Site maintenance related
Detecting when a site maintenance activity is the cause of the customer’s degraded experience
Companies require a highly specialized work force to root-cause customer complaints. Achieving a good level of accurate and consistent root causes analysis is challenging when working with multiple teams with different expertise and focus.
Lack of consistency due to team focus
Reduced accuracy due to knowledge segmentation
Any changes in the network or in customers' profiles potentially could have a negative impact on customers' service. It is easy to miss the problem and leave the customer with degraded services. The longer it takes to fix the problem, the higher is the risk of damaging customer satisfaction.
Easily missed customer issues
Higher risk of customer dissatisfaction
Due to the significant amount of repetitive and complex manual work, a higher volume of calls to a call center means higher operational costs on the front-line customer care and technical support teams. With limited human resources, any surge in volume of technical customer issues will cause further delays in complaint handling.
High cost on customer care and technical support teams
Long delay on handling customer issues
100x
Faster response
80%
Automation
100%
Consistency
20%
Less complaints
For categories of issues where AI Care gets high confidence, automatic actions can be directly triggered to resolve customer issues in closed loop, driving extra fast and zero-touch response to detected problems.
Automatic problem identification, root causing and routing of technical customer complaints.
Recommendations can be used by technicians in open loop.
Automatic actions can be directly applied to resolve customer issues before customer notices or even complains.
Automatic detecting and root causing customer problems by monitoring suspicious patterns from system logs.
Virtual tickets are created for reporting, analysis, and tracking purposes.
Get started and request a demo to learn how AI Care can help you.
Detecting when a site maintenance activity is the cause of the customer’s degraded experience
Detection of continuous connection jumps from one site to another pointing to a coverage or site issue
Problems caused by chronic degradation of site KPIs or Alarms causing performance issues for customers
Detecting if customer should be covered by more advanced technologies (4G, 5G) but stuck in old technologies (2G, 3G)
Identify coverage holes on all technology bands based on customer location
Problems caused by incorrect setup or connection failures of indoor devices (e.g., femtocell)
Incorrect provisioning creates conflicts between nodes, devices, billing etc., causing service's failure
Idle profile when disabling accounts could cause unnecessary usage of resources and potential conflicting provisioning issues
Missing required configurations in customer profile for a service that they should have available causing a specific service not to work
Accounts that should be temporarily disabled due to payment issues were not properly changed in downstream systems causing revenue holes
An incorrect service restriction has been set up for the customer, limiting their ability to make calls or send messages
An application used for messaging has been installed by the customer causing the stock messaging app not to be able to send messages
A device lacks the bands that the operator needs for optimal service
Below you will find answers to the most common questions about AI Care.
Tupl AI Care reduces the customer service team workload, quickly identifying & resolving end-customer issues by integrating with network and subscriber data.
Tupl AI Care uses all relevant Network and Customer data to find the most likely root cause of the customer problem and provides recommendations in natural language to Customer Service agents and Network Operations & Engineers.
The AI Engine correlates data from multiple sources to automate the detection, troubleshooting and action recommendations for customer incidents.
Tupl’s Proactive AI Care solution monitors the subscriber’s experience in near real-time to detect service impacting issues. When an issue is detected, it creates a virtual ticket and, when possible, resolves the issue.
When the issue cannot be resolved immediately, virtual tickets with automated troubleshooting and action recommendations are forwarded to engineers for final decisions.
Proactive AI Care SaaS is delivered in cloud service (e.g. AWS, Azure, etc.) and can also be deployed on-premises, in your private cloud or data center.
It is easy and quick to get started, fit for a faster procurement process, with a functional solution in operation within 2-3 weeks.
Monthly subscription. No strings attached. Stop at any time.
The more relevant data sources are used, the higher the accuracy. Our algorithms, verified by our customers' engineers, have reached over 90% accuracy.
Most of the project time is typically spent in arranging access from various sources. The integration and validation of functionality takes only between one and two months. Not your typical telecom timelines...
Yes. In fact, algorithms are designed so that we can use what is available, and additional data sources just increase the granularity of the prescriptive analytics automation. Some data sources are mandatory for obvious reasons, such as: tickets, KPIs, topology data.
AI Care is directly handling technical customer complaints, mainly related to engineering localized issues, which could be related to the network (e.g. faults, congestion), the location (e.g. coverage) or the handset equipment.
Get started and request a demo to learn how AI Care can help you.
