one or more components) of device500may perform one or more functions described as being performed by another set of components of device500.

FIG. 6is a flow chart of an example process600for utilizing machine learning to determine whether an in-person customer follows a merchant on social media. In some implementations, one or more process blocks ofFIG. 6may be performed by a device (e.g., processing platform420). In some implementations, one or more process blocks ofFIG. 6may be performed by another device or a group of devices separate from or including the device, such as a client device (e.g., client device410).

As shown inFIG. 6, process600may include receiving, from a point-of-sale device, transaction data associated with a transaction between a customer and a merchant associated with the point-of-sale device (block610). For example, the device (e.g., using computing resource424, processor520, communication interface570, and/or the like) may receive, from a point-of-sale device, transaction data associated with a transaction between a customer and a merchant associated with the point-of-sale device, as described above.

As further shown inFIG. 6, process600may include determining a customer email address of the customer and other data associated with the transaction, the customer, or the merchant, based on the transaction data and customer data identifying the customer (block620). For example, the device (e.g., using computing resource424, processor520, memory530, and/or the like) may determine a customer email address of the customer and other data associated with the transaction, the customer, or the merchant, based on the transaction data and customer data identifying the customer, as described above.

As further shown inFIG. 6, process600may include processing the customer email address, the other data, and social media data, with a machine learning model, to identify a social media account of the customer, wherein the social media data includes data identifying a plurality of social media accounts, and wherein the plurality of social media accounts includes the social media account of the customer (block630). For example, the device (e.g., using computing resource424, processor520, storage component540, and/or the like) may process the customer email address, the other data, and social media data, with a machine learning model, to identify a social media account of the customer, as described above. In some implementations, the social media data includes data may identify a plurality of social media accounts. In some implementations, the plurality of social media accounts may include the social media account of the customer.

As further shown inFIG. 6, process600may include determining, based on the social media data, whether the social media account of the customer follows the merchant (block640). For example, the device (e.g., using computing resource424, processor520, memory530, and/or the like) may determine, based on the social media data, whether the social media account of the customer follows the merchant, as described above.

As further shown inFIG. 6, process600may include performing one or more actions based on whether the social media account of the customer follows the merchant (block650). For example, the device (e.g., using computing resource424, processor520, memory530, storage component540, communication interface570, and/or the like) may perform one or more actions based on whether the social media account of the customer follows the merchant, as described above.

In a first implementation, the other data may include data identifying an identifier of the transaction, a geographical location of the transaction, a name of the customer, a geographical location of the merchant, a geographical location of the customer, an age of the customer, or demographics associated with the customer.

In a second implementation, alone or in combination with the first implementation, process600may include performing a sentiment analysis of the customer with the merchant; and performing one or more other actions based on the sentiment analysis.

In a third implementation, alone or in combination with one or more of the first and second implementations, performing the one or more actions based on whether the social media account of the customer follows the merchant may include providing, to a client device of the customer, information indicating how to follow the merchant on social media when the social media account fails to follow the merchant; providing, to the point-of-sale device, information identifying an offer for the customer when the social media account follows the merchant; or providing, to the point-of-sale device, information identifying a discount for the transaction when the social media account follows the merchant.

In a fourth implementation, alone or in combination with one or more of the first through third implementations, performing the one or more actions based on whether the social media account of the customer follows the merchant may include providing a message to the social media account of the customer when the social media account follows the merchant; providing, to the social media account of the customer, a request to follow the merchant when the social media account fails to follow the merchant; or retraining the machine learning model based on whether the social media account of the customer follows the merchant.

In a fifth implementation, alone or in combination with one or more of the first through fourth implementations, determining, based on the social media data, whether the social media account of the customer follows the merchant may include determining, based on the social media data, whether the social media account of the customer follows the merchant, a product of the merchant, or a service of the merchant.

In a sixth implementation, alone or in combination with one or more of the first through fifth implementations, determining, based on the social media data, whether the social media account of the customer follows the merchant may include maintaining a list of social media accounts that follow the merchant; comparing the social media account of the customer to the list of social media accounts that follow the merchant; and determining whether the social media account of the customer follows the merchant based on comparing the social media account of the customer to the list of social media accounts that follow the merchant.

FIG. 7is a flow chart of an example process700for utilizing machine learning to determine whether an in-person customer follows a merchant on social media. In some implementations, one or more process blocks ofFIG. 7may be performed by a device (e.g., processing platform420). In some implementations, one or more process blocks ofFIG. 7may be performed by another device or a group of devices separate from or including the device, such as a client device (e.g., client device410).

As shown inFIG. 7, process700may include receiving, from a point-of-sale device, a name of a customer conducting a transaction with a merchant, an image of the customer, and a geographical location of the merchant (block710). For example, the device (e.g., using computing resource424, processor520, communication interface570, and/or the like) may receive, from a point-of-sale device, a name of a customer conducting a transaction with a merchant, an image of the customer, and a geographical location of the merchant, as described above.

As further shown inFIG. 7, process700may include processing the name of the customer, the image of the customer, the geographical location of the merchant, and social media data, with a machine learning model, to identify a social media account of the customer (block720). For example, the device (e.g., using computing resource424, processor520, storage component540, and/or the like) may process the name of the customer, the image of the customer, the geographical location of the merchant, and social media data, with a machine learning model, to identify a social media account of the customer, as described above.

As further shown inFIG. 7, process700may include determining, based on the social media data, whether the social media account of the customer follows the merchant (block730). For example, the device (e.g., using computing resource424, processor520, memory530, and/or the like) may determine, based on the social media data, whether the social media account of the customer follows the merchant, as described above.

As further shown inFIG. 7, process700may include performing one or more actions based on whether the social media account of the customer follows the merchant (block740). For example, the device (e.g., using computing resource424, processor520, memory530, storage component540, communication interface570, and/or the like) may perform one or more actions based on whether the social media account of the customer follows the merchant, as described above.

In a first implementation, the name of the customer may be received from a transaction card associated with the customer.

In a second implementation, alone or in combination with the first implementation, the image of the customer may be received from a camera associated with the point-of-sale device.

In a third implementation, alone or in combination with one or more of the first and second implementations, the geographical location of the merchant may include a geographical location of the point-of-sale device.

In a fourth implementation, alone or in combination with one or more of the first through third implementations, performing the one or more actions based on whether the social media account of the customer follows the merchant may include providing, to a client device of the customer, information indicating how to follow the merchant on social media when the social media account fails to follow the merchant; providing, to the point-of-sale device, information identifying an offer for the customer when the social media account follows the merchant; or providing, to the point-of-sale device, information identifying a discount for the transaction when the social media account follows the merchant.

In a fifth implementation, alone or in combination with one or more of the first through fourth implementations, performing the one or more actions based on whether the social media account of the customer follows the merchant may include providing a message to the social media account of the customer when the social media account follows the merchant; providing, to the social media account of the customer, a request to follow the merchant when the social media account fails to follow the merchant; or retaining the machine learning model based on whether the social media account of the customer follows the merchant.

In a sixth implementation, alone or in combination with one or more of the first through fifth implementations, process700may include performing a sentiment analysis of the customer with the merchant; and performing one or more other actions based on the sentiment analysis.

AlthoughFIG. 7shows example blocks of process700, in some implementations, process700may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted inFIG. 7. Additionally, or alternatively, two or more of the blocks of process700may be performed in parallel.

FIG. 8is a flow chart of an example process800for utilizing machine learning to determine whether an in-person customer follows a merchant on social media. In some implementations, one or more process blocks ofFIG. 8may be performed by a device (e.g., processing platform420). In some implementations, one or more process blocks ofFIG. 8may be performed by another device or a group of devices separate from or including the device, such as a client device (e.g., client device410).

As shown inFIG. 8, process800may include receiving, from a point-of-sale device, transaction data associated with a transaction between a customer and a merchant associated with the point-of-sale device (block810). For example, the device (e.g., using computing resource424, processor520, communication interface570, and/or the like) may receive, from a point-of-sale device, transaction data associated with a transaction between a customer and a merchant associated with the point-of-sale device, as described above.

As further shown inFIG. 8, process800may include determining a customer email address of the customer and other data associated with the transaction, the customer, or the merchant, based on the transaction data and customer data identifying the customer (block820). For example, the device (e.g., using computing resource424, processor520, memory530, and/or the like) may determine a customer email address of the customer and other data associated with the transaction, the customer, or the merchant, based on the transaction data and customer data identifying the customer, as described above.

As further shown inFIG. 8, process800may include processing the customer email address, the other data, and social media data, with a machine learning model, to identify a social media account of the customer, wherein the machine learning model is trained based on historical customer email addresses, historical other data, and historical social media data, wherein the social media data includes data identifying a plurality of social media accounts, and wherein the plurality of social media accounts includes the social media account of the customer (block830). For example, the device (e.g., using computing resource424, processor520, storage component540, and/or the like) may process the customer email address, the other data, and social media data, with a machine learning model, to identify a social media account of the customer, as described above. In some implementations, the machine learning model may be trained based on historical customer email addresses, historical other data, and historical social media data. In some implementations, the social media data may include data identifying a plurality of social media accounts. In some implementations, the plurality of social media accounts may include the social media account of the customer.

As further shown inFIG. 8, process800may include determining, based on the social media data, whether the social media account of the customer follows the merchant (block840). For example, the device (e.g., using computing resource424, processor520, memory530, and/or the like) may determine, based on the social media data, whether the social media account of the customer follows the merchant, as described above.

As further shown inFIG. 8, process800may include performing one or more actions based on whether the social media account of the customer follows the merchant (block850). For example, the device (e.g., using computing resource424, processor520, memory530, storage component540, communication interface570, and/or the like) may perform one or more actions based on whether the social media account of the customer follows the merchant, as described above.

In a first implementation, process800may include performing a sentiment analysis of the customer with the merchant; and performing one or more other actions based on the sentiment analysis.

In a second implementation, alone or in combination with the first implementation, performing the one or more actions based on whether the social media account of the customer follows the merchant, cause the one or more processors may include providing, to a client device of the customer, information indicating how to follow the merchant on social media when the social media account fails to follow the merchant; providing, to the point-of-sale device, information identifying an offer for the customer when the social media account follows the merchant; providing, to the point-of-sale device, information identifying a discount for the transaction when the social media account follows the merchant; providing a message to the social media account of the customer when the social media account follows the merchant; providing, to the social media account of the customer, a request to follow the merchant when the social media account fails to follow the merchant; or retaining the machine learning model based on whether the social media account of the customer follows the merchant.

In a third implementation, alone or in combination with one or more of the first and second implementations, determining, based on the social media data, whether the social media account of the customer follows the merchant may include determining, based on the social media data, whether the social media account of the customer follows the merchant, a product of the merchant, or a service of the merchant.

In a fourth implementation, alone or in combination with one or more of the first through third implementations, determining, based on the social media data, whether the social media account of the customer follows the merchant may include maintaining a list of social media accounts that follow the merchant; comparing the social media account of the customer to the list of social media accounts that follow the merchant; and determining whether the social media account of the customer follows the merchant based on comparing the social media account of the customer to the list of social media accounts that follow the merchant.

In a fifth implementation, alone or in combination with one or more of the first through fourth implementations, process800includes receiving, from the point-of-sale device, a name of another customer conducting another transaction with the merchant, an image of the other customer, and a geographical location of the merchant; processing the name of the other customer, the image of the other customer, the geographical location of the merchant, and the social media data, with another machine learning model, to identify another social media account of the other customer; determining, based on the social media data, whether the other social media account of the other customer follows the merchant; and performing one or more other actions based on whether the other social media account of the other customer follows the merchant.

AlthoughFIG. 8shows example blocks of process800, in some implementations, process800may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted inFIG. 8. Additionally, or alternatively, two or more of the blocks of process800may be performed in parallel.