- H2O Driverless AI can build lead scores, producer performance segments and next best action models from your own quote, policy, commission and activity history.
- Every score carries reason codes, so an agent sees why a lead or household is on the list and compliance can review the logic.
- An agent and broker co-pilot can answer product and appetite questions with citations and draft the outreach; the agent decides what to send.
- No insurer runs agent and broker next best action on H2O today, so this page describes the pattern, not a customer outcome.
Give every agent and broker a data-backed reason to act
Most carriers sell through captive agents, independent agents, brokers and call-center sellers. Each one decides every day which lead to call, which household to review and which product to raise, usually from a list sorted by date or by gut feel.
Your distribution team sees the results in production reports, weeks later. What it rarely sees is why one agency writes and keeps more business than another with a similar book, so good leads go cold while sellers work weak ones, and renewal conversations happen too late.
How agent and broker performance and next best action works
H2O can be used to add explainable scores and a cited co-pilot above the agency management system and CRM your producers use today.
Step 1: join the distribution history
Start with leads, quotes, bound and lapsed policies, commissions, service contacts and agent activity, joined to what happened next. Automatic feature engineering builds signals such as days since the last contact, products held per household and quote-to-bind rate by channel.
Step 2: score every lead
Classification models rank inbound and purchased leads by how likely they are to bind, so your call center and agents work the likely buyers first.
Step 3: segment producer performance
Clustering groups agents and brokers by book, product mix, activity and results, so distribution leaders can compare each group with its peers and see which practices go with stronger new business and retention.
Step 4: suggest a next best action
Propensity models score each household for the next likely event: a cross-sell, a coverage gap, a renewal at risk. The agent sees one suggested action per household, with reason codes behind it.
Step 5: deploy and keep the models current
Each model ships with documentation written as it trains, so your actuarial and compliance teams can review what drives it.
An agent and broker co-pilot
A co-pilot puts the scores and your own documents in one place for the producer. An agent asks whether a risk fits appetite, which endorsement covers a gap, or what to say to a household at renewal, and gets an answer that cites the guide it came from. It can draft the email or call notes for a suggested action, and the agent reviews and edits every draft.