iotSymphony Customer Use Case - Truck Roll Optimization

  • user

    100+ Years of Enterprise
    Software Experience

  • gcp logo


  • concept

    Proof of Concept


Business Challenge:

Telco’s, cable providers and internet service providers need to improve consumer experiences and satisfaction with in-home consumer equipment (set-top boxes, cable modems, Wi-Fi routers, etc.).

  • While engineering teams monitor network and equipment performance continuously and understand when performance issues occur, they do not engage with customers.
  • Operations teams who respond to customer issues do not have access to the network information nor tools to understand the cause of performance issues.
  • Service technicians must triage in-home equipment to try and identify what is causing poor service experiences.

Outcome for the consumer:

Long calls with customer service, days without service while waiting for a scheduled service technician, work and family impacted waiting hours for the technician to arrive, then more waiting as the technician triages the problem. And if everything goes well, the problem gets resolved in one visit, and that is not often the case. Consumer frustration grows.

Outcome for the business:

Multiple independent departments involved (network operations, customer service, service operations) combined with long wait times drives low customer satisfaction, reduces customer loyalty and increases churn that impacts the bottom line.


A better way with iotSymphony:

By leveraging the iotSymphony software platform, organizations can dramatically change this process and ensure a much more satisfying experience for the consumer while reducing operations costs. And here is how:

  • As telemetry data streams from the router or set-top box, iotSymphony captures this data and runs it through business rules to determine if the data requires further analysis
  • The business rules engine determines what data requires immediate action or further analysis
  • Data that requires further analysis is merged with other pertinent data (manufacturing data, location data, etc.) and run through the analytics engine, where AI/ML models are executed to predict if the performance behavior of the device will result in failure
  • With failure predicted, the data is merged with additional pertinent data (customer profile data, etc.) and run through the decision engine to determine the best course of action
    • Based on the data and analytics, any number of decisions are possible
      • Performance activity does not lead to a probability of failure – do nothing
      • A software patch can be applied remotely to correct performance issue – dispatch remote software patch
      • A software patch has to be applied locally, and the customer is tech savvy – dispatch an SMS message with instructions and a link to apply the software patch
      • A software patch has to be applied locally, but it is not known if the customer is tech savvy – dispatch an SMS message explaining what is needed and asking customer if they are comfortable to manage a self-install patch
      • The router needs to be replaced and the customer is tech savvy – dispatch an SMS message informing the customer a new router is being sent to them along with instructions and return packaging
      • The router needs to be replaced and it is unknown if the customer is tech savvy – dispatch an SMS message informing the customer their router needs to be replaced with an automated schedule for a technician visit
  • The identified action is automatically generated in the appropriate operations system, such as dispatch system, CRM system, or any other operations system.

Outcome for the consumer:

The consumer is happy that their service was never interrupted, they are impressed that their provider is taking proactive measures to ensure their service remains active, and they are happy with the various options to resolve any issue while minimizing the impact to their lives. Customer satisfaction goes up, customer loyalty goes up and the consumer is less likely to churn


Outcome for the business:

A significant reduction in consumer complaint calls drives down call center call volumes and average handle times (AHT). If a consumer does call, less triaging is required as the data that determined the cause of their issue and the resulting action taking place are made available to the representative, resulting in lower call handle times and a reduction in repeat calls. There are fewer service technician dispatches and when a technician is dispatched, their MTTR (Mean Time To Repair) rate goes down as they will know exactly what needs to be done as well as having the right materials to resolve the issue. Operations costs go way down, employee satisfaction goes way up and bottom-line profitability goes way up.