By: John Blunk, Director of Client Services
When you have a bad experience with a company, what’s the first thing you do? If you’re like a lot of consumers, you probably tell your friends and family about it.
This scenario can be a nightmare for a company if it happens with a large number of customers on a frequent basis. But the good news is that by taking the time to understand customers, those negative reviews can be turned into positive ones. That’s where Net Promoter Score (NPS®) comes in.
What is NPS?
Developed in 2003 by Bain & Company’s Fred Reichheld, NPS is used to measure, understand, and track customer experience. It is based on one simple question:
What is the likelihood that you would recommend Company X to a friend or colleague?
Respondents are asked to rank their likelihood to recommend on a scale of 0-10, with 0 being the least likely to recommend and 10 being the most likely. Respondents are then placed into one of three categories:
- Promoters (9 or 10): These customers keep coming back to your product or service and refer their friends.
- Passives (7 or 8): This group was satisfied with their experience, but they may easily switch to competing companies and are not likely to recommend.
- Detractors (6 or below): Customers in this group had an unpleasant experience with your company and may voice their dissatisfaction to others.
To calculate NPS, you simply subtract the percentage of customers that are detractors from the percentage of promoters. According to Reichheld, an NPS of more than 50 is considered excellent, and world-class companies score between 75-80 percent.
Why is it important?
Although NPS does not measure how many people will actually go out and recommend or criticize your product, service, or brand, it gives companies an understanding of how customers feel about them. And since word-of-mouth recommendations continue to be the most effective form of advertising when it comes to driving sales, knowing if customers might be willing to promote your company can be extremely valuable.1 Plus, in 11 of the 14 case studies that Bain has compiled on NPS, likelihood to recommend proved to be the most powerful predictor of repurchases and referrals.2
Another reason why I recommend using NPS is its simplicity. While many of the metrics used in market research are often complex, NPS provides a quick snapshot of customer experience in a form that almost anyone can understand. A low NPS can alert a company that it needs to put more effort into improving its customer experience, while a high score can serve as evidence of a successful customer-centric business strategy. It is also measurable over time, so companies can use it to easily track their progress from one year to the next.
How We’ve Used it:
In a recent study for a leading power tool manufacturing company, we asked current customers if they would recommend this company to their friends and colleagues. We found that this company had an NPS of 80 percent, putting it in the same category as some of the world’s top companies.
Using NPS allowed us to provide the company with a benchmark report of how their current customer experience efforts were performing. Combined with additional other research, we were able to consult with them on how to maintain this level of success and continue growing.
Are you ready to turn your customers into loyal brand advocates? Contact us to learn more about our customer experience research capabilities and expertise.
 Under the Influence: Consumer Trust in Advertising, Nielsen, 2013
 Net Promoter SystemSM, Bain & Company, 2013
By: FGI Staff
Are there opportunities for consumer packaged goods (CPG) companies in the digital space?
Some executives say yes, others say....maybe. The problem with digital and CPG companies is the unknown. While it's true consumer shopping habits are most often done online, the products purchased are not normally those produced by CPG companies (at least not now). Industry experts are expecting that behavior to shift, causing top brands to start looking seriously into their digital strategies. In fact, leading consumer-packaged-goods companies are already pilot testing their presence in the online space, tapping into potential new buyers, developing new products, and making them more accessible to the ever-so-savvy consumer. Whether it be utilizing the ecommerce space or leveraging digital to gain better insights – more CPG companies will need to take part in a trend that likely won’t ever go away.
Myth: Shopping online is mainly used by the millennial.
False. According to a study by Deloitte, GenX and Baby Boomers are increasing their online shopping habits due to some not-so-obvious instances. One respondent to this study said they would strongly consider purchasing consumer goods online as age and mobility decreases. That insight opens up significant opportunity for companies who are looking to grow in the ecommerce space.
It’s no secret the shopping behaviors of consumers have changed drastically in the recent decade. CPG companies change their strategies to make their products more accessible to the consumer.
Some companies might go as far to ask, “does this mean traditional shelf space will become less important in the future?” Absolutely not. Having a presence in the ecommerce space should be part of the overall strategy for future growth, not the only strategy. While a digital presence is a significant part of growth, acquiring traditional shelf space is still a top priority for CPG companies.
Myth: Using the digital space overall doesn’t provide much value to the CPG industry.
False. Online communities are an emerging technology that provide companies with real-time answers to questions, allowing them to improve products or develop new ones. According to an article written by McKinsey, Gatorade utilizes online communities to monitor and analyze consumer answers, get ideas, and optimize landing pages. Kraft has also used online communities that resulted in their ever-popular Nabisco 100-calorie pack products, generating $100 million in sales within the first year of launch.
As you can see, the digital space offers valuable insights that CPG companies can action. By pairing strategic goals with data-driven insights and logic, brands can start seeing a measurable impact on growth and profitability.
Here are more questions online communities can answer:
Product development & innovation: Are there opportunities for additional products? How popular will these new products be with consumers? Companies should also test new pricing and promotion strategies to see which has a higher ROI.
Marketing and branding: Are consumers aware of your brand in the digital space? How do they feel about your brand? Are you communicating the right message?
Customer experience: What does the online purchase path of your buyers look like and what is their experience with it? What are your customers saying about you? Are they likely to refer?
Question: What are your thoughts on CPG companies in the digital space?
For more information on online communities, contact us here.
By: David Wilson, CEO
The Consumer Packaged Goods industry, along with all types of manufacturing industries, has become increasingly challenging. Manufacturers find themselves in industries and categories that are highly saturated with messages and SKU's. Thousands of companies are all competing for limited retail shelf space, consumer awareness, and new product trial. Unfortunately, these headwinds mean that more products fail than succeed.
Consider these statistics cited in an April 2011 Harvard Business Review article titled, “Why Most Product Launches Fail”:
“Less than 3% of new consumer packaged goods exceed first-year sales of $50 million—considered the benchmark of a highly successful launch.”
“...about 75% of consumer packaged goods and retail products fail to earn even $7.5 million during their first year. This is in part because of the intransigence of consumer shopping habits.”
“...American families, on average, repeatedly buy the same 150 items, which constitute as much as 85% of their household needs; it’s hard to get something new on the radar.”
Here’s the good news: when companies follow proven best practices for product innovation (such as Stage-Gate®), including the right research at each step along the way to launch, their success rates skyrocket. Here are just a few results achieved by companies using research-based product innovation 
- New product success rates are 3 times higher
- Time to market is 35% faster
- New products reach profitability targets 77% of the time
- New product projects are on time and on budget 79% of the time
Simply put, research-driven innovation improves your odds of success every time. Gains in revenue, share and margins are possible for almost every manufacturer in every category. Period.
At FGI we're helping many of the world’s most innovative manufacturers break barriers to growth with winning product concepts, price points that balance demand and margins, and marketing strategies that drive trial and repeat purchase. We replace water-cooler theories and hunches with a proven mix of strategic consulting, marketing research and advanced analytics. This is not guesswork. This is fact-based, research-based innovation and marketing that results in profitable growth.
Here are a few ways we can help you drive profitable growth:
FGI helps you tap the voice of the customer at every stage of your new product innovation process. From ideation to concept testing and market sizing, research-driven decisions will give you a powerful edge at every step along the way towards launch. Start listening to what your customers are saying and turn feedback into better products and more effective messages. The result? Increased trial, higher satisfaction scores, better word-of-mouth marketing and social media shares, and repeat purchase that grows share.
FGI helps you find the perfect price for your product, even if it’s in a new category. When you know the exact price elasticity of your product, you can set the exact price to reach your revenue growth and profit goals. Depending on your product’s unique features and benefits relative to your competition, you don't always have to price lowest to be competitive. Our advanced pricing models accurately project what your customers will pay for a product, so you can start maximizing share and margin potential.
Product Launch Marketing
FGI helps you decide on the launch strategy that best communicates your product’s value. We help you pick just the right words and messages that will move your customer to action. FGI’s name, package and message testing removes all the guesswork to give you confidence in every new product launch results. Say goodbye to ad campaigns that waste money and put your launch at risk.
We've done it. And we’ll do it again. Download our case study and see how we're helping leading manufacturers drive profitable growth with research-driven solutions they can trust.
 Stage-Gate International.
By David Wilson, CEO
With over 30 years of experience working with clients at FGI, we know firsthand just how valuable research can be in the product innovation process. In a chaotic global economy powered by escalating consumer expectations and hyper competition, simply having a great product idea is no longer enough to guarantee success. Instead, companies that seek profitable growth must commit to research-driven product and service innovation to keep up with—or surpass—the competition.
Put simply, companies must continually bring the voice of their customers into their innovation labs, engineering offices, marketing teams and executive boardrooms if they want to guarantee consistent growth.
Even though we’ve seen this claim proven time and time again over the years at FGI, it’s always helpful to see empirical evidence from the financial community about the direct correlation between research-driven innovation and long-term growth. Here’s the latest from Savita Subramanian, head of U.S. equity strategy at Merrill Lynch:
"From an equity investor’s perspective, we think that innovation is actually a very important factor to consider when investing in a company, and to that end we looked at the performance of companies that spend on research versus those that don’t. And we found that companies that are actually committed to research and committed to long term growth projects tend to outperform companies that don¹t by, on the margin, a couple of percentage points a year. So this is not an insignificant theme for the equity investor. In fact, we think that equity investors can be rewarded handsomely for finding companies that are on the leading edge of innovation.”
[Watch the full video here]
Companies everywhere launch new products and services every day. Unfortunately, roughly 9 out of 10 fail to meet their minimum expectations after being launched into the market. Why? They skimp on research—or even worse—they skip it entirely. Sound crazy? It’s true, especially among many middle-market companies.
Here’s the good news: when companies follow proven best practices for product/service innovation (such as Stage-Gate®), including the right research at each step along the way to launch, their success rates skyrocket. Consider these performance levels achieved by companies using research-based product innovation:
- New product success rates are 3 times higher
- Time to market is 35% faster 
- New products reach profitability targets 77% of the time 
- New product projects are on time and on budget 79% of the time 
The facts clearly support the formula that we all know is simple logic:
Research-Driven Innovation-->Long Term Growth-->Increased Shareholder Value
 Best Practices in Product Innovation authored by Dr. Robert G. Cooper & Dr. Scott J. Edgett with the American Productivity and Quality Center, 2003
 Comparative Performance Assessment Study by Dr. Abbie Griffin, 2004
 Best Practices in Product Innovation authored by Dr. Robert G. Cooper & Dr. Scott J. Edgett with the American Productivity and Quality Center, 2003
 Best Practices in Product Innovation authored by Dr. Robert G. Cooper & Dr. Scott J. Edgett with the American Productivity and Quality Center, 2003
By: Madeline Varney
At this year’s 2013 TSE Services Member Satisfaction Summit we heard how electric utility cooperative members use customer satisfaction research to more fully engage with consumers and improve their products and services.
Member engagement, member satisfaction and best practices were trending topics for speakers at the summit as different cooperatives shared the challenges they face and how those issues are being resolved.
Here's what some companies are saying about customer satisfaction research:
-“It’s a reality check.”
-“I go straight to the verbatim responses to see what customers are saying.”
-“[We’re] empowering customers to make good decisions.”
For Owen Electric, part of creating a “culture of customer satisfaction” involved increasing the scope of their market research efforts by joining over fifty other electric utility cooperatives to more accurately track satisfaction scores produced by TSE Services and FGI Research. Overall satisfaction scores now keep them on point, but delving into the verbatim responses from cooperative members has transformed Owen Electric’s internal sense of accountability.
When every member interaction can influence the next satisfaction survey interview being conducted, employees realize how they can affect the bottom line and are eager to know the cooperative’s satisfaction scores. Linking those customer comments back to office managers, linemen, and those responsible for the topic in question propels problem resolution.
During the summit FGI’s John Blunk and Rob Killough gave the annual Member Satisfaction Survey Update, detailing updates to the survey instrument and outlining best practices for phone research concerning survey length and how to create unbiased survey questions among other topics.
Satisfaction may be a state of mind, but exploring the hard data provides tangible benefits.
By: Andy Smith, New Business Development Manager
After attending the 2013 EMACS and my 5th Chartwell conference, the trending strategies for utilities is becoming clear: Listen. Connect. Understand. Participants at the conference show us how they're doing this:
Listening leads to better business.
On day one Southern Co kicked off the conference with one of the key themes “know the customer” and “giving customers options has value”. She described how they use segmentation, target offerings and being more like a retailer as their goals.
Home Depot took a potentially damaging problem and turned it into a 3-year, company-wide initiative that lead to a BIG win – improving sustainability and wood purchase policy. They launched their ECO line of products; low VOC paints, low flow toilets, and CFL’s to improve sales and keep pace with customer needs. All this was initiated by listening, and a good example of the benefits received from understanding customer attitudes and behaviors.
Ameren is also taking the step to listen to their customers, and discussed their assembly of a cross-functional customer experience team – the first for the company with the mantra “put the customer first.” By doing this, they will measure everything as a best practice to show ROI.
AEP described the fear of new marketing retailers moving in to Ohio and Texas that will be able to offer several plan options to their customers. AEP is using incentives, experimental rate plans, and choice plans to stay competitive.
Connecting leads to better communication.
Avista is staying current by using social media to drive brand value – to educate, inform and engage. Their strategy starts with “influencers” like media, news, and sports outlets. Advocates and Ambassadors are now part of the plan and include sponsorships and non-profits to spread content via Twitter and Facebook.
Understanding pain points leads to increased customer satisfaction.
Duke Energy discussed using customer journey mapping in its efforts to understand their customers and uncover issues. Their goal was to “take an outside view in”. They used digital personas and included channels and research. Customers were participating and in the room, some were moderate and others were heavy digital users to provide immediate feedback. The nine-day session brought to light 44 digital journeys and 150 pain points.
We also heard from ONCOR on using focus groups to revamp mobile apps, Tucson Power on customer analytics and how they used and analyzed multiple sources of data to improve rate plans and collect more revenue, KCP&L on consistent messaging and PPL on how “Storm Sandy” changed everything about handling outages.
Chartwell ended the 3-day conference with the 10 catalysts for change based on their member survey. This entails how the digital user will be a huge driver of change, along with big data analytics.
How are you using these trends and staying competitive in 2014?
by by Dino Fire, Chief Science Officer
Goodness good•ness [goo d-nis] the state or quality of being good, excellence of quality. (dictionary.com).
A good predictive model is only as good as its "goodness." And, fortunately, there is a well-established process for measuring the goodness of a logistic model in a way that a non-statistician—read: the senior manager end-users of your model—can understand.
There is, of course, a precise statistical meaning behind words we propeller heads throw around such as "goodness of fit." In the case of logistic models, we are looking for a favorable log likelihood result relative to that of the null, or empty, model. This description isn’t likely to mean much to an end-user audience (it barely means anything to many propeller heads).
Saying that your model will predict the correct binary outcome of something 81% of the time, however, makes a lot more intuitive sense to everyone involved.
It starts with a standard hold-out sample process, where the model is trained and modified using a random part—say, half—of the available data (the learning set) until a satisfactory result is apparent. The model is then tested on the second half of the data (the testing set) to see how "predictive" it is. For a logistic model, a "confusion matrix" is a very clean way to see how well your model does.
Using existing historical data, say we’re trying to predict whether someone will renew their association membership when their current contract is up. We run the model on the testing set, using the parameters determined in the initial model-building step we did on the learning set.
logit.estimate <- predict.glm(fit, newdata = testing, type = 'response')
Let's set the playing field by determining what the existing "actual" proportions of the possible outcomes are in the testing data.
# Actual churn values from testing set
testprops <- table(testing$status) # create an object of drop/renew (actuals)
prop.table(testprops) # express drop/renew in proportional terms
So historically, we see that 59% of people don't renew when their membership period is up. Houston, we have a problem! Good thing this is a hypothetical example.
The elegance of logistic regression—like other modeling methods—is that it provides a neat little probability statistic for each person in the database. We can pick some arbitrary value for this predicted probability—say anything greater than 50% —to indicate that someone will renew their membership when the time comes.
testing$pred.val <- 0 # Initialize the variable<
testing$pred.val[logit.estimate > 0.5] <- 1 # Anyone with a pred. prob.> 50% will renew
With those results in hand, we need to know 2 things. First, how well does the model do in pure proportional terms? In other words, it is close to the same drop/renew proportions from the actual data? This is knowable from a simple table.
testpreds <- table(testing$pred.val) # create an object of drop/renew (predicted)
prop.table(testpreds) # express drop/renew predictions in proportional terms
Recall that our original proportions from the "actuals" were 59%/41%...so far so good.
Second, and most importantly, how well does the model predict the same people to drop among those who actually dropped, and how does it do predicting the same people to renew among those who actually renewed? That's where the confusion matrix comes in.
In a perfect (but suspicious) model, cells A and D would be 100%. In other words, everyone who dropped will have been predicted to drop, and everyone who renewed will have
been predicted to renew. In our example, the confusion matrix looks like this:
# Confusion matrix
confusion.matrix <- table(testing$q402.t2b, testing$pred.val) # create the confusion matrix
confusion.matrix # view it
Drop 310 55
Renew 62 189
Assign each of the four confusion matrix cells a letter indicator, and run the statistics to see how well the model predicts renewals and drops.
a <- confusion.matrix[2,2] # actual renew, predicted renew
b <- confusion.matrix[2,1] # actual renew, predicted drop
c <- confusion.matrix[1,2] # actual drop, predicted renew
d <- confusion.matrix[1,1] # actual drop, predicted drop
n = a + b + c + d # total sample size
CCC <- (a + d)/n # cases correctly classified
CMC <- (b + c)/n # cases misclassified
CCP <- a/(a + b) # correct classification of positives (actual à predicted renew0
CCN <- d/(c + d) # correct classification of negatives (actual à predicted drop)
OddsRatio <- (a * d) / (b * c) # the odds that the model will classify a case correctly
At 81%, our model does a pretty fair job of correctly determining the proportion of members who will drop and renew. It is capable of predicting the individuals who will renew their membership 75% of the time. More importantly, the model will predict who will not renew 85% of the time…presumably giving us time to entice these specific individuals with a special offer, or send them a communication designed to address the particular reasons that contribute to their likelihood to drop their membership (we learn this in the model itself). If we send this communication or special offer to everyone the model predicts will drop their membership, we will only have wasted (aka "spilled") this treatment on 15% of them.
Now that's putting big data to work in a powerful way. I predict that it's some really useful analytics for your marketing managers.
Editor's note: Our short Q&As introduce you to the hardworking individuals that crunch the numbers, make the connections, and change the industry. This month, meet Kerri Meyer Forguites, Project Manager.
Where is your hometown?
I grew up in Paul Smiths and Lake Placid, New York.
I feel so fortunate to have been raised in such a charming area, and I still consider the Adirondacks to be one of the most beautiful places in this country. I was given countless unique opportunities growing up in the North Country that I wouldn’t trade for anything.
What do you do at FGI?
I’m a Project Manager at FGI. I began my career here in 2007, fresh out of college, as a Project Coordinator. Assisting fellow project managers allowed me to easily transition into a project management role in 2011.
I currently work on projects in the retail, nonprofit, CPG, publishing, and energy industries, among others. Working with companies in such diverse industries keeps my job challenging and exciting and I always feel like I’m learning something new!
Why do you work at FGI?
Every project that I work on at FGI is different from the last and next. This makes my job interesting, challenging and rewarding.
I also work with a truly amazing team! FGI is full of creative, caring, entertaining, and helpful people.
One thing not many people know about me is…
I am obsessed with my dog, Mr. Fredrick! Mr. Fredrick, also affectionately known as Fred-Fred, Nugget, Bad-Boy, and the list goes on, is a rescued Olde English Bulldogge with a killer personality.
I work remotely from a home office in Saratoga Springs, New York, so I consider Fred to be my Research Assistant. During the work day, you can usually find him napping under my desk.
If I’m ever on the phone with you, and you listen closely, you might even hear him snoring away in the background.
If you could be a superhero what would your powers be?
If I could be a superhero, I would definitely want to be able to read minds. I would find it fascinating to know what other people were thinking, especially if they didn’t know I was a mind reader.
Editor’s note: this post is by guest blogger, Corey Dall, who served as marketing manager for FGI Research for 2 years before starting her tenure as the digital marketing manager for a local bank. This is her reflection on her time here and some of the lessons she learned from our industry thought leaders.
I’ve been lucky these past 2 years, not only because I worked for a company nominated one of the best in the Triangle and the team here is like family, but because I learned a lot about marketing (in my day-to-day work) and about market research.
In light of this, I wanted to share with you some of the tips and advice I’ll be taking with me in the next step of my professional journey.
1. Ask the right questions
It’s truly staggering to think of the ways good, reliable research can shape a company’s future. But if you don’t proactively seek it out, you could end up making apologies and potentially back tracking on decisions, costing your business millions of dollars (Gap logo redesign, anyone?).
So whether I’m handling concept tests in the future, or brainstorming ways to boost customer satisfaction and retention, I’ll begin first by asking: what research can we do to make sure our conclusions are the right ones for this business? What are we missing? What changes will make the biggest impact (and how can we prove it)?
The right questions at the start make all the difference.
2. Qualitative research is really neat
It’s no surprise to me that online qualitative research is the next big thing in this industry. For example, in an insight community (aka MROC) platform, which looks like the social sharing sites we’re all used to, you can gather verbatim phrases and keywords to use in campaigns and in future pieces of contents.
You can also collect pictures, videos, and reactions to images, making it easy to shop, eat, or see inside the homes of your consumers. For a marketer like me, this stuff is a goldmine, whether it’s for testing web usability, ad impact, or the viability of a product.
And if that wasn’t enough, good MROC platforms allow for a little bit of quantitative research as well, in the form of quick polls or open-ended responses to carefully crafted questions.
How cool is that?
3. Persona development is a must
In the market research world, segmentation analysis is how a company can craft its personas from quantitative data. Not only do you find out who is buying your product, but also why and what they’re most likely to purchase in the future.
You can craft names and stories for these personas, identify—beyond your product category—their likes and dislikes, family size, and propensity for social sharing (positive or negative). All of which is backed by hard data and analysis.
Imagine being a home goods or tool manufacturing company and knowing exactly, down to the color preference, what works (or won’t) with your biggest spenders and most loyal customers. Is there a hidden group of consumers you might not have considered before (as was the case in this study FGI did through a combination of social media mining and segmentation)?
In light of a new focus on journey mapping and customer experience across a number of industries, I think segmentation will be more crucial than ever.
4. Predictive analytics are part of the future of marketing
Some companies spend a lot of money to boost their JD Power score (or NPS) to see it either stay flat or nominally increase. Others throw time and effort into customer engagement ideas only to see them go down in a blaze of glory (Jello’s recent #fml Twitter campaign comes to mind).
In addition to asking the right questions at the start, it’d sure be nice to know the exact ROI for a particular time or money investment. You can do that with predictive analytics (marketers insert cheer here).
So every time your CEO asks why that vertical was promoted through PPC rather than a case study, or why a brainstorming session about better customer service is more essential than one on product packaging redesigns, you can use predictive analytics for definitive proof.
At each stage, you can compare your future scores against your current benchmark, see what shifts each step forward might make, and move swiftly and smartly through decision making. That’s why I think marketing and predictive analytics will have a nice future together, and I’m glad to get the heads up now.
5. Don’t forget about big data
FGI has covered big data a few times on this blog, from what it is and how to get started to its use with the aforementioned predictive analytics.
With each post, I got a better grasp of the potential that lies in data mining and appending, especially when transactional data meets behavioral. Undoubtedly, it’s a big mountain to climb for most companies, but I think the view from the top must be pretty darn nice (just ask Amazon).
I’ll likely keep an eye on this space as the FGI big data experts are just getting warmed up on the topic.
We’ve already blogged about the challenges life insurance marketers will face this year, and how these insurers can effectively reach untouched consumer segments (e.g. low to middle markets) and adapt to changing consumer bases.
And in April 2013, FGI Research received over 1,500 responses from its SmartPanelists about their perceptions of life insurance (LI) coverage. From this data, we discovered consumer attitudes towards purchasing LI and found common patterns among those who do and don’t choose coverage. Below is an overview of some key findings.
Education and marriage
Of the 1,151 people (71%) who indicated that they do carry life insurance, most tend to have higher levels of education (a four-year college degree or higher) and are married than the 475 (29%) who are uninsured.
College graduates have higher employment rates than those who have attained less than a bachelor’s degree. This increased financial stability, paired with a husband or wife to lessen the individual economic burden of life insurance, helps explain this trend among those with coverage.
Major barriers to purchase
For the 475 respondents remaining uninsured (29%), many cited expenses and cost as a major barrier to purchasing life insurance. These expenses included mortgage/rent and employment situations. Others felt purchasing LI as unnecessary for the time being.
Economic trends of the past 2 years—namely instability and lack of growth that lead to higher levels of unemployment—help explain these attitudes. People directly affected by the slowed economy are uncertain about spending their limited income on an expense like life insurance. Other financial burdens, like student loans, mortgages, and credit card debt, remain at the forefront.
Furture purchase likelihood
Of those currently uninsured, 51% felt as though life insurance was not necessary in the future, especially when household income is greater than $75,000.
This attitude likely results from younger consumers’ lack of knowledge about life insurance, how it ties into overall future financial plans, and the benefits it offers.
Additionally, a lack of trust in financial institutions and their agents to fulfill their promises and give objective advice contributes to the overall feeling that life insurance is simply not a necessity, according to Deloitte’s 2013 Life Insurance and Annuity Industry Outlook.
How age comes into it
Finally, age is a major factor in likelihood of purchase. Of those respondents who indicated they will not purchase life insurance in the future who belong to Generation Y (which consists of adults ages 18 to 34), 50% said they do not see the value in life insurance. On the other hand, the Silent Generation (ages 77 and up) and Baby Boomers (ages 56 to 76) have made other plans to secure their future, such as a 401K, IRA or CD, for example.
With those likely to purchase life insurance, the differences in attitudes between generations is apparent as well. Generation X and Y feel it is an important part of future planning, but not so much as Baby Boomers. At 74% Baby Boomers describe life insurance as a necessary expense.
There is one thing all generations appear to agree upon, however: they don’t think LI is affordable yet at this point in their lives.
Overall, insurers must be aware of the many factors that influence attitudes towards life insurance coverage, including the state of the economy, marital status, education, and differing generational beliefs.
Whether it’s showing members of Generation Y the inherent value and benefits of LI or gaining consumer trust, understanding the above trends will help insurers succeed in the current market.