Tag Archives: eric schmidt

Insurtech: The Approaching Storm

Customer-centricity and mobile engagement: the next wave of innovation to disrupt the insurance industry?  

The individual customer has to be at the center of the marketing strategy of every company that wants to succeed. A customer-centric marketing approach starts with the realization that there is no “average” customer. Customers have different behaviors and preferences — and this presents rich opportunities to move past a “one-size-fits-all” marketing approach. Customer-centric marketing teams think of their customer base as their greatest long-term investment.

A customer-centric approach means targeting the right customer through the right channel and sending the right message — at the right time. It also helps teams align around a strategy that will drive long-term value to the business, acquiring high-value customers and keeping them coming back.

The consumption habits have deeply changed in a competitive environment where the customers face information overload.

The smartphone has become the primary reference when searching for information, comparing products, finding the best deals and connecting with a brand/organization. The smartphone has become the first screen, the reference for our daily activities.

“Mobile is the future.” With these very words in 2010, Eric Schmidt, the then-CEO of Google and now chairman of Alphabet, gave us a glimpse of what was going to happen. And he hit the target!

As citizens and customers, we live surrounded by dozens of different devices, and the screen of the smartphone has become the main reference for all our activities. Mobile is not just another channel, it is a proxy of the customer — an entirely new lifestyle.

The awareness that the rhythm of our existence is marked by the mobile revolution is certified by three common stats:

15: The minutes between when we wake up and when we turn on our smartphones.

150: How many times we check, on average, our smartphones during the day.

177: The minutes we spend, on average, every day looking at the screen of our mobile devices.

Customers today do not go online. They live online.

Better yet, they experience an endless sequence of moments — in a nonlinear balance between the online and offline worlds.

See also: Top 10 Insurtech Trends for 2017  

Your customers are ready to buy. They are ready to buy from you. They are just asking for one simple thing: that they can receive relevant information on their smartphone when it is the right time.

According to Google research on “micro-moments” that offer memorable experiences to customers, a retail brand must develop and cultivate three qualities:

Be There: The ability to show up when and where the customer has a need or desire.

Be Useful: The ability to be there with relevant content and to become a primary reference.

Be Quick: The ability to think and act fast. Speed is essential across all stages of the customer journey.

At the core, the brand-new customer is driven by technology.

The super-shoppers are tech-savvy, and you cannot even remotely think to engage and monetize them as you did with the clients in past decades. If you do not speak the new shoppers’ language, you will never capture their attention, and, ultimately, you will lose all relevance.

What does it mean to be relevant in the mobile age? Easy. Rethink the marketing strategy, how to connect with customers (online and in-store) and how to convey contents and values.

In a few words, you must use technology to establish your brand as a trustworthy source of information and inspiration. And you must do it not once and for all, but improving day after day after day.

Study and understand the super-shopper; be present in the micro moments that matter; stay relevant; and be epic. Only then you will conquer shoppers’ hearts and minds.

Shopping in the era of micro moments often starts when people have a need or desire to purchase a product. Once they feel this need, they start looking for ideas, a search that will lead them to online communities, social networks, video tutorials and company blogs. Only then will they evaluate the different options and eventually decide what (and where) to buy.

In these moments, you have to be there and be useful to win trust and loyalty.

“Be there” means you must identify the most important micro moments and commit to being there, whenever and wherever a shopper is searching, especially on mobile.

“Be useful” means you must provide valuable contents when your customers need them, on any channel — social media, point of sale, advertising, blog, social commerce, etc.

“Be quick” means you must provide the required and valuable information at the right time and in the right manner.

Has the moment come for an old-style industry like insurance to turn the page? Several experts, managers, entrepreneurs and investors engaged in the insurance space consider that 2017 will be the year of insurtech. Some strongly believe that every successful insurance company will be insurtech soon!

An intelligent use of the technology in this industry can generate opportunities to close the protection gap, reduce the anti-selection issue, optimize loss ratio with personalized proposals and reduce overall processing cost. All this in a customer-centric approach.

The Internet of Things and artificial intelligence are undoubtedly two main drivers of the evolution in the industry, and we have seen several interesting applications already on the market. A lot of insurtech startups are investing all around the world in these technologies, which enable insurance carriers to propose innovative and customized coverage to their customers while  “blue ocean” opportunities are appearing.

See also: 10 Predictions for Insurtech in 2017  

Traditionally, the insurance industry business model is focused on:

  • Identifying the pool of customers that might have risks assessed;
  • Targeting those customers and assessing the risk for each class;
  • Selling differently priced policies and spreading the risks over the pool of customers; and
  • Trying to retain those customers as long as possible, offering lower price for longer contracts.

This approach is, by definition, based on the concept of “standardization” — the opposite of “customized” from a marketing point of view — and, even if it was one of the golden rules of the insurance business for several decades, it has become obsolete nowadays.

The insurance industry has always been data-rich, but, traditionally , it is quite unstructured, or, at least, the models used are quite old and simple.

Being connected has become the talk of the town, and insurance companies are one of the main interested parties in this discussion — some of them even being actual promoters of change and innovation.

Consumers are becoming more and more connected, whether it is at home, at work, behind the wheel, when they engage in sports or leisure activities and so on.

The surrounding environment is becoming smart and is being incorporated in the connected ecosystem, thus creating opportunities for insurance companies — opportunities that must be managed appropriately to maximize value. Here, big data analytics play a huge role, as the quantity of collected data and variables is getting higher and higher.

The IoT real-time data collection and sharing power will create significant opportunities in finer product segmentation and more specialized pools of risk and predictive modeling to better assess risk, as well as improving loss control and accelerating premium growth.

The IoT is the network or system of related computing devices and sensors, and it can communicate with other devices on the network. These objects, or “things,” are capable of transmitting data.

In the end, for insurance carriers to harness the power of the IoT, each will have to first think creatively about what data to gather and how to use it.

A system based on IoT and big data analytics can identify patterns and provide optimized solutions based on real-time input. Up-front: A seamless user-friendly interface can transform the way companies communicate with policy holders.

The IoT’s impact within insurance is coming fully into focus. At the highest level, better use of IoT and sensor data means insurers have the opportunity to:

  • Establish direct, unmediated customer relationships;
  • Gain more granular and precise understanding of who their customers are and how their needs change over time; and
  • Individualize offerings of products and features.

Within IoT applications, artificial intelligence is also helping (or disrupting, depending on how you see the matter) the sector in different ways.

The abundance of data can be used to refine customer segmentation and provide personalized offers based on personal features.

Artificial intelligence offers predictive recommendations that are backed by complex algorithms and data and have the ability to analyze process flows for bottlenecks, improving overall company and customer satisfaction. Algorithms compare answers and information provided by customers to make appropriate recommendations for each risk scenario.

The algorithms are constantly at work to better understand humans and their thought processes through machine learning, which allows AI to analyze human behavior and provide predictive consulting based on each individual’s wants and needs.

So, AI can help increase customer engagement and retention with personalized offers delivered at the right time, in the right way, at the right price.

How to Capture Data Using Social Media

Insurance carriers looking to better market and manage risks should use social media as a rich component of a robust analytics platform. By augmenting existing big data projects with social media feeds, carriers can identify key information about their insureds that would otherwise be difficult to gather in a timely manner. Social media data analytics can be a competitive advantage leading to greater sales, lower claims and increased customer satisfaction. However, insurers should be careful with the data or risk crossing the “creepy line.”

With more than one billion users on Facebook and two billion total social media users across all platforms, the data shared is immense. The data that can be extracted from social media varies by platform, but in general the information goes far beyond pure text. Social graphs describe connections and relationships; profile updates highlight life change events such as marriage and the birth of children; geolocation tags highlight travel; and continuing communication can be parsed for activities and attitude.

Modern carriers looking to leverage analytics for a competitive advantage should already have a big data capability that pulls data from policy, billing and claims systems, call center logs, portal and app usage, third party enhancement tools such as Dun and Bradstreet and other sources to build a robust picture of each insured. This data can be mined using machine learning and neural networks to identify risks that should be exited, opportunities for cross-selling and best marketing opportunities to insureds and prospects. Social media is not a replacement for this data, rather a rich addition to it. By augmenting known facts with machine processing of social data, insurers can enable a more detailed and nuanced analysis that the same analytics routines can use to further refine analysis.

See also: Should Social Media Have a Place?

Examples of enhanced capabilities with this more robust analysis include:

  • Prescriptive marketing: Asses the marketing mechanisms and messaging that will be most effective in converting the prospect to an insured through analysis of social graphs, profile data and language usage. By parsing the semantics of a user’s language and analyzing their social graph for the type of language they are accustomed to seeing and, importantly, that they have chosen to see, marketing can be best tailored for the prospect.
  • Life event based cross-selling: Identify changes in relationship, location, job or family structure that enable marketing or sales to proactively contact the insured to recommend additional products or services. An example is increasing term life coverage for a new parent. By contacting insureds with relevant products at the moment of a life event, agents can be highly effective at converting new sales.
  • Continuous risk assessment: Continuously assess insureds’ risk profiles by expanding the analysis of an insured beyond their behaviors with the carrier to their behaviors with all other parties as evidenced in their social media communications. Updates about employment, travel, family circumstances or other items can impact how a framework understands the facts of an insureds’ interactions with the carrier. By understanding this, a carrier can better tailor reserve models or reevaluate whether to renew the policy.
  • Claim fraud detection: Identify potential claim fraud activity by monitoring geolocation, language and other data elements to confirm reported stories and check for telling language used in public communications. For example, a claim for workers compensation could be identified for potential challenge if a system identifies geolocation data from a golf course.
  • Customer sentiment: Be proactive with alerts of customer dissatisfaction with claim handling or price adjustments through text mining, allowing for remediation prior to losing a customer. By identifying dissatisfaction, the carrier can take better next steps in communication and outreach to maintain a client’s goodwill and business.

These aspects of insurance sales, risk management and claim management are beneficial for carriers. However, there are risks and challenges associated with social media data:  

  • Language is complex data: Because social media is so dependent on written words, language analysis is a common basis for analysis. Semantic assessment is useful in identifying underlying emotions and intent. However, words have different meanings in different sub-cultures, geographies, friend groups and even in different transmission medium. As such, language parsing should often be used to augment existing analysis, not to serve as a primary source of facts.
  • Usage of social media varies: In general, social media has widely different usage by age group and other demographic segments. Uptake rakes are not the same across all demographic groups, as demographic analysis of Facebook vs. Snapchat bear out and actual usage of the tools varies by group. The amount of data shared by younger users typically, but not always, dwarfs that of their parents. Analytical frameworks need to be configured to account for these differences and not draw unwarranted conclusions from different behavior patterns.  
  • Usage of social media starts and stops: Users of social media will start, stop and potentially resume use many times. Details of usage may also change as users’ needs or privacy concerns change. This requires analytical tools to be flexible in analysis — to understand that lack of data, limited data or infrequent posting is not necessarily an indicator of underlying behaviors of the prospect or insured.
  • Security is tricky: In the post-Snowden era, concerns about data privacy and usage are increasingly spotlighted by the media. Insurers should be cautious about how they collect, how they store and how they take action based on social media information. De-identification and storing only the analysis of the underlying data are potential paths among others. This should be continuously evaluated.

See also: 2 Concepts on Social Media and Analytics

A final note on risks: In 2010, then-Google CEO Eric Schmidt said, “Google policy is to get right up to the creepy line but not cross it.” This brought about much criticism from the public and watchdogs as many took it to mean Google would use the data it had in ways customers were not comfortable with. Insurance is as much about trust as it is about financial contracts. Therefore, insurers should be careful in using data that some may consider private or semi-private rather than public. They should also be cautious in drawing inferences and interpretations from data in a manner which would cause insureds to question them as warranted and justifiable. The use of data to further the carrier’s understanding of its customers must be approached as a relationship that can benefit both parties, and insurers must avoid being seen as “big brother” looking to squeeze extra premium from insureds.

Customers may not embrace the concept of their behaviors being analyzed. However, good analytics programs within insurance companies should be doing that today. By combining the facts of policy, billing and claims systems along with behavior evidenced in call center data, portals, digital apps and through other mechanisms, carriers should be analyzing customers robustly. In this framework, social media data becomes an enhancement layered on top that adds new dimensions and nuances to existing analysis. By leveraging neural networking and other machine learning approaches, carriers can better market, rate and manage risk and claims. These are net positives for insurers and potentially positives for customers. But, there are some substantial risks that must be managed as part of the total analytics strategy. By focusing first on the known facts and actual behaviors and only then expanding into the nuances of social media carriers, insurers can better enable robust and sound analysis that generates a return on investment for all parties.

How Google Is Wrong About the Internet

Eric Schmidt, the executive chairman of Google, said this week that the Internet will disappear — “There will be so many IP addresses, so many devices, sensors, things that you are wearing, things that you are interacting with, that you won’t even sense it,” he said. Now, Eric is a very smart fellow; he’s worth several billion dollars more than I am (the score is Schmidt, $8.3 billion, me, $0 billion); and he even has a better hairline than I do despite being two years older. He made his comments in Davos at the World Economic Forum, known as the gathering spot for very serious people. So his remarks have been getting quite a bit of attention and consideration. 

But he’s wrong.

He’s wrong for the same reason that people have been wrong since I started covering technology for the Wall Street Journal going on 30 years ago. That suggests to me that people will keep being wrong for the same reasons for some time to come, including in the world of insurance, where we are all having to try to figure out how the Internet of Things will play out. So let me point out the two issues that mean that even very smart people in very serious settings can’t just assume the sort of technological utopia that Schmidt is describing.

They are:

  • Decision rights
  • Transaction costs

Let’s look at those issues in the context of an article in the New York Times many years ago that got me mad enough to start thinking about the blind spot in the first place. A very bright reporter, and something of a friend, began by painting an idyllic vision of an automated future: A person hopping out of bed would step on a sensing device that would let the house know he was up. The house would then turn on CNN in the family room, start the coffee and probably do some other things that I no longer remember at this remove.

Nice image, right?

Decision rights

But what if I don’t want to watch CNN? What if I’m more interested in watching ESPN that morning? Or my kids were already awake and watching cartoons — would my stepping on the pad change the channel despite the screams that would surely result? What if I’m heading off to meet someone for breakfast and will have coffee there, not at home that day?

A key question with any sort of automation is: Who owns the decision rights? In the case of CNN and the coffee, do I want the house to have the decision rights, or do I want to retain them?

Transaction costs

How much effort do I have to put into the automation? Is it really worth it to put a sensor under my carpet or even to lay something on top of the carpet? What does that cost? How long does it take me to configure the TV and other systems in the house so that they react appropriately?

Those transaction costs then have to be compared against the benefits, which, in the case of CNN and the coffee, are trivial. It’s just not that hard to pick up the remote and click the TV on or to fix the coffee in the morning (which you would have had to do before going to bed in the automated scenario.)

People tend to get so excited about the George Jetson-like possibilities that they ignore decision rights and transaction costs and paint visions that simply won’t occur in any reasonable timeframe.

That’s how we ended up with:

— The talk back in the early ’90s about “agents” that would pull together what some called “The Daily Me,” a personalized newspaper that would gather all the news that it knew you were interested in and mix it in with your schedule and other things to lay out your day for you. The problem was that these personalized papers took a huge amount of effort and were so inaccurate that no one would turn all the decision rights over to an agent. What if you didn’t have time to read the news that day? What if you had become interested in some topic that you’d never read about before — how would your agent know?

(I took the talk of agents somewhat personally because the three pieces I wrote for the Wall Street Journal that easily got the most response from readers in my 17 years there were about: a time I sailed across the Atlantic in a small boat, having never sailed before, in what turned out to be some monster storms; my two-week career as a professional wrestler; and my mother (a piece written with my younger brother). I guarantee you that no one who picked up the Wall Street Journal the day those pieces appeared was looking for anything about sailing, professional wrestling, my mother or me, so no one would have ever seen them in a world of agents.)

This talk of agents is cropping up again, by the way, and is surely part of the reason that Schmidt wants to talk about having the Internet disappear. Google wants to make search so efficient that its engine knows what you want to find even before you think to look. The company has made impressive strides — if you type in Elm Street while looking for directions on your Android phone, Google Maps usually guesses quickly and correctly which Elm Street you want — but that’s a long way from the world that Google is describing, and the transaction-cost and decision-rights issues will still get in the way.

— The fuss over the “Internet refrigerator” that still crops up from time to time. The idea is that your refrigerator would sense when, say, you were low on milk and reorder it for you. But that requires an awful lot of engineering, both in the refrigerator and in whatever system of grocery delivery would be used, and only makes sense if you’re turning just about all your shopping over to your refrigerator — if you have to go to store anyway, it’s simple to grab some milk.

And there is always the issue of decision rights. What if a family goes on vacation? How long will the refrigerator keep ordering? I have a friend whose 21-year-old son drinks a gallon of whole milk a day. When the son — who is 6’5″, weighs 285 pounds and looks like he could bench press a cow — is home, they can’t buy milk fast enough, but when he’s gone at school they don’t need any. Do they have to let a few gallons of milk sour before the refrigerator figures out the son is gone?

— The excitement about home controls: remote-controlled lighting, the Internet thermostat that will sense who’s in a room and adjust lighting levels and temperature to personal preferences and so on. Those are just an awful lot of work for not much benefit — you can always flip a light switch or adjust a rheostat — and doesn’t resolve the issues that come up when one person likes a room cooler than the other.

Technology will make plenty of tasks disappear, but let’s not be too hasty. We need to think through the costs, the benefits and the potential for errors and conflicts in automated systems, to make sure we don’t fall victim to the seductive tendency to ignore transaction costs and decision rights.

The Internet won’t disappear in my lifetime, which I’m assuming will be at least 30 more years. I won’t even have sensors that turn on CNN and start my coffee when I get out of bed.