System, method, and computer program for providing proactive customer care for issues associated with setting up the billing process as part of the ordering process

A system, method, and computer program product are provided for providing proactive customer care for issues associated with billing or ordering processes. In use, a likelihood that a customer is going to call a call center to address at least one issue associated with at least one of an ordering process or a billing process is predicted. Additionally, it is determined whether the customer is likely to call the call center based on the predicted likelihood that the customer is going to call the call center. Further, the customer is proactively notified before the customer contacts the call center, if it is determined that the customer is likely to call the call center.

FIELD OF THE INVENTION

The present invention relates to product and service providers, and more particularly to providing proactive customer care to customers of such providers.

BACKGROUND

Customer loyalty and satisfaction are becoming increasingly important to the success of businesses. Issues that arise during an order process have the potential to influence customer loyalty and satisfaction.

Ordering is essentially a flow of actions that arise in response to a customer placing an order. Such an order may be for the provision of internet services, fixed line services, television, mobile subscriptions, or modifications and deletions of existing installations of such services, etc.

Ordering can be extremely complex and involves negotiation with the customer (e.g. to configure the product, determine pricing, collect billing/address information, etc.) followed by a provisioning system that connects with multiple independent systems related to port provisioning, account creation, billing setup, and address verification, etc.

Often, customers will call a call center when issues arise during the ordering process. Such calls may be a result of problems in the process, missing a due date, and other technical issues that prevent the completion of the order, as was planned originally. Handling calls at a call center is costly and pulls call agents away from working on other productive items.

SUMMARY

A system, method, and computer program product are provided for providing proactive customer care for issues associated with billing or ordering processes. In use, a likelihood that a customer is going to call a call center to address at least one issue associated with at least one of a billing or ordering process is predicted. Additionally, it is determined whether the customer is likely to call the call center based on the predicted likelihood that the customer is going to call the call center. Further, the customer is proactively notified before the customer contacts the call center, if it is determined that the customer is likely to call the call center.

DETAILED DESCRIPTION

FIG. 1illustrates a method100for providing proactive customer care for issues associated with billing or ordering, in accordance with one embodiment.

As shown, a likelihood that a customer is going to call a call center to address at least one issue associated with at least one of an ordering process or a billing process is predicted. See operation102. The issue may be any issue associated with a product and/or service and/or an ordering or billing process associated therewith. For example, issues related to setting up the billing process that occur at the same time as the ordering process may be detected.

Additionally, it is determined whether the customer is likely to call the call center based on the predicted likelihood that the customer is going to call the call center. See operation104.

Further, the customer is proactively notified before the customer contacts the call center, if it is determined that the customer is likely to call the call center. See operation106.

The proactive notification may include any type of notification. For example, in one embodiment, proactively notifying the customer may include proactively notifying the customer utilizing at least one text message. In another embodiment, proactively notifying the customer may include proactively notifying the customer utilizing at least one email. Of course, any notification may be utilized in various embodiments (e.g. based on a customer preference, etc.)

Further, proactive notifications may include any information associated with the issue, a product or service associated with the issue, and/or various other information. For example, proactively notifying the customer may include proactively notifying the customer that the issue is being addressed or has been addressed.

Furthermore, predicting the likelihood that the customer is going to call the call center may include a variety of techniques. For example, predicting the likelihood that the customer is going to call the call center may include predicting that the customer is going to call the call center within a specific time period.

Additionally, in one embodiment, predicting the likelihood that the customer is going to call the call center may include predicting the likelihood the customer is going to call the call center based on historical information associated with the customer. The historical information associated with the customers may include any type of past data. For example, the historical information may include a number of times a customer has contacted (e.g. called, emailed, etc.) a call center associated with a service provider, dates of the contacts, reasons for the contacts, and/or various other historical data associated with the customer. In one embodiment, the historical data may be stored in one or more databases associated with a service provider.

Further, in one embodiment, the historical information associated with the customer may be utilized to generate a behavior model for the customer. In this case, the behavior model for the customer may indicate a propensity for the customer to call the call center. In one embodiment, the behavior model for the customer may indicate a likelihood that proactively notifying the customer will backfire.

In another embodiment, at least one classification algorithm or model may be utilized to predict the likelihood that the customer is going to call the call center. In this case, the classification algorithm may utilize information from an order management system, a customer management system, and/or a system including demographic data to predict the likelihood that the customer is going to call the call center. In one embodiment, the classification algorithm may utilize historical information from an order management system, a customer management system, and/or a system including demographic data to predict the likelihood that the customer is going to call the call center.

Additionally, in one embodiment, the classification algorithm may be updated with currently received information (e.g. from the various systems, etc.). For example, the classification algorithm may be continuously updated in real time with the currently received information.

In one embodiment, the classification algorithm may be utilized to generate at least one call propensity model indicating a call propensity of a plurality of customers. As an option, the classification algorithm may be part of a big data analytics platform.

Further, in one embodiment, the method100may include predicting whether proactively notifying the customer will backfire. In this case, the customer may not be proactively notified if it is determined that proactively notifying the customer will backfire. A backfire refers to a case when a customer notifies the call center because the customer was proactively notified.

The method100addresses issues that may arise during an ordering process. The ordering process is essentially a flow of actions that arise in response to a customer placing an order. Such an order may be for the provision of internet services, fixed line services, television, mobile subscriptions or modifications and deletions of existing installations of such services, etc.

Ordering can be extremely complex and involves negotiation with the customer (e.g. to configure the product, determine pricing, collect billing/address information, etc.) followed by a provisioning system that connects with multiple independent systems related to port provisioning, account creation, billing setup, and address verification, etc.

The method100may be implemented to address ordering related calls to the call center, which are initiated by the customers. Specifically, the method100may address calls during the fulfillment phase of the order, once the negotiation phase is completed. Such calls may be a result of problems in the process, missing the due date, and other technical issues that prevent the completion of the order, as was planned originally.

Incoming calls are very costly to the service providers. Such calls may be prevented by proactively notifying the customer, by the service provider, using SMS, email, mail, etc. Each call that is prevented saves money and releases the agent to conduct more productive calls.

FIG. 2illustrates a system200for providing proactive customer care for issues associated with billing or ordering, in accordance with one embodiment. As an option, the system200may be implemented in the context of the details ofFIG. 1. Of course, however, the system200may be implemented in the context of any desired environment. Further, the aforementioned definitions may equally apply to the description below.

As shown, the system200includes a bootstrap model202and a real time prediction module204for predicting and handling incoming calls due to ordering issues. The real-time prediction module204is capable of being updated based on information received in real-time.

In one embodiment, the system200may be implemented as a big data analytics application to predict the likelihood that a customer will call a call center with a particular issue (e.g. ordering and billing processes issues, etc.) in a given time window (e.g. in an ordering case, during the fulfillment process until notification; and in a billing case, while modifying the billing process as a result of the order; etc.).

For those with a high likelihood to call, proactive notifications may be sent to the identified customers, notifying them in advance of any issue and ensuring things are in control. The system200may also function to predict which notifications might backfire (i.e. the customer will call as a result of the proactive notification).

The proactive notifications will reduce the number of calls of the customers to the call centers, thus saving money for the service providers. The past behavior of the service provider customers may be used to create behavioral models. The models may be used to predict future calls before they take place

Thus, the system200is a real time system that may ingest data directly from various systems including order management systems, customer management systems, and also ingest demographic data to generate predictions as to whether a customer will call about an identified issue and whether proactively notifying the customer will cause a backfire.

Although this is a real time, continuous learning system, the initial deployment of the architecture may be bootstrapped using historical data (e.g. generating the bootstrap model202). To achieve this, an extraction of ordering, customer, and demographic data may be acquired and each order may be labelled according to a corresponding end state. For example, there may be two labels, Call and OK, where OK refers to customers that did not call the call center. In one embodiment, the system200may utilize a binary classification algorithm that can be trained to differentiate between the class types (e.g. utilizing machine learning, etc.).

Once the system200is deployed, the system may continuously relearn the classification model to predict which orders/customers will initiate incoming calls. This is useful since the ordering system itself may be subjected to dynamic external influences that would not be captured by the initial bootstrap model202.

For example, local issues could trigger delays that would impact customer satisfaction and trigger calls and complaints. Similarly, new faulty advertising could result in misinformed customers ordering a product that is different from what they expect, leading to a bad experience and complaints.

Accordingly, the system200may function such that an order and/or customer that is predicted to initiate and incoming call may be handled in a proactive way that will prevent the incoming call and also prevent any backfire.

This system200relies on the classification paradigm in machine learning. Various algorithms may be suitable for such classification. As one example, a random forest algorithm may be utilized.

Moreover, any information may be used to predict the likelihood a customer will call, generate a call model, predict the likelihood of a backfire, and/or generate a backfire propensity model. For example, such information may include information associated with: customer demographics (e.g. for one customer or across an entire customer base, etc.), customer purchases (e.g. for one customer or across an entire customer base, etc.), customer billing information (e.g. for one customer or across an entire customer base, etc.), customer usage (e.g. for one customer or across an entire customer base, etc.), and/or customer notification information (e.g. for one customer or across an entire customer base, etc.).

FIG. 3illustrates a network architecture300, in accordance with one possible embodiment. As shown, at least one network302is provided. In the context of the present network architecture300, the network302may take any form including, but not limited to a telecommunications network, a local area network (LAN), a wireless network, a wide area network (WAN) such as the Internet, peer-to-peer network, cable network, etc. While only one network is shown, it should be understood that two or more similar or different networks302may be provided.

Coupled to the network302is a plurality of devices. For example, a server computer304and an end user computer306may be coupled to the network302for communication purposes. Such end user computer306may include a desktop computer, lap-top computer, and/or any other type of logic. Still yet, various other devices may be coupled to the network302including a personal digital assistant (PDA) device308, a mobile phone device310, a television312, etc.

FIG. 4illustrates an exemplary system400, in accordance with one embodiment. As an option, the system400may be implemented in the context of any of the devices of the network architecture300ofFIG. 3. Of course, the system400may be implemented in any desired environment.

As shown, a system400is provided including at least one central processor401which is connected to a communication bus402. The system400also includes main memory404[e.g. random access memory (RAM), etc.]. The system400also includes a graphics processor406and a display408.

The system400may also include a secondary storage410. The secondary storage410includes, for example, a hard disk drive and/or a removable storage drive, representing a floppy disk drive, a magnetic tape drive, a compact disk drive, etc. The removable storage drive reads from and/or writes to a removable storage unit in a well known manner.

Computer programs, or computer control logic algorithms, may be stored in the main memory404, the secondary storage410, and/or any other memory, for that matter. Such computer programs, when executed, enable the system400to perform various functions (as set forth above, for example). Memory404, storage410and/or any other storage are possible examples of tangible computer-readable media.