I can't stop thinking about algorithms. I am obsessed, and I want to tell you why.

Let's be clear: I am not a data scientist. I am a guy who finds technology and applications of technology fascinating. I am not writing this for technology nerds. I am writing this for professionals who want a working knowledge of technology.

If you are reading this, then you understand computers. A computer is nothing more than rules programmed by a human. Those rules are then executed and create an output.

But algorithms are so much more; they are breathtaking. An algorithm is a computer writing its own rules and then creating output from those rules.

It's easy to focus on the scary part of algorithms. In the Avengers movie, a super algorithm results in a machine - Ultron - bent on destroying the world. I will leave those scenarios to the Elon Musks of the world.

Algorithms can do so much good

Think about any repetitive task you do. An algorithm can be created to solve that task. Some algorithms are used for fun. For example, Facebook uses algorithms to suggest friends for you to connect with. Google Photos uses an algorithm to identify faces and group pictures of the same person together (which can lead to terrible results).

Algorithms are already being used in the insurance industry. Take a look at CoverHound or PolicyGenius; the algorithms behind these applications quote personal lines of insurance based on your needs.

How algorithms work (and why they are awesome)

Again, I am not a data scientist, but here is my simple explanation of how most if not all algorithms are created:

1. Create a seed set.

First, you identify a seed set, which is the core learning that is taught to the algorithm. Yes, that's right, even a computer algorithm has to be taught something from a human! For example, with the Facebook algorithm, I'm almost certain that the algorithm was first fed a giant spreadsheet that contained information about individuals and how they were connected (you do know your data created Facebook, Google and every other big data company you can think of, right?).

2. Feed the seed set to the algorithm.

The algorithm then reads all of the information it is fed and starts making its own rules. For example, the Facebook algorithm may determine: "Oh, I see, Jimmy likes Teenage Mutant Ninja Turtles, and he is connected with Bobby from the same city, and Bobby also likes Teenage Mutant Ninja Turtles. I bet Jimmy also knows Steve from the same city who also has a love for Donatello. They should connect."

3. A human reviews the results.

A human (see, you are still needed!) then reviews the output of the application of the algorithm rules. In the Facebook example, a human might determine if Jimmy and Steve should actually connect on Facebook. Maybe they are part of rival gangs, and the algorithm didn't recognize this. The human would then add this data to the spreadsheet and feed it back to the algorithm.

4. The algorithm rules are improved based on new input.

The algorithm creates rules to account for the new information. "Don't connect rival gang members even if they live in the same city and like the Teenage Mutant Ninja Turtles."

5. Steps three and four continue indefinitely.

Now stop for a second and think about all the rules that are built up in your head about people you connect with. Maybe you prefer to hang out with people who brew beer or read Harry Potter. There are literally hundreds of millions of personal preferences that human beings use to associate with people.

What if you could store all of those preferences and use them to connect people?

That's Facebook.

Algorithms are good for insurance workers

Now think about your work and all the stuff you know and all of the stuff your colleagues know. What if all of that information could be fed into an algorithm and used to create rules. You could then use those rules to more quickly do your work.

I can hear you thinking "But then I will be out of a job." Therein lies the rub, one that has been discussed ad nauseum (more than 9 million results for a Google search on "technology will destroy jobs"). Fatalists argue that algorithms and the advanced software programs they create will destroy jobs. Famous technologist and investor Marc Andreessen expressed as much when he proclaimed in 2011 that "software is eating the world."

But what happens if software starts doing repetitive tasks previously done by humans? I believe humans find new ways to be productive. And, I believe history supports my theory. But that's a blog post for another day.

I will leave you with two questions.

What repetitive tasks do you despise?

Wouldn't it be great if you could offload these tasks to a computer?

In 2006, UCLA Professor of Urban Planning Donald Shoup compiled the results of 16 surveys carried out between 1927 and 2001 on the time spent looking for a parking space. He reported that the average time spent looking for on-street parking was approximately eight minutes - a figure that has remained relatively unchanged since the 1930s.

This research also demonstrated that, on average, one vehicle in three in traffic is actually searching for somewhere to park. This figure has been confirmed more recently by a study from the San Francisco City Council, which concluded that an estimated one-third of weekday traffic was because of drivers looking for a parking space.

While solving the problem of road congestion via accurate traffic information has been looked at for decades - the RDS TMC protocol was invented in 1988 - and has already reached a good level of sophistication and accuracy, solving the parking problem via connected services is quite a recent topic and is still very much a work in progress.

As a matter of fact, most pure players in this field have been founded quite recently: as an example, JustPark in 2006; Parkopedia, ParkMe, Worldsensing and Anagog in 2009; and Parknav in 2011. The only companies to have emerged earlier are the parking payment companies, PayByPhone and Parkmobile, in 2000 and Pango in 2005.

On-Street and Off-Street

Parking essentially divides in two markets with two very different problems to solve: off-street and on-street. Connected services taking care of off-street parking are now quite advanced. In the three steps of information, booking and payment, the first is largely available (even if real-time data remains partial), but booking and advanced payment are still works in progress. Very few cars on the road today - or navigation apps - are able to find, book and pay seamlessly for a parking space in a garage.

The on-street parking problem is, by nature, more difficult to solve because detecting free parking bays in real time, at scale is complex and requires many sources of information. There are very different approaches to create this data.

Leveraging Traffic Probe Data for Parking

One is to make sense of the existing probe data currently used for real-time traffic. For example, Garmin is using this data to calculate the inflow and outflow of cars for each road segment in large cities and estimate availability (read here). The company has partnered with Parkopedia to include off-street parking information in their data model.

The GPS company launched this service in their mobile app during the third quarter in six German cities and is now adding cities in more countries: London, Amsterdam, Vienna and a few others coming in the U.S.

Inertial Data From Smartphones

Detecting parking and "unparking" events through inertial sensor data from drivers' smartphones is another approach used by Anagog, which built a software development kit now embedded in several million apps (watch here). Through a signal processing algorithm, the company detects out of gyroscope, accelerometer and location data (GPS, etc.) parking events that are fed to a big data cloud that is now nearing 1 billion historical parking events.

Data From Car Sensors

Car makers such as Volkswagen (read here) or General Motors are also looking at producing data using car sensors.

In the case of Volkswagen, a pilot launched by the company uses the existing ultrasonic proximity sensors (used for parking) to assess the availability of free parking spaces on the side of the road when the car drives along a street. The data is uploaded in real-time and matched against map data to eliminate false positive (parking space for disabled people, etc.).

Parking Meters

Using data from on-street parking meters is another opportunity to get real-time, on-street parking information. Because a significant number of these meters are connected to the cloud, it is possible to build predictive data based on historical trends. Parkeon, a worldwide leader in parking meters, is among the companies enabling that opportunity and rendering this data through a mobile app, Path To Park (read more here), which is now available throughout France and in a number of cities in the U.S. and Germany.

Street-Based Sensor Infrastructures

Lastly, companies such as Worldsensing are placing sensors on each parking bay in the street, which obviously provides the most accurate data, but at a cost. Worldsensing, based in Barcelona, just closed a series B round of funding (for an undisclosed amount). Its largest deployment to date was in Moscow, where the company covered 13,000 spots. The next stage of the deployment will include more than 50,000 sensors.

Image processing is also a technology that could be used to sense free parking bays in streets. Data from fixed CCTV (used for security or traffic monitoring), smartphone apps, connected dash cams or even cars could be used for that purpose.

Obviously, the best information will come from the aggregation of these data streams (historical and real-time). Inrix, which announced in June that it will supply on-street parking data to BMW, combines data from cities, mobile payment companies, real-time parking data, connected car-sharing services and Inrix's database of real-time vehicle GPS data (read here).

Parknav, a start-up based in the U.S. is also using a very diverse set of data (car-sharing, telecom, fleet, crowd-sourcing), including POI data (bars, schools, etc.) to infer probabilities about parking availability.

Accurate information about free on-street parking bays is a complex matter that will take many more years to solve, but the opportunities are huge for the whole car industry and beyond. The first opportunity is the time saved for drivers and the alleviation of stress and frustration. Once this first opportunity will be realized for drivers, its overall social impact will be big: less traffic, less pollution, less money spent on fuel.

Unused Parking Inventory

The last market opportunity in smart parking is to further eliminate barriers between the offer and the demand, between people circling in streets and empty parking bays, in enabling yield management of underused private parking inventory.

Residential buildings, companies, hotels, schools, hospital or churches have parking spaces that are empty or partially used during workdays, nights and weekends, vacations, etc. Companies like JustPark (UK) or Zenpark (France) are targeting this segment using connected technologies to unlock the value of this inventory and grow the total parking spots available.

On Jan. 28 in Brussels, the ConnecteDriver conference, in partnership with consulting firm Inov360, will gather the brightest minds and the most innovative companies to discuss the fascinating topic of smart parking:

- Hans-Hendrick Puvogel, COO at Parkopedia
- Anthony Eskinazi, head of product and co-founder, JustPark
- William Rosenfeld, CEO, ZenPark
- Bertrand Barthelemy, president of Parkeon
- Ruth Portas, sales manager, Worldsensing
- Ofer Tziperman, CEO, Anagog
- Martin Treiblmayr, product manager, Garmin
- Vincent Pilloy, co-founder and CEO, Inov360
- Parknow (speaker name to be confirmed)

If you love football, then you know how frustrating it is to be a football fan. Every offseason, you get excited about the potential for the coming season. Before the season begins, you read all of the articles and watch the analysts.

They all say, "This is the year." Your team added some of the top defensive players in the league. You're convinced the team has solved its offensive woes, too. Your team added a star wide receiver, and the running back is looking great in training camp.

Then the season starts, and your team suffers loss after loss. You question how professionals can spend so much time and money on the sport yet fail to improve. As the season continues to sputter, more and more people call for the team to fire the coach. At the end of the season, they fire the coach and hire a new star coach from a great team.

"Next year," you and the rest of the fan base tell each other.

The next season begins and your team still loses. Year after year, the cycle repeats itself.

When it comes to innovation, insurance company claims departments have a lot in common with your favorite underachieving football team. Top talent in every department. Great recruits from top companies. Lots of talk about the newest technology. But each year you get the same results.

How can you solve this problem?

The One Thing

In "The One Thing," Gary Keller shares several lessons we should apply to the insurance claims industry. He does so by simplifying the decision-making process. Whether you're the general manager of a football team or an insurance claims executive, you can apply Keller's lessons to your situation.

The Six Lies Between You and Success:

1. The idea that everything matters equally;
2. Multitasking;
3. Lack of discipline;
4. The belief that willpower is always on will-call;
5. A balanced life;
6. The idea that big is bad.

These "Six Lies" insurance claims departments. Claims professionals will get what they put in each day. If that's emailing about hundreds of claims, then claims professionals will get routine claim maintenance. They will not achieve innovation. By making routine claim maintenance the priority, claims departments are falling victim to the six lies standing between the claims department and innovation.

The Four Thieves of Productivity:

1. Inability to say "No";
2. Fear of chaos;
3. Poor health habits;
4. An environment that doesn't support your goals.

While I can't make any assumptions about whether there are poor health habits in your claims departments (unless your claims professionals are gorging on the vendor-sponsored food!), I can assume that the four thieves should resonate with you.

Insurance claims professionals do what they do because that's what everybody has always done. No one has ever been terminated for saying "yes" to a responsibility. People who follow the status quo feel safer than people who hinge their success on a business transformation. As a result, claims departments are productive at claims maintenance, but they often leave much to be desired when it comes to innovation.

The Focusing Question

Keller condenses the entire book into what he calls "The Focusing Question."

What's the one thing you can do now such that by doing it everything else will become easier or unnecessary?

Good questions are the path to great answers. By combining a small focus with a big goal, the "Focusing Question" provides you with the ideal starting point to achieve something great.

Claims innovation requires starting with "The One Thing" today: giving your best claims manager responsibility for transforming the claims department. While this may sound drastic, it truly is "The One Thing" that will transform an insurance company. I've seen it. With a strong leader dedicated to this project, executives will breeze through the process of selecting vendors, identifying key requirements, troubleshooting workflows and handling anything that stands in the way of true innovation.

Once "The One Thing" is addressed, many tasks will follow: assigning a good leader from the IT department, engaging an outside consultant and supporting the department with future-focused software. But until executives dedicate their best claims manager to "The One Thing," claims departments will suffer from unnecessary obstacles.

Claims departments and football teams will keep underachieving until they get their franchise quarterbacks. You can hire all the star free agents and coach your teams to change, but if your quarterback spends his time focusing on the same old plays, get ready for another year with the same results.

Who will be your company's Tom Brady?

Terrorist and other mass violence attacks, which occur with alarming regularity around the world, can threaten your people, operations and assets. Many companies look to insurance — mainly property terrorism and political violence coverage - to help manage the financial impact of these risks, which can include property damage and business interruption losses.

Terrorism Insurance or Political Violence Coverage?

Property terrorism insurance provides coverage for the physical damage and business interruption that can result from acts that are motivated by politics, religion or ideology. Political violence insurance provides coverage related to war, civil war, rebellion, insurrection, coup d’état and other civil disturbances.

Choosing which coverage - or combination - is best for your organization can be tricky. The line between what is considered "terrorism" and what is considered "political violence" is often blurry. For example, should attacks by particular groups be classified as acts of terrorism, or another form of political violence?

To help determine the best insurance program to manage these risks, here are a few things to think about:

• Ensure the limits of insurance that you buy provide enough protection for multiple loss scenarios.
• Review the location of your assets to determine the appropriate insurance solution.
• Understand the policy terms, conditions and limitations of terrorism and political violence insurance.
• Work with your advisers to understand your property and employee exposures so you can make an informed decision or mitigate potential losses.

Addressing the Risks

Along with insurance considerations, of course, you need to ensure the safety of your employees with integrated and well-practiced crisis and continuity plans in the event of a disaster. Events from terrorist attack to natural catastrophes can cause significant business interruption (BI) losses. Steps to take to manage BI risk include:

• Develop and test business continuity plans.
• Conduct scenario testing.
• Coordinate BI insurance with other coverages, including political violence and terrorism insurance.
• Be prepared to gather appropriate information in the event of a claim, including recording damage via photographs and video.
• Maintain separate accounting codes to identify all costs associated with the potential damage.

For more information on these topics, read Marsh's 2015 Terrorism Risk Insurance Report and our political risk insurance report, Strong Capacity Drives Buyer's Market for Political Risk Insurance.

For decades, the insurance industry has led the world in predictive analysis and risk assessment. And today, with the treasure trove of big data available from historical processes, IoT and social media, insurance companies have the opportunity to take this discipline to a whole new level of accuracy, consistency and customer experience.

The actuarial models that were once driven solely by large databases can now be fueled with tremendous quantities of unstructured data from social media, online research and news, weather and traffic reports, real-time securities feeds and other valuable information sources as well as by "tribal knowledge" such as internal reports, policies and regulations, presentations, emails, memos and evaluations. In fact, it is estimated that 90% of global data has been created in the past two years, and 80% of that data is unstructured.

A large portion of this data now comes from the Internet of Things -- computers, smart phones and wearables, GPS-enabled devices, transportation telematics, sensors, energy controls and medical devices. Even with the advancement of big data analytics, the integration of all this structured and unstructured data would appear to be a monumental achievement with traditional database management tools. Even if we could somehow blend this data, would we then need thousands of canned reports, or a highly trained data analytics expert in every operating department to make use of it? The answer to this dilemma may be as close as our smartphones.

Apps that Unleash the Power

As consumers, we are no stranger to the union of the structured and unstructured datasets. A commuter, for example, used to rely on Google Maps to get from his office to his home. But with the advent of apps like Waze, not only can he get directions and arrival times based on mileage and speed data, but can also combine this intelligence with feeds from social media and crowd-sourced opinions on traffic. Significant advances in the power of in-memory processing, machine learning, artificial intelligence and natural language processing have the potential to blend millions of data points from operational systems, tribal knowledge and the Internet of Things -- using apps no more complicated than Google Maps.

Using apps that harness the power of artificial intelligence and machine learning can provide far superior predictive analysis simply by typing in a question, such as: What are the chances of a terrorist act in Omaha during the month of December? Where is the most likely place a power blackout will occur in August? How many passenger train accidents will occur in the Northeast corridor over the next six months? What will be the effect on my fixed income portfolio if the Federal Reserve raises short term interest rates by .25 percentage point?

Using a gamified interface, these apps can use game theory such as Monte Carlo simulations simply by moving and overlaying graphical objects on your computer screen or tablet. As an example, you could calculate the likely dollar damages to policyholders caused by an impending hurricane simply by moving symbols for wind, rain and time duration over a map image. Here are some typical applications for AI app technology in insurance:

Catastrophe Risk and Damage Analysis

Incorporate historical weather patterns, news, research reports and social media into calculations of risk from potential catastrophes to price coverage or determine prudent levels of reinsurance.

Targeted Risk Analysis (Single view of customers)

With the wealth of individual information available on people and organizations, it is now possible to apply AI and machine learning principles to provide risk profiles targeted down to an individual. For example, a Facebook profile of a mountain climbing enthusiast would indicate a propensity for risk taking that might warrant a different profile than a golfer. Machine learning agents can now parse through LinkedIn profiles, Facebook posts, tweets and blogs to provide the underwriter with a targeted set of metrics to accurately assess the risk index of an individual.

Underwriting

Each individual assessor has his own predilection to assessing risks. By some estimates, insurance companies could lose hundreds of millions of dollars either through inaccurate risk profiling or through lost customers because of overpricing. AI apps provide the mechanics to capture "tribal knowledge," thereby providing a uniform assessment metric across the entire underwriting process.

Claims Processing

By unifying unstructured data across historical claims, it is possible to establish ground rules (or quantitative metrics) across fuzzy baselines that were previously not possible. Claims notes from customer service representatives that would previously fall through the cracks are now caught, processed and flagged for better claims expediting and improved customer satisfaction. By incorporating personnel records when a major casualty event occurs, such as a severe storm or flood, you can now dispatch the most experienced claims personnel to areas with the highest-value property.

Fraud Control

Integrate social media into the claims review process. For example, it would be very suspect if someone who just put in a workers' compensation claim for a severe back injury was bragging about his performance at his weekend rugby match on Facebook.

A Powerful Value Proposition

The value proposition of artificial intelligence apps for better insurance industry underwriting and risk management is too big to ignore. Apps have been transformational in the way we intelligently manage our lives, and App Orchid predicts they will be just as transformational in the way insurance companies manage their operations.

This article is an excerpt from a white paper, "Capturing Hearts, Minds and Market Share: How Connected Insurers Are Improving Customer Retention." In addition to the material covered here, the white paper includes specific recommendations on how to improve retention.

To download it, click here.

Insurers currently operate in a challenging environment. On the financial side, premiums are stagnant and interest rates low, and many cost-cutting measures have already been enacted. On the other hand, customer empowerment is growing. Customers are finding the information and offers they need to switch providers more freely than in the past - customers whom insurers can ill afford to lose.

For many carriers, the key to preserving customer relationships still lies in personal interaction, executed through traditional distribution and service models with tied agents and brokers. For some customer sets - those who strongly favor personal interaction - this business model works well. Yet a growing segment of customers, especially those 30 years old and younger, differ in some key aspects. While they still look for help and advice, they seek personal contact in the context of a holistic, omni-channel experience; they communicate and find information whenever, wherever and however they want. And even traditional customers appreciate if their agents have broader and faster access to the information and specialists they need on a case-by-case basis.

How can insurers keep - and even expand - these diverse customer sets, old and young alike? What factors drive retention and loyalty? To explore these questions, we surveyed more than 12,000 insurance customers in 24 nations about relationships with their insurers, what they perceive as valuable and in what ways they would like to interact and obtain new services going forward.

We found that while insurers understand well how to cover risks, they often fail to engage their customers on an individual basis. Even though insurance is complex, customers want to be involved, emotionally and rationally. When insurers act on this knowledge, customer share can rise.

The churn challenge

As a rule of thumb, the cost of acquiring new customers is four times that of retaining existing ones. To grow market share, insurers need new customers. But for the balance sheet, retention has a much larger impact.

For a long time, the insurance industry did not consider this lack of trust a problem. In the highly asymmetrical pre-Internet world, there was a necessary gatekeeper to information and knowledge about risks and coverages: the insurance intermediary. For insurers, the intermediary's trusted personal customer relationship was a guarantee of fairly reliable renewals and low customer churn - thus, keeping the most profitable customers.

The technological innovations of the digital age have altered this picture. Information asymmetry is diminishing. Although many customers still seek advice on insurance matters, the empowered digital customer does not need to rely solely on the gatekeepers of old for information. With communication being swift and ubiquitous, misinformation is quickly uncovered, leading to a steady erosion of trust, even with the personal adviser and insurer.

We have come to expect that only 43% of our survey respondents trust the insurance industry in general - a number that has stayed fairly stable since our first survey in 2007 - but only 37% trust their own insurers to a high or very high degree. Most customers are neutral, with 16% actually distrusting their providers.

As we have often seen in past studies, trust varies widely by market and culture. For example, only 12% of South Korean customers responded that they trust their insurers, compared with 26% in France, 43% in the U.S. and 51% in Mexico.

Low trust translates to high churn. Even though 93% of our respondents state that they plan to stay with their current insurers for their recently acquired coverage through 2015, almost a third came to that coverage by switching insurers. Why? Most commonly (for 41% of respondents), their old insurers couldn't meet their changing needs (see Figure 1).

The pattern of increasing customer empowerment and decreasing information asymmetry is continuing. New and non-traditional entrants to the insurance market are taking advantage of the opportunities of digital technologies. For example, Google recently launched an insurance comparison site for California and other regions of the U.S. This presents a real threat to both online insurers and traditional providers - not because of the comparison option itself, but because Google has collected a huge amount of information about each individual through his or her surfing habits, thus allowing better personalization and higher-value offers.

The three dimensions of retention

What do insurers need to do to increase trust and customer retention with the intent of improving both the top and bottom lines? The findings of our survey point to three courses of action:

• Know your customers better. Customer behavior is affected by experiences and underlying psychographic factors. Insurers need to know and understand customers better, not only as target groups but as individuals. Insurers also need to get their customers involved, rationally and emotionally.
• Offer customer value. As overused as the term is, a strong and individualized value proposition is exactly what insurers need to provide to their customers. Value is more than price; it includes many factors, including quality, brand and transparency.
• Fully engage your customers across access points. As Millennials become a significant part of the insurance market, speed and breadth of access has begun to matter much more than in the past. Insurers need to engage their customers as widely as possible, from in-person interactions at one extreme all the way to digital interaction models such as those made possible by the Internet of Things.

Customer perception and behavior

Ever since the Internet has become a viable way to shop for goods and services, much discussion has centered on the matter of price. In theory, insurance products are easy to compare, so shouldn't the cheapest one win out?

This view assumes that, aside from the price, all else is equal. If that were true, price would indeed be the sole tie-breaker. In reality, though, all else is never equal. Insurance is a product based on trust, for which perception matters. Perception, and thus customer behavior, is shaped by the individual customer's attitudes and experiences. Understanding a customer on an individual basis helps a carrier tailor these experiences by communicating the "right way."

To classify our respondents according to their attitudes, we used the same psychographic segmentation as in previous studies (see Figure 2).

One size seldom fits all

Overall, our respondents stated that the three most important retention factors are price (63%), quality of service (61%) and past experience (33%) - leading back to the price as the main tie-breaker. Yet a closer look across segments paints a more diverse picture: For a demanding support-seeker, quality is by far the most important (74%), while a loyal quality-seeker bases his renewal intentions on past experience more strongly than any other group (43%).

Assuming an insurer is targeting all these customer segments, it will need a diverse set of customer communication options, as each segment requires approaches tailored to its specific preferences (see Figure 3). This figure shows the five most-used insurance search options in the three segments where we are seeing the biggest shift among Millennials, who represent future customers.

The power of emotional involvement

Our data show that appropriate communication with customers sets off a positive chain reaction. First, it increased the use of that type of interaction. Customers perceived the interaction as more positive, and ultimately this increased emotional involvement with their providers - the "heart share" of our study title. Finally, emotional involvement is strongly connected to customer loyalty, so increasing involvement from medium to high had a dramatic impact on the loyalty index (see Figure 4).

What is the right way to communicate and increase involvement? As seen in Figure 3, the answer is "It depends," so there is no one right approach for all customers. But using current technology - specifically, social media analytics - can help insurers improve involvement.

With this tool, providers can "listen" to various online sources, understand how they are seen by customers, uncover trends and quickly tie this knowledge to specific actions. Providers can combine the findings of social media analytics with psychographic segmentation and an individual customer's place within the segmentation; the latter gained via more traditional customer analytics. With this customer view, insurers can even go beyond the personalized knowledge their tied agents tend to have: As customer wants and needs change and they articulate it on social channels, insurers will know and can react in close to real time.

Social media analytics

Social media analytics is a set of tools that allow insurers to analyze topics and ideas that are expressed by their actual or potential customers through social media. This can be on an individual basis, or per customer group. Through social media analytics, insurers can apply predictive capabilities to determine overall or individual attitude and behavior patterns, and identify new opportunities.

This article is an excerpt from a white paper, "Capturing Hearts, Minds and Market Share: How Connected Insurers Are Improving Customer Retention." In addition to the material covered here, the white paper includes specific recommendations on how to improve retention.

To download it, click here.

It's going to take how much longer?! It's going to cost how much more?!!

If those sound like all too familiar expressions of frustration, in relation to your data journey (projects), you're in good company.

It seems most corporations these days struggle to make the progress they plan, with regards to building a single customer view (SCV), or providing the data needed by their analysts.

An article on MyCustomer.com, by Adrian Kingwell, cited a recent Experian survey that found 72% of businesses understood the advantages of an SCV, but only 16% had one in place. Following that, on CustomerThink.com, Adrian Swinscoe makes an interesting case for it being more time/cost-effective to build one directly from asking the customer.

That approach could work for some businesses (especially small and medium-sized busineses) and can be combined with visible data transparency, but it is much harder for large, established businesses to justify troubling the customer for data they should already have. So the challenge remains.

A recent survey on Customer Insight Leader suggests another reason for problems in "data project land." In summary, you shared that:

• 100% of you disagree or strongly disagree with the statement that you have a conceptual data model in place;
• 50% of you disagreed (rest were undecided) with the statement that you have a logical data model in place;
• Only 50% agreed (rest disagreed) with the statement that you have a physical data model in place.

These results did not surprise me, as they echo my experience of working in large corporations. Most appear to lack especially the conceptual, data models. Given the need to be flexible in implementation and respond to the data quality or data mapping issues that always arise on such projects, this is concerning. With so much focus on technology these days, I fear the importance of a model/plan/map has been lost. Without a technology independent view of the data entities, relationships and data items that a team needs to do their job, businesses will continue to be at the mercy of changing technology solutions.

Your later answers also point to a related problem that can plague customer insight analysts seeking to understand customer behavior:

• All of you strongly disagreed with the statement that all three types of data models are updated when your business changes;
• 100% of you also disagreed with the statement that you have effective meta data (e.g. up-to-date data dictionary) in place.

Without the work to keep models reflecting reality and meta data sources guiding users/analysts on the meaning of fields and which can be trusted, both can wither on the vine. Isn't it short-sighted investment to spend perhaps millions of pounds on a technology solution but then balk at the cost of data specialists to manage these precious knowledge management elements?

Perhaps those of us speaking about insight, data science, big data, etc. also carry a responsibility. If it has always been true that data tends to be viewed as a boring topic compared with analytics, it is doubly true that we tend to avoid the topics of data management and data modeling. But voices need to cry out in the wilderness for these disciplines. Despite the ways Hadoop, NoSQL or other solutions can help overcome potential technology barriers -- no one gets data solutions for their business "out of the box." It takes hard work and diligent management to ensure data is used & understood effectively.

I hope, in a very small way, these survey results act as a bit of a wake up call. Over coming weeks I will be attending or speaking at various events. So, I'll also reflect how I can speak out more effectively for this neglected but vital skill.

On that challenge of why businesses fail to build the SCVs they need, another cause has become apparent to me over the years. Too often, requirements are too ambitious in the first place. Over time working on both sides of the "IT fence," it is common to hear expressed by analytical teams that they want all the data available (at least from feeds they can get). Without more effective prioritization of which data feeds, or specifically which variables within those feeds, are worth the effort - projects get bogged down in excessive data mapping work.

Have you seen the value of a "data labs" approach? Finding a way to enable your analysts to manually get hold of an example data extract, so they can try analyzing data and building models, can help massively. At least 80% of the time, they will find that only a few of the variable are actually useful in practice. This enables more pragmatic requirements and a leaner IT build which is much more likely to deliver (sometimes even within time & budget).

Here's that article from Adrian Swinscoe, with links to Adrian Kingwell, too.

What's your experience? If you recognize the results of this survey, how do you cope with the lack of data models or up-to-date meta data? Are you suffering data project lethargy as a result?

As is the case in any professional group, individual medical provider's performance runs the gamut of good, bad and iffy. The trick is to find good medical providers for treating injured workers, avoid the bad ones and scrutinize those who are questionable. To qualify as best for injured workers, medical providers need proficiency in case-handling as well as medical treatment.

High-value physician services

The first step is to clarify the characteristics of the best providers, especially in context with workers' compensation. One resource is an article published by the American College of Occupational and Environmental Medicine in association with the IAIABC (International Association of Industrial Accident Boards & Commissions) titled, "A Guide to High-Value Physician Services in Workers' Compensation How to find the best available care for your injured workers" It's a place to begin.

The article notes, "Studies show that there is significant variability in quality of care, clinical outcomes and costs among physicians." That may be obvious, but it also verifies the rationale for taking steps to identify and select treating doctors rather than pulling from a long list of providers to gain the discount. The question is, what process should be used to select providers?

Approach

Although considerable effort from scores of industry experts contributed to this article, the approach they recommend is complex, time-consuming and subjective. In other words, it is impractical. Few readers will have the expertise and resources to follow the guide. Moreover, one assertion made in the article is simply wrong.

Misstatement

The article states that it would be nice to have the data, but that the data is not available. "Participants in the workers' compensation system who want to direct workers to high-quality medical care rarely have sufficient data to quantify and compare the level of performance of physicians in a given geographic area."

Actually, the data is available from most payers whether they are insurers, self-insured, self-administered employers or third-party administrators (TPAs). However, collecting the data is the challenge.

Data silos

The primary reason data is difficult to collect is that it lives in discrete database silos. The industry has not seen fit to place value on integrating the data, but that is required for a broad view of claims from beginning and throughout their course.

At a minimum, claim data should be collected from medical billing or bill review, the claims system and pharmacy (PBM). The data must be collected from all the sources, then integrated at the claim level to get a broad view of each claim. It takes effort, but it is doable. Yet, there remains another data challenge.

Data quality

Payers have traditionally collected billing data from providers, through their bill review vendor. The payer's task has been paying the bill and sending a 1099 statement to providers at the end of the year. All that is needed is a provider name, address and tax ID so the payment reaches its destination. It makes no difference to payers that providers are entered into their systems in multiple ways causing inaccurate and duplicate provider records. One payment is a payment. The provider might receive multiple 1099s, but that causes little concern.

What is of concern is that when the same provider is entered into the payers' computer system in multiple ways, it can be difficult to ascertain how many payments were made to an individual provider. Moreover, when the address collected by the payer is a P.O. box rather than the rendering physician's location, matters become more complicated. This needs to change.

The new request

Now payers are being asked to accurately and comprehensively document individual providers, groups and facilities so the data can be analyzed to measure medical provider performance. They need to collect the physical location where the service was provided and it should be accurately entered into the system in the same way every time. (Note: This is easily done using a drop-down list function rather than manual data entry.)

Most importantly, a unique identifier is needed for individual providers, such as their NPI (national provider identification). Many payers are now stepping up to improve their data so accurate provider performance assessments can be made.

High-value, quality medical providers can be identified by using the data. However, quality data produces better results. Selecting the best medical providers is not a do-it-yourself project. Others will do it for you.

In the era in which wellness vendors were still claiming a return on investment (ROI_ on wellness (and more and more are not), I asked a number of them how they calculated the ROI. Not one calculated the ROI in a way that a steely-eyed CFO would endorse.

Below is a partial list of costs that wellness vendors should be considering, but rarely if ever do consider. If you have a wellness program and want to look for an ROI, make sure these costs are included:

1. Wellness vendor fees
2. Communication costs
3. Investments in materials (e.g., Fitbit) and facilities (e.g., onsite fitness centers)
4. The cost of biometric tests and health assessments
5. The cost of program incentives (awards, premium reductions, etc.)
6. The wages and benefits of the company's wellness team members
7. The wages and lost productivity for employees to sit through biometric tests and wellness meetings, to read wellness memos and other communications and to fill out health risk assessments. (If 10,000 employees spend eight hours per year in wellness meetings, reading wellness emails, filling out forms, etc, at an average wage of $20/hour, the cost is $1.6 million.)
8. The cost of following-up on false positives from asymptomatic employees going to doctors for ill-advised tests. This one is not uncommon. (I've personally witnessed people who've had false positives on wellness exams and spent thousands of plan dollars just to explore false positives. The largest one cost a shade less than $70,000 to get an all clear. If you want to know the true cost of a wellness program, this impact can't be ignored.)

Further, wellness vendors claim improvements in productivity, but most say the gains cannot be measured. That is a fallacy. Vendors need only look at a client's wages as a percentage of sales (with a few minor adjustments). If that ratio is not declining, employee productivity is not improving.

For an excellent discussion on failures of wellness productivity claims click here.

The same principles apply to value on investment (VOI) claims, as well. Click here for an excellent review of what some call the VOI scam.

This post may be flogging a dead horse. So be it.

This is part of a series of interviews by Shefi Ben Hutta with insurance practitioners who bring an interesting perspective to their work and to the industry as a whole. Here, she speaks with David Stegall, principal consultant with Risk Consulting & Expert Services, who often serves as an expert witness in insurance litigation.

To see more of the "A Word With Shefi" series, visit her thought leader profile. To subscribe to her free newsletter, Insurance Entertainment, click here.

Describe what you do in 50 words or less:

Risk Consulting & Expert Services is an insurance and risk management consulting firm providing services and counsel to commerce, industry and government on insurance, reinsurance and alternative risk transfer matters. I have more than 37 years of experience and often act as an expert witness in litigation.

What made you decide to start Risk Consulting & Expert Services?

After 30 years, I no longer had an interest in continuing to work within the industry as a purveyor of insurance.

And if it weren't for the appeal of working in insurance, what profession would you be in today?

Film and/or music production. I like the creative process.

Describe your typical client:

A litigation attorney with the need for an insurance or risk management professional who can offer a professional opinion on the usual and customary practices of the insurance industry or the required standard of care used within the industry and can explain that opinion to a judge and jury in plain, simple English.

Memorable court trial:

Very few cases go to trial, yet I recall the irony of testifying on a case regarding flood insurance at the Cameron Parish Court House in Louisiana, which is about a stone's throw away from the Gulf of Mexico.

Is there a carrier you would love to testify in court against?

I cannot answer that because I do not think of insurance companies as being either good or bad. They are only as good (or bad) as those individuals who are making decisions for them in a given instance, and even then the good (or bad) decision may be specific to that instant.

You have a talent for explaining complicated risk terms. In your experience, which P&C coverage is most baffling to consumers?

Water damage and flood. Flood is excluded in practically every insurance policy (except flood policies), and water damage may or may not be covered. Most people think of the terms synonymously, but they aren't. The simplest way to think of it is: If the water comes from above (without hitting the ground) it is covered (note that pipes are considered as being above). If the water comes from below (lake, river, stream, ocean), it is not covered. But please read your policy and ask questions of your insurance representative or call a consultant!

You have more than a few designations, one of which is the Chartered Property & Casualty Underwriter. Has the role of underwriting changed much from when you last practiced it?

There are fewer underwriters now, but they are extending specific yet limited underwriting authority to more general agents (or some form or position of limited underwriting authority) that specialize in a particular industry or product offering.

What emerging technology keeps you up at night from a litigation standpoint?

The same as everybody else: cyber risk. The risks are emerging at the same rate as the technologies.

Speaking of cyber, you recently published a whitepaper on "Cyber Risk & Insurance." The Ashley Madison hack is now correlated to at least two suicides; where do you think insurers should draw the line?

The same place they draw the line with the idea that, if you attend a baseball game, you might get hit by a foul ball. A person does take some risk by subscribing to any service or website - yes, there is an implicit, if not explicit, responsibility (in the form of statutes) to protect people's privacy but some activities carry innate risk that insurance can only partially address.

Favorite quote/s:

"Everything's Gonna Be Alright" (Muddy Waters and others) and "It is always getting too late and then it is." I hope I made that one up, but I'm sure I've heard it somewhere, and it resonated.

When you are not working, you are most likely…

Playing with my seven grandchildren or playing the harmonica.

What are you most excited about at the moment?

That I feel happy, healthy and terrific! A phrase made famous by a former insurance professional and fellow lover of Chicago, W. Clement Stone.