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Predictive Prospecting: How Big Data Analytics Could Be the Holy Grail of Marketing

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Arizona advisor Linda Patent rented a large seminar room, secured catering from a popular local restaurant and invited a lot of people to come and learn about the topics she had advertised. That event went very well last year, she recalled happily –  so well that she did another and another and another, eight events in all. But as time went on, she noticed fewer people were attending. It reached the point that by June, just 20 people, representing only 14 households, showed up. 
 
That got Patent to thinking it might be time to find other ways of reaching new prospects. “Could predictive analytics help?” she wondered. She got her answer, but more on her later.
 
Other producers are starting to ask the same question. Predictive analytics, also called “big data,” refers to the crunching of massive amounts of consumer information gleaned from company and public records, data merchants, and other sources. Mining the data reveals meaningful insights on customer preferences, habits, behavior and other details, which corporations and organizations use for a multitude of business purposes including marketing to likely-to-buy customers.
 
Some life and annuity carriers have adopted predictive analytics, and now insurance producers are wondering if they too can use it – to help with prospecting, marketing and lead generation, for example. They’ve been chatting about it at meetings and calling around for information. 
 
The straight-up answer is “Yes, producers can use big data.” In fact, a few already are experimenting with it. Distributors, such as independent marketing organizations (IMOs) and brokerage general agents (BGAs), are perhaps on the leading edge. It’s part of their ongoing search for strategies they can use to help their independent agent clients increase sales. Some carriers are exploring field applications of analytics, too. 
 
The Power of Analytics
Advisor interest in analytics often springs from stories advisors hear about its power. 
Some still recall the media stories from a few years back about an analytics team that had reportedly detected the pregnancy of an unidentified teenage girl, even before the girl’s father knew about it. Whether they believed it or not, several agents said they were enthralled by the potential. 
 
Carriers have predictive analytics ventures, too. For instance, Principal Financial Group reduced the time it takes for underwriting retail variable and universal life products from weeks to 48 hours with the help of predictive analytics, according to a study by The Economist Intelligence Unit and sponsored by SAP. Unum uses predictive analytics to identify potential claims fraud, according to a Social Security Administration report. Other carriers are using it to spot likely-to-buy customers, reduce underwriting time and identify emerging target markets. 
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Then there are the big data companies. In the past few years, they’ve been making the insurance rounds, loaded with – what else? – data that quantify the power of big data to enhance business activities. Producers say they know that the insurance divisions at these firms sell predictive analytics services to carriers, but some wonder if they have packages for insurance agencies, too.
 
Even producers who voice skepticism about the whole field of analytics say they want to learn more. 
No wonder. There’s a “big data flood” out there, according to Advisor 2020, a book that probes what’s ahead for insurance and financial advisors. Published earlier this year, the book is the work of the GAMA Foundation for Education and Research, with the sponsorship of the National Association of Insurance and Financial Advisors (NAIFA). 
 
“Every year, there is a 40 percent growth in global data generation,” the GAMA  researchers point out. Companies with more than 1,000 employees store 235 terabytes of data on average, much of it generated by consumers on various devices. “Consumers on Facebook alone generate more than 30 billion pieces of content every month.”
 
It’s that swelling of data that advisors find hard to ignore. That’s data on family status, purchase habits, business development plans, health matters, employment, hobbies, credit history, interests, travel and more. If they could only get their hands on some of it, said several producers contacted for this article, this could open up opportunities for uncovering more suitable prospects as well as better addressing customer needs. 
 
Chal Daniels, a financial advisor from Napa, Calif., expressed a common advisor response to the idea when he said, “It would help me be more effective, for example, by cutting back on my diagnosis time, which is time spent on finding likely candidates for certain products.” 
 
It’s on the Way 
Certain types of data may be closer to advisor fingertips than they realize. As this article was being written, IBM announced that it will make its cloud-based Watson Analytics service available for small business use. 
Watson will provide instant access to predictive and visual analytic tools for businesses. “Our intention is to make the base level accessible to everyone at no charge, forever,” IBM spokeswoman Faye Abloeser said in an email. “With that, professionals can upload data and perform basic analysis and get a sense of the tool.” 
Does this mean that life insurance and annuity agents could use Watson Analytics? “Absolutely, this is something SMBs [small and midsize businesses] can use,” Abloeser said.  They can use the free version or a paid version that allows them to work with “larger, more complex data sets from a wider array of sources.”
Because this will be a broad-based service, it’s possible that the “free” version of Watson may not be tailored enough for insurance producer purposes. But the mere fact that Watson will soon be available signals that analytics for small firms like insurance practices is no pipe dream.
 
What About Now?
Some IMOs and BGAs are already moving into analytics services for agents. These initiatives are not on the massive scale used by large corporations, but they are data-rich.
 
Take Shane Westhoelter, for example. He is chief executive officer of Gateway Insurance Solutions, an IMO in Walnut Creek, Calif. He began looking into analytics via another of his businesses, which sells real estate investment trusts (REITs).  Before recommending a REIT to clients, he said, he obtains studies from the REIT that provide insight into the properties owned by the REIT.
 
Those studies include demographic detail on the areas where the properties are located. Westhoelter said he discovered that the data in those studies could be useful for other business purposes, such as insurance marketing. For example, an agent might consider looking for REITs serving the retirement market, he suggested.
 
Westhoelter uses analytics in lead generation for agents, too. He said his firm “scours” social media sources like Facebook and LinkedIn. “We identify a target market and send out feelers to people we find there to see if they have an interest. If they say yes, we get their permission to follow up, and then we send the leads to brokers who pay for them.” 
That “scouring” process begins with the IMO purchasing advertising space on Facebook and LinkedIn. “The sites give us permission to mine out the site down to the ZIP code and street address,” he said. An example is to identify employees at a big company within a 100-mile radius. 
 
On Facebook, the filters might be Social Security, Alzheimer’s disease or dementia, he said.  “Or, we could screen for people who participated in the ice bucket challenge – people who are interested in philanthropy, helping charity and believe in a cause,” he said.
“Then we look at the results to decide whether to offer an educational workshop on the topic.” 
Once the system generates a list, “you can use social media to invite the people to an event to discuss investment options on, say, 401(k) plans or some other topic,” Westhoelter said.  “If they respond, indicating they want more information, then the contact shifts to traditional marketing.” 
 
He said his firm provides this service to agents who are looking for new prospects in an area. It has been doing this for five years. The producers do pay for the leads. As with any lead-generating service, “it’s up to the talent of the agent to turn the leads into paying customers,” he added.
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Westhoelter’s word to the wise: “Firms that use analytics need to take care not to abuse the data, because they have a lot of personal information.”
 
Probability Scores
For Jeremy Rettich, analytics is about probability of buying. The president of Virtue Advisors, a Nashville IMO, he has partnered with a data analytics firm to identify those probabilities. He calls the resulting system “psychographic marketing,” which is now a service that his firm offers to agents. 
The service identifies “buying clusters” by lifestyle, values, attitudes, interests and other personal characteristics, which are combined with basic demographics (age, income, marital status, etc.), Rettich said. 
 
This generates a “probability” score, which highlights the likelihood that particular households or individuals will do business with the agent during the coming year. Agents obtaining this information then focus on reaching out to those households rather than to everyone in a ZIP code having the typical demographic characteristics, he said.
 
Rettich’s word to the wise: With analytics, “you are not mailing advertising to people who aren’t interested. You are sending out fewer mailers … and the proportionate response rate is higher, too.” 
 
Ask the Right Questions
Andrew A. Falvey, the principal at CT Brokerage Systems in Cheshire, Conn., knows a thing or two about analytics. In addition to being a consultant and a life-licensed agent at Beachport Insurance, Falvey also has IT management experience.  
 
In his view, analytics results “will only be as good as the questions you ask,” which are the queries made in the software. For example, it’s not just about finding ZIP codes with a lot of people, he said.  “Look at the deeper layers, such as the characteristics of people in the ZIP codes, in the neighborhoods, and so on.”  
What you are seeking is knowledge, he said. “It starts with collecting data. That leads to information, and that leads to knowledge if you ask the right questions.” (See table below.)
 
Gaining knowledge is key, he said, because it “helps agents answer critical questions like: ‘Do I continue marketing to that group or not?’” That’s a question carriers ask, he allowed, but “agents need to know, too.”
To get the best answers, agents will want to get the largest volume of data and information possible to create the knowledge, Falvey said. The more they have, the better the ability to predict an outcome. 
It’s like looking at a street map with no information about current traffic or construction versus a map with both, he explained. The first map provides information, but the second provides knowledge that helps the driver find a better route.
 
The problem for agents is that they typically can’t obtain large volumes of data on their own, Falvey said. They need to get it from technology companies that do data analytics or acquire it from large organizations that provide access and have the expertise to sift through the data. “The firms they use must have knowledge of the business … and they must be able to leverage the information so it’s tailored to the producer’s needs, such as for targeted leads,” he cautioned. 
 
Falvey’s word to the wise: “Does predictive analytics cost? Yes. Is it expensive? I don’t know, because what you are looking for is perceived value, and that’s an open question at the moment.”
 
Use It for Marketing
Mike Ford, president of PFG Marketing Group, an IMO in Phoenix, said it comes down to agents wanting to get in front of more people. To do that, they need help with marketing.
He said his firm does analytics “as much as we can.” It does this to target agents who are the most likely to need and want his firm’s services. To get the analytics, the IMO rents a national database from a third-party vendor.
 
As for agents using analytics, Ford pointed out that “the industry is changing tremendously, so agents need ideas on how the market is shifting and how to address those shifts.” In fact, agents need training programs on the use of analytics, he said, adding, “It’s critical for them.” IMOs need training, too, “so we can provide meaningful information to our agents.”
 
Ford’s word to the wise: “We would look for guidance from the carriers and regulatory bodies in analytics matters that have to do with privacy, suitability, accuracy of data, and so on.”
 
An Agent’s Experience
It’s not just IMOs who are dabbling in predictive analytics. Agents are, too. Take Ryan J. Pinney, for example. The vice president of brokerage sales for Pinney Insurance Center in Roseville, Calif., said predictive analytics is one part of a three-pronged system he uses to reach customers. 
It starts with “demographic modeling,” or building an ideal client list or ideal group list based on known population characteristics such as age, income, occupation, gender, etc. (Data sources include public records and credit bureaus.) 
 
“Target marketing” comes next. He narrows the list to a manageable size and then uses social media, such as LinkedIn or Facebook, to advertise only to specific names. “On LinkedIn, for instance, you can target the CEO of every Fortune 500 company with, say, an ad about employee benefit plans,” Pinney said.
“Predictive analytics” is the third segment. Using an existing marketing list, he sends out general communication like e-newsletters. Then, he watches to see what items people click, and he scores the clicks as he goes. He then sends specific people ads based on their clicks – for instance, an ad on life insurance or retirement planning if they click on a related item. 
 
“Most agents already do some of this, in some fashion,” Pinney said. “For instance, they send out e-newsletters. But many don’t do it consistently, or they don’t track the clicks.” As a result, they don’t get accurate results, spot anomalies or get a sense of who is likely to buy. “That’s when it becomes predictive analytics,” he said.  
 
Pinney’s word to the wise: “Demographic modeling, target marketing and predictive analytics all go together. If agents have 400 to 500 clients and if they do these three things, they will never have to add another client.”  
 
What About the Holdbacks?
Linda Patent, the advisor who launched her own investigation into using an analytics service, decided not to do it right now. This wasn’t for lack of interest. It was a cost/benefit decision.
 
An investment advisor representative who is principal of L.R. Patent Financial Services in Prescott, Ariz., Patent said that what caught her eye is the possibility that she could use predictive analytics to figure out where to concentrate her marketing efforts more precisely (as opposed to, say, mailing to an entire ZIP code).
 
“It’s the gathering of the data by marketing criteria that interests me,” she said, noting that it’s discouraging to get general lists of people who are said to have $250,000 of investable assets but who turn out to have only $100,000. Her thought was, if the analytics help increase response rates as well as accuracy of information, that would make her marketing investments more affordable in the long run.
 
After looking at the cost of the service she was considering, however, Patent felt uncertain about the kind of return she would realize. “I don’t have data to prove predictive analytics methodology would increase my response ratios, sales and revenues,” she said. 
 
She still wants to try analytics, but she is hoping that the insurance companies will start to make analytics services available to their top advisors. That would be her foot in the door. 
 
Agents Just Getting Going on Analytics
 
Advisor use of predictive analytics is in its infancy. Here are some of the hurdles and perceptions that need to be addressed for it to become more mainstream. 
 
Lack of trust. Many sales reps simply don’t want to get into analytics, McKinsey researchers Matt Ariker and Nimal Manuel wrote in a recent blog post. Often, this is due to emotional resistance stemming from lack of trust, they said. That’s not rational, they said, noting that “recommendations based on advanced analytics can make a huge difference – if sales reps and customer service agents use them.” 
 
One solution the consultants recommend is for companies to turn their top performers into allies and advocates. “Top sales performers often have major influence within organizations,” they said.
 
Lack of knowledge. Many producers contacted for this article said they don’t know what predictive analytics is. Once they learn about how it might help them grow their business, though, they get interested. 
To help with understanding, Eric Sondergeld, corporate vice president of strategic initiatives for LIMRA, avoids focusing on the predictive aspect, Analytics doesn’t have to predict something in order to be valuable, he said. Instead, he talks about “analyzing data in new and innovative ways to get better results and make good business decisions.” Dave Edington, senior vice president of insurance for Epsilon, a Dallas-based technology firm, pointed out that “big data in and of itself has little value. It’s big data analytics that is key. That unlocks value from the data.”
 
Lack of awareness. Some producers know the term “predictive analytics,” but they are unaware that it will affect their business soon, if not already. 
 
This is bound to change. A KPMG survey last year found that 33 percent of 101 insurance executives listed data and analytics as their highest-priority investment area over the next two years, trailing only IT infrastructure. A Bedford, Massachusetts, software provider, FirstBest Systems, found that insurance executives believe predictive analytics helps improve underwriting profitability (56 percent), make better risk decisions (49 percent) and improve underwriting quality (35 percent). Celina Insurance Group, a property-casualty carrier selling through independent agents, invested in analytics after determining that it was a “question of survival,” wrote Nicolas Michellod, a senior analyst in Celina’s insurance practice.
 
It’s too expensive. Producers aren’t the only ones who worry about cost. Companies dwell on it, too. For example, 85 percent of the mega-carriers that FirstBest studied planned to spend more than $1 million in 2014 on data analytics initiatives, and most have spent more than $5 million. The smaller firms naturally expected to spend less, with 64 percent of small carriers targeting zero to $100,000 for the year. Some IMOs said privately that they spend up to $20,000 a year on certain analytics-related initiatives, although others spend much less. 
 
As analytics improve, “the costs will come down,” Sondergeld predicted, pointing to the way costs dropped for computers, handheld calculators and digital cameras. 
 
It’s an unknown and therefore risky. If an analytics venture backfires and doesn’t perform as expected, the approach could develop a reputation of not being worthwhile, Sondergeld observed. Among agents, it will be important to prepare them properly on what to expect and how to handle problems that may crop up, he said.
Complicating matters is that “many organizations are still in the earlier stages of analytic maturity,” Edington said, pointing to challenges in data quality, integrity, consistency, access to data and analytical staffing shortages. “Many organizations have a lot of work to do before they will be exploiting the value of big data.” 
 
Concerns about privacy. Agents have noticed the public outcry about highly publicized data breaches at household names like Target and Home Depot. They don’t want that to happen if they use analytics. Then again, the breaches usually involve retail businesses where the customer uses a check or credit card to buy, Sondergeld said. “Insurance is not retail in that sense – yet.”
 
Even so, he and several other sources interviewed for this article said that as predictive analytics use grows, carriers and producers will need to ensure they “treat the data appropriately, especially if it is attached to personally identifiable information.”
 
“My advice to agents and advisors is to be patient,” Sondergeld said. “This is a very new thing. Companies are jumping in now. There are pilot programs, and they will build over time. … The carriers will talk to their agencies after a while.”
 
Meanwhile, try working upstream with intermediaries or manufacturers, he suggested.  “Ask them, what are you doing with the analytics and how can it benefit me?”
 
Soon enough, companies will probably make analytics available to agents, possibly those who sell a certain volume of business, Sondergeld said. They will do this even though some agents might submit business through another company. But most agents will give more business to carriers that help them, he added. The carriers know that the agents will reciprocate. “It’s human nature.”
 
Linda Koco, MBA, is a contributing editor to InsuranceNewsNet, specializing in life insurance, annuities and income planning. Linda may be reached at [email protected]
 

 

Linda Koco, MBA, is a contributing editor to InsuranceNewsNet, specializing in life insurance, annuities and income planning. Linda can be reached at [email protected] [email protected].


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