Insurance Recommendation Engines That Reduce Friction

Rate increases, longer repair cycles, and higher claim severity have turned “shopping for insurance” into a stress test for customers and agents alike. The surprising part: most friction isn’t caused by price—it’s caused by irrelevance. The wrong message. The wrong offer. The wrong moment.

That’s why recommendation engines are quietly becoming one of the most practical applications of AI in insurance customer engagement. In a podcast conversation with Tiffany Grinstead, VP of Personal Lines Marketing at Nationwide, one point stood out: relevancy beats reach. If you can show up with the right next step—quote help, a coverage explainer, a self-service shortcut, or a cross-sell suggestion—you remove the “work” from the experience.

This post reframes the interview as a case study and connects it to a theme you’ve seen across our AI in Supply Chain & Procurement series: personalization at scale only works when the underlying data supply chain is healthy. Insurance recommendation engines run on the same fundamentals as procurement recommendations—clean data, clear rules, and feedback loops that keep learning.

Recommendation engines matter because customers are overloaded

The core problem is attention scarcity, not channel scarcity. Customers and agents are saturated with messages, and generative AI is only increasing the volume. In that environment, blasting more ads doesn’t help; it often makes outcomes worse.

Grinstead describes Nationwide’s approach as “the right message to the right audience at the right time,” powered by segmentation and behavioral triggers. That phrasing sounds familiar if you work in procurement: it’s the same logic as sending a buyer the right substitute supplier suggestion at the moment a shipment delay hits.

What “frictionless” actually means in insurance

A frictionless experience isn’t a magical UI. It’s a series of small moments where the customer doesn’t have to:

• Repeat information they already provided
• Guess what to do next
• Call support for something that should be self-service
• Worry that they’re underinsured because they don’t understand coverage

A recommendation engine becomes the “next best action” layer across that journey.

Where recommendation engines show up (even if you don’t label them)

Most insurers already run recommendation logic somewhere, even if it’s basic:

• Cross-sell prompts (auto customer nudged toward home/umbrella)
• Quote completion nudges (agent follow-up triggered by browsing behavior)
• Service deflection (recommend “download proof of insurance” instead of “call us”)
• Content recommendations (coverage explainers attached to quotes)

The shift now is from static rules to AI-assisted ranking and personalization—while still maintaining compliance guardrails.

The real engine is your data supply chain (and that’s the point)

Recommendation engines don’t fail because the model is weak. They fail because the data pipeline is messy. This is exactly the lesson from AI in supply chain management: if your supplier master data is fragmented, your AI forecasts wobble. Same story here.

Grinstead calls out a major strategic shift: moving away from heavy reliance on third-party data toward zero-party and first-party data.

• Zero-party data = data customers intentionally share (opting into telematics, smart home programs, preference centers)
• First-party data = data you already own through servicing, quoting, claims, and interactions

That shift is also a response to privacy changes and signal loss. But it’s more than compliance—it’s quality. Zero-party data tends to be higher intent and easier to justify.

Practical blueprint: the insurance “data supply chain” for recommendations

If you’re building or buying a recommendation engine, the most useful way I’ve seen to plan it is like a supply chain flow:

1. Data sources: policy, quote, claims, web/app behavior, CRM, call center, agency systems, telematics, connected home
2. Normalization: identity resolution, deduping households, standardizing product/coverage attributes
3. Feature layer: life events, risk signals, propensity to buy, churn risk, service friction indicators
4. Decision layer: rules + model ranking + eligibility constraints
5. Delivery: email, agent CRM, website/app modules, call center scripts, paid media audiences
6. Feedback: conversions, complaints, opt-outs, claim outcomes, retention, NPS/CSAT

This “data supply chain” framing resonates with procurement teams because it’s the same pattern: inputs → quality controls → decisions → execution → learning loop.

Use cases that actually drive acquisition and retention

The best recommendation use cases aren’t flashy—they’re measurable. In the interview, several themes connect directly to acquisition/retention economics.

Acquisition: find high-intent shoppers and make quotes feel guided

Grinstead describes using partners and data to “find shoppers in key life moments.” That’s the acquisition side of recommendation engines: detect intent and reduce drop-off.

High-impact acquisition recommendations include:

• Next best step during quote: recommend “talk to an agent” for complex home policies, not just more forms
• Coverage defaults based on risk + affordability: recommend deductible options with plain-language tradeoffs
• Channel switching recommendations: if someone starts online but shows uncertainty, prompt a call or agent handoff

One stance I’ll take: forcing everyone into fully self-serve is a mistake for homeowners and multi-line households. The engine should recommend the right level of help, not just the next product.

Retention: reduce bill shock and coverage confusion

The hard market is real: higher repair costs, longer cycle times, and weather volatility are pressuring rates. Grinstead shares a telling insight from Nationwide’s agency survey work: consumers often underestimate repair costs (e.g., expecting a minor fender bender to cost ~$2,000 when reality can be $4,000–$5,000 and take 6–8 weeks).

In that environment, retention recommendations that work are:

• Coverage education at renewal: recommend a short explainer video for the coverages that changed
• Self-service coaching: recommend the exact digital task (proof of insurance, vehicle change, claim status tracking)
• Agent enablement content: recommend talking points tailored to what the customer is likely reacting to (price, deductible, coverage)

A recommendation engine that reduces confusion reduces churn. That’s not theory—it’s how customers decide whether they’re being treated fairly.

Cross-sell and up-sell: do it without being creepy

The interview touches on a question many teams avoid: Can personalization go too far? Yes. And insurance has an extra trust burden.

The safe pattern is:

• Recommend products tied to clear household needs (home + auto + umbrella)
• Trigger recommendations at natural moments (new vehicle, new home, teen driver, claim, renewal)
• Explain the value exchange in plain language

If the customer thinks “How do they know that?” you’ve already lost. If they think “That’s helpful,” you’ve earned permission.

Telematics and smart home data: recommendations that prevent loss

The highest-value recommendations aren’t about selling more insurance. They’re about preventing claims. That’s where the interview becomes especially relevant to risk and operations teams.

Grinstead highlights two innovation categories:

1. Data-driven driving (telematics, connected car)
2. Proactive risk mitigation (connected home sensors: water, fire, security)

Here’s the connection to recommendation engines: once you have opt-in data, you can recommend actions that reduce loss.

Examples that insurers can operationalize:

• Recommend a distracted-driving coaching module to drivers showing risky patterns
• Recommend a water shut-off device to homes with risk indicators (age of plumbing, prior water losses, region)
• Recommend electrical fire monitoring for homes with older wiring profiles

This is also where supply chain and procurement thinking helps. You’re not just recommending a product—you’re curating an ecosystem of partners, devices, installers, and service workflows. That’s procurement.

A useful mental model:

A modern insurer is managing a “risk prevention supply chain,” not just a claims payout process.

How to implement recommendation engines without breaking trust (or compliance)

A recommendation engine in insurance must be explainable enough for agents, customers, and regulators. If your team can’t articulate why the recommendation happened, you’re building a liability.

A governance checklist I’d insist on

1. Eligibility rules first, model second: never recommend ineligible products/discounts
2. Reason codes: every recommendation needs a human-readable explanation
3. Bias and fairness testing: monitor outcomes, not just model performance
4. Privacy-by-design: explicit consent for sensitive streams (telematics, smart home)
5. Frequency caps: limit repetition and reduce “stalker” vibes
6. Audit logs: store what was recommended, when, and why

Build vs. buy: choose based on use cases, not demos

Grinstead notes there are many vendors and that carriers need to evaluate them “with an expertise lens.” I agree—and I’d add one more filter: integration reality.

Ask these questions early:

• Can the engine operate across channels (direct, agent, service), or is it stuck in marketing only?
• Can it work with your CRM workflows so sales follow-up happens fast?
• Does it support both B2C and B2B recommendations (customer + agency enablement)?

If it can’t plug into your operating system, it’s not a recommendation engine. It’s a slide deck.

“People also ask” questions (answered plainly)

Are recommendation engines just for cross-selling?

No. The most profitable recommendations often reduce service costs, improve retention, and prevent losses—especially via self-service and risk mitigation.

What data do you need to start?

Start with first-party data: quote and policy attributes, digital behavior, and CRM interactions. Then add opt-in streams (telematics, connected home) where the value exchange is clear.

How do you measure success?

Use a scorecard, not a single KPI:

• Quote-to-bind conversion
• Cross-sell attach rate
• Retention at renewal
• Call deflection/self-service adoption
• Complaint rate and opt-out rate (trust metrics)

Where this fits in the AI in Supply Chain & Procurement story

Recommendation engines in insurance are a customer-facing example of a bigger truth: AI outcomes depend on the health of your data supply chain and partner ecosystem. Procurement teams have been living this for years—supplier data quality, integration, governance, and continuous improvement.

Insurance teams are now facing the same reality, just expressed through customer engagement: your ability to personalize depends on whether your internal systems, agency tools, and external partners can share clean signals and act on them.

If you’re exploring AI recommendation engines for insurance—whether for acquisition, retention, or risk prevention—start by mapping your data supply chain, picking one high-ROI journey, and insisting on explainability from day one. That’s the difference between “personalized” and simply noisy.

What would happen to your retention numbers if every renewal included one recommendation that made the customer feel informed instead of cornered?