How does an agent know why a product was recommended?

Every recommendation carries a reason code showing which factors drove it, so the agent can open the conversation with the customer's own situation instead of a scripted pitch.

Does this replace our quoting or campaign tools?

No. It is the scoring layer underneath them; recommendations and reason codes feed the quoting, CRM and campaign tools your teams already use.

Can the models run inside our own environment?

Yes, on-premises, air-gapped, in a private cloud or in the cloud, so household and policy data stay where you choose.

Personalized product bundling for insurers

Q: What data does a next-product model use?

Most models start from the policies each household holds, quotes, life events you already capture, claims and contact history, joined to which products households took in the past.

Machine learning that predicts which additional policy or coverage each household is likely to need next, with a reason code, so agents, call centers and websites offer one relevant bundle instead of a generic package.

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Key takeaways

H2O Driverless AI can build next-product models from your own policy, household, quote and claims history, with a reason code on every recommendation.
A recommendation that explains itself gives an agent a reason to raise it and gives a customer a reason to listen.
The same scores can serve your website, call center and agent channels, inside the coverage and rates you have already filed.
Scores feed the quoting, CRM and campaign tools you already run; none of them is replaced.

Every household gets the same offer

You sell auto, home, renters, umbrella, life and small commercial cover, and most households hold only one or two of them. Each new policy in a household adds premium and gives that customer another reason to stay.

Generic cross-sell campaigns treat every customer the same. They push products that do not fit, annoy the people who already said no, and miss the moment when a household actually needs new cover, such as a new driver, a new home or a new business. Your agents and call center have seconds to decide what to raise, and without a clear, explained recommendation they fall back on the offer of the month.

How personalized product bundling works

Step 1: Build the household view

Combine policies held, quotes, life events you already capture, claims and contact history for each household, with which products each one took in the past.

Step 2: Predict the next product

H2O Driverless AI trains classification models for each product, and automatic feature engineering finds signals such as tenure, recent changes and coverage gaps.

Step 3: Rank the bundle

A recommendation ranks the policies and coverage options each household is most likely to accept, and clustering groups households with similar needs for campaign design.

Step 4: Explain each recommendation

Reason codes show why a product was suggested, so an agent can open the conversation with the customer's own situation.

Step 5: Score in every channel

Each model can be deployed so the same recommendation appears on the website, in the call-center script and on the agent desktop.

Watching for drift

Customer needs shift with the season, the economy and your own product changes, so watch for model drift and retrain on schedule.

Questions people ask before they call us.

It predicts which additional policy or coverage each household is likely to need and accept next, so agents, call centers and websites can offer one relevant bundle instead of a generic package.

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