In a complex landscape of old and new, cars and networks are being built to be self-aware, adaptable and communicative with one another and humans in real time. We live in extraordinary times where there is transformative experience with three kinds of cars — some fully automated, others with simple systems for accident avoidance/traffic routing and still others that account for today’s average car.
Appliances and sensors in smart homes are network-connected with seamless integration and intelligent collaboration between devices and analytics that puts homeowners in control, making them co-creators of customized experiences.
From managing chronic diseases at one end of the spectrum to preventing disease at the other, the social network of things is revolutionizing healthcare, too. A person’s data is continuously being gathered and used to diagnose illness and to align the best providers and treatments as quickly as possible. Devices in the predictive realm have the potential to detect the onset of a wide range of health risks, such as high blood pressure and early signs of delirium.
As insurers, we are paymasters in the business of protection. Not only do we have a vested interest in mitigating loss, but we also have a huge responsibility to support and incorporate prevention and early intervention techniques to provide real value to our consumers. With devices that are highly networked and predict, negotiate and have an impact on outcomes, we find ourselves at the brink of redefining the underlying concept of insurance — from one of pooling risk to sublimating risk altogether.
This article is an excerpt from a white paper, “Capturing Hearts, Minds and Market Share: How Connected Insurers Are Improving Customer Retention.”To download it, click here.
Part 1 of this series explained why retention is so much harder these days. This article explains how insurers can solve the problem.
Know Your Customers
Understand values and behaviors of your customers. Start with available data sources. Augment structured data from traditional back-end systems with unstructured data like those collected through call centers and written correspondence. With these data, you can deduce meaningful patterns and behavior-based customer segments.
Enter into active dialogues to establish meaningful relationships. Use social media analytics and conversations via social networks to increase customer touch points. Use the knowledge gained about their wants and needs to sustain intermittent conversation about things that are helpful to the customer.
Build an environment where sharing data creates mutual benefits for customer and insurer. Transparency is key. Create and publicize a “customer data policy” that specifies how and when you will use data shared directly or generated through means such as “big data gathering,” and how customers will benefit. Use shared data to create extra customer value, as detailed in the next section.
Offer customer value
It is no surprise that customer value – that is, the value that a customer derives from the relationship with his or her insurer – drives customer loyalty. In a previous study, we defined customer value as the adequate response to customers’ changing needs. How can insurers translate this to understand which value drivers influence retention?
The fairness zone: The first component of customer value we will discuss is – again – price. For most of our respondents, the absolute level of premiums mattered less than individual perception of price fairness – a too-low price has the same negative effect on loyalty as one that is too high (see Figure 5). This means that a customer to whom the price seems right is two to three times less likely to switch in a given year. The fairness of premiums is also an emotional component that insurers need to get right (and tools like social media analytics can support this).
What is the power of brand? The second value factor we examine is brand. What is the retention value of a good brand? According to our data, it’s less than expected. Only 21% of our respondents name “reputation” as one of the factors that cause them to stay with their chosen insurers. Could brand still be an implicit value driver?
Our recent consumer products industry study, “Brand enthusiasm: More than loyalty,” showed that brand consciousness and brand loyalty are changing, and our data echoes those findings. Only 12% of respondents have a high brand consciousness, and that is the only bracket where it has a strong effect on loyalty in the insurance world (see Figure 6).
This suggests that an extra investment in brand creates limited loyalty returns; a great brand only matters if your customers belong to the few who are brand conscious to begin with. Moving customers to the “high” consciousness bracket might prove difficult to achieve.
So how can insurers, many of whom already have a strong brand, make this work to their advantage? We propose adopting the concept of “brand enthusiasm.” Brand enthusiasm is influenced by the level of customer engagement, which we will explore in the next section, and again leads to the increased emotional involvement with the insurer that we call “heart share.”
Transparency, not complexity
Last but not least, we examined other product-related value drivers. We suspected that the often high complexity of insurance products has a negative effect on loyalty, but our data proved this hypothesis wrong. Although product complexity might be a deterrent to purchase (which was outside the scope of the survey), even those who perceived the product they bought to be highly complex did not show a higher propensity to switch.
In contrast, transparency about the product strongly influences loyalty in a positive way. Transparency leads the customer to understand and be more comfortable with the product (and the insurer) even when it is complex. Seventy percent of respondents who reported that their product understanding was high expressed high loyalty – almost three times as many as those with low product understanding. High transparency leads to rational involvement: the “mind share” in our study title.
What current technology can help insurers promote customer value? To give customers an emotional connection and involvement with a fair price and a transparent product, telematics is ideal. Regarding fairness, customers can see that the rate is based on their personal risk and influenced by their personal actions. Examples include a “pay-how-you drive” auto product or the use of exercise tracking devices in health insurance. Transparency of this sort of auto product is high, and for many telematics offerings, there is an additional fun factor by seeing how well you drove, thus competing against yourself for better driving scores.
Recommendations: Offer value
Support your customers in areas they personally value, even if they are not directly related to your core business. Offer information to your customers in useful areas that are widely related to their coverage: for example, traffic or weather information for auto insurers. Create communities of interest – in social networks or directly hosted by you – to share news, tips and enhance exchange among like-minded individuals and your organization.
Add risk mitigation or prevention into your products and services. Commercial insurers have been doing this for years. Start offering these at the outset of the contract relationship. Later, add tracking via telematics, plus assistance services.
Personalize offerings and provide pick-and-choose product options. Product flexibility starts in the back end. Your application architecture must enable a modular approach to products and services. Build a roadmap for flexibility using industry standards such as IAA. From the front end, add in-depth analytics to flexibly balance the offered options with market needs.
Fully engage your customers across access points
Incumbents at risk
One characteristic of the Millennial customer is the desire for omni-channel shopping for their goods and services. For insurance shoppers, this extends well beyond using traditional insurers – many Millennials are open to using adjacent providers and new entrants into the market (see Figure 7).
In the short run, offerings like Google Compare mainly replace existing aggregators; insurers still cover the actual risk. In the long run, online service providers – given their good customer knowledge across many products and services – could start to accept risk themselves. In this case, customers’ already-stated willingness to switch would become a real threat to incumbents.
In addition, the reason respondents gave for considering those providers should be troubling to insurers: They describe non-traditional providers as faster, more transparent and easier to reach (see Figure 8). To counter this, carriers need to engage with their customers across a broader range of access points than ever before.
The age of mobility
One option is to be more accessible on the go. Ninety-six percent of our respondents own some form of mobile device, most often smartphones (owned by 82 percent of respondents) and tablets (owned by 49 percent); they have become commonplace modern accessories throughout the world. Still, only 13 percent of respondents who bought their insurance online, either directly or via an aggregator, used their mobile devices to buy. On the other hand, 29 percent of all respondents stated they would like their insurers to offer an option to buy through a mobile device, and that this would increase their loyalty.
Expanding mobile offerings outside of searching and buying is an instant accessibility increase with potential loyalty gains. The biggest effects would be in submitting claims (42 percent) and in simple communication (43 percent). Many insurers have already invested in apps for claim submission, but again, they seem to be either unknown or too hard to use.
The effect of expanded mobile offerings differs widely by country, with the more empowered customers in developing markets increasing their loyalty more (see Figure 9). Still, given the larger market sizes in mature markets, investment in mobile services are still expected to generate returns.
Connecting everything, everywhere
Looking toward the longer term, insurers will also need to consider investing in the Internet of Things (IoT) to enhance customer engagement. A growing number of consumers either own or can imagine owning an Internet-connected device like a refrigerator or a washing machine (56 percent of millennials, 36 percent of boomers).
Currently, only a small percentage of customers told us they would be comfortable with insurers using the data from these devices (21 percent of millennials, 15 percent of boomers.) Still, for those respondents, the greater accessibility and convenience of the IoT would lead to an increase in loyalty. Insurers can make use of the IoT if they sell it right: with high transparency regarding how the data is used (and not used).
Recommendations: Fully engage
Embrace mobile to enable constant access for your customers. For your main set of lines of business, envision “customer journey maps.” These maps document the typical steps a customer must take during the provider relationship, from needs discovery through information gathering and purchase, all the way through after-sales services and claims processes. For each step, identify interaction options to generate a complete picture of potential mobile touch points.
Support decision making throughout each step of the sales process at the convenience of your customers. Create one unified front end for the customer, whether they come in through an agent, call center, the Internet or mobile devices. Make customer data and product information equally available at all touch points.
Have information available anytime, anywhere to support instantaneous fulfillment of client requests. Equip tied agents, underwriters, claims adjusters and other fulfillment roles with mobile technology like tablets and other handheld devices. This allows you to abandon a fixed workplace in favor of greater fulfillment flexibility – for example, claims can be adjusted directly on-site.
Ready or not – are you capturing the hearts and minds of your customers?
How are you using your in-house sources of customer knowledge? In what ways are you gathering and adding external information, such as that from social networks? How are you combining internal and external information? How is it used to generate greater customer value and loyalty?
Where and how are you using needs-based or persona-based segmentation approaches? How will you deepen your level of understanding individual customers?
To what degree can your customers pick and choose options from your product portfolio? What is your plan to remove the barriers to further customization?
How do you communicate with your customers? What is your approach to staying abreast of the ways they prefer to communicate, now and in the future?
In what ways are you engaging millennials? And how will you stay updated to address the customers of the future, such as Generation Z and beyond?
This article is an excerpt from a white paper, “Capturing Hearts, Minds and Market Share: How Connected Insurers Are Improving Customer Retention.”To download it, click here.
ITL Editor-in-Chief Paul Carroll recently hosted a webinar on “Captivating Customers With All-Channel Experiences,” featuring experts from Capgemini and Salesforce.com and the former chief customer experience officer at AIG. To view or listen to the webinar, click here. For the slides, click here.
In almost all cases, to provide experiences that captivate customers, insurers must modify their legacy technology infrastructure.
Some insurers are building an overlay, taking an innovative approach to the technology that customers touch, but that isn’t enough. Insurers need to take a broader look and make sure that new customer technology integrates effectively with back-end systems such as claims, policy administration, billing and enterprise resources planning (ERP). That way, all parts of the enterprise are driving toward providing the desired customer experience.
These changes will make agents more satisfied and efficient. The changes will also help captivate customers, who want to deal with all parts of the insurance process as one seamless operation. That means both upgrading the technology for agents and incorporating them tightly into the insurer’s systems.
Cloud solutions have proven to deliver capabilities insurers need faster and with less business disruption than traditional, on-premises alternatives. The result is lower total cost of ownership and significantly reduced project risk. Such an approach lets insurers remain firmly focused on the customer. Insurers can focus on designing the customer journey and experience rather than be burdened by the design, build, test and deployment of the technology.
To get there from here, insurers need to integrate the interactions among employees, customers and agents and among social networks, internal systems and business processes. The result needs to support any device, use unified business logic and provide access to data. There needs to be a consistent customer experience across all channels (self-service, agents and call centers).
Exhibit 3 provides a sample of the necessary components (in this case, on a Salesforce platform):
Customer Interaction Hub, which provides ease of use and information accessibility
Platform, which provides multi-device capabilities
Service Cloud, which helps agents track the history of customers and policies and engage regularly with customers
A cloud-based contact center telephony system. The system (in this case, Odigo) must provide services such as intelligent call routing, natural language recognition, mobile channel integration, biometrics or voice-based authentication, multi-site routing and management dashboards. The platform must allow customers to originate a transaction in one channel and take it forward in another, such as self-service.
Document signature software, to allow customers to sign quotes and policies online
Integration with popular insurance software packages for policy quotes, binding, claims
When developing for a multi-channel experience, it’s crucial to do lots of A/B testing – changing one variable at a time for a sample of customers, seeing how they react and incorporating those changes that produce better results. It’s also important to actually watch customers to see how they navigate a process – where they stop, where they start up again, where they get sidetracked, where they get confused. We’ve watched customers many times, and the results can be surprising enough to at least require considerable tinkering.
For example, with three releases each year, Salesforce has delivered 47 major releases since its inception. Each release is informed by learning from how users behave, adopt and use Salesforce’s features. As a result, more than 1,700 features have been sourced directly from Salesforce’s customer community. In insurance, Salesforce learns from more than 2,500 insurance customers. These continuing improvements happen in an agile fashion, and follow an iterative cycle of release, learn and improve.
The race to become a leading insurer that is able to attract, satisfy and retain customers is in full motion. Those insurers that can blend traditional channels and digital channels in a seamless way will lead the race, creating clear competitive advantage with the capabilities in place to capitalize on market disruption over the coming years.
The first two articles in this series are here and here. For the white paper from which these articles are adapted, click here.
Historically, “analytics” has referred to the use of statistical or data mining techniques to analyze data and make inferences. In this context, analytics typically explain what happened (descriptive analytics) and why (diagnostic analytics). If an insurer saw its customers moving to its competition, it would analyze the characteristics of the customers staying or leaving, the prices it and its competitors offer and customer satisfaction. The analysis would help determine what was happening, who was leaving and why. In contrast, predictive analytics focuses on what will happen in the future.
“Predictive analytics” has a fairly broad definition in the press but has a specific meaning in academic circles. Classical predictive analytics focuses on building predictive models where a subset of the available data is used to build a model using statistical techniques (usually some form of regression analysis — linear, logistic regression etc.) that is then tested for its accuracy with the “holdout” sample. Once a model with sufficient accuracy is developed, it can be used to predict future outcomes. More recent predictive analytics techniques use additional machine learning techniques (e.g., neural network analysis or Bayesian probabilistic techniques).
Insurers have used predictive analytics for almost two decades, but, despite its usefulness, it has two main drawbacks:
Focus on decision versus action: Predictive analytics can tell you what is likely to happen but cannot make recommendations and act on your behalf. For example, a predictive model on the spread of flu can determine the prevalence and spread of flu but cannot tell you how to avoid it. Similarly, a predictive model of insurance sales can determine weekly sales numbers but is incapable of suggesting how to increase them.
Reliance on single future versus multiple alternative futures: While we can learn from the past, we know that it may not be a good predictor of the future. Predictive models make linear predictions based on past data. They also make certain assumptions that may not be viable when extrapolating into the future. For example, regression requires the designation of a dependent variable (e.g., insurance sales), which is then described in terms of other independent variables (e.g., brand loyalty, price etc.). While this method can help predict future insurance sales, the accuracy of the numbers tends to decrease further into the future, where broad macro-economic and behavioral considerations will play a greater role in sales.
In response, there are a number of firms, authors and articles that propose “prescriptive analytics” as the next stage of the analytics continuum’s evolution. Prescriptive analytics automates the recommendation and action process and generally is based on machine learning techniques that evaluate the impact of future decisions and adjust model parameters based on the difference between predicted and actual outcomes. For example, insurers could use prescriptive analytics for automatically underwriting insurance, where the system improves its conversion ratio by adjusting price and coverage on a continual basis based on predicted take-up and actual deviations from it.
However, while prescriptive analytics does address the first of predictive analytics’ drawbacks by making and acting on its recommendations, it usually fails to address the second shortcoming. Prescriptive analytics relies on a single view of the future based on historical data and does not allow for “what if” modeling of multiple future scenarios. The critical assumption is that the variables used to explain the dependent variable are independent of each other, which in most cases is not true. While the analysis can be modified to account for this collinearity, the techniques still fail to use all of the available data from domain experts. In particular, prescriptive analytics does not take into account the rich structure and influences among all the variables being modeled.
In addition to prescriptive analytics, we believe that complexity science is a natural extension of predictive analytics. Complexity science is an inter-disciplinary approach to understanding complex systems, including how they form, evolve and cease to exist. Typically, a system that consists of a few well-known parts that consistently interact with each other in a way we can easily understand is a “simple” system. For example, a thermostat that can read (or sense) the temperature and reach a given target temperature is a simple system. At the other end of the spectrum, a system with a very large collection of entities that interact randomly with each other is a “random” system. We often use statistical techniques to understand the behavior of the latter. For example, we can gain an understanding of the properties of a liquid (like its boiling point) by looking at the average properties of the elements and compounds that compose it. The fundamental assumption about such systems is that its parts are independent.
In between simple and random systems are “complex” systems that consist of several things that interact with each other in meaningful ways that change their future path. For example, a collection of consumers watching advertisements, talking to others and using products can influence other consumers, companies and the economy as a whole. Complexity science rejects the notion of “independence” and actively models the interactions of entities that make up the system.
Complexity science identifies seven core traits of entities and how they relate to each other: 1) information processing, 2) non-linear relationships, 3) emergence, 4) evolution, 5) self-organization, 6) robustness and 7) if they are on the edge of chaos. Unlike a random system, the entities in a complex system process information and make decisions. These information processing units influence each other, which results in positive or negative feedback leading to non-linear relationships. As a result, properties emerge from the interaction of the entities that did not originally characterize the individual entities. For example, when a new product comes on the market, consumers may purchase it not just because of its intrinsic value but also because of its real or perceived influence on others. Moreover, the interactions between entities in a complex system are not static; they evolve over time. They are capable of self-organizing and lack a central controlling entity. These conditions lead to more adaptive behavior. Such systems are often at the edge of chaos but are not quite chaotic or entirely random.
Two parallel developments have led to complexity science’s increased use in practical applications in recent years. The first is the availability of large amounts of data (or big data) that allows us to capture the properties of interest within each entity and the interactions between them. Processing the data allows us to model each entity and its interactions with others individually, as opposed to treating them as an aggregate. For example, a social network is a complex system of interacting individuals. We can use complexity science to understand how ideas flow through the social network, how they become amplified and how they fade away.
The second development accelerating complexity science’s use is the inadequacy of classical or statistical models to adequately capture complexity in the global economy. Since the financial crisis of 2007/8, a number of industry bodies, academics and regulators have called for alternative ways of looking at the world’s complex social and financial systems. For example, the Society of Actuaries has published a number of studies using complexity science and a specific type of complexity science called agent-based modeling to better understand policyholder behavior. In addition, health insurers are building sophisticated models of human physiology and chemical reactions to test adverse drug interactions. As another example, manufacturers are modeling global supply chains as complex interacting entities to increase their robustness and resiliency.
Agent-based modeling is a branch of complexity science where the behavior of a system is analyzed using a collection of interacting, decision-making entities called agents (or software agents). The individual behavior of each agent is modeled based on available data and domain knowledge. The interaction of these agents among themselves and the external environment can lead to market behavior that is more than just the aggregate of all the individual behaviors. This often leads to emergent properties. Such models can be used to evaluate multiple scenarios into the future to understand what will happen or what should happen as a result of a certain action. For example, a large annuity provider has used individual policyholder data to create an agent-based model in which each one of its customers is modeled as an individual software agent. Based on specific policyholder data, external socio-demographic and behavioral data, as well as historical macro-economic data, the annuity provider can evaluate multiple scenarios on how each annuity policyholder will lapse, withdraw or annuitize their policy under different economic conditions.
In conclusion, as companies look to capitalize on big data opportunities, we will see more of them adopt prescriptive analytics and complexity science to predict not just what is likely to happen based on past events but also how they can change the future course of events given certain economic, political and competitive constraints.
A company’s reputation, which is core to its profitability and long-term competitiveness, faces new challenges as information speeds blindly through online media and social networks. Lanny Davis, former assistant to President Clinton on crisis management and principal in Lanny J. Davis & Associates, recently noted that, in the age of the Internet, “you never get a second chance to change a first impression. Once your reputation is smeared and your character unfairly attacked, the eternal misinformation echo chamber of the search engine allows the harm to continue eternally, unless you fight back — early, with all the facts, often yourself — until the truth gets in the way of the search engine lies.”
When a corporate reputation is tarnished, a company can lose its trust factor; investor confidence is weakened; and a company’s share price can be reduced. In extreme cases, a damaged reputation can lead to a company’s downfall. “Hackgate,” “Rupertgate,” or “Murdochgate” -– names given by the press to the News International phone-hacking scandal – led to the demise of News of the World newspaper.
Let’s make a list of some leading triggers to reputation failure:
unethical behavior such as Sears’ management team’s unrealistic performance quotas for its car repair business, which led to overbilling and created a scandal in the 1990s.
financial irregularities, such as those that led to Enron’s bankruptcy.
executive misconduct, such as the conviction tied to insider trading that led to Martha Stewart’s resignation.
environmental violations, such as Nike’s exploitation of workers in sweatshops, failure to provide work environments that are safe and contact with cotton factories using slave labor—issues that dogged Nike through the 1990s and beyond.
safety & health product recalls, such as followed allegations of “unintended acceleration” in Toyota cars.
security breaches, such as the recent one at Target in which tens of millions of people had credit-card data stolen.
In other words, much as Murphy’s Law says: “Anything that can go wrong will go wrong.”
What should a corporation do to protect its reputation?
Use your CEO: Fred Smith, FedEx’s legendary founder, is a good example. A good CEO embodies and reiterates a company’s values, code of ethics and vision. Your CEO regularly communicates honesty and transparency and is trusted with your corporate reputation.
Perform an S.W.O.T. analysis: Identify your company’s strengths, weaknesses, opportunities and threats.
Develop a corporate reputation strategy: Johnson & Johnson is still reaping reputation benefits more than 30 years after its swift and sweeping recall of Tylenol and institution of tamper-proof packaging after some maniac laced some pills with cyanide and put them in bottles on store shelves, killing seven people.
Monitor your reputation online. Constantly check social media sites and your own website. No company can afford to be reputation-blind, and no suit of armor is impenetrable.
Be honest, factual and open with the media.
Create a plan to manage an unexpected crisis. Execution is the cornerstone. Train everyone on identifying the crisis, what to do and who gets contacted. Preparation is essential to managing potential and actual crises in a timely fashion. Communication is no longer one-way; it’s now two-way.
Evaluate the purchase of corporate reputation insurance. For 20 years, the insurance industry has known that how a company manages a reputation crisis will have a dramatic impact on the cost of civil litigation arising out of that crisis. For this reason, insurance purchased for the risk of shareholder lawsuits, directors and officers insurance, has from time to time included an option to purchase, or included automatically, “crisis management” insurance. This reimburses the company for the cost of crisis management expert fees up to a set amount, usually $50,000 to $200,000.
However, since 2010, there has been an outbreak of “new and improved” reputation insurance policies from name-brand insurance carriers like Zurich (Brand Assurance), AIG (ReputationGuard), Munich Re (Reputation Insurance) and a number of Lloyds syndicates, including a standalone reputation policy produced by Steel City Re.
Some carriers emphasize reimbursement of crisis-management expenses while others are more geared toward reimbursing a company for a loss. Finding the right one, or right combination, can be challenging, but they are worth a look.
Be sure to check out Thought Leader Ty Sagalow's recent appearance on New York News!