Tag Archives: data driven

3 Ways to Boost Trust in Your Brand

It’s no secret that there is a newfound aggressive and competitive environment in insurance. A combination of outside competition focused on disrupting the distribution channel and an increase in tech-driven carriers is fostering this environment, and adapting to this change goes beyond just adopting technology. Everything hinges on a carrier’s ability to shed its conservative approach to business – both internally and when communicating to customers.

Although fairly new in the U.S., insurance price comparison sites are rising in both popularity and sophistication, enabling consumers to compare policies from insurers down to the last dollar in a matter of seconds. Carriers shouldn’t fear these sites but should be prepared with a strategy that allows them to stay successful amid this disruption.

Regardless of the changes happening, insurance still remains a complex purchase, and brand trust is a valuable way to differentiate your company. Nicholas Weng Kan, CEO of Google Compare, shares that there is a level of comfort that customers need before they make the commitment to buy a policy; that is why more than half of sales still happen after a conversation with an insurance expert.

So how do you gain trust for your brand? The catalyst for success in modern business is through transparency, which breeds trust – something insurance desperately needs. According to a recent Ernst & Young study, insurance suffers from lower levels of trust than any other industry, with 57% of consumers expressing dissatisfaction at the lack of interaction from their insurer.

Below are steps carriers can take to elevate trust in their brand:

First Step: Get to Know Your Customer

An important aspect of successful businesses today is the ability to create a relationship with the customer. Carriers don’t have the bandwidth to treat each customer with interpersonal attention but can still understand who they are and what they want. Through the use of analytics, carriers have quick access to valuable information about a customer. Brands that don’t begin adopting these technologies to match customer needs can’t keep up with those that do.

Netflix obliterated Blockbuster by using advanced analytics to know customers better. The same dynamic differentiates between the data-driven and traditional carrier: Once the customer acquisition approach is more segmented and targeted, carriers can also deliver the right price based on a more accurate risk profile.

While pricing may not be everything, competitive pricing is necessary. The E&Y survey found that 50% of consumers change their insurer because of price. Insurance is already a complex business that lacks linearity, and price is an important consideration for all customers. The most competitive carriers leverage predictive analytics as a useful tool to help distinguish the poor risks from the good risks so they can focus on the customer relationships that will benefit their business in the long term.

Second Step: Learn to Cater to the Millennial Generation

Millennials have overtaken the baby boomers as the largest consumer base in the country, at 75.3 million strong, and it is clear that they aren’t satisfied with the insurance buying process. According to the 2015 Capgemini World Insurance Report, less than 30% of insurance customers around the world enjoy a positive customer experience, with North America seeing the deepest decline in satisfaction.

Millennials crave transparency and a buying process that is painless and streamlined. Trust is bred when they know their insurers have their best interests at heart and care about their well-being, as well as the causes that are important to them. Above all, Millennials demand efficiency and personalization. Using innovative technologies to interest and retain Millennials is key to gaining their trust. Not only will this breed a positive experience, but it limits the amount of confusion from the consumer, thus creating fewer touch points for the insurer or agent.

Last Step: Be Transparent

The industry holds tightly to how products and services are priced. That’s just not going to work any more. Customers are sophisticated and expect insurance agents to connect them to information. Agents and insurers won’t garner consumer trust with a “black box” buying process.

Consumers have moved on from the notion that they are better off when an expert makes choices for them, but they do want to be guided by experts and understand what is happening in each step of the process. According to another E&Y study, nearly 70% of global customers feel they initiate the purchase of new policies because of the availability of digital channels. Based on results, it was inferred that difficulty in accessing information contributes to customers leaving their current carrier.

If insurers continues to be secretive about how they conduct business and price their policies, then many of the new insurance innovations run the risk of being perceived negatively by consumers. For example, Internet of Things has broken into homeowners, with partnerships between Nest and well-known insurers such as Liberty Mutual, but if the pricing structure and benefits aren’t clearly defined, there is a chance of a consumer backlash.

A Consumer Reports article earlier this year found that insurance is receiving criticism for using credit information to price personal auto policies. Of course, credit information is a strong indicator of loss and a smart predictive factor for any auto insurer, and it’s used by many different industries. However, there is perceptual damage, leaving the industry blindsided by critics who assume that secrecy must automatically mean untrustworthy.

Insurance needs to find the right balance between doing a good job managing risk and developing a more innovative and transparent culture. Creating a positive consumer experience involves being more transparent about price and clearly defining the benefits and service offered by the carrier. Almost 40% of consumers are either not confident or only somewhat confident that they have the appropriate coverage. This should be a wake-up call to the industry that all aspects of customer engagement need attention.

What Does ‘Data-Driven’ Really Mean?

The more things change, the more they stay the same. What remains constant are the fundamentals of what makes insurance a well-capitalized, reliable cornerstone of the U.S. economy. The basic model of assessing risk, collecting insurance premiums, investing and paying claims still works. What’s been completely upended is how carriers evaluate and acquire the best risks – and how much more important effective risk evaluation is today.

Advanced data and predictive analytics have changed the customer acquisition and retention game. When an insurer can pinpoint which policies are going to be the most profitable 10% and also know that same small segment is delivering 50% of total profit, you know the rules have changed.

The chart below represents a study of the portfolios from a diverse set of commercial insurers and lines of business. The study shows that that this surprising statistic holds true across companies. It helps demonstrate the real advantage — and potential threat — of data analytics. The insurer that can accurately identify the best 10% of the market is going to be able to compete on, and win, this business.

chart 1

What does being data-driven mean in practice?

Information is a business enabler; you don’t need to embark on “big data” or predictive analytics initiatives just for the sake of them. You shouldn’t feel pressured to lead the rallying cry to become a data-driven organization because everyone is talking about it. You consume data to gain insights that will solve problems that matter and achieve specific objectives.

Data-driven decision making is a commitment and a passion to go beyond the limits of heuristics, because you know it’s necessary to reach a new level of understanding of where your business is today and where it’s headed in the near term. Data-driven cultures have a disciplined curiosity and rigor to find credible patterns in the data before finalizing their conclusions – which is why everyone emphasizes how important it is to create a test-and-learn culture. Armed with a solid business case, transparency and good processes, data-driven organizations use analytics in combination with human expertise to make better decisions.

Why is this so urgent?

A recent Bloomberg article reported that the workers’ compensation industry posted its first underwriting profit since 2006, which is welcome news. At the same time, the article noted that this is directly related to how insurers have reacted to the current investment environment. In the absence of meaningful investment returns, insurers are keenly focused on bridging the gap by improving underwriting profits and enhancing operational efficiencies: “The reality is, in today’s interest-rate environment, we need to be driving combined ratios under 100,” said Steve Klingel, CEO of the National Council on Compensation Insurance (NCCI).

This isn’t limited to one line of business. As Robert Hartwig, president of the Insurance Information Institute, noted in a recent interview, “You’re not going to see vast swings you did 10 or 15 years ago, where one year it’s up 30% and two years later it’s down 20%”. The reason he gave: “Pricing is basically stable… The industry has gotten just more educated about the risk that they’re pricing.”

Now what?

No one said implementing data analytics in an underwriting environment is a small task or a quick fix. Many companies focus primarily on selecting the right predictive model. In reality, the model itself is just one part of a larger process that touches many parts of the organization.

Data analytics can only be successful if developed and deployed in the right environment. You may find that you have to retool your people so that underwriters don’t feel that data analytics are a threat to their expertise, or actuaries to their tried-and-true pricing models. Never underestimate the importance of the human element in moving to a data-driven culture.

Given the choice between leading a large-scale change management initiative and getting a root canal, you may be picking up the phone to call the dentist right now. It doesn’t have to be that way: Following a thoughtful, straightforward process that involves all the stakeholders early and often goes a long way.

3 Key Steps for Predictive Analytics

The steady drumbeat about the dire need for data and predictive analytics integration has been there for several years now. Slowly, many carriers have started to wake up to the fact that predictive analytics for underwriting is here to stay. According to Valen Analytics’ 2015 Summit Survey, 45% of insurers who use analytics have started within the past two years, and, of those that don’t currently implement analytics, 56% recognize the urgency and plan to do so within a year. Although it used to be a competitive advantage in the sense that few were using predictive analytics, it can now be viewed as table stakes to protect your business from competitors.

The real competitive advantage, however, now comes from how you implement predictive analytics within your underwriting team and focus its potential on strategic business issues. New competitors and disruptors like Google won’t politely wait around for insurers to innovate. The window to play catch-up with the rest of tech-driven businesses is getting narrower every day, and it’s either do or die for the traditional insurance carrier.

All of this buzz about data and predictive analytics and its importance can be deafening in many ways. The most important starting point continues to center on where to get started. The most pertinent question is: What exactly are you trying to solve?

Using analytics because everyone is doing it will get you nowhere fast. You need to solve important, tangible business problems with data-driven and analytic strategies. Which analytic approach is best, and how is it possible to evaluate the effectiveness? Many insurers grapple with these questions, and it’s high time the issue is addressed head-on with tangible steps that apply to any insurer with any business problem. There are three key steps to follow.

First Step: You need senior-level commitment.

You consume data to gain insights that will solve particular problems and achieve specific objectives. Once you define the problem to solve, make sure that all the relevant stakeholders understand the business goals from the beginning and that you have secured executive commitment/sponsorship.

Next, get agreement up front on the metrics to measure success. Valen’s recent survey showed that loss ratio was the No. 1 one issue for underwriting analytics. Whether it’s loss ratio, pricing competitiveness, premium growth or something else, create a baseline so you can show before and after results with your analytics project.

Remember to start small and build on early wins; don’t boil the ocean right out of the gate. Pick a portion of your policies or a test group of underwriters and run a limited pilot project. That’s the best way to get something started sooner than later, prove you have the right process in place and scale as you see success.

Finally, consider your risk appetite for any particular initiative. What are the assumptions and sensitivities in your predictive model, and how will those affect projected results? Don’t forget to think through how to integrate the model within your existing workflow.

Second Step: Gain organizational buy-in.

It’s important to ask yourself: If you lead, will they follow? Data analytics can only be successful if developed and deployed in the right environment. You have to retool your people so that underwriters don’t feel that data analytics are a threat to their expertise, or actuaries to their tried-and-true pricing models.

Given the choice between leading a large-scale change management initiative and getting a root canal, you may be picking up the phone to call the dentist right now. However, it doesn’t have to be that way. Following a thoughtful and straightforward process that involves all stakeholders early goes a long way. Make sure to prepare the following:

  • A solid business case
  • Plan for cultural adoption
  • Clear, straightforward processes
  • A way to be transparent and share results (both good and bad)
  • Training and tech support
  • Ways to adjust – be open to feedback, evaluate it objectively and make necessary changes.

Third Step: Assess your organization’s capabilities and resources.

A predictive analytics engagement is done in-house or by a consultant or built and hosted by a modeling firm. Regardless of whether the data analytics project will be internally or externally developed, your assessment should be equally rigorous.

Data considerations. Do you have adequate data in-house to build a robust predictive model? If not, which external data sources will help you fill in the gaps?

Modeling best practices. Whether internal or external, do you have a solid approach to data custody, data partitioning, model validation and choosing the right type of model for your specific application?

IT resources. Ensure that scope is accurately defined and know when you will be able to implement the model. If you are swamped by an IT backlog of 18-24-plus months, you will lose competitive ground.

Reporting. If it can be measured, it can be managed. Reporting should include success metrics easily available to all stakeholders, along with real-time insights so that your underwriters can make changes to improve risk selection and pricing decisions.

Boiling this down, what’s critical is that you align a data analytics initiative to a strategic business priority. Once you do that, it will be far easier to garner the time and attention required across the organization. Remember, incorporating predictive analytics isn’t just about technology. Success is heavily dependent on people and process.

Make sure your first steps are doable and measurable; you can’t change an entire organization or even one department overnight. Define a small pilot project, test and learn and create early wins to gain momentum by involving all the relevant stakeholders along the way and find internal champions to share your progress.

Recognize that whether you are building a data analytics solution internally, hiring a solution provider or doing some of both, there are substantial costs involved. Having objective criteria to evaluate your options will help you make the right decisions and arm you with the necessary data to justify the investment down the road.

Chasing the Right Numbers on Claims

Managing a claims operation is challenging. There are so many moving parts, dynamics and procedures. Information comes gushing in like a fire hose, making it difficult for many companies to effectively assemble and organize it. It’s crucial to help claims divisions focus on the right numbers instead of chasing numbers that have no value.

Most claims leaders know that there are a few factors that affect the majority of claim outcomes. However, many times organizations will mistakenly target metrics “for metrics’ sake,” at the expense of common sense.

Traditionally, a claims supervisor or branch manager will receive metric targets from senior leadership. Unfortunately, the intent of these goals is skewed dramatically by the time they reach front-line personnel. For example, let’s take a company that wants to improve customer service by inspecting vehicle damage the same business day. While this is a noble idea and has the potential to increase customer satisfaction, branch level managers are often forced to abandon rational thinking to meet a specific “inspection metric” or quota. Managers will chase the numbers to obtain an inspection, often having staff appraisers take photos of damaged vehicles over fences or taking shortcuts in an attempt to meet requirements. This often leads to compromised accuracy and raises the question — “Does it really make sense?” It does to the manager who needs to meet goals and protect her job but does it truly increase customer satisfaction? Not necessarily.

Having a goal at the top doesn’t mean that the numbers will retain their true meaning by the time they get to the daily staff. It’s crucial to focus on figures that actually create better claim outcomes and customer experiences.

Here’s another example of how differing goals within a claims organization can skew overall results when managers are forced to manage to the wrong numbers:

Let’s say your insured damages another vehicle and that claimant decides to go through his own carrier for repairs. Now the carrier sends in a subrogation demand that includes excessive rental, overlapping operations, duplicate invoices and mathematical errors. Would it be a good idea to just pay what is being asked without reviewing for accuracy?

Well, for some insurers that don’t have the staffing or the expertise in the subrogation department, quite often an excessive demand like this might just be rubber-stamped. The subrogation department may be overseen by an individual who has been compartmentalized away from day-to-day claims. If this manager’s goals and metrics don’t include accuracy, he may just pay this overinflated demand.

Chasing the wrong numbers can give the misperception that the manager is achieving goals, but the best possible outcome wasn’t achieved.

So what’s the answer?

The key is matching numbers to desirable outcomes that make sense. Eliminate any metrics that provide little value and only serve to create busywork. With the wealth of data that companies are able to gather and analyze, the focus should be on information that has a direct impact on customer retention and quality service.

One must carefully focus on the right numbers to add value and help push the organization forward to achieving that ideal balance of client satisfaction and operational efficiency.