- AI predictive customer support predicts the likely cause of an issue from past tickets and resolutions, network events, device data and weather.
- The agent sees the top likely causes and the next step for each, so the customer answers fewer questions and the issue is fixed sooner.
- Fewer unnecessary technician visits free your field teams for real network and hardware repairs.
- H2O Driverless AI builds the models with reason codes, and the platform runs in the deployment option you choose.
The customer explains the problem three times
When a customer's service stops working, they call, move between agents, answer the same questions again, and often end the call with a technician visit booked rather than a fix. That visit costs you money, and sometimes it was not needed.
Your agents feel it too. They take the calls from frustrated customers, work through long scripts, and rarely get to solve the problem themselves.
How predictive customer support works
A model learns from past support cases which signals point to which cause, then ranks the likely causes for each new call.
Step 1: join the data you already hold
Past tickets and their resolutions, network alarms and outages, device and modem readings, account and plan details, and weather for the service area.
Step 2: learn the patterns
H2O Driverless AI runs automated machine learning, with feature engineering on time series data such as recent outages and repeat contacts.
Step 3: rank the likely causes
Multiclass classification scores each open call against the causes in your history and returns the top few, each with a suggested next step.
Step 4: show the reasons
Explainable AI reason codes show the agent why a cause ranked first, such as an outage on the node or a modem that dropped offline twice this week.
Step 5: decide on the visit
The model predicts whether a technician visit is likely to be needed, so a visit is booked when the fault is in the home and avoided when a remote fix will do.
Step 6: monitor and retrain
Track model drift as your network, devices and plans change, and retrain on new resolutions.
Measuring the change
Capture a baseline before you start: first-call resolution, repeat calls within seven days, and technician visits that found no fault. Measure the change against it.
Proof
H2O can be used to predict the likely cause of a support call and whether a technician visit is needed, using the support, network and weather data an operator already holds. No customer result is published for this use case, so this page quotes none.