Tag Archives: business

A Practical Tool to Connect to Customers

I recently led a workshop at the BRITE Conference at Columbia University on how to connect to customers and was honored to be among speakers including Shelly Lazarus, Ogilvy’s chairman emeritus; Vikram Somaya, ESPN’s global CDO; Linda Boff, CMO of GE; and Columbia Professor and innovation thought leader Rita McGrath. Organized by faculty members David Rogers, Matt Quint and Bernd Schmitt, and now in its ninth year, BRITE promotes dialogue on top brand, innovation and technology trends across business and academia.

I’ve condensed about half the workshop into a self-directed exercise, so you can try it.

The workshop started with three premises:

  1. People-based offerings are the basis for market relevance. Product pushing cannot endure. We are doing business in an “I want” world where companies like Amazon and Apple have set an “anything is possible” standard. The standouts will be companies that know how to walk in the shoes of the people they aspire to serve. These successful brands will follow the customer’s journey through life with authenticity — not just fixated on how to push product selection and purchase.
  2. Customers wear different hats – they may be users, buyers or payers for your offering. People see different brand benefits based on their role. Building brand/customer connections requires you to parse these roles and tune into the relevant benefits. The benefits may not be the same — this matters when it comes to product, communications and experience decisions.
  3. Network thinking overrides linear thinking and action. Building a business through binary relationships with suppliers on the one hand and customers on the other hand has been supplanted by businesses driven by value networks, or “value constellations.” Once you have a clear picture of the user, buyer and payer roles, you have in hand raw material to begin to assemble the members of your constellation. More on this topic in a future post.

Growth and Transformation: The Holy Grail

There’s not a conversation I’ve had with a senior executive in the past few years – irrespective of business size or sector – that didn’t share two linked priorities: growth and transformation. Technological possibilities, customer expectations and the need for speed demand a departure from historically beneficial but now outmoded strategies.

To Solve A Big Problem, You Have to Chunk It Down

To paraphrase a favorite colleague of mine from my days at American Express, “you just have to chunk” the big, hairy problems to make progress toward solving them.

Traditional business strategy starts with questions like: “What business are we in?” and “What core competencies can we use to compete?” These are inside-out questions whose answers assume “sustainable competitive advantage” is something you can achieve and own.

Set these assumptions aside. Our economy demands you define your strategy from the “outside” — where the customer is. Twentieth-century notions of strategy revolved around your position relative to competition. Twenty-first century strategy revolves around the customer.

This means the first chunk to work at is “Who is our customer?” And next, “Can we engender a transformational relationship with our customer, starting with focusing on needs, and then align all of our activities and decisions to deliver?”

A Simple, DIY Tool to See Your Customers as People, Not Data Points

Here’s a tool you can use to deepen your brand’s connection to customer needs and begin to conceptualize new business models for enablement.

Whether you complete it in your head or around the table at a team meeting, this simple template can nudge even stubborn traditionalists to ask new questions about how customer insight translates into business results.

Milton Rokeach: The Hierarchy of Needs and the User/Buyer/Payer Model

Rokeach, a 20th-century social psychologist, conducted research resulting in an inventory of desired end states for human existence. These end states, or values, are summarized below:

POSTPeopleBased

How Does This Theory Apply to Brands and Innovation?

Brand managers tend to enumerate product features to explain value to customers. Better brand strategists get to the benefits, too. But almost always, brands stop short of the much richer territory – connecting the brand to the values people strive toward in life.

By pushing a little harder to understand which values your brand satisfies (i.e., back to Rokeach’s inventory) you can find new growth levers, and pragmatic transformation priorities can emerge.

What Does Soup Have To Do With It?

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So, in the simple example of a can of soup purchased for my family, the benefits may be a tasty, quick, low-cost meal that satisfies my daughter’s hunger and provides some nutrition. But as a mom, my values are things like fulfilling my sense of duty to family, maintaining family harmony at the dinner table, keeping my life under control and getting time back in my day. Brands that demonstrate connection to these sorts of deeper values will win my perpetual loyalty. Features and benefits are temporal. Values endure.

Next, by delineating what is sought by users vs. buyers vs. payers (and understanding what the implications are when these roles are played by different people), you will establish a new angle on segmentation and shine a light on otherwise hidden innovation opportunities.

So back to the can of soup, note the differences below between the benefits that matter to the user, the buyer and the payer. These may be one, two or more people. But even when one person plays all three roles, the benefits that one person sees through each lens are different.

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So what about features?

Features may provide reasons to believe in the brand benefits, or even ladder up to the brand values. But by themselves, they will almost never endear customers to you. And, in fact, they may burden people with detail that distracts from a quick determination of whether the brand represents a good choice. At a minimum, features must be shared for the sake of ingredient transparency – the latter representing a brand value that has gained in importance especially for millennial buyers.

Try to complete the user/buyer/template model as a team exercise or on your own. See how it can get you thinking about improving customer focus and engagement by connecting to the higher-order needs of whatever marketplace you serve.

Helping Data Scientists Through Storytelling

Good communication is always a two-way street. Insurers that employ data scientists or partner with data science consulting firms often look at those experts much like one-way suppliers. Data science supplies the analytics; the business consumes the analytics.

But as data science grows within the organization, most insurers find the relationship is less about one-sided data storytelling and more about the synergies that occur in data science and business conversations. We at Majesco don’t think it is overselling data science to say these conversations and relationships can have a monumental impact on the organization’s business direction. So, forward-thinking insurers will want to take some initiative in supporting both data scientists and business data users as they work to translate their efforts and needs for each other.

In my last two blog posts, we walked through why effective data science storytelling matters, and we looked at how data scientists can improve data science storytelling in ways that will have a meaningful impact.

In this last blog post of the series, we want to look more closely at the organization’s role in providing the personnel, tools and environment that will foster those conversations.

Hiring, supporting and partnering

Organizations should begin by attempting to hire and retain talented data scientists who are also strong communicators. They should be able to talk to their audience at different levels—very elementary levels for “newbies” and highly theoretical levels if their customers are other data scientists. Hiring a data scientist who only has a head for math or coding will not fulfill the business need for meaningful translation.

Even data scientists who are proven communicators could benefit from access to in-house designers and copywriters for presentation material. Depending on the size of the insurer, a small data communication support staff could be built to include a member of in-house marketing, a developer who understands reports and dashboards and the data scientist(s). Just creating this production support team, however, may not be enough. The team members must work together to gain their own understanding. Designers, for example, will need to work closely with the analyst to get the story right for presentation materials. This kind of scenario works well if an organization is mass-producing models of a similar type. Smooth development and effective data translation will happen with experience. The goal is to keep data scientists doing what they do best—using less time on tasks that are outside of their domain—and giving data’s story its best possibility to make an impact.

Many insurers aren’t yet large enough to employ or attract data scientists. A data science partner provides more than just added support. It supplies experience in marketing and risk modeling, experience in the details of analytic communications and a broad understanding of how many areas of the organization can be improved.

Investing in data visualization tools

Organizations will need to support their data scientists, not only with advanced statistical tools but with visualization tools. There are already many data mining tools on the market, but many of these are designed with outputs that serve a theoretical perspective, not necessarily a business perspective. For these, you’ll want to employ tools such as Tableau, Qlikview and YellowFin, which are all excellent data visualization tools that are key to business intelligence but are not central to advanced analytics. These tools are especially effective at showing how models can be used to improve the business using overlaid KPIs and statistical metrics. They can slice and dice the analytical populations of interest almost instantaneously.

When it comes to data science storytelling, one tool normally will not tell the whole story. Story telling will require a variety of tools, depending on the various ideas the data scientist is trying to convey. To implement the data and model algorithms into a system the insurer already uses, a number of additional tools may be required. (These normally aren’t major investments.)

In the near future, I think data mining/advanced analytics tools will morph into something able to contain more superior data visualization tools than are currently available. Insurers shouldn’t wait, however, to test and use the tools that are available today. Experience today will improve tomorrow’s business outcomes.

Constructing the best environment

Telling data’s story effectively may work best if the organization can foster a team management approach to data science. This kind of strategic team (different than the production team) would manage the traffic of coming and current data projects. It could include a data liaison from each department, a project manager assigned by IT to handle project flow and a business executive whose role is to make sure priority focus remains on areas of high business impact. Some of these ideas, and others, are dealt with in John Johansen’s recent blog series, Where’s the Real Home for Analytics?

To quickly reap the rewards of the data team’s knowledge, a feedback vehicle should be in place. A communication loop will allow the business to comment on what is helpful in communication; what is not helpful; which areas are ripe for current focus; and which products, services and processes could use (or provide) data streams in the future. With the digital realm in a consistent state of fresh ideas and upheaval, an energetic data science team will have the opportunity to grow together, get more creative and brainstorm more effectively on how to connect analytics to business strategies.

Equally important in these relationships is building adequate levels of trust. When the business not only understands the stories data scientists have translated for them but also trusts the sources and the scientists themselves, a vital shift has occurred. The value loop is complete, and the organization should become highly competitive.

Above all, in discussing the needs and hurdles, do not lose the excitement of what is transpiring. An insurer’s thirst for data science and data’s increased availability is a positive thing. It means complex decisions are being made with greater clarity and better opportunities for success. As business users see results that are tied to the stories supplied by data science, its value will continue to grow. It will become a fixed pillar of organizational support.

This article was written by Jane Turnbull, vice president – analytics for Majesco.

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In Third Parties We (Mis)trust?

Technology is transforming trust. Never before has there been a time when it’s been easier to start a distant geographical relationship. With a credible website and reasonable products or services, people are prepared to learn about companies half a world away and enter into commerce with them.

Society is changing radically when people find themselves trusting people with whom they’ve had no experience, e.g. on eBay or Facebook, more than with banks they’ve dealt with their whole lives.

Mutual distributed ledgers pose a threat to the trust relationship in financial services.

The History of Trust

Trust leverages a history of relationships to extend credit and benefit of the doubt to someone. Trust is about much more than money; it’s about human relationships, obligations and experiences and about anticipating what other people will do.

In risky environments, trust enables cooperation and permits voluntary participation in mutually beneficial transactions that are otherwise costly to enforce or cannot be enforced by third parties. By taking a risk on trust, we increase the amount of cooperation throughout society while simultaneously reducing the costs, unless we are wronged.

Trust is not a simple concept, nor is it necessarily an unmitigated good, but trust is the stock-in-trade of financial services. In reality, financial services trade on mistrust. If people trusted each other on transactions, many financial services might be redundant.

People use trusted third parties in many roles in finance, for settlement, as custodians, as payment providers, as poolers of risk. Trusted third parties perform three roles:

  • validate – confirming the existence of something to be traded and membership of the trading community;
  • safeguard – preventing duplicate transactions, i.e. someone selling the same thing twice or “double-spending”;
  • preserve – holding the history of transactions to help analysis and oversight, and in the event of disputes.

A ledger is a book, file or other record of financial transactions. People have used various technologies for ledgers over the centuries. The Sumerians used clay cuneiform tablets. Medieval folk split tally sticks. In the modern era, the implementation of choice for a ledger is a central database, found in all modern accounting systems. In many situations, each business keeps its own central database with all its own transactions in it, and these systems are reconciled, often manually and at great expense if something goes wrong.

But in cases where many parties interact and need to keep track of complex sets of transactions they have traditionally found that creating a centralized ledger is helpful. A centralized transaction ledger needs a trusted third party who makes the entries (validates), prevents double counting or double spending (safeguards) and holds the transaction histories (preserves). Over the ages, centralized ledgers are found in registries (land, shipping, tax), exchanges (stocks, bonds) or libraries (index and borrowing records), just to give a few examples.

The latest technological approach to all of this is the distributed ledger (aka blockchain aka distributed consensus ledger aka the mutual distributed ledger, or MDL, the term we’ll stick to here). To understand the concept, it helps to look back over the story of its development:

 1960/’70s: Databases

The current database paradigm began around 1970 with the invention of the relational model, and the widespread adoption of magnetic tape for record-keeping. Society runs on these tools to this day, even though some important things are hard to represent using them. Trusted third parties work well on databases, but correctly recording remote transactions can be problematic.

One approach to remote transactions is to connect machines and work out the lumps as you go. But when data leaves one database and crosses an organizational boundary, problems start. For Organization A, the contents of Database A are operational reality, true until proven otherwise. But for Organization B, the message from A is a statement of opinion. Orders sit as “maybe” until payment is made, and is cleared past the last possible chargeback: This tentative quality is always attached to data from the outside.

1980/’90s: Networks

Ubiquitous computer networking came of age two decades after the database revolution, starting with protocols like email and hitting its full flowering with the invention of the World Wide Web in the early 1990s. The network continues to get smarter, faster and cheaper, as well as more ubiquitous – and it is starting to show up in devices like our lightbulbs under names like the Internet of Things. While machines can now talk to each other, the systems that help us run our lives do not yet connect in joined-up ways.

Although in theory information could just flow from one database to another with your permission, in practice the technical costs of connecting databases are huge. Worse, we go back to paper and metaphors from the age of paper because we cannot get the connection software right. All too often, the computer is simply a way to fill out forms: a high-tech paper simulator. It is nearly impossible to get two large entities to share our information between them on our behalf.

Of course, there are attempts to clarify this mess – to introduce standards and code reusability to help streamline business interoperability. You can choose from EDI, XMI-EDI, JSON, SOAP, XML-RPC, JSON-RPC, WSDL and half a dozen more standards to “assist” your integration processes. The reason there are so many standards is because none of them finally solved the problem.

Take the problem of scaling collaboration. Say that two of us have paid the up-front costs of collaboration and have achieved seamless technical harmony, and now a third partner joins our union, then a fourth and a fifth … by five partners, we have 13 connections to debug, by 10 partners the number is 45. The cost of collaboration keeps going up for each new partner as they join our network, and the result is small pools of collaboration that just will not grow. This isn’t an abstract problem – this is banking, this is finance, medicine, electrical grids, food supplies and the government.

A common approach to this quadratic quandary is to put somebody in charge, a hub-and-spoke solution. We pick an organization – Visa would be typical – and all agree that we will connect to Visa using its standard interface. Each organization has to get just a single connector right. Visa takes 1% off the top, making sure that everything clears properly.

But while a third party may be trusted, it doesn’t mean it is trustworthy. There are a few problems with this approach, but they can be summarized as “natural monopolies.” Being a hub for others is a license to print money for anybody that achieves incumbent status. Visa gets 1% or more of a very sizeable fraction of the world’s transactions with this game; Swift likewise.

If you ever wonder what the economic upside of the MDL business might be, just have a think about how big that number is across all forms of trusted third parties.

2000/’10s: Mutual Distributed Ledgers

MDL technology securely stores transaction records in multiple locations with no central ownership. MDLs allow groups of people to validate, record and track transactions across a network of decentralized computer systems with varying degrees of control of the ledger. Everyone shares the ledger. The ledger itself is a distributed data structure held in part or in its entirety by each participating computer system. The computer systems follow a common protocol to add transactions. The protocol is distributed using peer-to-peer application architecture. MDLs are not technically new – concurrent and distributed databases have been a research area since at least the 1970s. Z/Yen built its first one in 1995.

Historically, distributed ledgers have suffered from two perceived disadvantages; insecurity and complexity. These two perceptions are changing rapidly because of the growing use of blockchain technology, the MDL of choice for cryptocurrencies. Cryptocurrencies need to:

  • validate – have a trust model for time-stamping transactions by members of the community;
  • safeguard – have a set of rules for sharing data of guaranteed accuracy;
  • preserve – have a common history of transactions.

If faith in the technology’s integrity continues to grow, then MDLs might substitute for two roles of a trusted third party, preventing duplicate transactions and providing a verifiable public record of all transactions. Trust moves from the third party to the technology. Emerging techniques, such as, smart contracts and decentralized autonomous organizations, might in future also permit MDLs to act as automated agents.

A cryptocurrency like bitcoin is an MDL with “mining on top.” The mining substitutes for trust: “proof of work” is simply proof that you have a warehouse of expensive computers working, and the proof is the output of their calculations! Cryptocurrency blockchains do not require a central authority or trusted third party to coordinate interactions, validate transactions or oversee behavior.

However, when the virtual currency is going to be exchanged for real-world assets, we come back to needing trusted third parties to trade ships or houses or automobiles for virtual currency. A big consequence may be that the first role of a trusted third party, validating an asset and identifying community members, becomes the most important. This is why MDLs may challenge the structure of financial services, even though financial services are here to stay.

Boring ledgers meet smart contracts

MDLs and blockchain architecture are essentially protocols that can work as well as hub-and-spoke for getting things done, but without the liability of a trusted third party in the center that might choose to exploit the natural monopoly. Even with smaller trusted third parties, MDLs have some magic properties, the same agreed data on all nodes, “distributed consensus,” rather than passing data around through messages.

In the future, smart contracts can store promises to pay and promises to deliver without having a middleman or exposing people to the risk of fraud. The same logic that secured “currency” in bitcoin can be used to secure little pieces of detached business logic. Smart contracts may automatically move funds in accordance with instructions given long ago, like a will or a futures contract. For pure digital assets there is no counterparty risk because the value to be transferred can be locked into the contract when it is created, and released automatically when the conditions and terms are met: If the contract is clear, then fraud is impossible, because the program actually has real control of the assets involved rather than requiring trustworthy middle-men like ATM machines or car rental agents. Of course, such structures challenge some of our current thinking on liquidity.

Long Finance has a Zen-style koan, “if you have trust I shall give you trust; if you have no trust I shall take it away.” Cryptocurrencies and MDLs are gaining more and more trust. Trust in contractual relationships mediated by machines sounds like science fiction, but the financial sector has profitably adapted to the ATM machine, Visa, Swift, Big Bang, HFT and many other innovations. New ledger technology will enable new kinds of businesses, as reducing the cost of trust and fixing problems allows new kinds of enterprises to be profitable. The speed of adoption of new technology sorts winners from losers.

Make no mistake: The core generation of value has not changed; banks are trusted third parties. The implication, though, is that much more will be spent on identity, such as Anti-Money-Laundering/Know-Your-Customer backed by indemnity, and asset validation, than transaction fees.

A U.S. political T-shirt about terrorists and religion inspires a closing thought: “It’s not that all cheats are trusted third parties; it’s that all trusted third parties are tempted to cheat.” MDLs move some of that trust into technology. And as costs and barriers to trusted third parties fall, expect demand and supply to increase.

cloud

Secret Sauce for New Business Models?

Insurance companies were built to bring stability to an unstable world.

So, why do factors such as market instability, technological upheaval and consumer pressure seem to throw so many insurers into panic? In many cases, insurers can simply point to their rigid foundations.

It didn’t take many California earthquakes to convince California builders that foundations would need to be built with flexibility in mind. In insurance, it won’t take many disruptive upheavals to teach businesses that current foundations are ripe for disaster. New foundations are needed to support a perpetually shifting business.

In Reinventing Insurance: Leveraging the Power of Data, Analytics and Cloud Core Systems, a Majesco white paper issued in cooperation with Elagy, we look closely at how fundamental changes in the insurance business can be met with a new view of insurance infrastructure. By assembling cloud components into a fully functional virtual infrastructure, insurers remove the lethargy and overhead that bogs down everything from data aggregation and analytics to testing and product development. The goal is to build an insurance enterprise that can capitalize on market opportunities.

Risk vs. Time

To assess potential cloud value, Majesco first looked at the relationship between insights and risk assessment and at how insights are traditionally gathered and used. Traditional risk assessment regards claims experience across time and population as the best kind of informant regarding risk within any particular insurance product. This kind of risk assessment is proven. Actuarial science has been honed. Insurers have become adept at long-term predictive capabilities, and regulations have kept consumers and insurers protected from failure through adequate margins of error.

The experience of time, however, has become the sticking point. To meet market demands, every insurance process has to be shortened. The new predictive fuel of data provided through real-time digital sources (as well as increasingly insightful technologies) can give insurers a much better view of risk in a much more appropriate timeframe. But even if they can gather data and assess the data quickly, they will, in most cases, still be held back by a product development and testing infrastructure that isn’t prepared to respond to fast-acting competitive pressure. The transparency that offers such promising opportunity is widely available to anyone, not just insurers, and it is highly coveted by agile, tech-savvy, entrepreneurial disrupters.

Competition vs. Time

Entrepreneurs love innovation and crave a new, marketable idea. They especially enjoy turning age-old processes on end, because these moments are often akin to striking gold. With technology’s rapid application of telematics, sensors, geolocation information and improved data management, nearly anyone can tap into the same data pools. Creative entrepreneurs, educated investors and innovative organizations are teaming up in a new kind of gold rush where rapid opportunity recognition will be met with rapid product development and relevant marketing. At a time when consumers seem to be susceptible to instant access product messages, disruptive companies will soon be feeding them instant-access products.

Once again, the development time of legacy platforms can’t offer a competitive solution to insurers. The foundation is now susceptible to cracking because of its inflexibility.

Legacy vs. Time

Insurers still maintain dozens of advantages in the industry, the first and the foremost being experience. All of today’s new data sources, new channel options and modern infrastructure possibilities have more promise in the hands of insurers than in the hands of non-insurance disrupters. Legacy systems, however, are restrictive. They aren’t plug and play. Most aren’t operating in a unified data environment with data consolidated and available across multiple databases. So, insurers’ opportunities will be found in a system built to fit the new insurance business and infrastructure model.

Majesco’s report discusses how insurers can align cloud solutions with business strategies to capitalize on new risks, new products and new markets. With data aggregation, for example, cloud solutions available through Majesco and data-partner Elagy are rewriting analytic- and decision-making processes. A cloud data solution can integrate claims experience with third-party data and newly available data sets to relieve the need for additional IT overhead.

A Satellite Office Approach

Small and medium-sized insurers, in particular, stand to gain through a reinvention of their operational model. Market drivers—such as agents’ lack of marketing insights, the availability of relevant data and the need for low-cost process efficiencies—make an excellent case for change. The hurdles are real, however. Many insurers don’t have the needed resources to take advantage of these opportunities, and they are constrained by technology and a lack of operational capability.

The ideal solution would be to transfer the whole pipeline to the cloud, migrating the enterprise infrastructure into a cloud-based infrastructure where partners and innovators can plug their solutions into a cloud-based core administration system.

In the real world, most insurers would be served by a better strategy. When companies in any industry hope to move to a new geographic region, they sometimes open a satellite office. The satellite office is the new footprint in the foreign territory. It’s the place where testing and acclimation happen, and its approach is somewhat analogous to what insurers can do when looking at cloud development.

Insurers will find excitement and freedom running a new and improved model alongside the old model. While the organization practices its newfound agility, it will maintain the stability of legacy systems for as long as they are needed or are practical. A cloud-based insurance platform will quickly bring the insurer to the realm of data-fueled experience and competitive advantage. Its new processes and capabilities will breathe fresh life into insurers that are ready for resilient foundations.

Jurors and Questions on Insurance Coverage

For most potential jurors, questions of insurance coverage do not usually arise in common conversation. Seldom cut and dried, usually subject to numerous definitions and intricacies, coverage issues can be boring and puzzling for even an experienced adjuster. Asking a lay person to try to classify an “occurrence” as defined by a policy, or whether a third party is covered as an additional insured, may prompt, at best, glazed-over eyes or, even worse, a negative commentary about insurance companies. While it may be best in some situations for a judge to determine the issue of insurance coverage, this is not always possible. Sometimes, coverage questions arise in litigation, and those interpreting policy language and determining the outcome are jurors. If jurors are deciding the issues, certain challenges then arise, such as how to clarify policy language, present a clear and concise argument and overcome negative preconceptions about the insurance industry.

Can the Judge Decide Coverage Issues?

In Louisiana, general rules regarding issues that are triable by a jury are set forth in Louisiana Code of Civil Procedure articles 1731 – 1736. These establish the general rule that a demand for a trial by jury will result in a trial by jury of all issues. However, exceptions to the general rule exist when: (a) the parties stipulate that the jury trial shall be as to certain issues only; (b) a party in his demand specifies the issues to be tried by a jury; or (c) the right to trial by jury as to certain issues does not exist. Where a jury trial has been demanded by one or both parties, the case must be tried by a jury unless both parties consent to trial without a jury or the trial court finds that a right to a trial by jury does not exist.

More particularly, La. C.C. P. art. 1562(D) specifically codified the general principle found in La. C.C. P. art. 1736 requiring a stipulation between or the consent of the parties before the trial judge can order that insurance coverage issues be tried separately, with the “court alone” deciding the issue of insurance coverage.

La. C.C.P. art. 1562(D) states:

“If it would simplify the proceedings or would permit a more orderly disposition of the case or otherwise would be in the interest of justice, at any time prior to trial on the merits, the court may order, with the consent of all parties, a separate trial on the issue of insurance coverage, unless a factual dispute that is material to the insurance coverage issue duplicates an issue relative to liability or damages. The issue of insurance coverage shall be decided by the court alone, whether or not there is to be a jury trial on the issue of liability or damages.”

The leading case on the subject is Citgo Petroleum Corp. v. Yeargin, Inc., 95-1574 (La. App. 3 Cir. 7/3/96), 678 So.2d 936, writ granted, remanded, 96-2000 (La. 11/15/96), 682 So.2d 746 and 96-2007 (La. 11/15/96), 682 So.2d 747. There, the court stated that La. C.C.P. art 1562(D) provided that, if principals of judicial efficiency or justice would be served then the court may order a separate trial on the issue of insurance coverage. However, the trial judge’s discretion is not unfettered. The judge’s ability to take the issue away from the jury is severely restricted because, under the article, all of the following conditions must exist: (1) it would simplify the proceedings, permit a more orderly disposition of the case, or be in the interest of justice; (2) the consent of all parties; (3) the non-existence of a factual dispute material to the coverage issue that duplicates an issue relative to liability or damages; and (4) the order must be rendered before trial on the merits.

Therefore, the requirements set forth in the article effectively leave the judge with no discretion, as it requires the consent of all parties. The court further noted that, while the issue of insurance coverage under an insurance policy is a narrow issue of the law between the alleged insured and the insurer, a jury is not prohibited, by statute or otherwise, from deciding this issue. Further, there is no exception to the right to trial by jury for issues that the trial judge may think are too technical or too complex for the jury to understand. Even if the trial judge believes that he is more capable than the jury of deciding the issue of coverage, he cannot take this issue away from the jury once the issue is included within the scope of issues for which a jury trial was requested, unless the conditions of La. C.C.P. art. 1562(D) are met.

As such, if a trial by jury has been requested, but an insurer is presenting technical questions of coverage and believes that a judge would be best suited to decide the coverage issue, a stipulation or the consent of all parties would be necessary before the judge could take the coverage issue away from the jury. Unfortunately, often the consent of all parties to separately try the coverage issue cannot be obtained, and the insurer is left with a jury to decide intricate and potentially costly coverage issues.

Selecting the Best Jury for Your Coverage Case

If coverage issues must be decided by a jury, the persons who make up that jury can make a difference in the outcome of the case. Questioning prospective jurors in voir dire about their current insurance policies and other contracts can provide some insight into how they view insurance companies and the potential for coverage. People often believe that they are “fully covered” under their insurance policies, and that insurers are large, prosperous companies that should be able to “help out” individuals. However, further questioning can reveal that potential jurors do understand that there are limitations as to what is covered under certain policies and what has been negotiated.

Questioning a potential juror about a policy he may currently have in place, whether that policy has a limit and if he understands that the insurance company would not be required to pay more than that limit, can show that the potential juror does understand some limitations to coverage. Additional questions may involve who the current policies provide coverage to and the limitations on that coverage. Even simple, and almost obvious, questions can help illustrate a potential juror’s understanding of coverage limitations. For example, discussing how an automobile policy might provide coverage for certain damage to an owned vehicle but would not cover general maintenance, oil changes or a monthly car payment can help provide insight into whether an individual may be able to understand the issues and be a constructive juror.

Additionally, general questions regarding the potential jurors’ opinion of insurance companies in general, personal claims experiences or inferences regarding insurers that the potential juror has taken from the media can provide insight into whether the potential juror might be favorable or undesirable from the insurer’s standpoint.

Presentation at Trial – Concise and Comprehensible

After a jury has been selected, helping jurors understand and follow the language and logic of the coverage argument is vital. The following tips may help simplify the coverage case and overcome obstacles when faced with presenting coverage issues to a jury.

1. Walk Jurors Through the Basics

Although often complex, insurance policies are simply contracts. They define a relationship between parties and outline who will do what, when and under what circumstances. Presenting the insurance policy as a simple contract, by identifying the promise between the parties and what each may receive in exchange for their promise, may help jurors be less apprehensive when approaching coverage issues.

A good place to start is with the basics of the policy and how it is structured. Discussing the declarations, insuring agreement, exclusions, definitions, conditions and endorsements allows jurors to get comfortable with the policy. After the policy and its purpose are explained, the specific provisions at issue can be addressed. An effective way to do this is by using demonstrative evidence, such as blowups of certain pages or Power Point presentations illustrating specific language and what it means. Presenting the policy through large exhibits helps break down the technicality for jurors and show that it is a logical and consistent contract.

Further, preparing an exhibit naming and listing the experience of all of the individuals who are involved in creating the policy, the claim investigation, adjustment and the coverage decision shows that time and thought of real individuals went into creating a well-organized document and making a well-thought-out coverage decision.

2. Humanize the Issues

Jurors often bring their own experiences to the courtroom and, sometimes, a bad impression of insurance companies. Further, oftentimes coverage disputes are coupled with bad faith claims, exacerbating the notion that insurance companies are malicious. To overcome these perceived notions and prejudices, it is key to humanize the insurer’s operations and show the jurors that real people have drafted the policies and handled the claims. Showing that the insurer is not just a large, faceless corporation, but individuals making decisions and doing their jobs, will help negate the insured’s presented image of an uncaring, profit-seeking business entity. While testimony from a vice president may be impressive, the agent who issued the policy or the adjuster who handled the claim may help put a more relatable face to the company.

Additionally, many insurers have adopted vision statements outlining a code of ethics or a commitment to the community. Using this at trial, and showing how the company is committed to its values or involved in the community, helps dispel negative ideas of an uncaring corporation.

Lastly, insurers should be careful about attacking the insured’s credibility or positions. While it may be necessary, the way this is presented to the jury can have a big impact and can erroneously further the negative ideas about the insurance company.

3. Show All Negotiations

Jurors will generally understand the concept of “you get what you pay for.” They know that if they contracted with their cable company and pay for only the basic channels, they do not get premium channels, such as HBO. It follows that jurors should understand that if underwriting documents or other evidence show what was discussed and understood between the parties, and this is reflected in the contract, this should be what governs. If evidence of negotiations is available, this should be presented to the jury. This concept may be particularly helpful in litigating commercial policies, where there is usually more negotiation, and in showing the application of policy exclusions.

4. Keep It Simple

As a general rule, the simpler the better. It is important to keep the insurance policy language from sounding too technical. Avoid overuse of legal terms and phrases, as this will only confuse jurors and may cause them to fall back on the generally accepted legal principle that “any ambiguity must be construed against the insurer.” A straightforward presentation, relying on only one or two strong coverage arguments, should be used. Presenting every argument possible is not always the best strategy, as this could bog down the jury and cause them to lose focus. When one or two key arguments are made, the case is tight and allows jurors to concentrate on the big picture, rather than trying to follow several moving parts.

Another tactic that may help bring the issues to a comfortable level is to compare the policy to other contracts jurors may have entered into. Outlining the limits and duties imposed by contracts that jurors may be more familiar with, such as a purchase agreement for a car, or a lease agreement for an apartment, may also help jurors realize that there are also limitations and duties imposed by insurance contracts, just like the contracts with which they are more familiar.

Additionally, working backward from the result being sought provides a road map for a streamlined argument and helps create a unifying theme throughout the litigation. Starting from the verdict form or jury instructions helps to keep concentration on the elements that need to be established or explained.

5. Apply Basic Jury Concepts

Basic concepts of persuasion, which apply to all jury litigation, can also be used effectively in a coverage case. Fairness must be stressed and run as a theme throughout the presentation of the coverage case. Jurors want to be fair and will try their best to do so. Additionally, any obvious weaknesses in the case should be addressed. Holes in the case, if not admitted to or explained, will create doubt.

Presenting a coverage case to a jury is sometimes unavoidable, but need not be too difficult or incomprehensible for jurors. Carefully questioning and selecting potential jurors, along with presenting a simple yet logical argument, while humanizing the insurance company, can help achieve a successful presentation of the case in the courtroom and, with that, a successful result.