Invented by Salil Dhawan, Rahul VYAS, Nice Ltd
The Nice Ltd invention works as follows
The present invention provides a computerized method that uses a cloud computing environment to improve client service in a call center. The computerized method includes retrieving the context of a question and a time limit from a CTI and trying to retrieve data in order to evaluate average resolution times for the context received. If the data is located, the agent will compare the evaluated resolution time to the received limit and send a notification if the received limit is lower than the evaluated resolution time. The client can also be given a list of other options for contacting the server. If the data cannot be found or the received time limit is higher than the evaluated average time resolution, an agent dashboard can display the time-limit and update parameters accordingly. This will improve client service by considering the client’s time-limit before addressing a query.Background for Optimized agent desktop and consideration of customer time up front in a call center
The organizations are constantly trying to reduce contact center costs and improve the service they offer to clients. These two goals may seem contradictory, but they can be achieved when every inbound contact in the call center leads to a meaningful and quick query resolution, which prevents client dissatisfaction.
For instance, the extra costs that are paid by clients who are unhappy due to a long waiting time on an inbound call queue could be saved for the organization. Clients may be unhappy if, on top of waiting a long time for an agent to become available, the agent does not meet their time constraints. Organizations may strive to make their customers happy.
Also, satisfied clients can contribute to a higher client retention rate.” If clients aren’t satisfied, their base can shrink. They may also leave and spread negative word of mouth, which leads to a low client retention rate.
In addition, client satisfaction impacts not only the bottom line of the business, but also the morale and retention rate of the agents in the contact center. Businesses may survive with angry customers who make only one purchase, but they will not thrive. For an organization, focusing on customer satisfaction may be key to its success.
Some market research shows that a higher level of customer satisfaction is the key to a lasting relationship with clients. A company must deliver value to clients continuously. One market study found that 81% more satisfied customers were likely to return to a company if they enjoyed a positive experience. After a bad experience, 95% of people will share their concerns with family and friends or “churn”.
Another aspect to customer satisfaction is that a continuous satisfaction of customers can lead to loyalty. Customers will continue doing business with an organization once they have put their trust in it and know that the company is going to continue to provide value to them. Customers who are satisfied with the service of an organization will also recommend it to their friends. Customer advocacy has been proven to be an effective form of marketing. Small businesses, for example, estimate that 85% come from referrals. It may be more cost-effective to keep existing customers satisfied rather than try to attract new ones. “It’s six-times more expensive to win a new client than to keep an existing customer.
A high FCR (First-Call Resolution) is often associated with high customer satisfaction. This means that a contact centre’s ability to solve customer problems or questions the first time they call without any follow-up is high. The FCR is used to measure how well a contact center is conducting its business. It is affected by many factors including the types and complexity of transactions, the agent’s experience, the agent’s training and the tools implemented such as remote control and knowledge management.
There are instances when the customer is time-bound and has a limited time to interact with a contact center agent. In this case, neither the contact centre nor the agent who will be handling the call is aware that the customer has a limited time frame. The agent will likely treat the call as if it were a normal call. When the agent does not know the time restrictions of the customer, and the interaction is longer than their time limits, it may result in: (a), the customer dropping the call abruptly while speaking to the agent; (b), the agent holding the call repeatedly to check the knowledge base, or transfer the call. The customer might become dissatisfied.
To achieve a high FCR, and to make an agent more efficient, the agent must take into account the client’s time constraints when attending the interaction. The agent can then work to resolve the call accordingly. When the agent knows the client’s time constraints, he or she will not put the client on hold and won’t transfer them to another agent. “No interaction between a customer and agent will be abruptly ended without agreeing to the next steps in order to resolve the query, if the call could not be completed within the client’s time-constrained limit.
Therefore there is a requirement for a system that will improve client service, by giving agents greater clarity on the time constraints of the client, i.e. the time limit for the phone call. This will increase the client’s satisfaction, as the agent will handle the call in the best way possible.
In other words there is a requirement for a method and system that will improve customer service by ensuring that the call center takes customer time constraints into consideration when answering a client query.
According to some embodiments, there is a computerized method for using cloud-based computing environments for improving customer service in a call center.
According to some embodiments, a computerized device with a processor, a historical database and a memory for storing the database is configured, on an inbound call, to run an Interaction Manager microservice in order to receive a Computer Telephony Integration event (CTI), and then send it to the Agent Desktop Optimizer module (ADO). The ADO module can be part of an Automatic Contact Distributer component within a contact centre system.
Furthermore…, according to some embodiments, the ADO Module may be configured in order to retrieve the context of a CTI event and the time limit of a client.
The ADO module can also, according to some embodiments, attempt to retrieve historical data in order to determine the average resolution time of the context received. The time limit may be expressed as minutes.
Furthermore…, according to some embodiments, when data is found in an historical database, the ADO Module may compare the evaluated resolution time with received time limits and, when received time limits are below the evaluated resolution time, it may send a delay notification and give the client options for querying via other channels.
Furthermore in accordance with certain embodiments of this disclosure, if the data cannot be found or if the received time limit is higher than the evaluated average resolution, the ADO Module may present the time limit of the client on an agent dashboard via a display and update parameters within the agent dashboard while the inbound call is in progress.
The ADO module can improve the service to the client by taking into account the time limit of the customer before addressing the query.
The delay notice can also include, according to some embodiments, the average time needed for resolution of the query.
Furthermore…, according to some embodiments of this disclosure, you can evaluate the average resolution time by retrieving the time total for the total number interactions related with the context received during a preconfigured time period and dividing that time total by the number of interactions.
Furthermore…, according to some embodiments of this disclosure, upon selection by the client of time-queue consideration, the ADO Module may update the time-limit received by reducing the evaluated average waiting time of the received query from the time-limit received before attempting data extraction from the database of historic data.
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