US20160253688A1 - System and method of analyzing social media to predict the churn propensity of an individual or community of customers - Google Patents

System and method of analyzing social media to predict the churn propensity of an individual or community of customers Download PDF

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US20160253688A1
US20160253688A1 US15/052,831 US201615052831A US2016253688A1 US 20160253688 A1 US20160253688 A1 US 20160253688A1 US 201615052831 A US201615052831 A US 201615052831A US 2016253688 A1 US2016253688 A1 US 2016253688A1
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customer
churn
social
social media
customers
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Aaron David NIELSEN
Qiao Pang
Ebrahim Bagheri
Mohammad-Amin Jashki
Fattane Zarrinkalam
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Thinkcx Technologies Inc
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Thinkcx Technologies Inc
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Priority to US15/052,831 priority Critical patent/US20160253688A1/en
Publication of US20160253688A1 publication Critical patent/US20160253688A1/en
Assigned to THINKCX TECHNOLOGIES, INC. reassignment THINKCX TECHNOLOGIES, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: BAGHERI, Ebrahim, JASHKI, MOHAMMAD-AMIN, NIELSEN, AARON DAVID, PANG, QIAO, VAZIFEDOOST, ALIREZA, ZARRINKALAM, FATTANE
Priority to US16/565,207 priority patent/US11392968B2/en
Priority to US17/744,439 priority patent/US20230044619A1/en
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/335—Filtering based on additional data, e.g. user or group profiles
    • G06F16/337—Profile generation, learning or modification
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G06F17/30572—
    • G06Q10/40—
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking

Definitions

  • This invention is related to the automatic prediction of customer churn in service and subscription based businesses by using intelligent and real-time analysis of customer's social media profiles, engagement and community.
  • the purpose of the present invention is therefore to help service providers attack churn in a much more predictive and proactive manner, which in turn will generate a financial return for the service provider through higher retention rates, lower loyalty incentive costs, and a superior customer experience.
  • the present invention generally relates to a computer-implemented method to characterize social influence and to predict behavior of a user, said user being part of a social network, and more particularly to a computer-implemented method which comprises creating a dynamically updatable social influence profile of the user, predicting future behavior of the user based on influence given by the user and received by the user from his social circles, and thereafter predicting the user's predisposition to either leave a subscription or a service or reduce his/her engagement with a subscription or a service.
  • the method and system of the invention depends upon the characterization of influence among social media users, and in assigning feature vector similar cohorts to a user in order to predict future behavior of that user, using the proprietary “churn” analysis of the invention.
  • customer save rates can be increased by increasing the sophistication and variety of ways to attempt to save the customers from subscription or service cancellation.
  • the tools and techniques discussed herein relate to tailoring computer-based customer saving procedures to a particular customer who may not have yet explicitly signaled to the company/provider the desire to cancel or reduce a service or a subscription.
  • the invention “catches” the user at least one step before such conveyance.
  • the method and system of the present invention have substantial benefits for service and subscription-based businesses, which lose substantial revenue each year due to customer churn. To mitigate these losses, companies have implemented customer retention programs; however, these programs are limited in their effectiveness. There are several reasons for this, including but not limited to:
  • the present invention provides, in one aspect, a method for the early prediction of communities of customers who are likely to cancel their subscriptions or services. This is accomplished by analyzing a customer's social media profile (SMP).
  • SMP social media profile
  • the present invention provides, in another aspect, a computerized system for locating the social profiles for customers, collecting their SMP, analyzing the information, and delivering the results to businesses so they can proactively make efforts to retain their customers before they decide to cancel their products.
  • the present invention provides, in another aspect, a method for finding individuals with low degrees of loyalty to their service providers. These predictions are achieved by analyzing the historical social media content of each individual and looking for features indicating customer estrangement to service providers.
  • the present method and system is able to accurately and proactively identify the churn risk of an individual customer or community of customers. As a result, this method provides the following benefits:
  • the end result of implementing a churn prediction and management program as outlined herein is to develop a better understanding of the causes of churn and of the characteristics of customers who will likely churn in the future and to generate a target list of the most likely future churners.
  • the business/enterprise may implement a much more efficient and much more effective customer retention program.
  • the present invention provides, in one aspect, a computer implemented method of collecting/mining data relating to social media influence around a customer, and analyzing said data to predict a customer's predisposition to either leave a subscription or a service or reduce his/her engagement with a subscription or a service which comprises: a) receiving a plurality of social media inputs associated with the customer; b) determining a churn probability for the customer; and c) performing an action based on the determined churn probability.
  • the present invention provides, in another aspect a computer-implemented method to characterize social influence and to predict behavior of a user, said user being part of a social network which comprises a) creating a dynamically updatable social influence profile of the user, b) predicting future behavior of the user based on influence given by user and received by the user from his social circles, and thereafter c) predicting the user's predisposition to either leave a subscription or a service or reduce his/her engagement with a subscription or a service.
  • the present invention provides, in another aspect a computer implemented method of collecting/mining data relating to social media influence around a customer, and analyzing said data to predict a customer's predisposition to either leave a subscription or a service or reduce his/her engagement with a subscription or a service comprises:
  • the present invention provides, in another aspect a system, comprising: an information module that is configured to identify a user of a service; a probability module that is configured to determine a churn probability for the user of the service; and an action module that is configured to perform an action based on the determined churn probability.
  • the present invention provides, in another aspect a computer implemented method of designing an efficient customer retention program for managing customer churn among customers of a business, the customer retention program including an analysis of the causes of customer churn and identifying customers who are most likely to churn in the future, so that appropriate steps may be taken to prevent customers who are likely to churn in the future from churning, the method comprising:
  • the present invention provides, in another aspect a computer-implementable method for predicting and delivery of churn signals for customers that are at risk of terminating their subscription and/or service to the customer retention units at the provider company, wherein the churn predictions are generated by analysis of full social media profiles of customers.
  • the present invention provides, in another aspect, a machine implemented system that predicts and delivers churn signals to customer relationship management (CRM) software of service-based or subscription businesses for customers who are at risk of cancelling their services which comprises:
  • the present invention provides, in another aspect, a non-transitory, tangible computer-readable medium storing instructions adapted to be executed by a computer processor to perform a method for generating customer churn prediction, for an entity in need of such prediction, said method comprising the steps of: extracting and receiving, by a churn prediction program executing on the computer processor, a variety of social media inputs; pre-processing the social media inputs to identify relevant social media posts, data trends and social network structures (pre-processed data); extracting and engineering features of the pre-processed data, such features comprising at least one of i) assessed social media postings, ii) assessed life events, iii) assessed engagement with the entity and competitors of said entity iv) assessed trend predisposition of customers to the entity based upon their prior churns, v) assessed one or more communities of customers to the entity and predisposition of the customers to the entity to churn based upon churn risk of the one or more communities; create feature vector
  • FIG. 1 is a diagram depicting the complete computer system 100 used to identify communities with high likelihood of churn and alert the customer retention departments.
  • FIG. 2 is a diagram of the Social Aggregator 200 component of the present invention that creates a database of social media profiles 240 .
  • FIG. 3 is a diagram 300 of the Social Predictor sub-component 121 that can predict the churn risk of patrons.
  • FIG. 4 is an example 400 of how cohorts of churned 411 and not churned 421 customers will be utilized to predict the likelihood of churn for new customers 401 .
  • FIG. 5 is a flow chart 500 of how the Social Predictor Scheduler 122 triggers a computation of churn risk on the condition that the generated social media contents are relevant.
  • FIG. 6 is a flow chart 600 of how influential connections 641 in the social network of a customer 611 are determined.
  • FIG. 7 is a depiction 700 of a console where customer retention agents would view a queue of churn risk signals.
  • FIG. 8 is a depiction 800 of a page where customer retention agents would view details about a customer including the insights that contributed to their churn risk assessment.
  • FIG. 9 is a flow chart of the general architecture of the data integration, preprocessing, feature engineering and extraction, feature vector generation and model generation.
  • FIG. 10 is a flow chart of “life event participation” in churn prediction, in accordance with one aspect of method of the invention.
  • FIG. 11 is a schematic of the “life event” prediction component of the method of the invention.
  • An embodiment of the invention may be implemented as a method of as a machine-readable non-transitory storage medium that stores executable instructions that, when executed by a data processing system, causes the system to perform a method.
  • An apparatus such as a data processing system, can also be an embodiment of the invention.
  • invention and the like mean “the one or more inventions disclosed in this application”, unless expressly specified otherwise.
  • device and “mobile device” refer herein to any personal digital assistants, Smart phones, other cell phones, tablets and the like.
  • the term “e.g.” and the like mean “for example”, and thus does not limit the term or phrase it explains.
  • the term “e.g.” explains that “instructions” are an example of “data” that the computer may send over the Internet, and also explains that “a data structure” is an example of “data” that the computer may send over the Internet.
  • both “instructions” and “a data structure” are merely examples of “data”, and other things besides “instructions” and “a data structure” can be “data”.
  • the function of the first machine may or may not be the same as the function of the second machine.
  • social media profile includes, but is not limited to, social streams, follows (e.g., likes and follows), community influence, personality types and social media engagement with peers, family members, the company and competitors across social networks such as TWITTER®, FACEBOOK®, LINKEDIN® INSTAGRAM® GOOGLE+® REDDIT® YELP® and WORDPRESS®.
  • analysis is carried out on the processed SMP to intelligently infer the desired communities.
  • a customer of a service or subscription (whose social media behaviors and influences are mined and analyzed in accordance with the present invention) is also a user of at least one social media platform or network.
  • a plurality of feature vectors of the user are identified against which cohorts of the user may be computed in order to predict the likelihood of the user either leaving a subscription or a service (with which he/she is a customer) or reduce his/her engagement with a subscription or a service (with which he/she is a customer).
  • churn or “churn rate” refers to a number of individuals that leave a group or other collection over a certain period of time, such as a number of customers that leave a subscription-based service. Churn, therefore, is similar to attrition, and may be the opposite of retention. For example, a customer-based service model may succeed when customer churn is low (and retention is high), and may fail when customer churn is high (and retention is low), among other things. However, specifically within the scope of the invention, churn may also refer to a user reducing the type or nature of services from a supplier/company/industry (while not fully leaving the service entirely).
  • any given numerical range shall include whole and fractions of numbers within the range.
  • the range “1 to 10” shall be interpreted to specifically include whole numbers between 1 and 10 (e.g., 1, 2, 3, 4, . . . , 10) and non-whole numbers (e.g., 1.1, 1.2, . . . 1.9).
  • the terms “component” and “system” are intended to encompass computer-readable data storage that is configured with computer-executable instructions that cause certain functionality to be performed when executed by a processor.
  • the computer-executable instructions may include a routine, a function, or the like. It is also to be understood that a component or system may be localized on a single device or machine or distributed across several devices or machines.
  • attribution refers to the technology's ability to associate or connect a company's individual customers with their social profiles in such a way that the past and future messages they post are automatically attributed back to that specific customer. Without an attribution component, it is nearly impossible to predict individual churn situations. Further, the methods of obtaining and analyzing social messages can be prohibitively expensive if not done efficiently, and so the technology used to perform these operations must be carefully designed and built with cost efficiencies in mind. Currently available technologies are not able to deliver against these two requirements.
  • the present invention addresses and resolves the aforementioned challenges and provides a computer-implementation method and system for predicting the customers that are highly likely to cancel or change their service or subscription. With this information, companies can proactively implement early retention strategies that are lower cost and more effective.
  • One aspect of the present invention relates to the use of a customer's “social media profile” (SMP) to predict whether they are at risk of churning or not.
  • SMP social media profile
  • the rise of social media outlets such as TWITTER, FACEBOOK, Blogs, and INSTAGRAM has generated a wealth of publicly available user information. Human subjects use these social platforms to express their frustration, excitement, and opinions. And their statements with regards to the services they are subscribed to are no exception. While some people might not have a social media presence most do and each customer of a company can be characterized by his/her SMP.
  • This profile includes a history of their generated social content across all social media outlets. SMPs can be leveraged to determine whether a particular customer is at risk of cancelling their service/subscription. This information can then be delivered to the businesses that are at risk. Note that it makes a considerable difference to know about this risk upfront, even before the customer knows they might churn, than to wait until they contact the company and ask to terminate their contract.
  • Examples of how the method in the present invention uses real time and historical SMPs to help the businesses retain at risk customers, include:
  • the present invention processes SMPs of customers in real time and generates churn notifications if the customers are engaged in conversations with their competitors.
  • This engagement can be a signal of their intention to explore the opportunity of switching service/subscription providers. While a single posting to a competitor does not necessarily indicate the customer will churn, the cumulative engagement with rival companies can significantly strengthen the signal and more definitively indicate an upcoming switch.
  • the present invention analyzes the whole history of the SMPs with a goal of determining how loyal each customer generally is to services and subscriptions.
  • the present invention analyzes the whole history of the SMPs with a goal of determining how loyal each customer generally is to services and subscriptions.
  • not every individual has the same personality type when it comes to dealing with their subscriptions. Some individuals frequently change service providers while others only change if absolutely required (e.g. they move to a new city and the old provider is not available in the new geographical area).
  • Customers sometimes display this predisposition in social media outlets.
  • By analyzing the customer's history on social media platforms it is possible to determine the customer's predisposition to and frequency of switching changing providers. This information can be used in determining the customer's churn risk with a specific provider.
  • Another embodiment of the present invention is to address the influence of the social network connections on the company's customers. If a customer's very close friend had an alarming experience with the same company and shares it on a social media platform, it is likely that the customer is going to reconsider their relationship with the company.
  • one aspect of the invention is to predict communities of customers with certain churn behaviors.
  • clusters of patrons are created. Individual customers are then compared to these clusters and the individual's churn probability can be inferred from the community that they belong to.
  • the present invention calls these groups of customers with similar churn behaviors “cohorts”. While the people in each cohort may not necessarily share the same demographics, they are similar in terms of their SMPs, engagement with rival companies, degree of loyalty towards services/subscriptions, and also influences from their social network. Therefore, their churn risk scores are to be very close to each other based on those constituents.
  • the present invention leverages existing clustering algorithms to create these churn cohorts. Similar to any clustering algorithm, a set of useful features are extracted from customers' SMPs. Some of these features are natural language components while others are numerical. Each of the natural language features goes through a pipeline of natural language processing techniques and is eventually transformed to a numerical feature. The clustering algorithm will work with a vector of numeric features.
  • the initial set of cohorts is created from a manually annotated set of customers. This is the training data set that the cohorts are constructed from and used for answering churn prediction questions. The customers in the training set are annotated with churned and not churned labels. Features for each customer are extracted and the clustering algorithm computes the cohorts from these feature vectors. Most features are calculated for three time periods: 1) short term 2) medium term, and, 3) long term history. For the purposes of the present invention, 1 week, 1 month, and 6 months are used respectively for each time periods but other alternatives can also be employed.
  • Neg_Dir_ 1 w (Direct negative in the past week): Negative sentiment score inferred from social contents that are directed at the current company. This is a score that is calculated from the SMP of the customer in the past week.
  • Neg_Dir_ 1 m (Direct negative in the past month): Negative sentiment score inferred from social contents of a specific customer that are directed at the current company. This is a score that is calculated from the SMP of the customer in the past month.
  • Neg_Dir_ 6 m Direct Negative in the past 6 months: Negative sentiment score inferred from social contents of a specific customer that are directed at the current company. This is a score that is calculated from the SMP of the customer in the past 6 months.
  • Neg_Indir_ 1 w (Indirect negative in the past week): Negative sentiment score inferred from social contents of a specific customer that are indirectly mentioned about the current company. These include contents about the industrial expertise of the company. This is a score that is calculated from the SMP of the customer in the past week.
  • Neg_Indir_ 1 m (Indirect negative in the past month): Negative sentiment score inferred from social contents of a specific customer that are indirectly mentioned about the current company. These include contents about the industrial expertise of the company. This is a score that is calculated from the SMP of the customer in the past month.
  • Neg_Indir_ 6 m (Indirect Negative in the past 6 months): Negative sentiment score inferred from social contents of a specific customer that are indirectly mentioned about the current company. These include contents about the industrial expertise of the company. This is a score that is calculated from the SMP of the customer in the past 6 months.
  • Pos_Dir_ 1 w (Direct positive in the past week): Positive sentiment score inferred from social contents of a specific customer that are directed at the current company. This is a score that is calculated from the SMP of the customer in the past week.
  • Pos_Dir_ 1 m (Direct positive in the past month): Positive sentiment score inferred from social contents of a specific customer that are directed at the current company. This is a score that is calculated from the SMP of the customer in the past month.
  • Pos_Dir_ 6 m Direct positive in the past 6 months: Positive sentiment score inferred from social contents of a specific customer that are directed at the current company. This is a score that is calculated from the SMP of the customer in the past 6 months.
  • Pos_Indir_ 1 w (Indirect positive in the past week): Positive sentiment score inferred from social contents of a specific customer that are indirectly mentioned about the current company. These include contents about the industrial expertise of the company. This is a score that is calculated from the SMP of the customer in the past week.
  • Pos_Indir_ 1 m (Indirect positive in the past month): Positive sentiment score inferred from social contents of a specific customer that are indirectly mentioned about the current company. These include contents about the industrial expertise of the company. This is a score that is calculated from the SMP of the customer in the past month.
  • Pos_Indir_ 6 m (Indirect positive in the past 6 months): Positive sentiment score inferred from social contents of a specific customer that are indirectly mentioned about the current company. These include contents about the industrial expertise of the company. This is a score that is calculated from the SMP of the customer in the past 6 months.
  • News_ 1 w (News about current company in the past week): Neutral news announcement score inferred from social contents of a specific customer about the current company. This is a score that is calculated from the SMP of the customer in the past week.
  • News_ 1 m (News about current company in the past month): Neutral news announcement score inferred from social contents of a specific customer about the current company. This is a score that is calculated from the SMP of the customer in the past week.
  • News_ 6 m (News about current company in the past 6 months): Neutral news announcement score inferred from social contents of a specific customer about the current company. This is a score that is calculated from the SMP of the customer in the past week.
  • Comp_Quest_ 1 w (Asking questions from a competitor in the past week): Engaging in questions score inferred from questions posed on competitor company's social media platforms by a specific customer. This is a score that is calculated from questions posted on social media accounts (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) of rival companies in the past week.
  • social media accounts e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Comp_Quest_ 1 m (Asking questions from a competitor in the past week): Engaging in questions score inferred from questions posed on competitor company's social media platforms by a specific customer. This is a score that is calculated from questions posted on social media accounts (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) of rival companies in the past week.
  • social media accounts e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Comp_Quest_ 6 m (Asking questions from a competitor in the past week): Engaging in questions score inferred from questions posed on competitor company's social media platforms by a specific customer. This is a score that is calculated from questions posted on social media accounts (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) of rival companies in the past week.
  • social media accounts e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Comp_news_ 1 w (News about a competitor in the past week): Neutral news announcement score inferred from social contents about the rival company by a specific customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • social media contents e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Comp_news_ 1 m (News about a competitor in the past month): Neutral news announcement score inferred from social contents about the rival company by a specific customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past month.
  • social media contents e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Comp_news_ 6 m News about a competitor in the past 6 months: Neutral news announcement score inferred from social contents about the rival company by a specific customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past 6 months.
  • social media contents e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Cancel_ 1 y Service cancellation score inferred from the number of service/subscription cancellation announcements by a specific customer. This is a score that is calculated from the SMP of the customer in the past year.
  • Cancel_ 2 y Service cancellation score inferred from the number of service/subscription cancellation announcements by a specific customer. This is a score that is calculated from the SMP of the customer in the past 2 years.
  • Renew_ 1 y Service renewal score inferred from the number of service/subscription renewal announcements by a specific customer. This is a score that is calculated from the SMP of the customer in the past year.
  • Renew_ 2 y Service renewal score inferred from the number of service/subscription renewal announcements by a specific customer. This is a score that is calculated from the SMP of the customer in the past year.
  • the social network features that are used to train the social network influenced cohorts are presented in the following. Other features may be included as applicable.
  • Net 1 _Neg_ 1 w (Negative in close social network in the past week): Negative sentiment score inferred from social contents that are generated from the customer's first circle of social network about the current company. First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 1 _Neg_ 1 m (Negative in close social network in the past month): Negative sentiment score inferred from social contents that are generated from the customer's first circle of social network about the current company. First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past month.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 1 _Neg_ 6 m (Negative in close social network in the past 6 months): Negative sentiment score inferred from social contents that are generated from the customer's first circle of social network about the current company. First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past 6 months.
  • the social media contents e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 1 _Pos_ 1 w (Positive in close social network in the past week): Positive sentiment score inferred from social contents that are generated from the customer's first circle of social network about the current company. First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • social media contents e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 1 _Pos_ 1 m (Positive in close social network in the past month): Positive sentiment score inferred from social contents that are generated from the customer's first circle of social network about the current company. First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past month.
  • social media contents e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 1 _Pos_ 6 m (Positive in close social network in the past 6 months): Positive sentiment score inferred from social contents that are generated from the customer's first circle of social network about the current company. First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past 6 months.
  • the social media contents e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 2 _Neg_ 1 w (Negative in distant social network in the past week): Negative sentiment score inferred from social contents that are generated from the customer's second circle of social network about the current company. Second circle includes acquaintances that are direct connections and but are not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 2 _Neg_ 1 m (Negative in distant social network in the past month): Negative sentiment score inferred from social contents that are generated from the customer's second circle of social network about the current company. Second circle includes acquaintances that are direct connections and but are not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past month.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 2 _Neg_ 6 m (Negative in distant social network in the past 6 months): Negative sentiment score inferred from social contents that are generated from the customer's second circle of social network about the current company. Second circle includes acquaintances that are direct connections and but are not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past 6 months.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 2 _Pos_ 1 w (Positive in distant social network in the past week): Positive sentiment score inferred from social contents that are generated from the customer's second circle of social network about the current company. Second circle includes acquaintances that are direct connections and but are not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • social media contents e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 2 _Pos_ 1 m (Positive in distant social network in the past month): Positive sentiment score inferred from social contents that are generated from the customer's second circle of social network about the current company. Second circle includes acquaintances that are direct connections and but are not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past month.
  • social media contents e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 2 _Pos_ 6 m (Positive in distant social network in the past 6 months): Positive sentiment score inferred from social contents that are generated from the customer's second circle of social network about the current company. Second circle includes acquaintances that are direct connections and but are not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • social media contents e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 1 _Disc_ 1 w (Discourage current service in close social network in the past week): Discouragement sentiment score inferred from social contents that are generated from the customer's first circle of social network and discouraging the use of current company services.
  • First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • Net 1 _Disc_ 1 m (Discourage current service in close social network in the past month): Discouragement sentiment score inferred from social contents that are generated from the customer's first circle of social network and discouraging the use of current company services.
  • First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past month.
  • Net 1 _Disc_ 6 m (Discourage current service in close social network in the past 6 months): Discouragement sentiment score inferred from social contents that are generated from the customer's first circle of social network and discouraging the use of current company services.
  • First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past 6 months.
  • Net 1 _Enc_ 1 w (Encourage joining competitor in close social network in the past week): Encouragement sentiment score inferred from social contents that are generated from the customer's first circle of social network and encouraging the use of rival companies' services.
  • First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • Net 1 _Enc_ 1 m (Encourage joining competitor in close social network in the past month): Encouragement sentiment score inferred from social contents that are generated from the customer's first circle of social network and encourage the use of rival companies services.
  • First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past month.
  • Net 1 _Enc_ 6 m (Encourage joining competitor in close social network in the past 6 months): Encouragement sentiment score inferred from social contents that are generated from the customer's first circle of social network and encourage the use of rival companies services.
  • First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past 6 months.
  • Net 2 _Disc_ 1 w (Negative in distant social network in the past week): Discouragement sentiment score inferred from social contents that are generated from the customer's second circle of social network and discourage the use of current company services. Second circle includes acquaintances that are direct connections and but not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 2 _Disc_ 1 m (Negative in distant social network in the past month): Discouragement sentiment score inferred from social contents that are generated from the customer's second circle of social network and discourage the use of current company services. Second circle includes acquaintances that are direct connections and but not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past month.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 2 _Disc_ 6 m (Negative in distant social network in the past 6 months): Discouragement sentiment score inferred from social contents that are generated from the customer's second circle of social network and discourage the use of current company services. Second circle includes acquaintances that are direct connections and but not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past 5 month.
  • the social media contents e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 2 _Enc_ 1 w (Encourage joining competitor in distant social network in the past week): Encouragement sentiment score inferred from social contents that are generated from the customer's second circle of social network and encourage the use of rival companies services.
  • Second circle includes acquaintances that are direct connections and but not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • Net 2 _Enc_ 1 m (Encourage joining competitor in distant social network in the past month): Encouragement sentiment score inferred from social contents that are generated from the customer's second circle of social network and encourage the use of rival companies services.
  • Second circle includes acquaintances that are direct connections and but not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past month.
  • Net 2 _Enc_ 6 m (Encourage joining competitor in distant social network in the past 6 months): Encouragement sentiment score inferred from social contents that are generated from the customer's second circle of social network and encourage the use of rival companies services.
  • Second circle includes acquaintances that are direct connections and but not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past 6 months.
  • Net 1 _Churn_ 1 w (Churn in close social network in the past week): Churn announcement score inferred from social contents that are generated from the customer's first circle of social network and indicates that they churned. First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 1 _Churn_ 1 m (Churn in close social network in the past month): Churn announcement score inferred from social contents that are generated from the customer's first circle of social network and indicates that they churned. First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 1 _Churn_ 6 m (Churn in close social network in the past 6 months): Churn announcement score inferred from social contents that are generated from the customer's first circle of social network and indicates that they churned. First circle includes acquaintances that are direct connections and also have high influence on the customer. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 2 _Churn_ 1 w (Churn in distant social network in the past week): Churn announcement score inferred from social contents that are generated from the customer's second circle of social network and indicates that they churned. Second circle includes acquaintances that are direct connections and but not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past week.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 2 _Churn_ 1 m (Churn in distant social network in the past month): Churn announcement score inferred from social contents that are generated from the customer's second circle of social network and indicates that they churned. Second circle includes acquaintances that are direct connections and but not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past month.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • Net 2 _Churn_ 6 m (Churn in distant social network in the past 6 months): Churn announcement score inferred from social contents that are generated from the customer's second circle of social network and indicates that they churned. Second circle includes acquaintances that are direct connections and but not in the first circle. This is a score that is calculated from the social media contents (e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.) in the past 6 months.
  • Twitters e.g. Tweets, FACEBOOK posts, GOOGLE+ posts, etc.
  • the present invention utilizes several features in churn risk prediction as provided herein. Cohorts are selected/composed based on social network influence. For greater detailed descriptions, these are named with Net 1 or Net 2 prefixes herein.
  • An aspect of the invention employs “first circle versus second circle” in a user's social network(s).
  • the first and second circles are assumed to be direct friends in a customer social network, but the difference is that the first circle has a greater influence on the customer behavior than the second circle. It is worth noting, that even though first and second circle are expressed as direct friends in a social network, within preferred aspects of the present invention, the understood scope/definition of first and second circle is more generic and is applicable to nth order connections (e.g. friend of a friend, or friend of a friend of a friend) and direct connections are only used as one example.
  • connection has various terminologies in different social networks.
  • a connection is equivalent to “friend”, “follower/followee”, and “connection” in FACEBOOK, TWITTER, and LINKEDIN respectively.
  • “connection” is used as the generic terminology that represents that two accounts are related to each other directly on a social network.
  • the following metrics are calculated for each connection of a customer. These metrics are then added together to create a general “influence” score. All connections with an influence score above a threshold are considered to be in the first circle and the rest of the connections are defined as the second circle.
  • v Customer Cohort is the set of vectors that are used for creating one category of clusters that are mainly based on features that represent the individual customer.
  • the second set of vectors, v Network Cohort is the vector set that are mainly composed of features that represent the social network of the customer and hence incorporate the social network influence for a particular patron.
  • multiple clustering analyses generate “cohorts” of customers.
  • a voting mechanism computes a probability from both customer cohorts and network cohorts, and eventually a weighting scheme calculates the final churn possibility of a customer.
  • the present invention uses, in a preferred aspect, one or more of the following components to effectively predict churn risk:
  • FIG. 1 100 summarized the system of the present invention.
  • the invention starts from a collection of inputs for each service-based or subscription company and finishes with a continuous stream of churn signal deliveries.
  • the system is comprised of the following components:
  • FIG. 2 200 depicts the details of how the Social Aggregator works. This component is responsible for creating and maintaining “social media profiles” for all customers. This component is comprised of the following:
  • FIG. 3 300 illustrates the sub-components of the Social Predictor component. This part is responsible for generating the churn signals for a community of users from their SMP history.
  • the sub-components are:
  • FIG. 4 400 shows an example of a new customer whose proximity with multiple cohorts, and hence his similarity to each customer within those cohorts, determines his churn risk.
  • the circle around the customer illustrates an example of how the neighbors of the customer across all clusters will help to determine his churn risk. His characteristics are closer to that of the churned group in this example and he will likely be flagged as high risk.
  • FIG. 5 500 demonstrates how the Social Prediction Scheduler subcomponent 112 works with Social Aggregator 115 and Social Predictor 121 to filter out the contents that are not going to affect churn risk but trigger a re-prediction when relevant social media content is generated.
  • FIG. 6 600 is a flow chart of how influential connections 641 in the social network of a customer 611 are determined.
  • the influential connections are also called “first circle” and non-influential connections are named “second circle” in parts of the present invention.
  • Multiple metrics are computed for each pair of customer and customer connection. The metrics participate in a weighted addition and the result of that addition goes through a threshold to decide if that connection is in first circle or not.
  • FIG. 7 700 is a depiction of a console where customer retention agents would be able to view a queue of churn risk signals.
  • FIG. 8 800 is a depiction of a page where customer retention agents would be able to view additional details about a customer including the insights that contributed to their churn risk assessment.
  • the present invention provides a non-transitory, tangible computer-readable medium storing instructions adapted to be executed by a computer processor to perform a method for generating a customer churn prediction, for an entity in need of such prediction, said method comprising the steps of: extracting and receiving, by a churn prediction program executing on the computer processor, a variety of social media inputs; pre-processing the social media inputs to identify relevant social media posts, data trends and social network structures (pre-processed data); extracting and engineering features of the pre-processed data, such features comprising at least one of i) assessed social media postings, ii) assessed life events, iii) assessed engagement with the entity and competitors of said entity iv) assessed trend predisposition of customers to the entity based upon their prior churns, v) assessed one or more communities of customers to the entity and predisposition of the customers to the entity to churn based upon churn risk of the one or more communities; create feature vectors based
  • the present invention further provides computer architecture and system to support the implementation of the methods described and claimed herein.
  • one or more described webpages may be associated with a networking system or networking service.
  • alternate embodiments may have application to the retrieval and rendering of structured documents hosted by any type of network addressable resource or web site.
  • a user may be an individual, a group, or an entity (such as a business or third party application).
  • a network cloud generally represents one or more interconnected networks, over which the systems and hosts described herein can communicate.
  • Network cloud may include packet-based wide area networks (such as the Internet), private networks, wireless networks, satellite networks, cellular networks, paging networks, and the like.
  • Networking system is a network addressable system that, in various example embodiments, comprises one or more physical servers and data stores.
  • the one or more physical servers may be operably connected to computer network via, by way of example, a set of routers and/or networking switches.
  • the functionality hosted by the one or more physical servers may include web or HTTP servers, FTP servers, as well as, without limitation, webpages and applications implemented using Common Gateway Interface (CGI) script, PHP Hypertext Preprocessor (PHP), Active Server Pages (ASP), Hyper Text Markup Language (HTML), Extensible Markup Language (XML), Java, JavaScript, Asynchronous JavaScript and XML (AJAX), Flash, ActionScript, and the like.
  • CGI Common Gateway Interface
  • PHP PHP Hypertext Preprocessor
  • ASP Active Server Pages
  • HTML Hyper Text Markup Language
  • XML Extensible Markup Language
  • Java Java
  • JavaScript JavaScript
  • AJAX Asynchronous JavaScript and XML
  • Flash ActionScript, and the like.
  • Physical servers may host functionality directed to the operations of the networking system.
  • the data store may store content and data relating to, and enabling, operation of networking system as digital data objects.
  • a data object in particular embodiments, is an item of digital information typically stored or embodied in a data file, database, or record.
  • Content objects may take many forms, including: text (e.g., ASCII, SGML, HTML), images (e.g., jpeg, tif and gif), graphics (vector-based or bitmap), audio, video (e.g., mpeg), or other multimedia, and combinations thereof.
  • Content object data may also include executable code objects, podcasts, etc.
  • the data store corresponds to one or more of a variety of separate and integrated databases, such as relational databases and object-oriented databases, that maintain information as an integrated collection of logically related records or files stored on one or more physical systems.
  • the data store may generally include one or more of a large class of data storage and management systems.
  • the data store may be implemented by any suitable physical system(s) including components, such as one or more database servers, mass storage media, media library systems, storage area networks, data storage clouds, and the like.
  • the data store includes one or more servers, databases (e.g., MySQL), and/or data warehouses.
  • the data store may include data associated with different networking systems, users and/or commercial entity (client) systems.
  • churn rate measures a number of individuals (for example, customer, clients, subscribers) that leave a group or other collection over a certain period of time.
  • a relevant example is the number of customers that leave a subscription-based service.
  • Churn therefore, is similar to attrition, and may be the opposite of retention.
  • a subscriber-based service model may succeed when subscriber churn is low (and retention is high), and may fail when subscriber churn is high (and retention is low), among other things.
  • Churn prediction and tracking system of the invention is generally a computer or computing device including functionality for communicating (e.g., remotely) over a computer network.
  • Churn prediction and tracking system may be a desktop computer, laptop computer, personal digital assistant (PDA), in- or out-of-car navigation system, smart phone or other cellular or mobile phone, or mobile gaming device, among other suitable computing devices.
  • PDA personal digital assistant
  • Churn prediction and tracking system may execute one or more applications, such as a web browser (e.g., Microsoft Internet Explorer, Mozilla Firefox, Apple Safari, GOOGLE CHROME, and Opera), to access and view content over a computer network.
  • a web browser e.g., Microsoft Internet Explorer, Mozilla Firefox, Apple Safari, GOOGLE CHROME, and Opera
  • a webpage or resource embedded within a webpage may include data records, such as plain textual information, or more complex digitally encoded multimedia content, such as software programs or other code objects, graphics, images, audio signals, videos, and so forth.
  • One prevalent markup language for creating webpages is the Hypertext Markup Language (HTML).
  • HTML Hypertext Markup Language
  • Other common web browser-supported languages and technologies include the Extensible Markup Language (XML), the Extensible Hypertext Markup Language (XHTML), JavaScript, Flash, ActionScript, Cascading Style Sheet (CSS), and, frequently, Java.
  • HTML enables a page developer to create a structured document by denoting structural semantics for text and links, as well as images, web applications, and other objects that can be embedded within the page.
  • a webpage may be delivered to entities/commercial clients/service providers/clients as a static document; however, through the use of web elements embedded in the page, an interactive experience may be achieved with the page or a sequence of pages.
  • network interface provides communication between hardware system and any of a wide range of networks, such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc.
  • Mass storage provides permanent storage for the data and programming instructions to perform the above-described functions implemented in servers, whereas system memory (e.g., DRAM) provides temporary storage for the data and programming instructions when executed by processor.
  • I/O ports are one or more serial and/or parallel communication ports that provide communication between additional peripheral devices, which may be coupled to hardware system.
  • Hardware system may include a variety of system architectures and various components of hardware system may be rearranged.
  • cache may be on-chip with processor.
  • cache and processor may be packed together as a “processor module,” with processor being referred to as the “processor core.”
  • certain embodiments of the present disclosure may not require nor include all of the above components.
  • the peripheral devices shown coupled to standard I/O bus may couple to high performance I/O bus.
  • only a single bus may exist, with the components of hardware system being coupled to the single bus.
  • hardware system may include additional components, such as additional processors, storage devices, or memories.
  • An operating system manages and controls the operation of hardware system, including the input and output of data to and from software applications.
  • the operating system provides an interface between the software applications being executed on the system and the hardware components of the system.
  • Any suitable operating system may be used, such as the LINUX Operating System, the Apple Macintosh Operating System, available from Apple Computer Inc. of Cupertino, Calif., UNIX operating systems, Microsoft® Windows® operating systems, BSD operating systems, and the like.
  • the functions described herein may be implemented in firmware or on an application-specific integrated circuit.
  • FIG. 9 the general architecture of the data pipeline of a preferred aspect of the social predictor engine in this invention is shown.
  • the social predictor engine extracts patterns that are used for predicting the churn probability of customers, from different sources of social data, for example, TWITTER.
  • the integrated data is provided to the social predictor engine by the social aggregator. This data is raw and is preprocessed to be used by the other components in the higher levels of the data pipeline.
  • the extracted patterns are based on selected cues or features which are calculated based on data per customer. These features may be based on both the behavior of the user or the network.
  • the sentiment based features calculate some features that are indicated by each user's behavior, while network based features capture these indications from the network and calculate their impact on the churn probability of each user.
  • a machine learning model which is essentially an estimation of real behavior of users, is trained based on the different features, calculated to capture different aspects of user's behavior.
  • This layer stores data and exposes data through a REST API.
  • the main components of this layer are the Social Aggregator and the Social Provider.
  • the Social Aggregator contains multiple long running processes which tap into the corresponding social network's streaming APIs in a real-time manner. As some social networks only provide REST APIs, the Social Aggregator is also capable of polling data from a REST API. Data then is stored in highly available databases, and exposed to the other parts of the system via load balanced REST APIs.
  • This layer is responsible for pre-processing the data received from the Social Data Aggregator.
  • This pre-processing is mainly to extract relevant data from the raw data received from Data Integration Layer and to properly format this data for passing to the next layer. Different types of pre-processing may be employed based on the features that are going to be extracted on the next layer. Three main pre-processing components in this layer may be: Relevant Post Extraction, Trend Data Extraction, and Social Network Structure Extraction.
  • a major role of this step is to extract relevant social media inputs or posts (for example, Tweets) from raw data.
  • This step uses a manually designed lexicon to estimate relevancy of each single post to telecom domain. In this lexion there are a set of words and a relevancy score it attributed to each term. The relevancy of each post is calculated based on below formula:
  • T is the set of terms in each Post and L is the set of terms in the lexicon and N is the length of the post.
  • the score function returns the relevancy scores based on the lexicon. If the relevancy score of a post is greater than a selected threshold, it is considered as a “relevant” one.
  • the threshold varies and can be adapted, case by case, to achieve the best results in a cross-validation method.
  • the BrandTagger component tags the relevant terms in a post. BrandTagger essentially looks for the terms related to the target company identities and their variants. Target companies are both the current company of a user and its competitive companies. For example if the current company of a user is ‘wind’ it will look for ‘windmobile’, ‘Wind’, ‘Wind company’ and so on.
  • This component has an API to check if an input text contains a specific company or its competitor brands, social media IDs, and other similar company related identities. This information will be used subsequently in calculating features. It is preferred that only essential metadata, like the userID or createdTime and retained and the other irrelevant metadata will be removed prior to passing to the next level.
  • the preprocessed data is written to file (or can be passed directly) for being used by next layer.
  • data is reformatted to be used for trend analysis over the social media data.
  • Relevant pieces of information that are changing during time are extracted. Examples include changes in membership in groups, changes in following/unfollowing friends or companies, changes in number of followers, changes in posting behavior, changes in user's activity in a social media and so on.
  • the changes could be related to a specific user or the behavioral changes in the whole network that can indicate current user behavioral trends.
  • the formatted data is passed to the next layer for feature extraction.
  • Information is extracted regarding the structure of social network for a customer/user/subscriber. Namely in this step, relevant data is extracted for recreating the surrounding network of the customer/subscriber/user.
  • This layer comprises a plurality of steps, each of which serves to extract the features based on the previously preprocessed data. Each step is designed to extract a different type of feature.
  • An important set of features, usable in the churn analysis of the present invention, are based on a “sentiment” of each company's customers tweets.
  • This component uses, preferably, three sources for assessing the sentiment of related tweets.
  • Using a third package is preferred for tie breaking and for fusion of the three sentiments.
  • a feature extraction module the assessed data and extracts proper features based on the sentiments. Table 1 shows a list of such features and their descriptions.
  • TweetChurnLevelAssessment is a proxy script that uses a package for identifying these signals based on bag of words model.
  • the sentiment of “related people” is assessed for aggregation.
  • the sentiments of 1-10 related people are assessed using the features listed in Table 1.
  • This step is similar to tweet sentiment assessment component, described above. The only difference is that it uses the FACEBOOK data for assessment and extracting features.
  • Another set of features which may be extracted and aggregated relates to life events.
  • Events are selected and extracted from user posts in social media using, for example, a machine learning model.
  • a plurality of life events may be searched for and used, including, but not limited to moving, going to college, getting married, leaving a job and starting a new job.
  • the selected extracted events may be used to calculate a set of features given in Table 2.
  • This step collects, analyzes and aggregates posts on a variety of social media platforms pages of a competitor or a target company (to the business or entity) to find evidence and frequencies of churn by the customer.
  • temporal features which are necessary for analysing the trend of churn for a single user based upon his/her previous churn scores and also based on the overall trend of churn from the company are collected and assessed. Examples, of such features are presented in Table 3 below. These features are used for trend analysis and predicting the trend in a pre-determined/pre-selected time increment, for example in the following month.
  • TemporalSingleChurnScore The churn scores in past 12 months for each user for each month TemporalOveralChurnScore The mean churn score of users during past 12 month TemporalGroupMemberShip Changes in Membership of a user in groups during time TemporalConnectionCount changes in following/unfollowing friends or companies each month TemporalPostCount Changes in number of posts during each month (will reflect internet usage behavior)
  • a significant body of data relates to and can be extracted from the various communities to which a customer belongs. For example friends who work in the same place as a customer will be more likely to use or not use a specific subscription or service. Changes in the preferences and behaviors of members in a community directly affects others in that same community and the aggregation and analysis of that data is used in churn prediction.
  • communities are detected and scored based on their churn risk. Such score is calculated based on the ratio of the number of users of a target company to the count of all members in this community.
  • Features are then calculated based on membership of users to such communities.
  • the different feature types for each user are combined together to create a single feature vector for each user.
  • the outcome is a single feature vector per user as follow:
  • Feature Type Sentiment Based userID fs1 fs2 fs3 fs4 fs5 1 .5 .3 0 1 .2 2 .3 .2 ⁇ .2 1 0 . . . . . . . . . . . . . . . Feature Type: Life Event Based userID ft1 ft2 ft3 tf 1 .3 .1 .2 0 2 .4 0 .1 1
  • fs 1 to fs 5 could be related to semantic based features and ft 1 to ft 4 to life event based features. These features are calculated separately by different components in the assessment layer. However for training a machine learning model one feature vector per user, at least, is required. Therefore these separate feature vectors are combined to create a single feature vector. There is unique identifier key field like user ID which is considered to be preserved in the different featured vectors (coming from different assessment components). This key field is used for joining the feature vectors.
  • model building is built over the extracted features given by the below layer.
  • Various different machine learning algorithms may be used for model building such as, for example, xgboost and random forest. Applying random forest, the implementation that is provided by sklearn package in python was used.
  • Life events are preferably significant trackable events that occur in individual's life. They could be something that happens in the personal life of someone such as getting married or they could be a professional circumstance such as starting a new job.
  • Capturing data relating to life event is an aspect of the churn prediction method of the invention as such life events can affect the customer behavior and might encourage or motivate him/her to switch their service providers. These events, properly tracked and engineered, can provide valuable insight into the churn prediction solution and hence life events metrics comprise an important part in a preferred method of the present invention.
  • the life event workflow starts from collecting social data for each chosen life event, curating those events for each event type, using a machine learning solution to generate both probabilities of a life event for a single social content and also a binary result. These generated life event results are used to predict customer churn.
  • Table 4 is a list of non-limiting life events and a brief description of each used in the current invention.
  • FIGS. 10 and 11 depict how the individual life prediction components create features that are used in the churn prediction component in the current invention.
  • each life prediction component is built in a multi-step process:
  • Cloud computing services provide shared resources, software, and information to computers and other devices upon request.
  • software can be accessible over the Internet rather than installed locally on in-house computer systems.
  • Cloud computing typically involves over-the-Internet provision of dynamically scalable and often virtualized resources.
  • the database resources aggregated and collected and arranged within the scope of the invention may be stored and provided in a cloud computing context.
  • implementation of the method of the invention may be via CRM or via the exchange and conveyance of data and information via an API to the company/client.
  • an churn prediction platform integrates a contact center, agent stations, and, optionally, a customer relation management (CRM) server.
  • the contact center, the agent station(s), and the customer relation management server are coupled over one or more networks, which may be the Internet, a private network, or a telephone network.
  • the customer relation management server may be physically located within the contact center and maintained by a third party, or located remote from the contact center and still operated thereby.
  • the elements of this agent state model are dynamic and updated in real-time as an agent (delivering a service) seeks to acquire information in regards to the likelihood of a customer of that service “churning”.
  • the system of the invention implements a web-based customer relationship management (CRM) system.
  • CRM customer relationship management
  • the system includes application servers configured to implement and execute CRM software applications as well as provide related data, code, forms, webpages and other information and retrieve from, a database system related data and content, related to the delivery of the method of the invention.
  • API application programming interface
  • SOAP Simple Object Access Protocol
  • REST Representational State Transfer
  • API can be a body of industry standard or proprietary Remote Procedure Call (“RPC”) technologies.
  • An API may use a directive dispatcher to dispatch device-neutral directives to one or more directive processors.
  • Directive processors can be included for any number of features.
  • An API can ease the work of programming GUI components. For example, an API can facilitate integration of new features into existing applications (a so-called “plug-in API”). An API can also assist otherwise distinct applications with sharing data, which can help to integrate and enhance the functionalities of the applications.
  • APIs often come in the form of a library that includes specifications for routines, data structures, object classes, and variables.
  • an API is simply a specification of remote calls exposed to the API consumers.
  • an admin user of a computer architecture within the scope of the invention can utilize administrator console software program to implement the method of the invention.
  • a software program can be a Web browser application, software installed on a computer system, or an application (“app”) installed on a tablet or smart phone.
  • the admin user can supply “directives” to via an admin console.
  • “Directives” can be commands, scripts, software packages, configuration manifests, configuration policies, software licensing keys, workflows, user and hardware catalogs, and other such inputs that the admin desires to implement over a population of devices and external systems.

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Cited By (62)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160314484A1 (en) * 2014-04-23 2016-10-27 Tencent Technology (Shenzhen) Company Limited Method and system for mining churn factor causing user churn for network application
US20170230477A1 (en) * 2016-02-10 2017-08-10 Curtail Security, Inc. Comparison of behavioral populations for security and compliance monitoring
US20170337486A1 (en) * 2016-05-17 2017-11-23 Futurewei Technologies, Inc. Feature-set augmentation using knowledge engine
US20180144352A1 (en) * 2016-03-08 2018-05-24 Arizona Board Of Regents On Behalf Of The University Of Arizona Predicting student retention using smartcard transactions
US20180218062A1 (en) * 2017-01-31 2018-08-02 Bank Of America Corporation Data aggregator
CN109784959A (zh) * 2017-11-10 2019-05-21 广州腾讯科技有限公司 一种目标用户预测方法、装置、后台服务器及存储介质
CN109960777A (zh) * 2017-12-25 2019-07-02 北京京东尚科信息技术有限公司 物品评论个性化推荐方法、系统、电子设备及存储介质
US20190213511A1 (en) * 2018-01-09 2019-07-11 International Business Machines Corporation Predictive service-reduction remediation
WO2018129422A3 (en) * 2017-01-06 2019-07-18 Veritonic, Inc. System and method for profiling media
CN110196979A (zh) * 2019-06-05 2019-09-03 深圳市思迪信息技术股份有限公司 基于分布式系统的意图识别方法及装置
US20190272421A1 (en) * 2016-11-10 2019-09-05 Optim Corporation Information processing apparatus, information processing system, information processing method and program
US10432659B2 (en) 2015-09-11 2019-10-01 Curtail, Inc. Implementation comparison-based security system
US10580079B1 (en) * 2015-06-23 2020-03-03 Allstate Insurance Company Enterprise nervous system
US10635987B1 (en) * 2015-11-02 2020-04-28 State Farm Mutual Automobile Insurance Company System for improving user sentiment determination from social media and web site usage data
US20200134648A1 (en) * 2017-07-24 2020-04-30 Beijing Didi Infinity Technology And Development Co., Ltd. Methods and systems for preventing user churn
CN111222923A (zh) * 2020-01-13 2020-06-02 秒针信息技术有限公司 一种判断潜在客户的方法及装置、电子设备和存储介质
US10685131B1 (en) * 2017-02-03 2020-06-16 Rockloans Marketplace Llc User authentication
US20200211035A1 (en) * 2018-12-31 2020-07-02 Microsoft Technology Licensing, Llc Learning system for curing user engagement
JP2020130528A (ja) * 2019-02-18 2020-08-31 沖電気工業株式会社 感情推定装置、感情推定方法、プログラム、及び感情推定システム
US10861103B1 (en) * 2011-02-24 2020-12-08 Allstate Insurance Company Social network risk analysis
US10885131B2 (en) 2016-09-12 2021-01-05 Ebrahim Bagheri System and method for temporal identification of latent user communities using electronic content
US20210089979A1 (en) * 2019-09-24 2021-03-25 cg42 LLC Analytics system and method for a competitive vulnerability and customer and employee retention
CN112749335A (zh) * 2020-09-08 2021-05-04 腾讯科技（深圳）有限公司 生命周期状态预测方法、装置、计算机设备和存储介质
US10999632B2 (en) 2019-02-15 2021-05-04 Rovi Guides, Inc. Churn analysis and methods of intervention
CN112767045A (zh) * 2021-01-27 2021-05-07 支付宝(杭州)信息技术有限公司 流失用户的挽回方法、装置和电子设备
US11126986B2 (en) * 2019-09-23 2021-09-21 Gregory Tichy Computerized point of sale integration platform
US20210342808A1 (en) * 2020-05-01 2021-11-04 Capital One Services, Llc Utilizing machine learning to determine whether an in-person customer follows a merchant on social media
US11182810B1 (en) * 2015-06-26 2021-11-23 Groupon, Inc. Method, apparatus, and computer program product for merchant classification
US11222377B2 (en) * 2017-09-29 2022-01-11 AVAST Software s.r.o. Smart recommendation engine for preventing churn and providing prioritized insights
US11308428B2 (en) * 2019-07-09 2022-04-19 International Business Machines Corporation Machine learning-based resource customization to increase user satisfaction
US20220131770A1 (en) * 2018-10-10 2022-04-28 Sandvine Corporation System and method for predicting and reducing subscriber churn
US20220138780A1 (en) * 2016-07-13 2022-05-05 Airship Group, Inc. Churn prediction with machine learning
US11379863B1 (en) * 2017-04-28 2022-07-05 Wells Fargo Bank, N.A. Systems and methods for determining customer metrics
US11392968B2 (en) * 2015-02-24 2022-07-19 Thinkcx Technologies, Inc. System and method of analyzing social media to predict the churn propensity of an individual or community of customers
WO2022174169A1 (en) * 2021-02-15 2022-08-18 Scribd, Inc. Archive offer personalization
US11488086B2 (en) * 2014-10-13 2022-11-01 ServiceSource International, Inc. User interface and underlying data analytics for customer success management
US11494746B1 (en) 2020-07-21 2022-11-08 Amdocs Development Limited Machine learning system, method, and computer program for making payment related customer predictions using remotely sourced data
US20220391928A1 (en) * 2021-06-02 2022-12-08 Infosys Limited System and method for analysing customer experience from unstructured social media data
US20230134035A1 (en) * 2021-11-01 2023-05-04 Level 3 Communications, LLC. Systems and methods for prioritizing repair and maintenance tasks in telecommunications networks
US20230206254A1 (en) * 2021-12-23 2023-06-29 Capital One Services, Llc Computer-Based Systems Including A Machine-Learning Engine That Provide Probabilistic Output Regarding Computer-Implemented Services And Methods Of Use Thereof
US20230224540A1 (en) * 2022-01-12 2023-07-13 Allstate Insurance Company Systems and methods for generating a personality profile based on user data from different sources
US20230222527A1 (en) * 2022-01-12 2023-07-13 Zoho Corporation Private Limited Omnichannel data processing and analysis
US11836202B2 (en) 2021-02-24 2023-12-05 Open Weaver Inc. Methods and systems for dynamic search listing ranking of software components
US11836069B2 (en) 2021-02-24 2023-12-05 Open Weaver Inc. Methods and systems for assessing functional validation of software components comparing source code and feature documentation
US11853948B2 (en) * 2018-04-05 2023-12-26 International Business Machines Corporation Methods and systems for managing risk with respect to potential customers
US11853745B2 (en) 2021-02-26 2023-12-26 Open Weaver Inc. Methods and systems for automated open source software reuse scoring
CN117422181A (zh) * 2023-12-15 2024-01-19 湖南三湘银行股份有限公司 一种基于模糊标签的代发客户流失预警方法及系统
US11893385B2 (en) 2021-02-17 2024-02-06 Open Weaver Inc. Methods and systems for automated software natural language documentation
US11921763B2 (en) 2021-02-24 2024-03-05 Open Weaver Inc. Methods and systems to parse a software component search query to enable multi entity search
US11947530B2 (en) 2021-02-24 2024-04-02 Open Weaver Inc. Methods and systems to automatically generate search queries from software documents to validate software component search engines
US11954692B2 (en) 2020-02-05 2024-04-09 International Business Machines Corporation Mitigating user dissatisfaction related to a product
US11960492B2 (en) 2021-02-24 2024-04-16 Open Weaver Inc. Methods and systems for display of search item scores and related information for easier search result selection
US11985270B2 (en) * 2020-12-23 2024-05-14 Acqueon Technologies Inc. System and methods for adaptive campaign management and predictive customer engagement utilizing a customer data platform
CN118246963A (zh) * 2024-05-29 2024-06-25 杭州高能云科技有限公司 一种金融行业客户数据处理方法与系统
US12106094B2 (en) 2021-02-24 2024-10-01 Open Weaver Inc. Methods and systems for auto creation of software component reference guide from multiple information sources
US12164915B2 (en) 2021-02-26 2024-12-10 Open Weaver Inc. Methods and systems to classify software components based on multiple information sources
US12197912B2 (en) 2021-02-26 2025-01-14 Open Weaver Inc. Methods and systems for scoring quality of open source software documentation
US12271866B2 (en) 2021-02-26 2025-04-08 Open Weaver Inc. Methods and systems for creating software ecosystem activity score from multiple sources
US12277126B2 (en) 2023-06-30 2025-04-15 Open Weaver Inc. Methods and systems for search and ranking of code snippets using machine learning models
CN120355462A (zh) * 2025-06-23 2025-07-22 绍兴亿都信息技术股份有限公司 一种基于gis的crm客户生态圈动态可视化管理系统
US20250245589A1 (en) * 2024-01-25 2025-07-31 Hitachi, Ltd. System and Method for Parts Usage and Replacement Forecasting
US12380119B1 (en) 2012-04-13 2025-08-05 Sprout Social, Llc System and methods for generating optimal post times for social networking sites

Families Citing this family (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11538048B1 (en) * 2018-12-22 2022-12-27 Pinterest, Inc. Predictively identifying activity subscribers
US20220207284A1 (en) * 2020-12-31 2022-06-30 Oracle International Corporation Content targeting using content context and user propensity
CN114188032B (zh) * 2021-12-20 2023-01-20 中晗控股集团有限公司 一种基于人工智能的数字化医疗信息防护方法及存储介质
US11756065B2 (en) 2022-01-06 2023-09-12 Walmart Apollo, Llc Methods and apparatus for predicting a user churn event
US12288235B2 (en) 2022-01-06 2025-04-29 Walmart Apollo, Llc Methods and apparatus for predicting a user acquisition event
CN114663159B (zh) * 2022-04-01 2024-12-20 中国工商银行股份有限公司 用户流动性识别方法、装置、设备、介质和程序产品
KR102620080B1 (ko) * 2022-12-27 2024-01-02 쿠팡 주식회사 주문 데이터를 처리하는 방법 및 장치
US12496529B2 (en) 2023-07-07 2025-12-16 Sony Interactive Entertainment Inc. Method for personalizing a video game trophy
US20250010207A1 (en) * 2023-07-07 2025-01-09 Sony Interactive Entertainment Inc. Method for churn detection in a simulation
CN117893256B (zh) * 2024-03-14 2024-05-31 浙江卡赢信息科技有限公司 一种基于大数据的app用户智能管理系统
CN119444306A (zh) * 2025-01-09 2025-02-14 厦门黑谷网络科技股份有限公司 一种ai智能获客处理方法及系统

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070156673A1 (en) * 2005-12-30 2007-07-05 Accenture S.P.A. Churn prediction and management system
US20090292583A1 (en) * 2008-05-07 2009-11-26 Nice Systems Ltd. Method and apparatus for predicting customer churn
US20120047219A1 (en) * 2010-08-18 2012-02-23 At&T Intellectual Property I, L.P. Systems and Methods for Social Media Data Mining
US20120053990A1 (en) * 2008-05-07 2012-03-01 Nice Systems Ltd. System and method for predicting customer churn
US20130054306A1 (en) * 2011-08-31 2013-02-28 Anuj Bhalla Churn analysis system
US20130185245A1 (en) * 2012-01-16 2013-07-18 International Business Machines Corporation Social network analysis for churn prediction
US20130218882A1 (en) * 2012-02-21 2013-08-22 Salesforce.Com, Inc. Method and system for providing information from a customer relationship management system
US20140119522A1 (en) * 2012-10-30 2014-05-01 Alcatel-Lucent Usa Inc. System and method for generating subscriber churn predictions
US20140244664A1 (en) * 2013-02-25 2014-08-28 Telefonaktiebolaget L M Ericsson (Publ) Method and Apparatus For Determining Similarity Information For Users of a Network
US20140297580A1 (en) * 2013-03-28 2014-10-02 Linkedln Corporation Determining a churn probability for a subscriber of a social network service
US20150074122A1 (en) * 2013-09-09 2015-03-12 International Business Machines Corporation Social recommendation across heterogeneous networks

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130204682A1 (en) * 2012-02-03 2013-08-08 Metro PCS Wireless, Inc. System and method for reducing churn for a communications service
US20160253688A1 (en) * 2015-02-24 2016-09-01 Aaron David NIELSEN System and method of analyzing social media to predict the churn propensity of an individual or community of customers

Patent Citations (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070156673A1 (en) * 2005-12-30 2007-07-05 Accenture S.P.A. Churn prediction and management system
US20090292583A1 (en) * 2008-05-07 2009-11-26 Nice Systems Ltd. Method and apparatus for predicting customer churn
US20120053990A1 (en) * 2008-05-07 2012-03-01 Nice Systems Ltd. System and method for predicting customer churn
US20120047219A1 (en) * 2010-08-18 2012-02-23 At&T Intellectual Property I, L.P. Systems and Methods for Social Media Data Mining
US9262517B2 (en) * 2010-08-18 2016-02-16 At&T Intellectual Property I, L.P. Systems and methods for social media data mining
US20130054306A1 (en) * 2011-08-31 2013-02-28 Anuj Bhalla Churn analysis system
US8630892B2 (en) * 2011-08-31 2014-01-14 Accenture Global Services Limited Churn analysis system
US8843431B2 (en) * 2012-01-16 2014-09-23 International Business Machines Corporation Social network analysis for churn prediction
US20130185245A1 (en) * 2012-01-16 2013-07-18 International Business Machines Corporation Social network analysis for churn prediction
US20130218882A1 (en) * 2012-02-21 2013-08-22 Salesforce.Com, Inc. Method and system for providing information from a customer relationship management system
US20140119522A1 (en) * 2012-10-30 2014-05-01 Alcatel-Lucent Usa Inc. System and method for generating subscriber churn predictions
US20140244664A1 (en) * 2013-02-25 2014-08-28 Telefonaktiebolaget L M Ericsson (Publ) Method and Apparatus For Determining Similarity Information For Users of a Network
US20140297580A1 (en) * 2013-03-28 2014-10-02 Linkedln Corporation Determining a churn probability for a subscriber of a social network service
US9189737B2 (en) * 2013-03-28 2015-11-17 Linkedin Corporation Determining a churn probability for a subscriber of a social network service
US20150074122A1 (en) * 2013-09-09 2015-03-12 International Business Machines Corporation Social recommendation across heterogeneous networks
US9710470B2 (en) * 2013-09-09 2017-07-18 International Business Machines Corporation Social recommendation across heterogeneous networks

Cited By (80)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10861103B1 (en) * 2011-02-24 2020-12-08 Allstate Insurance Company Social network risk analysis
US11727496B1 (en) 2011-02-24 2023-08-15 Allstate Insurance Company Social network risk analysis
US12380119B1 (en) 2012-04-13 2025-08-05 Sprout Social, Llc System and methods for generating optimal post times for social networking sites
US20160314484A1 (en) * 2014-04-23 2016-10-27 Tencent Technology (Shenzhen) Company Limited Method and system for mining churn factor causing user churn for network application
US10748166B2 (en) * 2014-04-23 2020-08-18 Tencent Technology (Shenzhen) Company Limited Method and system for mining churn factor causing user churn for network application
US11488086B2 (en) * 2014-10-13 2022-11-01 ServiceSource International, Inc. User interface and underlying data analytics for customer success management
US11392968B2 (en) * 2015-02-24 2022-07-19 Thinkcx Technologies, Inc. System and method of analyzing social media to predict the churn propensity of an individual or community of customers
US10580079B1 (en) * 2015-06-23 2020-03-03 Allstate Insurance Company Enterprise nervous system
US11182810B1 (en) * 2015-06-26 2021-11-23 Groupon, Inc. Method, apparatus, and computer program product for merchant classification
US10986119B2 (en) 2015-09-11 2021-04-20 Curtail, Inc. Implementation comparison-based security system
US10432659B2 (en) 2015-09-11 2019-10-01 Curtail, Inc. Implementation comparison-based security system
US11637856B2 (en) 2015-09-11 2023-04-25 Curtail, Inc. Implementation comparison-based security system
US11120355B1 (en) 2015-11-02 2021-09-14 State Farm Mutual Automobile Insurance Company System for improving user sentiment determination from social media and web site usage data
US11625634B2 (en) 2015-11-02 2023-04-11 State Farm Mutual Automobile Insurance Company System for improving user sentiment determination from social media and web site usage data
US10635987B1 (en) * 2015-11-02 2020-04-28 State Farm Mutual Automobile Insurance Company System for improving user sentiment determination from social media and web site usage data
US10462256B2 (en) * 2016-02-10 2019-10-29 Curtail, Inc. Comparison of behavioral populations for security and compliance monitoring
US11122143B2 (en) * 2016-02-10 2021-09-14 Curtail, Inc. Comparison of behavioral populations for security and compliance monitoring
US20170230477A1 (en) * 2016-02-10 2017-08-10 Curtail Security, Inc. Comparison of behavioral populations for security and compliance monitoring
US20180144352A1 (en) * 2016-03-08 2018-05-24 Arizona Board Of Regents On Behalf Of The University Of Arizona Predicting student retention using smartcard transactions
US20170337486A1 (en) * 2016-05-17 2017-11-23 Futurewei Technologies, Inc. Feature-set augmentation using knowledge engine
US20220138780A1 (en) * 2016-07-13 2022-05-05 Airship Group, Inc. Churn prediction with machine learning
US10885131B2 (en) 2016-09-12 2021-01-05 Ebrahim Bagheri System and method for temporal identification of latent user communities using electronic content
US10755094B2 (en) * 2016-11-10 2020-08-25 Optim Corporation Information processing apparatus, system and program for evaluating contract
US20190272421A1 (en) * 2016-11-10 2019-09-05 Optim Corporation Information processing apparatus, information processing system, information processing method and program
WO2018129422A3 (en) * 2017-01-06 2019-07-18 Veritonic, Inc. System and method for profiling media
US20180218062A1 (en) * 2017-01-31 2018-08-02 Bank Of America Corporation Data aggregator
US11250028B2 (en) * 2017-01-31 2022-02-15 Bank Of America Corporation Data aggregator
US12099620B1 (en) 2017-02-03 2024-09-24 Rockloans Marketplace Llc User authentication
US10685131B1 (en) * 2017-02-03 2020-06-16 Rockloans Marketplace Llc User authentication
US11379863B1 (en) * 2017-04-28 2022-07-05 Wells Fargo Bank, N.A. Systems and methods for determining customer metrics
US20200134648A1 (en) * 2017-07-24 2020-04-30 Beijing Didi Infinity Technology And Development Co., Ltd. Methods and systems for preventing user churn
US11222377B2 (en) * 2017-09-29 2022-01-11 AVAST Software s.r.o. Smart recommendation engine for preventing churn and providing prioritized insights
CN109784959A (zh) * 2017-11-10 2019-05-21 广州腾讯科技有限公司 一种目标用户预测方法、装置、后台服务器及存储介质
CN109960777A (zh) * 2017-12-25 2019-07-02 北京京东尚科信息技术有限公司 物品评论个性化推荐方法、系统、电子设备及存储介质
US20190213511A1 (en) * 2018-01-09 2019-07-11 International Business Machines Corporation Predictive service-reduction remediation
US11853948B2 (en) * 2018-04-05 2023-12-26 International Business Machines Corporation Methods and systems for managing risk with respect to potential customers
US20220131770A1 (en) * 2018-10-10 2022-04-28 Sandvine Corporation System and method for predicting and reducing subscriber churn
US11902114B2 (en) * 2018-10-10 2024-02-13 Sandvine Corporation System and method for predicting and reducing subscriber churn
US20200211035A1 (en) * 2018-12-31 2020-07-02 Microsoft Technology Licensing, Llc Learning system for curing user engagement
US12063417B2 (en) 2019-02-15 2024-08-13 Rovi Guides, Inc. Churn analysis and methods of intervention
US11722736B2 (en) 2019-02-15 2023-08-08 Rovi Guides, Inc. Churn analysis and methods of intervention
US10999632B2 (en) 2019-02-15 2021-05-04 Rovi Guides, Inc. Churn analysis and methods of intervention
JP2020130528A (ja) * 2019-02-18 2020-08-31 沖電気工業株式会社 感情推定装置、感情推定方法、プログラム、及び感情推定システム
JP7172705B2 (ja) 2019-02-18 2022-11-16 沖電気工業株式会社 感情推定装置、感情推定方法、プログラム、及び感情推定システム
CN110196979A (zh) * 2019-06-05 2019-09-03 深圳市思迪信息技术股份有限公司 基于分布式系统的意图识别方法及装置
US11308428B2 (en) * 2019-07-09 2022-04-19 International Business Machines Corporation Machine learning-based resource customization to increase user satisfaction
US11126986B2 (en) * 2019-09-23 2021-09-21 Gregory Tichy Computerized point of sale integration platform
US20210089979A1 (en) * 2019-09-24 2021-03-25 cg42 LLC Analytics system and method for a competitive vulnerability and customer and employee retention
WO2021061669A1 (en) * 2019-09-24 2021-04-01 cg42 LLC Analytics system for a competitive vulnerability and customer and employee retention
CN111222923A (zh) * 2020-01-13 2020-06-02 秒针信息技术有限公司 一种判断潜在客户的方法及装置、电子设备和存储介质
US11954692B2 (en) 2020-02-05 2024-04-09 International Business Machines Corporation Mitigating user dissatisfaction related to a product
US20210342808A1 (en) * 2020-05-01 2021-11-04 Capital One Services, Llc Utilizing machine learning to determine whether an in-person customer follows a merchant on social media
US11494746B1 (en) 2020-07-21 2022-11-08 Amdocs Development Limited Machine learning system, method, and computer program for making payment related customer predictions using remotely sourced data
CN112749335A (zh) * 2020-09-08 2021-05-04 腾讯科技（深圳）有限公司 生命周期状态预测方法、装置、计算机设备和存储介质
US11985270B2 (en) * 2020-12-23 2024-05-14 Acqueon Technologies Inc. System and methods for adaptive campaign management and predictive customer engagement utilizing a customer data platform
CN112767045A (zh) * 2021-01-27 2021-05-07 支付宝(杭州)信息技术有限公司 流失用户的挽回方法、装置和电子设备
US11961119B2 (en) 2021-02-15 2024-04-16 Scribd, Inc. Archive offer personalization
WO2022174169A1 (en) * 2021-02-15 2022-08-18 Scribd, Inc. Archive offer personalization
US11475482B2 (en) * 2021-02-15 2022-10-18 Scribd, Inc. Archive offer personalization
US11893385B2 (en) 2021-02-17 2024-02-06 Open Weaver Inc. Methods and systems for automated software natural language documentation
US11947530B2 (en) 2021-02-24 2024-04-02 Open Weaver Inc. Methods and systems to automatically generate search queries from software documents to validate software component search engines
US11960492B2 (en) 2021-02-24 2024-04-16 Open Weaver Inc. Methods and systems for display of search item scores and related information for easier search result selection
US12106094B2 (en) 2021-02-24 2024-10-01 Open Weaver Inc. Methods and systems for auto creation of software component reference guide from multiple information sources
US11921763B2 (en) 2021-02-24 2024-03-05 Open Weaver Inc. Methods and systems to parse a software component search query to enable multi entity search
US11836069B2 (en) 2021-02-24 2023-12-05 Open Weaver Inc. Methods and systems for assessing functional validation of software components comparing source code and feature documentation
US11836202B2 (en) 2021-02-24 2023-12-05 Open Weaver Inc. Methods and systems for dynamic search listing ranking of software components
US12164915B2 (en) 2021-02-26 2024-12-10 Open Weaver Inc. Methods and systems to classify software components based on multiple information sources
US12271866B2 (en) 2021-02-26 2025-04-08 Open Weaver Inc. Methods and systems for creating software ecosystem activity score from multiple sources
US11853745B2 (en) 2021-02-26 2023-12-26 Open Weaver Inc. Methods and systems for automated open source software reuse scoring
US12197912B2 (en) 2021-02-26 2025-01-14 Open Weaver Inc. Methods and systems for scoring quality of open source software documentation
US20220391928A1 (en) * 2021-06-02 2022-12-08 Infosys Limited System and method for analysing customer experience from unstructured social media data
US20230134035A1 (en) * 2021-11-01 2023-05-04 Level 3 Communications, LLC. Systems and methods for prioritizing repair and maintenance tasks in telecommunications networks
US20230206254A1 (en) * 2021-12-23 2023-06-29 Capital One Services, Llc Computer-Based Systems Including A Machine-Learning Engine That Provide Probabilistic Output Regarding Computer-Implemented Services And Methods Of Use Thereof
US20230224540A1 (en) * 2022-01-12 2023-07-13 Allstate Insurance Company Systems and methods for generating a personality profile based on user data from different sources
US20230222527A1 (en) * 2022-01-12 2023-07-13 Zoho Corporation Private Limited Omnichannel data processing and analysis
US12277126B2 (en) 2023-06-30 2025-04-15 Open Weaver Inc. Methods and systems for search and ranking of code snippets using machine learning models
CN117422181A (zh) * 2023-12-15 2024-01-19 湖南三湘银行股份有限公司 一种基于模糊标签的代发客户流失预警方法及系统
US20250245589A1 (en) * 2024-01-25 2025-07-31 Hitachi, Ltd. System and Method for Parts Usage and Replacement Forecasting
CN118246963A (zh) * 2024-05-29 2024-06-25 杭州高能云科技有限公司 一种金融行业客户数据处理方法与系统
CN120355462A (zh) * 2025-06-23 2025-07-22 绍兴亿都信息技术股份有限公司 一种基于gis的crm客户生态圈动态可视化管理系统

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