content customer lifetime value

Understanding and Improving Customer Lifetime Value Through Insurance Analytics


The whitepaper is prepared by:

Geo Whiting, Principal, GWhiting.com

Brittany Reyes, VP of Financial Services & Insurance, FC Business Intelligence

Disclaimer The information and opinions in this document were prepared by FC Business Intelligence

Ltd and its partners. FC Business Intelligence Ltd has no obligation to tell you when

opinions or information in this document change. FC Business Intelligence Ltd makes every

eort to use reliable, comprehensive information, but we make no representation that it

is accurate or complete. In no event shall FC Business Intelligence Ltd and its partners be

liable for any damages, losses, expenses, loss of data, loss of opportunity or prot caused by

the use of the material or contents of this document.

No part of this document may be distributed, resold, copied or adapted without FC Business Intelligence prior written permission.

FC Business Intelligence Ltd 20147-9 Fashion Street, London, E1 6PX

www.analytics-for-insurance.com/USA

With contributions from*:

Munish Arora, Senior Manager, Insurance Analysis, CSAA Insurance Group

Partha Srinivasa, Senior Vice President & CIO, HCC Service Company

Maroun Mourad, former CEO & Chairman, Middle East, Zurich Insurance Group& Author of The Insurance Management Playbook: A Leaders Guide

* Views expressed by our experts represent their sole thoughts on the topic of Insuranceanalytics. They do not necessarily represent the views of their current organizations and should not be seen as an endorsement of any group, product or strategy.


Analytics for Insurance USAConference & ExhibitionMarch 25-26, 2015 Chicago Position Analytics As Your MostStrategic Asset: Improve Operations,Pricing, Claims and More www.analytics-for-insurance.com/usa

3

INTRODUCTION

Insurance walks a delicate balance between assuming new risk and develop-ing new policies, but this high wire act becomes less of a concern when com-panies are able to grow sales from current customers. Whether times are lean or markets are expanding, savings are almost always present when pursuing the existing possibility.

To further the upsell and cross-sell opportunities, insurers large and small are turning to analytics to understand the behaviors, preferences, and mentality of their customers. Assigning a risk to these and evolving that risk with a pro-file allow insurers to best understand customers and their lifetime value.

This is a long-tail view of the customer, and it is proving to be fertile ground thats difficult to till. Todays analytics and business intelligence professionals are determining how to measure customer lifetime value and how best to apply it to customers and organizations.

Many insurers are still dealing with the growing pains of these models: deter-mining how best to treat high-value customers to increase business while not deserting low-value customers and suffering small, but consistent revenue losses.

This paper aims to provide top-level insights from industry thought leaders to help develop an understanding of customer lifetime value and its impact on the insurance space. Company understanding of the processes is essential to suc-cess and the makeup of these analytics units will require a new level of finesse.

While insurers face many hurdles, this guidance aims to provide the spark needed to manage customer lifetime value on systems, within organizations, and when interacting with the customer as they change.



4

CREATING CUSTOMER AND COMPANY UNDERSTANDING

Defining Customer Lifetime ValueCustomer lifetime value (CLV) is a descriptive model that projects the revenue a customer will generate over their entire lifetime with the insurer. This is the net cash flow value, typically in present-day dollars, for that customer over a standard timeframe.

Depending on sophistication, models may generate a CLV relative to single products and projected rate increases, with the likelihood of claims based solely on an initial risk projection, or they may include additional products based on changing needs over the consumers lifetime.

Using CLV in modeling for insurance is representative of a shift in data practic-es. The transactional mentality is slowly giving way to a broader approach of placing products relative to customer need. It does not represent a true shift to a customer-centric model but it is a step in that direction.

Whats required for the paradigm to change is stronger analytics and model-ing that adapt over time to changing profiles and personas. Thought leaders in the industry often say insurers are struggling to adopt systems and mentali-ties required to predict how customer risk profiles change over time.

Those predictions are being looked at today because when CLV is done right, it presents an opportunity to raise revenue with little cost. CLV projections also help guide companies toward new lead conversion where one customer may be 2x to 10x more valuable than another.

The larger the customer lifetime value is, the lesser their expense is. The cost of getting a new customer is much higher than keeping a current one, so CLV is a critical element, said Srinivasa.

One resounding theme from our experts this year is: Always start with the cus-tomer and work from their perspective. That has become the essential quality in CLV modeling to link value to attractive offers with strong conversion rates.

Most of the time, everything is conceived from the insurance companys point of view. The sales and service propositions at most insurance companies today are product-driven instead of customer-focused. The industry must overcome that, said Mourad. Theres no way on Earth you can achieve a higher product density if your focus is on the product instead of the customers lifestyle or business needs.

If you have a customer with five products and they make a claim on one, then you still have four very profitable products. That puts the insurer in a better position and makes the customer more profitable than if: they only have one policy, make a claim, their rate goes up, and then they leave.



5

Setting Proper ExpectationsCustomer lifetime value analytics rests on a foundation of many other ana-lytics platforms because of its data-intensive nature. This should be used to guide initial expectations for results in both timeframe and application.

According to our experts, CLV is best viewed as guidance for products and services as they relate to overall business decisions. Early CLV calculations will turn to predictive analytics for future values, plus focus on historical data and analysis for near-term modifications. Unlike many other analytics platforms, CLV lessons are designed to have a cultural impact on an organization.

Influence is sometimes viewed as sway over a specific product or sector, but CLV represents an opportunity for analytics to touch a much broader spec-trum of business decisions.

Our plan over the next couple of years is to utilize customer lifetime value models to measure quality of business production, optimize renewal reten-tion campaigns and increase the effectiveness of the sales quality assurance process. Over time, it would become a basic yardstick for wider applications in insurance operations, said Arora. We first intend to use CLV models for our exception review process to optimize the underwriting expense while man-aging the expected risk effectively.

One goal can be to see the benefits of other predictive analytics and expand those benefits over a short term to increase the amount of buy-in for the unit as a whole. Improvements and benefits translate into a larger budget and focus for CLV.

Insurance companies have an internal structural issue. A product-driven internal structure gets in the way of serving customers and maximizing the lifetime val-ue. Most companies are writing half the premiums they could be writing if they focused on the customer instead of the product, cautioned Mourad. This issue is further accentuated when companies have different operational and customer management structures across their Life and General Insurance business.

This type of cultural shift is not easy and will require consistent buy-in from the C-suite as well as individual business unit leaders.

Making The Business CaseOften, one of the biggest challenges from a data perspective is getting the lev-el of investment needed to create and improve systems. Insurers can sell many of their analytics concepts by saying: if we do these analytics we could get more business, but this needs a backbone of success and a proper estimate.

Putting this data in the concept phase of improvement is good. Everyone gets it. Its motherhood and apple pie. But when it comes to actually getting



6

financial support theres a struggle about explaining the value of the invest-ments and getting the funding, said Srinivasa.

That puts many analysts at risk of promising too much when trying to make their case. Every factor must be addressed, especially those with financial impacts. While personalization can provide the opportunity for better leads, cross-selling, and up-selling, it wont take outside factors into consideration. Without inputting new data, no system can predict and react to these factors, such as when competitors raise or lower prices, which will impact how mod-els perform.

Removing BiasAnalytics is often at odds with intuition. Understanding customer lifetime risk and value can exacerbate this divide. If a customer is viewed as a risk but the data says otherwise, the suggestion will be to target them with multiple products to increase their CLV.

This requires the data to be objective and credible enough to not only overcome an initial objection, but do so with such force that it is persuasive enough for a multi-product pitch. Demand must related to lost costs and relative risk, all with direct linkages back to the consumers risk characteristics and a projection on how those characteristics change over time.

The same type of bias often exists when analytics prompt a conversation about changing behaviors to match customer view points and preferences. Its the role of the data unit to provide comfort, and in business that comes down to tying change to bottom-line improvements and retention.

Addressing the bias requires analytics leads to quantify as much as possible. This gives data and suggestions the appropriate weight and can help keep the conversation on track. Data will not be persuasive when it or its lessons are left in the abstract.

Bias reduction requires competence and confidence. These are perhaps best achieved through small successes and a carefully crafted team that addresses data and CLV from its very core.

Cross-Sell, Up-Sell Focus The cross-sell and up-sell are major factors in customer lifetime value, but its not just for these. Its also about looking to profile the customer from a risk perspective. Theres a need to profile a person with data, credit history and other information. We can use claims and fraud perspectives to bring in the holistic view of the customer, and thats valuable, said Srinivasa.

The holistic customer view is a best practice for developing models and strat-egies in multiple realms of analytics. Where CLV starts to differ is its approach



7

to increasing value for the business. Increasing value is an internally driven thought from the point of view of the insurance company, but the push can often be too strong from the customers point of view.

Insurers need to move away from the mindset of How can I make as much money off of a product today and think about What is the relationship between us and our customers over the next 20 years; what is that worth to us? Most companies live quarter-to-quarter, but they need to shift away from that mindset, especially given the long-tail nature of a big part of our business said Mourad.

Even the mental model can change from increasing lifetime value to helping customers understand more. Increasing the overall financial education of cus-tomers and offering lifestyle-based solutions can lead to consistent wins, es-pecially when it comes to less expensive products such as renters insurance.

Nobody wakes up in the morning and says Oh great, I want to buy insurance, even though they should. We need to do a better job at managing the image of the insurance industry, highlight the benefits of our products and services, and contribute to increasing the publics overall financial literacy, said Mourad.

That image management comes from working with the customer and meet-ing their needs, as long as the approach is tailored without crossing a line.

Avoiding the Creepy Factor of PersonalizationAnalytics, whether it drives CLV or marketing, must strike a balance between not being too intrusive for the customer and being helpful. A risk-mapping tool for the customer lifestyle or business profile should help map out the risk journey and match it up with solutions along the way. However, the solutions must rely on information that the individual has shared or does not hold as too personal.

Insurers have enough insight from the customer point of view to have a nar-row persona. Using analytics can help insurers determine what products can actually be added and sold. Its a more personalized view today; it ties people to a community, geography and other group. We can match to personality, such as pairing a life policy to someone that is more risk adverse, noted Srinivasa.

Insurers can avoid this creep factor by taking a customer-centric approach and managing all of their work through that lens. Create a customer-centric culture doesnt mean beating the competition or extracting as much money as possible from customers in a single transaction. Its providing a service instead of a product.

Companies should act as risk engineers and advisors to their customers. Pro-file them, understand their lifestyle and business needs, and suggest products and services that actually help them as opposed to just helping insurers sell



8

more products. This approach should lead to high lead conversion and cus-tomer retention rates. said Mourad.

Looking outside of the insurance industry may provide some key tips for handling personalization without becoming creepy. A common best practice is to simply ask customers more questions and give them more options in terms of opt-outs and preferences. Establishing a preference center that cov-ers marketing message frequency and avenue goes a long way to improving relationships.

This type of build-out, alongside other systems to manage customers as they move through the product lifecycle is an essential part of developing a busi-ness unit that can properly hone and address CLV.



9

BUILDING A CLV-CAPABLE UNIT

Creating the Right TeamModern analytics programs often specialize with data scientists, or further with actuaries in the insurance industry. However, the rapid expansion of plat-forms and analytical needs is changing the desired combination.

Understanding and quantifying customer lifetime value requires a lot of di-verse, advanced modeling techniques. This includes the capability to develop new strategies and analytics that arent traditionally done in the insurance space. While these units are often part of a broader actuarial organization, the focus must be on the data science itself.

If you can consolidate all of your data scientists, analysts and programs, its a great thing to do if you can set the strategy for the company. This helps you understand the data and leverage it from all across, with unified metrics across everything, said Srinivasa.

Stronger teams are pairing statisticians and scientists with economists and those who have experience in the product side of insurance. Unfortunately, this effort is hampered by a poorin terms of size onlytalent pool that is not expected to make significant gains for the next three to five years.

Our philosophy is that we are not building models for sake of doing science experiments. We need to take the perspective of expenses, losses, premiums, retention, and other related business metrics and observe how CLV models consume and impact the bottom and top lines, said Arora. That requires people who are talented at building the models using state-of-the-art data sciences as well as adept at understanding the operational aspect of business. An effective CLV modeler would be one who can marry these two aspects into one.

Improving Touch PointsCustomer lifetime value is an ongoing process and it requires more consistent interactions with customers than the typically one to three that most insur-ers will see per customer annually: renewals, claims, and customer-instituted changes such as getting a new car or moving.

The amount of touch points and the level of data thats available to us are all increasing rapidly. IT is becoming even more important because a lot of the automation that we couldnt do in the past is now facilitating model growth and adjustment without us continually needing to process that information, said Srinivasa.

What we noticed when building the three underlying models premium, cost, retention separately and then combining them, was that they each rely on different data attributes captured at different touch points. Its essentially a



10

tracking system that draws upon data from customer interactions and up-dates over time when combined as part of customer lifetime value measure-ments. The models we are building today need to evolve into self-learning systems that update instantly with each new interaction, said Arora.

From a sales point of view, analytics facilitate data mining to segment cus-tomers, match personal or business risk needs with the right services, at the right time. Everyone talks about Big Data, but thats yesterdays news. Com-panies want data, and need it, right now! Customers behavior changes from day-to-day. We need to monitor that, adjust, and tweak along the way using event-triggered analytical capabilities, said Mourad.

That tweaking will not come through traditional channels in either data mod-eling or location. The next balancing act to observe is about the nature of data itself and its sources in particular.

Datas Part and LocationDeveloping and adjusting the models for CLV takes powerful systems that can integrate with a broad spectrum of tools and data sources. Customer lifetime value analysis, for many, draws upon all other existing data sets and a variety of third-party information that can bolster its efficacy.

While other analytics platforms can be niche and siloed for data processing, CLV requires an open approach to pull in relevant information from all dispa-rate systems. On top of that, the CLV platform must also allow for data gover-nance and cleanup before processing. The goal is always to work with mod-el-ready data, but organizations are consistently finding that they must do some cleanup, whether theyre working with internal or external data sources.

Data itself must play a neutral role in company operations for it to lend analyt-ics any credence. Starting a project with a stated goal of proving or disproving something inherently makes the system less objective. Losing objectivity is a prime way that models degrade and become less accurate.

Accuracy also depends on information sources. The proper mix of internal and external data is up for debate. Some of our experts are focusing on their existing data to manage CLV, while others say that the largest increase in data for proper CLV measurement must come from outside of the organization.

Insurers must determine how to balance their own data that may have fewer updates and external data, which is ultimately available to all of their compet-itors as well.

We use a mixture data and have access to a lot of third-party sources. There are individual insurance data, company data, and other information thats legally available, Srinivasa said. Our goal is to use a lot of external data, and



11

we would never want to slow that down, but we would also like to use some of the secret sauce with our internal data thats not available to competitors to provide a unique value to the customer.

If you want to use text mining such as underwriting, claims adjuster or customer service notes or any relevant text data we need better systems to find that useful nugget. We need something that understands the insurance jargon and properly identifies useful information from the organic text captur-ing practices across insurance operations, said Arora.

Does Social Media Have a Role?Social represents a promising place for expansion, but it still struggles with some adoption hurdles around quality, veracity, and processing.

Social mining will give us a lift in our models. Theres also some learning that will go with text mining. It provides a way to improve our predictive models, but must be a slow and well-thought application to be done correctly so it can be used in enterprise-wide models, said Arora.

And as with many other industries, social media comes with a variety of caveats.

The amount of external data is also huge. The problem is determining how good that external data is. When youre looking at external data, quality matters. Without quality, its no good to you, and you have to determine this before using it, noted Srinivasa.

Social media will likely be part of an analytics future for CLV because of its accessibility and sheer volume. It can serve as a starting point for a continual monitoring of customer information and it provides the insurer with a plat-form to start the conversation. Public social media posts are a place where the customer has signified that theyre willing to provide information.

The starting point should be external data if you really want to get serious about analytics. This should be coupled with internal data but most insur-ers really have a few basic data points about customers because they only interact with them two to three times per year. Compare this level of custom-er interaction to that of Facebook, Google, or even ones local supermarket. We should aspire to follow the customers life journey, when she checks in at a new location for example and use event-triggered analytics to notify her about a risk profile change and then suggest ways to mitigate it through the use of insurance, said Mourad.

These types of nanosecond level systems exist for major service providers, but are they possible in insurance? In many areas, real-time analytics are in big demand. While this hasnt reached CLV, it will soon start to bleed over, accord-ing to our experts.



12

The problem is cost not the capability. We can get it, but we have to afford it. It becomes a question of whats important and what we can live without, cau-tioned Srinivasa. There are many services that can provide instant, real-time options, but we also dont need it for each insurance vertical. We have to be smart and know when we dont need the Cadillac solution, but still get the services we need.

Feedback Loops and Starting PointsTackling CLV requires a strong buy-in from executive leadership, and that means a foundation of proper analytics and proven results before the conver-sation can begin. For such an in-depth process, many in leadership roles will want to see analytics wins for short-term and long-term projects.

That necessitates a slow rollout for CLV for some industries, but refining different models with varying feedback loops ultimately makes CLV analytics stronger. The key is to balance wins for buy-in and understanding of value for business cases with different return timeframes.

Unless you have a lot of historical data and youre just going to clean that, analytics and CLV processing does take at least a year, sometimes up to three years, for your tests. Is that a burden? Certainly. But thats also why predictive analytics is playing a larger role. Were able to provide some guidance about what the future will look like, said Srinivasa.

Some models, such as demand models, have a very quick feedback loop. Insurers can quickly crunch the numbers on how well a new product launch is faring in relation to how the issue rate has improved or declined. These present a good place to start when developing buy-in, but CLV also requires a foundation in longer-term projects.

Industry experts suggest looking to areas such as retention that have imme-diate and longer-term loops. Tracking both can help insurers refine models to address both price increases at renewal and observe trends that happen during the year that may be related to policy loss at non-renewal times.

It can be a burden in the sense that it takes longer to fine tune models and make adjustments, but technologies have evolved and they can help insurers reduce many of these cycles.

In-House VS. Off-The-ShelfThe analytics space is moving swiftly into the digital space and third-party vendors are working to capitalize this expanding market. While opinions differ on the quality of robust offerings available to the market, all of our experts noted that more off-the-shelf products are being adopted.

Customer lifetime value is among the most complex modeling tasks, chiefly



13

because of its reliance upon data from other modeling, which often neces-sitates that systems have at least some level of in-house development. The debate of whether to build or to buy is multifaceted and sometimes becomes a choice of when to develop in-house in order to foster growth in a particular skill or competency.

The complexity of the decision typically makes the mix-and-match approach viewed as a best practice, especially in growing departments. This allows companies to build and buy based on usage, and then expand models based on what works best in data exploration and staging.

It behooves us to have multiple tools in our toolbox. Having a mix of services in our toolkit allows us to use the best in each area. From model to model and problem to problem, we can best approach the need with this mix. Have you ever fixed a car with many problems with one tool? Our business is like that, said Arora.

Theres no single technology. Its always a set of technologies that we need to work to integrate and customize based on our needs. We have to provide a level of support and internal development, noted Srinivasa. For customer lifetime values we are already seeing some tools to look at risk and add these options into broader systems.

There is also no single approach that can guarantee success, but modern an-alytics are slowly turning to reach industry and niche needs, so the landscape may change dramatically and very quickly as outside companies realize how much money is to be made by addressing insurances fertile grounds.



14

MOVING FORWARD

Hurdles to CLV AnalyticsThe customer should be at the heart of everything we do. If you let this princi-ple guide your behavior, youre likely to see your customer lifetime value in-crease in a way that is higher than what youve ever expected, said Mourad. This mindset, however, is not as prevalent as it should be and needs to start from the top. Customer-centricity is just isnt present in enough corporate cultures.

Unfortunately, there are points in time where policies and projects arent ready to be customer-centric because there is not enough data available at present. In these circumstances, insurers must turn to strong leadership to steer the course of action. A business strategy is needed to make the business change, and then modeling and analysis can be applied. This leadership also often dictates the hiring process when it is time to expand.

The Talent pool will always be a challenge. If youre looking to expand over the next three years, it may become even more of a challenge. Most insurers plan to grow their staff and that means more competition for these types of scientists, noted Arora. The challenge grows because very few people have CLV in their skillset profiles. A lot of predictive modelers have an IT skillset or parallel industry knowledge, but very few have insurance domain knowledge and a predictive modeling skillset.

One point our experts touched on was the potential for a role between data scientists and insurance companies taking part in the education process. Oth-er sectors, especially manufacturing, have seen improvements in the future workforce by partnering with universities and colleges to guide programs.

Guidance and partnerships allow degree programs to focus on the right skillset and theyve been successfully applied on varying degrees of skill and intensity.

Having the insurance domain knowledge plus data science skills is a very strong combination. Its going to be hard to find that in the next couple of years, said Arora. The challenge will remain for the P&C insurance industry: how do we bring the business perspective to these data scientist graduates?

Final ThoughtsTo close this report and help spur the industry toward understanding and adopting a customer lifetime value approach, were turning over the spotlight to our experts.

These salient points and specific takeaways are designed to help you under-stand company structure, client perspectives, and expected industry challeng-es as you look to implement CLV models and create company mindshare for your data-driven workforce.



15

The biggest challenge that I have seen is that there are very few vendors who are creating high-quality, insurance-domain trained platforms with wider predictive modeling capabilities. Those who create these platforms are using limited modeling methodologies or require expensive integration and special-ized knowledge. Having stronger insurance-mainframe products that are end-to-end predictive analytics products and facilitate seamless integration with the policy and claims operational systems is one area that needs a lot of attention. There are some efforts but it will likely be five years before we see more than just a few products mature, said Arora.

Analytics and CLV systems must be seen as a business initiative and not an IT initiative. As an insurer, you are going to struggle unless you have a focused team and can show how analytics and customer lifetime value is a core business benefit. Companies who have a lot of historical data are doing well and that should be a sign to others to invest. Its going to be difficult for some to catch up, but the last thing you want is to become another Blockbuster, said Srinivasa.

Dont focus internally, focus externally, and start with the customer. If you really focus on solving the customers problems by offering them solutions that respond to their lifestyle and business needs, then you have a higher probability of achieving greater product density, better profitability, higher customer satisfaction, and retention. At the end of the day, its the customer who decides whether or not your company succeeds, said Mourad.

_GoBack

content customer lifetime value

Documents

customer lifetime value

insurance analysis

insurance analyticsanalytics

highvalue customers

lowvalue customers

insurance management

current customers

zurich insurance group