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Tag: Amazon.com

Can Amazon Dominate In Insurance, Too?

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In January 2013, LIMRA reported that 90% of industry executives it had surveyed believe that insurance companies will continue to form strategic alliances with “non-traditional organizations” to expand distribution. The example cited was MetLife’s trial alliance with 200 Wal-Mart stores. Then Accenture’s “Customer-Driven Innovation Survey” found that more than two-thirds of customers would consider purchasing home, auto and life insurance from businesses other than insurers—23% were open to purchasing from online service providers like Amazon or Google (which acquired auto insurance aggregator BeatThatQuote.com way back in 2011 in the UK).

Amazon has proven leadership as an e-commerce distributor, while Google is seen primarily as an information organization, so I would like to elaborate exclusively on the compelling reasons for insurers and Amazon to create a distribution model to match ever-evolving customer demands.

Customer demands

Every information source and every analyst report on insurance in the recent past points to changes in customer’s preferences. Generation X, Generation Y and Millennials prefer doing business with companies that provide:

  • Convenience of on-demand buying and self-service, predominantly through digital channels such as web and mobile.
  • Personalization of product and service delivery, including helping the customer choose the right product.
  • Building trust through transparency in pricing, simplified products and clear articulation of benefits.

So, insurers must innovate in personalizing products, providing transparency in the value of products and services and demonstrating excellence in on-demand distribution. Innovation must also touch “moments of truth” such as claims and policy changes. It is also critical that the distribution lifecycle should be an iterative process to consistently review the value of benefits and help customers fine tune the products and services they purchase.

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Amazon.com as a Delivery Model for Population Health

There’s that line about art, “good artists copy, great artists steal.” There’s some debate about if Picasso said it first, but most of us geeks know it from Steve Jobs.

Often, I see things from companies and industries outside of healthcare —processes, products, best practices —which inspire me. I like these little inspirations because they often aren’t rocket science, but nonetheless fuel some creative thoughts about their applicability in healthcare.

The other night, around 9:00 PM on a holiday Monday, I ordered some obscure aviation stuff from Amazon. I needed a new headset, a leg-mounted chart holder, a paper calculating tool called an E6B computer and a portable canister of oxygen.

I have Amazon Prime, their subscription service which provides expedited 2 day shipping, so I expected to see my stuff on Wednesday afternoon. I was blown away when there was an Amazon box outside my door by 9:00 AM the next morning, Tuesday.

A box showed up early, big deal, right?

Here’s what I think happened and why I’m so impressed. I had been browsing for some aviation stuff for a few days. Amazon clearly knows and tracks my window shopping. It’s how they suggest items when you come back to the site.

I believe they preemptively moved some of those obscure aviation items to the closest distribution center in anticipation of my purchase. In fact, Amazon was awarded a patent for exactly that process last week.

By predicting my purchasing behavior, Amazon was able to beat my expectations for delivery – a known threat to their model is the instant gratification of local retail – and get my package to me in 12 hours.

We’ve got a lot of data in healthcare. That’s to the lagging but persistent implementation of electronic medical records, doctors and health systems are beginning to apply some big data science to their patient populations. For instance, any credible EMR can tell a physician how many of her patients have asthma.

More advanced systems, including bolt on solutions can look at disease panels and cross sample against last visit date. Mr. Smith, we see it’s been a year since your last visit, how’s your arthritis? Can we schedule you and appointment with Dr. Jones?

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