Recently, I wrote about a startup called Aviva. (My comments were based on an article I read.) Aviva's CEO said, “What’s our long-term goal? To go from ask-it-once to ask-it-never — so customers don’t have to answer any questions at all.” How can coverage be booked without asking ANY questions? Why, using big data, of course. Wouldn’t a better goal be to first ask the necessary questions to assist consumers in identifying their unique exposures to loss, then match those exposures (where possible) with the proper insurance package to minimize the likelihood that a consumer will experience a serious or catastrophic financial loss? At my semi-annual checkups, my doctor asks me a lot of questions. Would it be an improvement if he didn’t ask me any questions? Maybe for his bottom line, but not for mine. Who can’t spare an hour once a year to prevent financial ruin? See also: Forget Big Data; You Need Fast Data   In another blog post, I wrote about the startup Slice, which apparently plans to write on-demand home-sharing and ride-sharing insurance without an application. How? Presumably by using big data, of course. In still another blog post, I wrote about Lemonade, which writes homeowners insurance using a phone app without a lot of pesky questions that are designed to identify exposure gaps of individuals and families. Lemonade, too, seems to be relying on black-box algorithms and our friend big data. Let’s take Slice. It claims: “All the information that insurance carriers ask you is all publicly available. So instead of taking up your time to give us this info, we use our clever SliceBots to collect it.” So, ALL of the information that Slice needs to properly insure all of your unique exposures to loss is publicly available? At one time, I saw a Zillow logo on a startup’s web site. Is that where, for example, homeowners' information might be obtained? Or might such a startup go directly to tax and other records where this information is obtained? How reliable is this “big data”? Is it vetted at all if customers are not asked any questions? Still another startup is Hippo. Backed by a number of investors, including Trulia, this is how Hippo's big data approach works, according to an article from Forbes: “According to the company, with Hippo, consumers can go from quote to purchase in minutes, as quotes are delivered in 60 seconds after answering three simple questions. Customers can get a personalized Hippo quote online, by phone or even through Facebook Messenger. The company leverages technology and data from multiple sources (such as property records, permit filings and aerial photography of roof conditions) to streamline the application process and provide ongoing risk monitoring. By leveraging data, Hippo saves customers time, while also garnering more accurate information that cannot be provided from subjective human answers alone. By cutting out the middleman, more accurately assessing risk and increasing technology efficiencies, Hippo is able to pass savings on to consumers.” There happens to be a home for sale in my neighborhood. Out of curiosity, I checked it out on both Zillow and Trulia. Zillow says it’s a 1-story home, Trulia says it has two stories. Zillow says two-and-a-half baths, Trulia says three-and-a-quarter baths. Zillow says the lot is 1.6 acres, Trulia says it's 0.48 acres. Zillow says the home is 2,968 sq. ft., Trulia says it’s 3,891 sq. ft. Just in the replacement cost valuation of the home alone, think these discrepancies might make a difference in coverage limits? See also: Healthcare Needs a Data Checkup   In my case, I owned a home that was 1,000 sq. ft. larger than the country tax records showed. Over the course of 30-plus years, attic space had been converted to living space, but the records from which “big data” might be drawn were never updated. When discussing this issue in an online forum, one of the participants said Zillow showed his home being 2,400 sq. ft. (the same size in the tax rolls), whereas it’s actually 4,683 sq. ft. Big data is one thing. Big, BAD data is another. Who is vetting the information, bots and algorithms? Certainly not regulators, given the open-arms welcome one startup got from a state insurance department. Is anyone listening? Does anyone care?

One of the most basic lessons you learn in first year business school is the SWOT analysis - strengths, weaknesses, opportunities and threats. And it's a great framework to apply to your business to understand what you do well, what you can improve on and where the greatest threats to your company lie. But how about a SWOT analysis on ourselves? Where are our blind spots? What do you struggle with? Here's a simple framework to give it a go: Strengths: What are your strengths as an entrepreneur? What do you do particularly well? Or, in the words of Chris Sacca, what's your "unfair advantage?" Perhaps you're great with product design. Or perhaps your distinguishing characteristic is your ability to sell. Or maybe you can work a room like nobody's business. Knowing your strengths tells you what added value you can uniquely bring to your business. See also: The Need for Agile, Collaborative Leaders   Weaknesses: You might be a terrible planner. Or you might procrastinate like nobody's business. Or you might dread making sales. You might also feel uncomfortable admitting it or talking about your weaknesses. But unacknowledged weaknesses are business killers. They slowly eat away at the core of your business, with little hope of ever changing the situation. So pay particular attention to weaknesses as you do your personal SWOT -- and be as honest as possible with yourself as you do. Opportunities:  Opportunities can be chances to build on your strengths and rectify your weaknesses - either through self-improvement or by adding additional members to the team with complementary skills. But of course, opportunities can only be leveraged if weaknesses are recognized and acknowledged -- yet another reason that honesty is so essential in the process of conducting your personal SWOT. Threats:  Finally, threats can come from multiple places. Your skills may no longer fit the needs of the business you're in. You might face competition from others who do have these skills -- and if you're unable to acknowledge (and work on) your weaknesses while at the same time leveraging and accentuating your strengths -- you could find yourself in a precarious professional position. Along these lines is the threat that you as the leader might lack the self-awareness or courage to look yourself in the mirror and conduct an honest, self-reflective SWOT analysis in the first place. Doing an honest, self-reflective personal SWOT analysis is useful for anyone at any stage of a career. But it's especially useful for entrepreneurs, who need such a wide-ranging set of skills to achieve their goals and find success in their business. Have you conducted a personal SWOT analysis? If not, what's holding you back? See also: Where Are All Our Thought Leaders?   Visit here to receive my free guide to 10 cultural codes from around the world, and here for my very best tips on stepping outside your comfort zone at work. Andy Molinsky is the author of Reach and Global Dexterity. 

Some of the best innovation success stories are built out of the lessons learned from watching the attempts of others as they either falter or flourish. The following is a compilation of what we believe are the best practices for innovation, through anecdotal and use case examples. In an effort to help and inspire insurers along the innovation journey, SMA has grouped the 10 innovation best practices into three phases and then defined each of the 10 best practices. See also: Innovation: ‘Where Do We Start?’   Top 10 Best Practices for Innovation Phase 1: Getting Started: Begin the Innovation Journey

1. Don’t Be a Lone Wolf
2. Institutionalize Innovation
3. Reframe Business Strategies and Plans
4. Explore the Insurtech Landscape

Phase 2: Gaining Momentum: Learn From Successes and Failures

1.  Have a Champion for Each Cause
2. Pivot, and Then Pivot Again
3. Expand Your Failure Appetite

Phase 3:  Creating Advantage: Leverage Innovation for the Competitive Edge

1. Leverage Customer Insights
2. Innovate Across the Enterprise
3. Make Innovation Continuous

P&C insurers spend as much as 30% of the cost of the product on distribution. That’s a hefty price to pay to get your offerings to consumers, only to have them be dissatisfied with the experience. As consumers demand more convenient options for purchasing insurance, leading P&C insurers have found a way to reduce costs and improve the ease of the buying experience by digitizing manual processes. What’s Holding Insurers Back? What’s holding most insurers back from meeting the speed and performance of a customer experience leader like Amazon? In a nutshell, siloed legacy systems. See also: P&C Core Systems: Beyond the First Wave   We know that insurers that have overcome legacy system challenges to achieve top digital capabilities grow revenue at 1.5 times the rate of less enabled competitors. We’ve said this before, so let’s break it down to discover what’s holding the rest of the industry—those still married to their aging systems and processes—from achieving the same results:

• Sub-prime processes: Consider what it’s like to purchase items from an online retailer. You search for an item; peruse the list of available products; find the one that fits your parameters, and in a few seconds make a purchase. This shopping experience pervades our modern culture, so why should it be any different in insurance? The answer again is legacy systems, as customers must provide reams of data on everything from the type of engine that runs their car to the framing in their house just to receive a price on available coverage. Entering this level of information takes time, adding on costs and generating consumer frustration.
• Product silos: In P&C insurance, everything lives in its own universe. Auto policies operate from one back-office system, homeowners from another, motorcycle from yet another and so on. When a customer wants to purchase multiple lines of coverage, information needs to be entered into all of those systems, requiring separate applications and sometimes separate agents to do the job. Because this data gets entered manually, the work costs the insurer in efficiency and errors.
• Non-standardized data: Given that P&C insurers operate from silos, what happens when P. John Smith, (he likes to be called John) living on Main Street in Scituate, RI, approaches carrier agents for auto and home coverage or even starts the process online himself. For the majority of insurers, information would have to be entered twice, once for auto and once for home, into two different systems. Suppose the agent, or John himself, enters the required identifying data for auto. Then, when it’s time to re-enter the data for the homeowners policy, John or a different agent decides to speed up the process by omitting his first initial, shortening his name to John Smith and his street address to Main St. We now have non-standardized data, where one individual is represented in two different ways across the insurer’s data warehouse. These simple discrepancies make it difficult to locate John when he calls with questions or about his renewal, reducing agent and online efficiency.

In case you didn’t realize it, all of the scenarios above relate to manual or inefficient data handling, something insurers can improve to reduce costs and acquire more customers. Why Digital Reigns Supreme (Hint, It’s Automation) Digital leaders grow faster in part because they eliminate much of the inefficiency and costs that plague their less-enabled counterparts. They also create a more satisfying customer experience, resulting in stronger acquisition and retention. McKinsey estimates that as much as 45% of work activities could be automated today, but let’s focus on the quote-to-issue lifecycle for a moment. This is where most of the manual data entry occurs. Automating the quote-to-issue lifecycle takes much of the chore of data entry off the plate of agents and consumers. With a leading digital distribution platform, the small amount of information that is subject to manual entry is entered only once, while the nitty-gritty details are drawn from verified third-party sources. Applications are completed in a fraction of the time, streamlining the quoting, binding and issuance process, while eliminating many of the manual tasks associated with generating a policy. Insurers can see double-digit error rate reductions, as much as a 70% decrease in data entry costs and a 50% increase in agent efficiency. But that’s only the tip of the iceberg. The real story is in customer satisfaction. When insurers use a digital distribution platform that unites product silos, consumers and agents are able to quote, bind and issue multiple products from a single application. Imagine agents and consumers quoting multiple products in less time than it used to take to quote one. By uniting product siloes and adding application prefill capabilities, a leading insurer has reduced data entry and streamlined the quoting, binding and issuance of products. The result is quote conversion rates of 35% through agency channels and 53% direct-to-consumer. See also: Data and Analytics in P&C Insurance   As consumers unite to turn the insurance industry on its ear, it’s time for insurers to leave the dark ages behind and emerge into the light of 21^st-century distribution. To learn how to evolve quickly and simply, without major upgrades or overhauls to existing systems, download our infographic, Direct-To-Consumer: The Future Of P&C Insurance.

When “Car and Driver” magazine debuted more than 60 years ago (originally titled Sports Cars Illustrated), nobody could have envisioned the approaching changes that would transform life as we knew it – including all things automotive and consumer. Today, the expression “car and driver” suggests a completely different meaning as automobiles are becoming “driven” by software and technology and their owners are becoming passengers – and increasingly we are riding in vehicles we don’t even own but rather share or rent. But while we await our future, current innovations in vehicle and consumer technologies have already emerged to create a transition period full of complex challenges and issues accompanied by potentially significant opportunities for all participants. While much attention is being paid to the emergence of telematics and the connected car, and seemingly endless amounts of investment capital are flowing to the many innovative and promising startups sprouting in this fertile global environment, something even more consequential is also beginning to evolve. Auto insurers and auto makers – once basically adversaries – are beginning to cooperate around many of the related opportunities.   See also: 3 Technology Trends Worth Watching   These two industries, which serve and share a common customer base, have traditionally been wary of one another because they had so many conflicting interests. Carriers insure the people who drive the cars that OEMs make, and, when accidents inevitably occur, liability is frequently brought into question to protect the interests of one from the other. In addition, franchised new car dealers, upon whose success OEMs depend for sales and vehicle distribution, earn significant revenues from selling a variety of related products and services – including warranties and insurance, another area of potential conflict. Finally, when insured vehicles end up in collision repair shops as a result of accidents (which happens more than 20 million times a year), insurance carriers do their best to manage repair costs by encouraging these shops to find and use less expensive parts, which costs OEMs and their franchised new car dealers significant parts sales revenues. And, at a higher level, insurers and OEMs value and fiercely protect their customer relationships and have no interest in sharing them with others.    However, these dynamics are quickly changing as new mobile technologies are rapidly transforming consumer behavior and expectations and as new connected car and automated driver assist technologies begin to present significant new challenges as well as exciting opportunities to both auto insurers and OEMs. It is far from a given that today’s auto market share leaders will enjoy similar shares of future autonomous vehicle sales, and it is equally uncertain as to by whom and how these vehicles will be insured. Tesla is positioning itself to do both. And so the ancient proverb that “the enemy of my enemy is my friend” seems to apply very well here. Evidence of insurer/OEM partnerships, both direct and indirect, is plentiful and growing daily. Insurer/OEM connected car partnerships date back to as early as 2012 and include State Farm/Ford, Progressive/GM OnStar, Allstate/GM OnStar and Nissan/Liberty Mutual. In 2015, Ford conducted a “Data Driven Insurance” pilot program that provided participating drivers with their driver history for use in obtaining auto insurance. In 2017, GM OnStar began offering its subscribers 10% discounts on auto insurance from participating carriers including National General, 21st Century, Liberty Mutual, State Farm and Plymouth Rock.   And data and analytics information providers Verisk and LexisNexis Risk Solutions, which collect data and analytics solutions for use by the insurance industry, have both recently launched telematics data exchanges with OEM participants including GM and Mitsubishi. Consenting connected-car owners have the option to contribute their driving data and seamlessly take advantage of insurers’ usage-based insurance (UBI) programs designed to reward them for how they drive. Other innovative telematics data models include BMW CarData, which allows owners to share customized data with pre-approved third-parties such as insurers, auto repair shops and other automotive service providers. Drivers can obtain custom insurance coverage based on their exact number of miles driven while repair shops could automatically order parts in advance of service appointments. For carriers, existing data pools and analytics tools will become less useful than real-time data streaming from connected cars coupled with increased proficiency in predictive modeling and machine learning. OEM/insurer partnerships can enable both parties to share the costs and co-develop big data mining technologies and advanced analytics methodologies to benefit their respective businesses. Insurers can improve underwriting and claims processes while OEMs can improve vehicle safety, design and performance. Data provided by connected-car devices could be used to initiate claims processing, order damaged parts, triage required collision repair and manage other third-party services (e.g. towing, rental, appraisal) and record accident dynamics as well as occupant placement. OEM/insurer partnerships sharing this data could lead to better claims service and satisfaction and more reliable injury claim evaluation. OEMs could use this data to improve vehicle and occupant safety and could ensure that repairs are performed at properly certified collision repairers and that appropriate parts are used in the repair. OEMs and insurers can partner to offer customers innovative customer experiences, becoming primary points of contact for risk prevention and new hybrid insurance products as well as dealer parts, service and sales opportunities. New revenue sources for both parties could include Intelligent GPS for theft recovery, real-time notifications of traffic and other travel inconveniences, intelligent parking, location-based services, safety and remote maintenance services. Cost duplication from currently overlapping services such as roadside assistance and towing could be eliminated by single-sourcing such services. See also: The Evolution in Self-Driving Vehicles   To be sure, other telematics data business models have emerged that could threaten OEM/insurer partnerships.  In June 2017, BMW and IBM announced the integration of the BMW CarData network with an IBM cloud computing platform that could help as many as 8.5 million German drivers who grant permission to diagnose and repair problems save on car insurance, and take advantage of other third-party services. IBM can also collect data from other OEMs over time, and BMW plans to expand the program to other markets. And technology companies, including Automatics Labs and Otonomo, are seeking consumer consent to sell data through their exchange platforms. While we await the day that self-driving vehicles dominate our roadways – which will no doubt make many of these driver data initiatives basically irrelevant – we have the most pragmatic of all reasons why OEM/insurer partnerships make sense. Participants can mitigate their risk and reduce their investments in these costly but still relatively short-term opportunities as they position their companies for the as-yet-undefined future of transportation and insurance.

In May, I wrote about The Three Phases Insurers Need for Real Big Data Value, assessing how insurance companies progress through levels of maturity as they invest in and innovate around big data. It turns out that there’s a similar evolution around how insurers consume and use feeds from the Internet of Things, whether talking about sensor devices, wearables, drones or any other source of complex, unstructured data. The growth of IoT in the insurance space (especially with automotive telematics) is one of the major reasons insurers have needed to think beyond traditional databases. This is no surprise, as Novarica has explained previously how these emerging technologies are intertwined in their increasing adoption. The reality on the ground is that the adoption of the Internet of Things in the insurance industry has outpaced the adoption of big data technologies like Hadoop and other NoSQL/unstructured databases. Just because an insurer hasn’t yet built up a robust internal skill set for dealing with big data doesn’t mean that those insurers won’t want to take advantage of the new information and insight available from big data sources. Despite the seeming contradiction in that statement, there are actually three different levels of IoT and big data consumption that allow insurers at various phases of technology adoption to work with these new sources. See also: 7 Predictions for IoT Impact on Insurance   Phase 1: Scored IoT Data Only For certain sources of IoT/sensor data, it’s possible for insurers to bypass the bulk of the data entirely. Rather than pulling the big data into their environment, the insurer can rely on a trusted third party to do the work for it, gathering the data and then using analytics and predictive models to reduce the data to a score. One example in use now is third-party companies that gather telematics data for drivers and generate a “driver score” that assesses a driver’s behavior and ability relative to others. On the insurer’s end, only this high-level score is stored and associated with a policyholder or a risk, much like how credit scores are used. This kind of scored use of IoT data is good for top-level decision-making, executive review across the book of business or big-picture analysis of the data set. It requires having significant trust in the third-party vendor’s ability to calculate the score. Even when the insurer does trust that score, it’s never going to be as closely correlated to the insurer’s business because it’s built with general data rather than the insurer’s claims and loss history. In some cases, especially insurers with smaller books of business, this might actually be a plus, because a third party might be basing its scores on a wider set of contributory data sets. And even large insurers that have matured to later phases of IoT data consumption might still want to leverage these third-party scores as a way to validate and accentuate the kind of scoring they do internally. One limitation is that a third party that aggregates and scores the kind of IoT data the insurer is interested in has to already exist. While this is the case for telematics, there may be other areas where that’s not the case, leaving the insurer to move to one of the next phases on its own. Phase 2: Cleansed/Simplified IoT Data Ingestion Just because an insurer has access to an IoT data source (whether through its own distribution of devices or by tapping into an existing sensor network) doesn’t mean the insurer has the big data capability to consume and process all of it. The good news is it’s still possible to get value out of these data sources even if that’s the case. In fact, in an earlier survey report by Novarica, while more than 60% of insurers stated that they were using some forms of big data, less than 40% of those insurers were using anything other than traditional SQL databases. How is that possible if traditional databases are not equipped to consume the flow of big data from IoT devices? What’s happening is that these insurers are pulling the key metrics from an IoT data stream and loading it into a traditional relational database. This isn’t a new approach; insurers have been doing this for a long time with many types of data sets. For example, when we talk about weather data we’re typically not actually pulling all temperatures and condition data throughout the day in every single area, but rather simplifying it to condition and temperature high and low at a zip code (or even county) on a per-day basis. Similarly, an insurer can install telematics devices in vehicles and only capture a slice of the data (e.g. top speed, number of hard breaks, number of hard accelerations—rather than every minor movement), or filter only a few key metrics from a wearable device (e.g. number of steps per day rather than full GPS data). This kind of reduced data set limits the full set of analysis possible, but it does provide some benefits, too. It allows human querying and visualization without special tools, as well as a simpler overlay onto existing normalized records in a traditional data warehouse. Plus, and perhaps more importantly, it doesn’t require an insurer to have big data expertise inside its organization to start getting some value from the Internet of Things. In fact, in some cases the client may feel more comfortable knowing that only a subset of the personal data is being stored. Phase 3: Full IoT Data Ingestion Once an insurer has a robust big data technology expertise in house, or has brought in a consultant to provide this expertise, it’s possible to capture the entire range of data being generated by IoT sensors. This means gathering the full set of sensor data, loading it into Hadoop or another unstructured database and layering it with existing loss history and policy data. This data is then available for machine-driven correlation and analysis, identifying insights that would not have been available or expected with the more limited data sets of the previous phases. In addition, this kind of data is now available for future insight as more and more data sets are layered into the big data environment. For the most part, this kind of complete sensor data set is too deep for humans to use directly, and it will require tools to do initial analysis and visualization such that what the insurer ends up working with makes sense. As insurers embrace artificial intelligence solutions, having a lot of data to underpin machine learning and deep learning systems will be key to their success. An AI approach will be a particularly good way of getting value out of IoT data. Insurers working only in Phase 1 or Phase 2 of the IoT maturity scale will not be building the history of data in this fashion. Consuming the full set of IoT data in a big data environment now will establish a future basis for AI insight, even if there is a limited insight capability to start. See also: IoT’s Implications for Insurance Carriers   Different Phases Provide Different Value These three IoT phases are not necessarily linear. Many insurers will choose to work with IoT data using all three approaches simultaneously, due to the different values they bring. An insurer that is fully leveraging Hadoop might still want to overlay some cleansed/simplified IoT data into its existing data warehouse, and may also want to take advantage of third-party scores as a way of validating its own complete scoring. Insurers need to not only develop the skill set to deal with IoT data, but also the use cases for how they want it to affect their business. As is the case with all data projects, if it doesn’t affect concrete decision-making and business direction, then the value will not be clear to the stakeholders.

With a population of approximately 102 million, the Philippines was the fastest-growing economy in Asia in 2016 and is one of the fastest-growing in the world. With more than half of the population under the age of 25 and buoyed by $50 billion in remittances and outsourcing annually, the economy is expected to outperform its peers over the coming years. This, combined with a $160 billion infrastructure plan, will set the stage for a rapid increase in the size of the middle class. See also: Insurtech Ecosystem Emerging in Asia The Philippine insurance sector is one of the oldest in the region, with development dating back more than 200 years — and it is among the region's most mature and competitive markets. There are now more than 32 million Filipinos covered by insurance, with 28 million of those covered by rapidly expanding micro insurance products. The ratio of coverage increased dramatically from 19% in 2010 to nearly 33% in 2016. There are currently 63 brokers, 31 life insurers, 71 non-life insurers and one reinsurance firm operating in the country. There is also a growing international presence, with global firms such as Axa and Mapfre investing heavily in the market. We believe that a country’s insurance market is ripe for disruption when it has: 1. A rapidly growing middle class; 2. Strong and sustained economic growth; 3. Increasing demand for insurance products; 4. Increasing levels of leisure and family-oriented activities; and 5. Growing levels of disposable income and, most importantly, society that is embracing digital technology and connections. The Philippines clearly has the first four attributes, but what about the digital aspect? Consider the following: The Philippines is the third-largest and fastest-growing market in smartphones in SE Asia. Three in 10 Filipinos own a smartphone. The average smartphone user spends three hours and 14 minutes a day on the internet via smartphone. Of that time, 78 minutes a day is spent on entertainment and related content, 56 minutes a day on apps and 40 minutes a day on communications services. Within 15 minutes of waking up in the morning, 79% of Filipinos have already checked their smartphones, and 40 million Filipinos are active on social media. Of those, 81% use Androids, with the remaining 19% using IOS. As to demographics and usage, 88% of users are under the age of 34, with 53% under the age of 24. The usage and social media aspects are even more telling, with 94% of all users on Facebook. And 32% of Filipino smartphone users download six or more apps per month, while 45% of those have paid for apps or made in-app purchases. Mobile banking is now used by 14% of smartphone owners, and this usage is increasing 25% per year. See also: Why Southeast Asia Is Ready for Disruption Is the Philippine insurance industry ripe for disruption? Is the Pope Catholic!

The abundance of data -- and the technology used to capture it -- is driving profound disruption in the relationship structure of the insurance industry. As the traditional gatherers and guards of massive amounts of data, insurers face threats from new, tech-savvy competitors that can adapt to changes more quickly. There are very powerful trends coming together to cause serious industry disruption. That can be a big threat, but if insurers start responding now and embracing the change, it could also be a big opportunity. What is Insurtech? Google defines insurtech as referring to the use of technology innovations designed to squeeze out savings and efficiency from the current insurance industry model. "Insurtech" is a portmanteau of “insurance” and “technology” that was inspired by the term "fintech" (financial technology). Longstanding reluctance to change is preventing many organizations in the workers’ comp industry from embracing new technology, especially technology that streamlines processes and worker performance. However, this reluctance is no longer sustainable. Simply stated, those who cling only to the old-time culture and ways will be disrupted. Think Amazon or Uber. Big data and analytics are forcing insurers to adjust their processes when it comes to collecting and using data. With the expansion of the Internet of Things, sensor technology, machine learning and artificial intelligence, there is more information available than ever before. See also: What’s Your Game Plan for Insurtech? Data, the asset Organizations that continue to ignore the facts will wonder why they are no longer competitive. Those that are open to new approaches using new technology will experience positive results. It is a matter of attitude and willingness to try newer methods. Nevertheless, insurtech need not be invasive or costly. To make a positive impact on processes and outcomes, an organization must first take the position of "data-centeredness," believing data is its valued asset. Accepting and incorporating new technology requires focusing on only three basic initiatives: data quality, data analysis and smart application of the intelligence gained through analytics. Data quality If data is an asset, then its quality must be valued and protected. Using poor or erroneous data never ends well. Information gained from poor quality data will not improve an organization’s processes or outcomes and will lead to poor decisions and detrimental actions. Therefore, resources must be applied to guaranteeing quality data input. Moreover, considerable resources may be needed to improve historic data. Over the last 25 years, organizations have focused on collecting data, but little attention has been paid to insuring that the data is accurate and complete. That must change. A data-centered organization will also guarantee that its data is pristine. Analytics The second initiative needed to avoid insurtech disruption is to analyze the organization’s data. Collect and analyze all data over the previous five years. Methods such as predictive analytics can be applied to gain greater understanding of the organization, how well it operates and what are the cost drivers both operationally and at the transaction level. This is simply a matter of analyzing historic data and monitoring concurrent data to reveal trends, threats, and possibilities. Know thyself. See also: Insurtech Is Ignoring 2/3 of Opportunity   Intelligent knowledge application Having quality data and analyzing it leads to the next critical step of designing intelligent solutions to problems identified during the analysis phase. Apply the knowledge gained to specific areas of need by creating “apps” that solve problems and improve processes in the organization. Alert the right person when conditions or events pose a risk to the organization or work product identified in the analysis phase. Deliver key intelligence to specific individuals or groups at the exact time they need it for decision support. Facilitate timely communication within the organization. Knowledge assistance provided at the right time to the right persons saves time and creates accuracy, efficiency and greater profitability. Stepping into the world of insurtech and avoiding disruption is largely a matter of perspective and attitude. It requires a view that data is an asset and, when properly managed, lets the organization define its destiny.

Let me set the scene. It’s 1966 in a Clint Eastwood movie where the two of you meet eye to eye in front of a saloon. The tension rises. You want to take the first shot. We feel you. That's how it can feel dealing with a bad customer. Maybe it's someone coming in two minutes before close (because, face it, you have no life!), or it's the customer who insists on taking a call while you serve them. We’ve all been there! It takes a lot of patience to handle the array of customers whom we are confronted with on a daily basis. It takes superpowers! But don’t get mad – get to laughing. See also: Much Higher Bar for Customer Service   We have collated a list of the most common personalities who do not make the greatest customers, for a little fun. Think of those customers like a business consultant advising on best business practices (come on, they’re basically doing you a favor!) We'll begin with the one who knows how to do your job better than you (and your boss). Then there’s the ninja who envies you so much that he wants to be you. He’s like your apprentice, posing as a customer to learn more from you. Slightly more frustrating is the one who is the greatest of all hagglers; he treats your company more like a Sunday car boot sale than actual established business. Just breathe! And the ugliest one of them all, the very vocal customer. He’s angry, he’s loud and you’re going to hear about it. We share with you this fun infographic of some of the most challenging customers and strategies to help you cope. As Clint Eastwood would say, “Go ahead, make my day” As always, please let us know your comments or any questions on Twitter (@thewebsitegroup ).

Imagine for a minute that a new competitor started calling on your customers and offering the same—or better—product, coverage or services for much less cost. Are your relationships strong enough that your customers would ignore the prospect of an offering of better, faster, cheaper? Certainly, some would at least be inclined to explore the offer, would they not? No need to imagine this scenario; it’s the new reality. And in this new reality, highlighted by insurtech startups, software incubators and service accelerators, small to medium-sized insurers are going to be more challenged than ever to keep up with the extraordinary changes taking place in the industry, all while trying to achieve growth in their organizations. See also: Innovation: ‘Where Do We Start?’   It’s no secret that smaller insurers are much more sensitive to loss of business, swings in expense and loss of knowledge-based staff. This makes small insurance operations vulnerable to carriers or competing services that are working with new insurance technologies to forge new products, services and business models. In fact, a new survey of 400 global executives by Forbes Insights and Gap International, “Challenge or Be Challenged: How to Succeed in Today’s Business Environment,” revealed that 57% of business leaders across a variety of vertical markets named startups and new technologies as their biggest competitors, while 70% say they are “extremely concerned” or “somewhat concerned” as to whether their company will still be relevant and competitive in two years. What does this mean for small insurer operations? Clearly, ignoring the changes taking place doesn’t mitigate the risks down the road unless you have a micro-monopoly in a service segment. Texas recently saw the closure of a fairly large public insurance pool that couldn’t navigate the current. So, what options are there for the small insurer? Maybe there’s safety in numbers. Merger, acquisition, strategic partnerships? It’s certainly been a successful approach for many small insurers around the country, like Beta Fund in California. It’s hard to say what would work for you. Then, is combining the only option? Well, leaders of established companies, large or small, might worry less about being disrupted by a startup if they focused more on organic growth, says Pontish Yeramyan, founder and CEO of Gap International. “When you’re connected to organic growth and your passion is about growth, then you’re busy innovating and being in front of the marketplace, rather than being victimized by change,” Yeramyan said in a recent Forbes report. This means investments in modern, affordable technologies and R&D, rather than looking outward for companies that might want to merge or be acquired. Having an eye on organic growth means continuous improvements, whether by development of your staff, new products, service enhancements or innovating your business model. It also means keeping an eye on strategic vision, adopting the most appropriate technologies and staffing with the right skillsets. The Forbes/Gap report also revealed that our new demanding and shifting business environment requires a change in how leaders think and act, namely, making innovation a part of the working institution, starting at the executive level and cascading into the entire organization. Small insurers can make this modification much more easily than can their behemoth brothers. “An organization’s ability to change and innovate quickly is a key competitive advantage,” Yeramyan says. See also: Insurers Are Catching the Innovation Wave   Technology is certainly an enabler in effecting change; done right, it enables insurers to experience the hallmark of organic growth, expanding their market share and reach even further. Insurers such as Diamond Insurance Group and Utah Business Insurance, both leading regional providers of insurance coverage, understand this first-hand. Both companies implemented a cloud-based insurance software system to enable a variety of insurance processes, including policy, underwriting, billing and claims integration for compliance reporting. And by focusing on improvements in their internal abilities, both companies report that they are able to deliver greater value to their external customers. For Diamond, these changes, coupled with their hard work, have resulted in a 20% increase in revenue in just the first quarter this year. How ready are you?