For centuries, people have lived in a world where data was largely proprietary, creating asymmetry. Some had it. Others did not. Information was a currency. Some organizations held it, and profited from it. We are now entering an era of tremendous data balance — a period of data symmetry that will rewrite how companies differentiate themselves.
The factors that move the world toward data symmetry are time, markets, investment and disruption.
Consider maps and the data they contained. Not long ago, paper maps, travel books and documentaries offered the very best views of geographic locations. Today, Google allows us to cruise nearly any street in America and get a 360° view of homes, businesses and scenery. Electronic devices guide us along the roadways and calculate our ETA. A long-established map company such as Rand McNally now has to compete with GPS up-and-comers, selling “simple apps” with the same information. They all have access to the same data. When it comes to the symmetry of geographic data, the Earth is once again flat.
Data symmetry is rewriting business rules across industries and markets every day. Insurance is just one industry where it is on the rise. For insurers to overcome the new equality of data access, they will need to understand both how data is becoming symmetrical and how they can re-envision their uniqueness in the market.
It will be helpful to first understand how data is moving from asymmetrical to symmetrical.
Let’s use claims as an example. Until now, the insurer’s best claims data was found in its own stockpile of claims history and demographics. An insurer that was adept at managing this data and applied actuarial science would find itself in a better position to assess risk. Competitively, it could rise to the top of the pack by pricing appropriately and acquiring appropriately.
Today, all of that information is still very relevant. However, in the absence of that information, an insurer could also rely upon a flood of data streams coming from other sources. Risk assessment is no longer confined to historical data, nor is it confined to answers to questions and personal reports. Risk data can be found in areas as simple as cell phone location data — an example of digital exhaust.
Digital exhaust as a source of symmetry
Digital exhaust is the data trail that all of us leave on the digital landscape. Recently, the New York City Housing Authority wished to determine if the “named” renter was the one actually living in a rent-controlled apartment. A search of cell phone tower location records, cross-referenced to a renter’s information, was able to establish the validity of renter occupation. That is just one example of digital exhaust data being used as a verification tool.
Another example can be found in Google’s Waze app. Because I use Waze, Google now holds my complete driving history — a telematics treasure trove of locations, habits, schedules and preferences. The permissions language allows Waze to access my calendars and contacts. With all of this, in conjunction with other Google data sets, Google can create a fairly complete picture of me. This, too, is digital exhaust. As auto insurers are proving each day, cell phone data may be more informative to proper pricing than previous claims history. How long is it until auto insurers begin to look at location risk, such as too much time spent in a bar or frequent driving through high-crime ZIP codes? If ZIP codes matter for where a car is parked each night, why wouldn’t they matter for where it spends the day?
Data aggregators as a source of symmetry
In addition to digital exhaust, data aggregators and scoring are also flattening the market and bringing data symmetry to markets. Mortgage lenders are a good example from outside the industry. Most mortgage lenders pay far more attention to comprehensive credit scores than an individual’s performance within their own lending operation. The outside data matters more than the inside data, because the outside data gives a more complete picture of the risk, compiled from a greater number of sources.
Within insurance, we can find a dozen or more ways that data acquisition, consolidation and scoring is bringing data symmetry to the industry. Quest Diagnostics supplies scored medical histories and pharmaceutical data to life insurers — any of whom wish to pay for it. RMS, AIR Worldwide, EQECAT and others turn meteorological and geographical data into shared risk models for P&C insurers.
That kind of data transformation can happen in nearly any stream of data. Motor vehicle records are scored by several agencies. Health data streams could also be scored for life and health insurers. Combined scores could be automatically evaluated and placed into overall scores. Insurers could simply dial up or dial down their acceptance based on their risk tolerance and pricing. Data doesn’t seem to stay hidden. It has value. It wants to be collected, sold and used.
Consider all the data sources I will soon be able to tap into without asking any questions. (This assumes I have permissions, and barring changes in regulation.)
Real-time driving behavior.
Retail purchases and preferences.
Exercise or motion metrics.
Household or company (internal) data coming from connected devices.
Household or company (external) data coming from geographic databases.
These data doors, once opened, will be opened for all. They are opening on personal lines first, but they will open on commercial lines, as well.
Now that we have established that data symmetry is real, and we see how it will place pressure upon insurers, it makes sense to look at how insurers will use data and other devices to differentiate themselves. In Part 2 of this blog, we’ll look at how this shift in data symmetry is forcing insurers to ask new questions. Are there ways they can expand their use of current data? Are there additional data streams that may be untapped? What does the organization have or do that is unique? The goal is for insurers to innovate around areas of differentiation. This will help them rise above the symmetry, embracing data’s availability to re-envision their uniqueness.
The way people and companies interact with each another is tremendously different from the way they conducted business just 10 years ago. Technology is pushing the boundaries of how and when business is conducted between businesses and their customers. That being said, the insurance industry’s customer journey over the last 100 years has not evolved or diverted from its basic business model: Brokers and agents are still the primary means for insurance companies to market and sell their products. This broker-dependent model served the industry well and remained the same while other industries have evolved their delivery channels. While there are some exceptions—such as Progressive and Geico, which use direct channels quite successfully—the industry’s most prevalent delivery channel remains with agents and brokers.
Given the insurance industry’s stability and profitability over time, the notion of a distribution chain realignment or agent disintermediation seems quite unlikely. This is bolstered by the fact that many large and successful companies played by the old business model quite profitably. Accordingly, there had been little incentive in the past to alter this business model. Today, however, insurance distribution is ripe for technological disruption, and carriers that ignore this trend are doing so at their own peril. We are on the verge of the perfect storm; the magnitude of technological availability and shifting demographics in the U.S. has the potential to disrupt and reorganize almost all aspects of the insurance customer journey.
Technology’s Adoption and Diffusion: Its effects on the general population
During earlier periods of technological growth, technology created more efficiency within the brick-and-mortar framework. Businesses were able to cut costs, automate design and streamline processes. The ultimate consumer did not necessarily enjoy lower prices or a better buying experience as a direct function of improving economies of scale. Moreover, consumers did not have additional access to pricing information, product research, reviews or product promotion pieces in real time. Instead, the average consumer bought through the retail channel that businesses sold through without any alternative.
Today, access to information is widely available in real time. If you want a product review on something you are interested in at your local store, you Google it. Then, if the review is satisfactory to you, you can go to a brick-and-mortar location and purchase it, or you can log on to an online store and purchase it from your sofa. The average consumer has more information and power at his disposal than ever before. He can search for prices at no cost to him and then make purchases. According to the U.S. Census in 2013, 84% of U.S. households reported computer ownership, with 79% of all households having a desktop or laptop computer and 64% having a handheld computer. 74% of all households reported Internet use, with 73% reporting a high-speed connection.
Complementing this growth in computer home ownership is the increasing popularity of tablets. In just three short years (between 2010 and 2013), tablet ownership increased from 3% to 34%. With this advance in personal technology there comes access to information.
All these statistics raise the question, “Why is technology growth at the individual level important to the insurance industry?” Because many products offer information on the web just by clicking, there is a fundamental shift in buying behavior because of the speed of information. There is a certain convenience factor individuals currently enjoy by using digital channels for research. Convenience is a key factor along the customer journey. As an example, when buying an airline ticket, do you call the airline or simply log on to a travel site to research options and make a purchase?
Many in the insurance industry state that insurance products’ complex nature will require that consumers use agents and specialty advisers to assist with product selection. Many would agree with that statement, with some qualification. For large commercial and other extremely complicated risks, the agent and broker channel will exist, but for small commercial and personal lines the delivery channels will blur.
Some consumers will always pick up the phone or meet with someone to get a better understanding of risk products. That preference, however, may be a generational one. People born in the 1960s and 1970s did not have computers and tablets from a young age. The millennial generation is used to the convenience and the speed that digital technology affords.
As an example, a 24-year-old told the story of his first experience purchasing automobile insurance. He called a national firm’s local office to inquire about a policy. The agent was friendly but was not available to meet with him for several days. Thinking that was ridiculous, he declined the appointment and used a website to research, evaluate and price a policy. Following that, he spoke to a customer service representative who explained coverages and what they were. At the conclusion of the phone call, he paid for the policy and was done. His primary goal was to 1) get information quickly, 2) evaluate the coverages, 3) determine that the price was fair and 4) purchase his policy. This was also accomplished after business hours when it was convenient for him, not the agent. All told, using digital channels first and later interacting with a call center was the optimal delivery channel path for him.
Technology and New Channel Formation
With the widespread growth of personal computing devices in the U.S. increasing each year, insurance companies have begun to take notice. It’s not uncommon to see websites that outline the company’s products. As a general rule, however, when it comes to pricing policies, insureds are still referred to agents. Consumers of insurance products demand information on multiple channels. Many want the ability to research and evaluate products on their own, without an agent (this is an evolutionary change), but this does not mean they might not want to BUY insurance from the agent. The agent will be there to answer any final questions and to fit the product into the overall financial situation of the consumer. The real challenge for most agents is remaining relevant and finding a way to create value within the digital customer journey. To that end, agents must find a way to help expedite how information is distributed and consumed. If agents relegate themselves to becoming just order-takers, they will quickly become irrelevant and will add very little value to the process. In other words, the agent’s role must evolve to avoid obsolescence. The agency distribution channel is not dead.
While there will always be agents representing insurance companies, their roles and their interactions with the industry and insureds will change over time as new distribution channels manifest themselves. The questions of “where” and “how much value” are what is changing. Some customers will use channels differently, but it is up to agencies and brokers to understand their target market’s preferences for channel selection. Agencies who do not use an omni-channel strategy will lose business to other agencies that do. Also, agencies need to create value through content, creating a clearly defined holistic- and flexible-guidance value that resonates with customers. Those who are able to evolve will continue to thrive, but those who do not will either continue to lose business or will close their doors. If you look at the travel agent industry, the number of travel agents has declined markedly, but there are still agencies in business that provide value to their customers. These agencies simply evolved and realigned their value proposition and targeted their customer segments quite successfully. The result is that there are far fewer agencies than there were 10 years ago. The same will occur with the agency channel.
The Rise of Omni-Channel Delivery
Under the old insurance distribution model, consumers were expected to shop for insurance with their agent, who would also be there for their subsequent questions or for submission of claims. Today, consumers increasingly expect to interact with their insurance provider on the consumer’s schedule through omni-channels. Subsequently, the agency delivery channel’s role is changing.
Perhaps, spoiled by a streamlined customer experience in other industries, consumers now want to research their purchases online and then decide whether to buy online or through brick-and-mortar stores. Blogs and consumer reviews are also important to today’s consumer. The way people shop is evolving at a rapid rate, and insurance companies need to recognize that. Carriers like Plymouth Rock, for example, are experimenting with an “option direct” delivery strategy. It allows prospective insureds to quote policies and, at their option, bind the business directly with the company. If the prospective insured does not purchase the policy online, it is released to an “agent exchange” where an agent purchases the lead and then follows up to cross-sell, up-sell or quote other companies. Using this approach, Plymouth Rock allows for a direct distribution channel with an option to work with an agent for coverage advice.
Time will tell if Plymouth’s model is successful, but, given the demands for omni-channel availability, it certainly makes sense that the company tests the model’s efficacy. This test presents an interesting business practice. Testing new distribution channels is a must. No one person—or expert—truly knows how distribution channels will evolve over the next few years. What is widely known, however, is that these channels exist and that they are viable alternatives with lower cost structures to insurance carriers. Also, what doesn’t work this year may work quite well five years from now. These new channels may just be a step in the customer journey, or they may turn out to be the point in the customer journey where purchases are made: i.e. the moment of truth. Either way, understanding target customer preferences is critical in an omni-channel world. Successful insurance companies will constantly test their channels to determine what the most effective strategy is for sales conversions.
Omni-Channel and Commoditization
With the proliferation of multiple distribution options, insurance companies are increasingly forced to compete on price instead of features. The growth of price comparison sites and aggregators makes buying insurance based on price even easier for the consumer. These channels provide a list of insurance policies ranked in ascending price order. On the surface, this presents challenges. From the carriers’ perspective, this is not the optimal solution because price alone does not explain the value of a policy or a company’s ability to pay claims. From the consumers’ perspective, buying solely on price potentially subjects them to improper or incomplete coverage. Yet, despite these challenges, over the last decade insurance product commoditization has occurred (e.g. personal auto).
To counter commoditization, insurance companies need to position themselves effectively to differentiate their product offerings. Evaluating the demographic preferences and buying habits allows insurance companies to more effectively target their customer base and not rely on price alone as the distinguishing factor. Deciding on a differentiation framework is even more important today given the changes in the market. Companies can compete on service (e.g. fast, no hassle claims), 24/7 accessibility, customer experience, unique product offerings, speed to market, leadership in the industry, etc., but they must fight to make sure these differentiators are made known in the midst of increasingly commoditized interfaces, distribution and thinking. To counter commoditization in the digital era, it might behoove insurers to select strategies other than price to compete and stand out from the competition and, secondly, to make sure these strategies are obvious and well understood by the consumers who might tend to look first at price.
The Importance of Millennials and their Preferences
The demand for omni-channel customer journeys is in its infancy. Consequently, there are fundamental differences in Internet use and shopping behavior by millennials, as compared with other generations. As baby boomers and Generation X age out, millennials and the subsequent generations who have experienced technology from an early age are going to drive market behavior on a larger scale. They are comfortable with an omni-channel approach and expect to find information available on the Internet so they can research their purchases. These consumers have skills, beliefs and requirements that previous generations did not have. (How many children help their parents and grandparents with their online challenges?) If one were to summarize some of the millennials’ characteristics and their digital preferences, a number of the following points deserve mention:
Based on their familiarity with technology, they are open to using digital channels as an option for purchases;
Millennials currently make up 25% of the population but will make up 75% of the population in 2025. Some of them are going to rise to the management level;
Convenience and ability to purchase goods and services 24/7 is important to them;
Online reviews and blogs are widely used in their decision making;
Millennials interact with brands on Facebook and other social media sites;
Opinions of others—particularly friends and family—influence buying decisions.
The power of insurance customers to voice their opinion is particularly strong with digital channels. A dissatisfied customer has the ability to vent his negative experiences to a massive audience. Online reviews and blogs are a powerful information source for current and potential customers, and these
sources can—and do—influence customer behavior. This shift in power drives home the importance of customer experience. With today’s social media, a negative experience could go viral and give a company a public relations nightmare. Conversely, publishing success stories that prove alignment with customer needs is an excellent way to demonstrate a company’s core values and reinforce its positioning as an insurer that fosters an excellent customer experience.
As stated earlier, over time, millennials’ buying preferences will become more and more important to numerous industries, including insurance. Because the millennials’ demographic will make up 75% of the workforce in 2025, many insurers will need to evolve their distribution channels and their customer interaction strategy to better serve this demographic. As far as personal lines are concerned, this demographic group will influence distribution channels more immediately because millennials are now at the age where they need to purchase insurance products. What is not clear today is which omni-distribution channel is the most effective for insurance distribution. Recognizing that, providing omni-channel delivery ensures that all options are covered and that marketing opportunities for customer touch are available.
It is the prevailing wisdom that the more an insurance company interacts with its customers, the more likely it is that customers will renew their coverage. In the old agency model, the only touch points for an insurance company are the claims and billing processes. To accomplish additional touch points, publishing content works quite well. Today, content- and information-sharing is one of the main avenues for adding value to customers. As an example, some homeowners insurance companies send out text warnings to areas in the path of a hurricane or tornado to guard against loss of life and property. Others use content quite differently. Topics that are relevant to a customer base (that are not insurance-related) work equally well. As another example, one insurance company sends out gardening suggestions based on demographic data.
Because insurance is a low-interest category to most consumers, insurers that publish content that interests their customers will create engagement and, consequently, develop a connection with their insureds. Only a small percentage of consumers actually file claims, and most insureds have little or no contact with their carrier. As a result, a content strategy allows insurers to interact with the majority of their customers other than just in claims or billing situations. This greatly increases customer touch and provides the opportunity to improve the customer experience. In the near future, however, content will become commonplace and expected, while user experience will determine the winners and losers in the marketplace.
Additional Demographic Shifts
The U.S. of 2050 will look very differently from that of today: Caucasians will no longer be the majority. The U.S. minority population, currently 30%, is expected to exceed 50% before 2050. No other advanced country will see such diversity. In fact, most of the U.S.’s net population growth will be among its minorities, as well as in a growing mixed-race population. Latino and Asian populations are expected to grow threefold, and the children of immigrants will become more prominent. Today in the U.S., 25% of children under five years old are Hispanic; by 2050, that percentage will be almost 40%. As a direct consequence, insurance companies need to start their long-term planning for these demographic shifts and must have strategies to serve these segments. In addition, the number of women in the workplace is increasing. As women grow in the management ranks, their influence on buying decisions will increase accordingly. Currently, women are responsible for 85% of all consumer purchases, including everything from autos to healthcare. Farnaz Wallace—the founder of Farnaz Global, a strategic consulting firm—said, “In the New World Marketplace, women, youth and multiculturalism are shaping our future economically and culturally, and companies must find ways to stay relevant in a world different than the one taught in textbooks.” He also said, “Millennials are the most racially and ethnically diverse generation in American history—gender-neutral and colorblind—transforming business norms.”
Throughout business history, products have fulfilled human needs. Think about how the automobile, air travel and the microwave oven changed the way we live. All these innovations took place on the company side of the value chain. In the past, these products disrupted other products. What makes disruption more likely in the insurance industry today? The major shift in the customer journey. Today, information is available to consumers on a massive scale and is virtually free. The agent is no longer the sole channel for information and product delivery. This disruptive cycle is substantially different because it empowers customers to use different channels during the purchase journey, channels that never existed before. Additionally, a generation of insurance purchasers are coming online with a major predisposition for utilizing omni-channel approaches. Companies that ignore these shifts are taking a major risk with their future viability because these shifts have already occurred and will continue with tremendous momentum.
The IDC “2016 Global Chief Marketing Officer FutureScape” predicts CMO turnover continuing at 25% per year or higher through 2018.
This is not surprising, as marketing continues to be disrupted and reinvented.
The CMO must anticipate the expectations of the connected consumer, master an accelerating digital learning curve and negotiate a new role and relationship to the CEO – who himself must come to terms with marketing playing a new position in the organization.
While this is true across companies in all sectors, it is a special consideration in insurance, where marketing is emerging from a historical “back seat” role in sales support and becoming the leader of customer-centricity and digital transformation efforts.
The CMO is now often expected to be a superhero – one who speedily turns customer-centricity into P&L results … uses technology and data analytics to drive performance … delivers marketing ROI … drives leads to sales channels … and advances capabilities to keep up with marketplace opportunities. She is a leader who gets beyond intellectualizing the need for change and quickly makes change happen. She gets Millennial consumers to flock to the brand.
Being data-driven is core to the wiring of the CMO who can accomplish all of this. Being a member of the Millennial generation may be useful, too. But I’m hearing a hunger for even more, from start-ups to Fortune 500 leaders.
These leaders are looking for a CMO who demonstrates:
Strategic, visionary and transformational wiring, with the ability to execute
Skill at seeding and scaling innovation
Analytical, technical and creative abilities
A collaborative style – someone who is a motivator and a networker
Digital native instincts and intuition
Links to P&L performance
A sense of urgency
This profile is a tall order. To find your marketing superhero:
Define what marketing means in your business. Marketing can be the high-impact discipline that connects your company’s brand with customers to create growth. If you have defined marketing as the advertising, promotions and research function, my definition proposes a much-expanded view with implications for the broader team, goals and metrics and alignment. Being clear on the function’s role is the basis for picking the must-have CMO qualities.
Maximize the CMO’s potential by envisioning a function that can:
Be immersed in customers’ lives and be the internal advocate for their needs
Surface, synthesize and apply market insight and data – pushing beyond demographics to a segment-based understanding of attitudinal, behavioral and cross-cultural attributes
Create experiences that attract customers and strengthen relationships
Test and learn – acquiring and applying data to get better
Have a P&L focus – connecting customer behavior to financial outcomes
Be a collaborator with colleagues, especially technologists and data scientists
Look to the CMO to adapt the mature methodologies that matter, and meld these with what technology and data now make possible. Segmentation, A/B testing and positioning methodologies work and are essential in an environment of channel proliferation and media fragmentation. Apply these alongside customer journey mapping, machine learning capabilities and the best social, mobile, community and other connection tactics to motivate customer engagement.
Hold the CMO accountable for metrics that make sense. The best metrics focus on the drivers of prospect and customer behavior that marketing can affect. While awareness, intent to buy and volume of qualified leads are on the list, more rigorous metrics linked to P&L outcomes also belong on the marketing scorecard – accounts opened, sales closed, evidence of loyalty such as repeat purchase and recommendation to others. Be aware of the dependencies beyond marketing, across a multi-functional business, to move these levers.
Provide sponsorship. Marketing will continue to transform irrespective of the size or stage of maturity of the business. The function’s success increases in a culture of customer commitment and insight, where leaders keep the customer at the center of decisions.
Chances are your CMO will be mortal. So, how will she succeed? Whether digital migrant, native or newbie, data-driven or intuitive, CMOs will rise to superhero status when they:
Operate with a relentless customer focus.
Achieve differentiation that matters to your target.
Build and motivate a diverse team – creating, in effect, the composite superhero marketer.
Lead with openness, trust and collaboration, self-awareness and humility, clarity of vision and connection to execution.
The robots are here. Not the humanoid versions that you see in Hollywood movies, but the invisible ones that are the brains behind what look like normal online front-ends. They can educate you, advise you, execute trades for you, manage your portfolio and even earn some extra dollars for you by doing tax-loss harvesting every day. These robo-advisers also are not just for do-it-yourself or self-directed consumers; they’re also for financial advisers, who can offload some of their more mundane tasks on the robo-advisers. This can enable advisers to focus more on interacting with clients, understanding their needs and acting as a trusted partner in their investment decisions.
It’s no wonder that venture capital money is flowing into robo-advising (also called digital wealth management, a less emotionally weighted term). Venture capitalists have invested nearly $500 million in robo-advice start-ups, including almost $290 million in 2014 alone. Many of these companies are currently valued at 25 times revenue, with leading companies commanding valuations of $500 million or more. This has motivated traditional asset managers to create their own digital wealth management solutions or establish strategic partnerships with start-ups. Digital wealth management client assets, from both start-ups and traditional players, are projected to grow from $16 billion in 2014 to roughly $60 billion by end of 2015, and $255 billion within the next five years. However, this is still a small sum considering U.S. retail asset management assets total $15 trillion and U.S. retirement assets total $24 trillion.
What has caused this recent “gold rush” in robo-advice? Is it just another fad that will pass quickly, or will it seriously change the financial advice and wealth management landscape? To arrive at an answer, let’s look at some of the key demographic, economic and technological drivers that have been at play over the past decade.
The need for digital wealth management and the urgent need to combine low-cost digital advice with face-to-face human advice have arisen in three primary market segments, which many robo-advisers are targeting:
Millennials and Gen Xers: More than 78 million Americans are Millennials (those born between 1982 and 2000), and 61 million are Gen Xers (those born between 1965 and 1981); accordingly, this segment’s influence is significant. These groups demand transparency, simplicity and speed in their interactions with financial advisers and financial services providers. As a result, they are likely to use online, mobile and social channels for interactive education and advice. That said, a significant number of them are new to financial planning and financial products, which means they need at least some human interaction.
Baby Boomers: Baby boomers, numbering 80 million, are still the largest consumer segment and have retail investments and retirement assets of $39 trillion. Considering that this segment is either at or near retirement age, the urgency to plan for their retirement as well as draw down a guaranteed income during it is critical. The complexity of planning and executing this plan typically goes beyond what today’s automated technologies can provide.
Mass-Affluent & Mass-Market: Financial planning and advice has largely been aimed at high-net-worth (top 5%) individuals. Targeting mass-affluent (the next 15%) and mass-market (the next 50%) customers at an affordable price point has proven difficult. Combining automated online advice with the pooled human advice that some of the digital wealth management players offer can provide some middle ground.
Technical advances have accompanied demographic developments. The availability of new sources and large volumes of data (i.e., big data) has meant that new techniques are now available (see “What comes after predictive analytics?”) to understand consumer behaviors, look for behavioral patterns and better match investment portfolios to customer needs.
Data Availability: The availability of data, including personally identifiable customer transactional level data and aggregated and personally non-identifiable data, has been increasing over the past five years. In addition, a number of federal, state and local government bodies have been making more socio-demographic, financial, health and other data more easily available through open government initiatives. A host of other established credit and market data companies, as well as new entrants offering proprietary personally non-identifiable data on a subscription basis, complement these data sources. If all this structured data is not sufficient, one can mine a wealth of social data on what customers are sharing on social media and learn about their needs, concerns and life events.
Machine Learning & Predictive Modeling: Techniques for extracting insights from large volumes of data also have been improving significantly. Machine learning techniques can be used to build predictive models to determine financial needs, product preferences and customer interaction modes by analyzing large volumes of socio-demographic, behavioral and transactional data. Big data and cloud technologies facilitate effective use of this combination of large volumes of structured and unstructured data. In particular, big data technologies enable distributed analysis of large volumes of data that generates insights in batch-mode or in real-time. Availability of memory and computing power in the cloud allows start-up companies to scale on demand instead of spending precious venture capital dollars setting up an IT infrastructure.
Agent-Based Modeling: Financial advice; investing for the short-, medium- and long-term; portfolio optimization; and risk management under different economic and market conditions are complex and interdependent activities that require years of experience and extensive knowledge of numerous products. Moreover, agents have to cope with the fact that individuals often make investment decisions for emotional and social reasons, not just rational ones.
Behavioral finance takes into account the many factors that influence how individuals really make decisions, and human advisers are naturally skeptical that robo-advisers will be able to match their skills interpreting and reacting to human behavior. While this will continue to be true for the foreseeable future, the gap is narrowing between an average adviser and a robo-adviser that models human behavior and can run scenarios based on a variety of economic, market or individual shocks. Agent-based models are being built and piloted today that can model individual consumer behavior, analyze the cradle-to-grave income/expenses and assets/liabilities of individuals and households, model economic and return conditions over the past century and simulate individual health shocks (e.g., need for assisted living care). These models are assisting both self-directed investors who interact with robo-advisers and also human advisers.
Evolution of Robo-advisers
We see the evolution of robo-advisers taking place in three overlapping phases. In each phase, the sophistication of advice and its adoption increases.
First Generation or Standalone Robo-Advisers: The first generation of robo-advisers targets self-directed end consumers. They are standalone tools that allow investors to a) aggregate their financial data from multiple financial service providers (e.g., banks, savings, retirement, brokerage), b) provide a unified view of their portfolio, c) obtain financial advice, d) determine portfolio optimization based on life stages and e) execute trades when appropriate. These robo-advisers are relatively simple from an analytical perspective and make use of classic segmentation and portfolio optimization techniques.
Second Generation or Integrated Robo-Advisers: The second generation of robo-advisers is targeting both end consumers and advisers. The robo-advisers are also able to integrate with institutional systems as “white labeled” (i.e., unbranded) adviser tools that offer three-way interaction among investors, advisers and asset managers. These online platforms are variations of the “wrap” platforms that are quite common in Australia and the UK, and offer a cost-effective way for advisers and asset managers to target mass-market and even mass-affluent consumers. In 2014, some of the leading robo-advisers started “white labeling” their solutions for independent advisers and linking with large institutional managers. Some larger traditional asset managers also have started offering automated advice by either creating their own solutions or by partnering with start-ups.
Third Generation or Cognitive Robo-Advisers: Advances in artificial intelligence (AI) based techniques (e.g., agent-based modeling and cognitive computing) will see second generation robo-advisers adding more sophisticated capability. They will move from offering personal financial management and investment management advice to offering holistic, cradle-to-grave financial planning advice. Combining external data and social data to create “someone like you” personas; inferring investment behaviors and risk preferences using machine learning; modeling individual decisions using agent-based modeling; and running future scenarios based on economic, market or individual shocks has the promise of adding significant value to existing adviser-client conversations.
One could argue that, with the increasing sophistication of robo-advisers, human advisers will eventually disappear. However, we don’t believe this is likely to happen anytime in the next couple of decades. There will continue to be consumers (notably high-worth individuals with complex financial needs) who seek human advice and rely on others to affect their decisions, even if doing so is more expensive than using an automated system. Because of greater overall reliance on automated advice, human advisers will be able to focus much more of their attention on human interaction and building trust with these types of clients.
Implications to Financial Service Providers
How should existing producers and intermediaries react to robo-advisers? Should they embrace these newer technologies or resist them?
Asset Managers & Product Manufacturers: Large asset managers and product manufacturers who are keen on expanding shelf-space for their products should view robo-advisers as an additional channel to acquire specific type of customers – typically the self-directed and online-savvy segments, as well as the emerging high-net-worth segment. They also should view robo-advisers as a platform to offer their products to mass-market customers in a cost-effective manner.
Broker Dealers and Investment Advisory Firms: Large firms with independent broker-dealers or financial advisers need to seriously consider enabling their distribution with some of the advanced tools that robo-advisers offer. If they do not, then these channels are likely to see a steady movement of assets – especially of certain segments (e.g., the emerging affluent and online-savvy) – from them to robo-advisers.
Registered Independent Advisers and Independent Planners: This is the group that faces the greatest existential threat from robo-advisers. While it may be easy for them to resist and denounce robo-advisers in the short term, it is in their long-term interest to embrace new technologies and use them to their advantage. By outsourcing the mechanics of financial and investment management to robo-advisers, they can start devoting more time to interacting with the clients who want human interaction and thereby build deeper relationships with existing clients.
Insurance Providers and Insurance Agents: Insurance products and the agents who sell them also will feel the effects of robo-advisers. The complexity of many products and related fees/commissions will become more transparent as the migration to robo-adviser platforms gathers pace. This will put greater pressure on insurers and agents to simplify and package their solutions and reduce their fees or commissions. If this group does not adopt more automated advice solutions, then it likely will lose its appeal to attractive customer segments (e.g., emerging affluent and online-savvy segments) for whom their products could be beneficial.
Product manufacturers, distributors, and independent advisers who ignore the advent of robo-advisers do so at their own risk. While there may be some present-day hype and irrational exuberance about robo-advisers, the long-term trend toward greater automation and integration of automation with face-to-face advice is undeniable. This situation is not too dissimilar to automated tax-advice and e-filing. When the first automated tax packages came out in the ’90s, some industry observers predicted the end of tax consultants. While a significant number of taxpayers did shift to self-prepared tax filing, there is still a substantial number of consumers who rely on tax professionals to file their taxes. Nearly 118 million of the 137 million tax returns in 2014 were e-filings (i.e., electronically filed tax returns), but tax consultants filed many of them. A similar scenario for e-advice is likely: a substantial portion of assets will be e-advised and e-administered in the next five to 10n years, as both advisers and self-directed investors shift to using robo-advisers.
Recently, it seems that developing public segmentations of your customers or citizens and then sharing it for all to see is becoming fashionable.
In part, this is to be applauded and welcomed.,/p>
The trend highlights a key tool within the customer insight toolkit, encourages greater focus on understanding people and embraces the need for greater transparency. However, there is also an inherent risk, that readers fail to understand the purpose, design and limitations of such segmentations and thus unwittingly apply them where they will not help.
This reminds me of a time many years ago when psychometric segmentations were very popular in business circles. Myers Briggs (MBTI) and many other profiles were enthusiastically applied and team members categorized into their “type.” Sadly, all too often, this perception about some important differences between team members was filed away following the team-building exercise and never used again. Screening interview candidates via psychometric segments was also “flavor of the month” at one stage, although I hear it being much more rarely used now (or only as part of a mix of “facts” to be considered).
Perhaps part of the problem can be a misunderstanding of the role of segmentation. As posted previously, segmentation is just one of a number of statistical tools available, and each segmentation will be designed to achieve a particular purpose. For this reason, more than one segmentation of customers may be entirely appropriate and insightful for a business that is able to handle such complexity (though most business leaders dislike this idea).
While this appears a useful segmentation to help the FCA understand and focus on more vulnerable segmentation with regard to financial understanding or access, it is also important to recognize its limitations. A 10-segment model will only ever be appropriate for understand macro attitudes and behaviors. My own experience of segmenting consumers within different product markets tells me that both attitudes and behaviors can vary widely once you drill down to specific needs or products. So, it’s important to realize that this segmentation has been designed to focus on dimensions like vulnerability, detriment and financial risk. Thus it is most relevant for the FCA itself, to help target communications.
This is another interesting segmentation, as it seeks to highlight and track changing social attitudes, family structures and pressures on modern families of many different types. However, once again it is important to realize the limitations of this survey. It is an attitudinal segmentation, constructed from a combination of “qual and quant” survey results, interpreted by an expert panel drawn from academia, social care and commerce. As such, this is a subjective perspective evidenced by self-reported attitudes and behaviors. Although such an understanding can be very rich, the inability to overlay this segmentation onto customer databases means that actual behavior cannot be verified or targeted actions or communications executed (often a drawback of attitudinal segments).
Once again, it’s encouraging to see this segmentation exercise being undertaken and the transparency regarding approach and progress. However, it does also appear to run the risk of a number of other “hybrid segmentations.” That is the risk that certain differences highlighted in various research studies or other sources are “cherry picked” to construct a patchwork quilt of apparently rich understanding that is not evidenced on a consistent basis. This can be seen in the infographic embedded in the above article. Even constructing a behavioral/demographic framework for a segmentation on that basis and then consistently surveying each segment runs the risk of masking important differences because of the averaging effect of artificially constructed segments. It will be interesting to see how government advisers and agencies avoid those risks.
I hope you found that interesting and are also engaged with the level of focus on segmentation in today’s government and media. If these are approached carefully and interpreted appropriately, they should be another driver of greater influence and seniority for customer insight leaders. That is our cause celebre.