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How Adaptive Conjoint Improves Your New Product Development Results


New Product Failure Rates Are High

New products fail at an alarming rate. Dozens of studies put failure rates at 60% to 80%.

So, why do so many new products fail?

Most companies rely on some form of product research to understand their consumers' choices, preferences and buyer journeys. However, fewer companies dig deeper to understand how these preferences differ across multiple segments and how they change in response to new competitive offerings, prices and disruptions.

In addition, many decision makers don't have good information to determine if investments in new product features will earn a suitable return in market share and margins. Finally, even when you launch an innovative new product at a very aggressive price point, you cannot expect your competitors to sit still. They will counter your move with price reductions and/or new product features of their own. These and other challenges contribute to the high failure rates of new products.

These innovation challenges put a great deal of pressure on brand managers, product managers and other marketing decision makers. To survive and thrive in this environment, decision makers must rely on the right kinds of new product development research throughout the innovation process.


How Some Product Testing Methods Contribute to Innovation Failures

Simply put, many product testing methods are too simple. When you ask customers what they want, they usually say "all of the best features at the lowest price." If your product research and development relies on simple rating scales about preferences and willingness to pay, you will almost always fail to understand how your customers really make their buying choices during their buying journeys. Why? Because this type of over-simplified research fails to understand the trade-offs that all customers make when choosing among a variety of alternative products with different brands, features, benefits and prices.

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Adaptive Choice-Based Conjoint Predicts Purchase Behaviors

The use of conjoint analysis in the product innovation process goes beyond simple research. It simulates a real-life approach for understanding your customers' attitudes, opinions and buying behaviors in the competitive market. At its basic level, conjoint analysis learns which combinations of features your customers prefer at different price points.

This type of product market research builds your customers' actual decision-making processes and preferences into your product design, marketing and pricing decisions. What's more, the conjoint-based market simulator tells you where you will take market share and how you may need to react when your competitors shift their own product/price strategies. Remember, they will not sit still.

Today's innovation leaders must be able to predict the purchase behaviors of their target customer segments when they are faced with multiple brands, features and prices. This is where the power of conjoint simulators really shines. Managers can accurately predict how their target customers will purchase when they offer certain products and assume certain competitive responses. This flexible and forward-looking approach to innovation can separate the failures from the successes.


In fact, when used in 3,000+ products over 20+ years, product concept development research and predictive analytics (as part of a formal innovation process such as Stage-Gate) produce success rates that are 2X higher than average companies and 3X higher than the bottom 20% of companies. 

How Adaptive Choice-Based Conjoint Works

So, how does it work?

Adaptive Choice-Based Conjoint (or ACBC) is one of the most advanced approaches to consumer choice (or preference) modeling. ACBC combines the strongest elements of Choice-Based Conjoint (or CBC) with Adaptive Conjoint Analysis (or ACA). Unlike traditional CBC, the ACBC survey research is done in stages. At first, the customer is asked to build his or her “optimal product configuration”. This is often called the build your own product stage (or BYO). Next, the “must-have” and “unacceptable” features are considered. Finally, the customers’ real-life decision making trade-offs are assessed by offering different product configurations (brands, features, benefits, prices, etc.).

Now, what are the real business benefits?

Benefits to Product Managers, Brand Managers and Marketers

This approach to product and service innovation delivers benefits across a wide variety of industries, including: food, beverages and other consumer packaged goods (or fast moving consumer goods, FMCG), consumer electronics, life insurance, health insurance, financial services, retail, telecom and others. Regardless of your industry or category, here are some key benefits you can expect from this approach:

1) You identify the optimal product configuration, price and marketing strategy based on how customers buy products in your unique category and market

2) You identify the prices your customers will pay and if they will drive sufficient margins (price testing)

3) You accurately forecast profitability and/or market share for your product vs. competitive alternatives

4) You anticipate competitors' reactions and plan your counter strategies to their price changes, features additions, benefit claims, etc.

5) You forecast the effectiveness of different advertising strategies, names, logos, messages, and benefit statements

6) You forecast the impact of different pricing strategies in the event your competitors make changes or your cost structure changes (up or down)

7) You measure the value of your brand vs. competing brands and estimate how consumers make trade-offs between brands, price, features and benefits (this is a practical measure of your brand equity vs. your competitors)

8) You can segment your consumers based on their product "choice drivers" so you can offer specific product configurations (and price points) that will satisfy each segment


Improve Your Odds of Innovations Success

In summary, the use of advanced conjoint analysis provides real, immediate and long-term benefits to decision makers throughout the innovation process. It accurately measures consumer preferences from among thousands of product (or service) alternatives. Conjoint analysis allows you to simulate and predict how your customers will think and buy in the real world. As a result, you identify the best product features, benefits and price points that will win in the real market. In a world where 60-80% of innovations fail, these and other new product market research methods will give you a far greater chance of success.

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Topics: pricing, new product development, product innovation

10 Advantages of Online Communities vs. Traditional Focus Groups (Part 1)



Editor's Notes:

1) This post has already generated a tremendous number of comments and ideas on various social media sites. Thanks to everyone who has taken time to contribute. Please keep your ideas and thoughts coming.

2) To be clear, this is Part I of a two-part post. So, while the title says "10 Advantages," Part I has just the first five advantages. Stay tuned for the next five in Part II.

3) While we extol the virtues of online communities (MROCs) in this post, we could easily write a post entitled "10 Advantages of Focus Groups vs. Online Communities." [And come to think of it, we may do just that in the near future.] We want to make it very clear that both solutions deserve a prominent place in everyone's MR toolbox. Focus groups continue to be extremely valuable. That said, the purpose of this post is to challenge your thinking and open your eyes to some truly unique and powerful advantages of online communities.




The venerable focus group has been a go-to market research solution since first invented by Robert Merton at the Bureau of Applied Social Research. But, times change and innovation marches on. Today, online communities are clearly a much better choice for many important market research projects. In Part 1 of this 2-part blog post, we'll cover five important advantages of online communities vs. focus groups.

First, let's establish a baseline definition for focus groups and online communities.

Focus Groups

A focus group is a form of qualitative research where a group of people are asked about their opinions, perceptions, ideas and feedback about a product, service or any topic of interest. Questions are asked in an interactive group setting where participants are free to interact with each other and the moderator. Focus groups are typically conducted in person at a central location (or online), last two hours, and include 8-12 participants from a single segment.


Online Communities

An online community, also known as a "Market Research Online Community" (or MROC), is also a form of qualitative research where a group of people are asked about their opinions, perceptions, ideas and feedback about a product, service or any topic of interest. Questions are posed to the entire group of participants and/or in private, one on one exchanges through a series of interactive research activities such as polls, image markups, discussion boards, and multimedia exercises. Participants interact with each other and the moderator via their computer, tablet and/or mobile phone. Online communities are conducted virtually, last one to two weeks, and include 50-150 participants (often from multiple segments).


As you can see from a simple comparison of the definitions, online communities offer many advantages over focus groups. So, let's briefly touch on the first five.

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The First 5 Advantages of Online Communities

1. Mobile

Online communities allow consumers to participate from their computer, tablet or mobile phone. They can share their opinions and insights anywhere, anytime and with any device. Unlike in-person focus groups, their feedback doesn't stop after two hours in a conference room (or online chat session). This offers multiple advantages to the marketers and researchers who need to better understand their consumers' real lives and experiences. With an online community, you can engage participants when they are using a product or service, in a store, or at any other important location. Do you want to know their perception of your product on the shelf? Send them into the store and ask them questions as they stand in the isle. Want real-time usability or e-commerce feedback? Give them a link to click and ask them for immediate feedback. The ideas and research applications are limitless once your participants are free to roam.


2. Millennials

Of course, not every study is focused on Millennials, but when they are your target, online is better. And, online and mobile is best. Let's face it, Millennials don't want to meet at a central focus group facility. And even if they did, it's not their natural habitat. They think online, digital and mobile. They can easily engage with you in the myriad different ways that online communities allow.

3. Multiple Segments

Focus groups generally include 8-10 people from a single segment. On the other hand, online communities allow you to engage 50-150 people across 4-5 different segments in a single study. So, once you've completed your online community, you can easily compare opinions and ideas across each segment of consumers. As a result, a single online community can provide more insights than five focus groups.


4. More Participants & More Insights

As mentioned previously, a single online community can engage 5-10 times more consumers than a single focus group. While engaging 100+ people in a focus group is impossible, with an online community, it's very straightforward. A a result, companies can harvest 10 times more insights from a single online community in the time it would take ten focus groups to be completed.

5. More Research for Your Budget & Time

Focus groups are very time intensive. For in-person groups, you must travel to the central location and deal with all of the associated travel costs and logistics. When focus groups are out of town, you typically need to allow for 2-3 days of travel time and thousands in travel costs, for a single person. If you are conducting a series of focus groups in multiple regions and cities (or even countries), these time and travel investments can grow exponentially. Online communities eliminate all of these travel and cost barriers. All participants, moderators and observers can join the online community from any location and any device. And because most community "tasks" or engagement activities are asynchronous, participants can share their feedback at any time. So, in most cases, a single online community can deliver insights from 50-150 participants and five or more segments in the the same or less budget (and time) required for a single focus group.

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Is Online Community Research Right for You?

Clearly, the focus groups are a great fit for many research needs. FGI continues to recommend and execute them when appropriate. That said, the many advantages of online communities make them a much better research solution in more and more situations. We hope these advantages (and the next 5 in Part 2) will encourage you to consider this powerful research approach in the future.

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If you would like to learn more about online community research, contact your FGI representative or click here now to request more information.

Click here now to learn more about online communities

Stay tuned for the next 5 online community advantages in Part 2 of this blog post.


Topics: mobile research, online communities, qualitative research, focus groups

Introduction to FGI Video

Introduction to FGI:

Below is a brief (2:23) explainer video that introduces FGI. It touches on our innovative solutions for product testing, package testing and other critical marketing needs.

Here's the transcript...

Today, marketers are under intense pressure to produce better and better results. They must launch winning products, grow revenue and profits, and create a better customer experience.

The problem is, many critical marketing decision are still being made with oversimplied research tools, outdated methods, or last-minute guesswork. Even worse, many viatal source of big data are stuck in their own silos, or abandoned altogether.

But, what if you could use more data sources, optimization algorithms, and predicctive analytics to make faster and better marketing decisions? What if your data-driven decisions suddenly resulted in better prodcuts, better packages, and better marketing messages? And, what if you could put all of this together to improve your financial performance, year afer year?

With FGI Research, you can.

Now, instead of guessing which marketing decisions are optimal, you'll know...before you make a costly mistake.

FGI is changing how innovative companies win, and the results are impressive. Using our approach within a formal innnovation and marketing process, company after company improves their marketing performance. New product succes rates are 3X higher. Time to market is 35% faster. And, profit targets are achieved almost 85% of the time. And, these results are based on thousands of products and services across every imaginable industry, customer segemnt and category.

So, why wait?

Learn how FGI can help you drive profitable growth today.

Topics: About FGI

Analytics Over Intuition: The Numbers Don’t Lie -- Market Research and Advanced Analytics Power Top Performing Companies

by David W. Wilson, CEO, FGI Research & Analytics

Analytics word cloud image resized 600

Analytics or intuition?

What’s most important? There’s a spirited debate around this question. Are the big data and analytical “quants" really going to rule the new world? Or, will the more intuitive and creative types offer leading companies the competitive edge?

A quick review of three independent surveys across thousands of companies yields this answer: analytics is your winner.

Let’s take a look at our three sources: Bain & Company, MIT Sloan Management Review, and the Product Development Institute (related to Stage-Gate International).

Bain & Company

Bain surveyed executives at over 400 companies ($1 billion and up) to produce its report entitled "The Value of Big Data: How Analytics Differentiates Winners." Bain’s research concluded "companies who use analytics the best are 2x more likely to have top quartile financial performance. [However], we found that only 4% of companies are really good at analytics, an elite group that puts into play the right people, tools, data and intentional focus.” 

"Companies who use analytics the best are 2x more likely to have top quartile financial performance.”

-- Bain & Company

The Bain report went on to say this:

"Leading companies embed analytics into their organizations by resolving to be data driven and defining what they hope to accomplish through their use of Big Data. The CEO and the top leadership team need to describe how analytics will shape the business’s performance, whether by improving existing products and services, optimizing internal processes, building new products or service offerings, or transforming business models. Top-performing organizations do this well, often building their organizations around data and a commitment to make data-driven decisions.” 

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MIT Sloan Management Review

MIT surveyed over 3,000 executive managers (around the world and across all sectors) to produce its report entitled “Big Data, Analytics and the Path From Insights to Value." They also surveyed leading researchers. MIT’s research concluded that "Top performers view analytics as a differentiator: Top-performing companies are three times more likely than lower performers to be sophisticated users of analytics, and are two times more likely to say that their analytics use is a competitive differentiator.”

"Top-performing companies are 3x more likely than lower performers to be sophisticated users of analytics."

--MIT Sloan Management Review

The MIT report went on to say this:

 "Our study clearly connects performance and the competitive value of analytics. We asked respondents to assess their organization’s competitive position. Those who selected “substantially outperform industry peers” were identified as top performers, while those who selected “somewhat or substantially underperforming industry peers” were grouped as lower performers. We found that organizations who strongly agreed that the use of business information and analytics differentiates them within their industry were twice as likely to be top performers as lower performers. The biggest obstacle is not the data: Despite the enormous challenge felt by most organizations to “get the data right, ”that’s not what executives name as the key barrier to achieving the competitive advantage that “big data” can offer — the top two barriers are “lack of understanding of how to use analytics to improve the business” and “lack of management bandwidth.” 

Product Development Institute

The Product Development Institute (in it’s affiliation with Stage-Gate International) has worked with thousands of companies (and tens of thousands of new product launches) over the past 30 years. Along they way, they have compiled a massive database of product launch and innovation performance results. Consistently, their research clearly shows that companies using a proven idea-to-launch process (like Stage-Gate) and advanced market research enjoy “new product success rates that are 3x higher” than competitors.

"Companies using a proven idea-to-launch process (like Stage-Gate) and advanced market research enjoy new product success rates that are 3x higher than competitors."

-- The Product Development Institute

None of this should really surprise us. As we face challenges every day, we are constantly looking for data and analysis that can help us make better and faster decisions to obtain better results. Of course, we also rely on intuition and creativity to create new and better products, decisions and futures…innovations that would have been unborn if left purely to data warehouses in the clouds that are being mined by the quants.

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The Bottom Line

Einstein wrote “Intuition does not come to an unprepared mind.” We've proven that today's top performing companies are prepared to take full advantage of analtyics for long term competitive advantage. Top performing companies of the future will have invested heavily in market research and advanced analytics. Indeed, they will make these consumer and market-driven insights the very foundation of their businesses. At the same time, the best companies will blend this unprecedented new level of insight with the age-old and awe-inspiring ability of humans to dream, create and invent a better world for us all.

"The best companies will blend this unprecedented new level of insight with the age-old and awe-inspiring ability of humans to dream, create and invent a better world for us all."


Topics: performance improvement tips, David Wilson, market research, big data, data analysis for business success, primary research, new product development, predictive analytics, data mining

Ohio Meets the 2014 FIFA World Cup -- What Twitter Big Data Mining & Visual Analytics Reveal About Who Really Cares in the USA

by Dino Fire, Chief Science Officer, FGI Research & Analytics

*** Important Note to Readers: Some of the graphics in this blog post can be hard to read based on their native form from r. However, I wanted to illustrate what gets produced from these public domain solutions. They must be reproduced with other packages to yield client-ready reports. In any event, you will "get the picture" based on my analyses and commentaries associated with each graph. Thanks in advance for your understanding. ***

usasoccer resized 600

Growing up in Northeast Ohio, I do not recall ever seeing, let alone actually kicking, a soccer ball.  In those times and in that place the term “football” meant something entirely different.  It meant an oblong, leather-clad, brown inflated ball.  It meant glorious Friday nights at Mollenkopf Stadium.  It meant watching the Ohio State Buckeyes stomp on the University of the Sisters of the Poor every Saturday afternoon.  And it meant exploring new and exciting ways to express one’s displeasure and disgust at the Cleveland Browns every Sunday.  So naturally I wondered how the 2014 FIFA World Cup was playing in this nether world of chauvinistic American sport.

"I wondered how the 2014 FIFA World Cup was playing in this nether world of chauvinistic American sport."

Through the magic of the Twitter API, R code, and a few extra moments of time on my hands, I set forth on the journey to find out.

The Twitter REST API enables users to set a geographic parameter to limit searches to a specific geographical area.  The search terms were limited to #WorldCup, #worldcup2014, or #Brazil.  These terms were subsequently eliminated from the analyses, because we’re interested in what people are saying about those terms, not about counts of the terms themselves.


I started with the latitude and longitude of Columbus, Ohio, and specified a 200-mile radius.  A word cloud, of course, yields larger, more prominent displays of words with higher frequencies.  The basic word cloud of Ohioans’ tweets demonstrate some interest in the Spanish and Croatian futbol teams.  Speaker of the House John Boehner garnered a few honorable mentions as well.  What that has to do with the World Cup, I do not know.

Next I made a little side excursion that explored the tweets from the Youngstown/Warren area with those of residents of Youngstown’s sister city, Salerno, Italy.  The outcomes were predictable but nuanced. The Youngstown and Warren folks tweeted about the generic USA.  Could’ve been the soccer team, could’ve been native cuisine, like hot dogs, and could’ve been anything.  Not so with the Italians, though; the national football club was front and center.


Ohioans are people of few words, at least as far as tweeting about the World Cup is concerned.  The vast majority of Ohioans’ tweets comprised 8 or 10 unique words.  The base R program provides a nice histogram.


"What’s the difference between England and a teabag? The teabag stays in the Cup longer."

Before we get into the deeper statistical analysis, I should point out that THE BIG BUZZ at the time was about England getting unceremoniously booted from the tournament in the opening round.

What’s the difference between England and a teabag? The teabag stays in the Cup longer.

A hierarchical cluster analysis of Ohioans’ tweets is intended to depict how words tend to cluster together in Euclidean space.  It’s a fancy way of seeing how words correlate.  And here are the results.


One group of tweets centered on England’s demise, and another seemed to be about who was showing up in Rio de Janeiro.  Yet another group of words dealt with the Italy – Costa Rica match, while a fourth cluster seemed to inquire about who was supporting US soccer.

Disregarding the clustering of words, we can review the correlations themselves.  I’m proud to say that Ohioans are expert analysts of English soccer.


Despite a seemingly infinite number of startups claiming to do better social media mining better than anyone else, sentiment analysis is an iffy proposition at best.  For those who aren’t blessed with 50 unsolicited emails a day from social media mining companies, sentiment analysis refers to an evaluation of a tweet from a subjective, qualitative standpoint.  The analysis tries to classify tweets or other textual content “scraped” from various websites into “good” or “bad,”  “happy” or “sad,” or other such bipolar sentiments.  But often that’s where the problem arises.  For example, the following tweet would be classified as “good:”

Well, England, that was a good effort.

But unfortunately, so would this one:

Well, England, THAT was a good effort.

He or she whom invents a sentiment algorithm that can accurately interpret sarcasm wins the prize.  Yeah, THAT will happen.  Scrape THAT, you bums.

Nevertheless, I’ll hop upon the sentiment analysis bandwagon and see how Ohioans feel about the World Cup so far. First of all, we see that there is no transformation of the sentiment-scored data required.  The results reflect a very normal distribution, not skewing one way or another too badly.


We see that the sentiment scores are more positive than not, but as of this writing, the USA team is 1 – 0.  Those scores are subject to shift later, to be sure.


In this case, the sentiment scoring algorithm freely admits that it is clueless about the context of many of the words it encountered.  Still, it seemed predisposed to find and tag joyful comments.

"In this case, the sentiment scoring algorithm freely admits that it is clueless about the context of many of the words it encountered."

The sentiment scoring algorithm output a nice comparison word cloud, which visually demonstrates the words and their respective classifications based on frequency.  Yes, I always associate the term “snapshot” with “disgust.”  Interestingly, “Redskins” got lumped into that classification as well.


So are Ohioan’s beliefs about the World Cup different from other, surrounding, and, some would believe, inferior types of people (based on their state of residence)?  Well, let’s see.


Sentiment scores in Ohio, Michigan, West Virginia, Pennsylvania, and Indiana lean uniformly positive.  But a careful look at the boxplots show that Ohioans and Indianans opinions tend to cluster in the middle:  not too positive, and not too negative.  That’s not the case among Michiganders, who tend to be extremely more positive or extremely more negative.  Those Michigan folks represent very nicely the dangerous reality about averages: You can be standing with your feet in a bucket of ice water and your head in a roasting hot oven.  But on average you feel just fine.

"Ohioans are losing interest, and starting to turn their attention toward Wimbledon. And West Virginians don’t seem to care much about the World Cup at all."

A comparison cloud shows just how different the tweets from these separate states really are.  Michiganders seem obsessed with the Italy – Costa Rica match.  Indianans seem strangely interested in the Forza Italia political movement.  Pennsylvanians are engrossed in a game of “where’s Ronaldo?”  Ohioans are losing interest, and starting to turn their attention toward Wimbledon.  And West Virginians don’t seem to care much about the World Cup at all.



What do you think about the ability of Twitter, r, and visual analytics to shed light on USA's real and lasting interest "the beautiful game?" Let me know with your comments below.

Topics: Twitter, data visualization, big data, predictive analytics, data mining

How to Maximize the Profit from Your Term Life Insurance Block...and Maximize Your Customers' Experience...Using Market Research and Predictive Analytics

by David W. Wilson, CEO, FGI Research & Analytics

Are you losing millions of dollars as your term life policies lapse at PLT?

Maximize Profit from Enforce Term BlockLife insurance companies lose hundreds of millions of dollars every year as their term life customers drop policies that reach post level term (PLT).  As term life policyholders receive their rate change notices in the mail (often with very little warning, 60-90 days, and with no personal reminders or consultation from their original agents), these orphaned customers are usually quite shocked to see that their premiums will soon increase 5-10 times the rate they have been paying during their level period. Sadly, what happens next is very predictable. As much as 80-90% (or more) of policyholders lapse their contracts at PLT, or a few months thereafter when the reality of the premium increase hits their pocketbooks. 

"Often called the "shock lapse," these extremely low PLT persistency levels can have a devastating impact on the revenue and profitability of in-force term blocks."

Often called the "shock lapse," these extremely low PLT persistency levels can have a devastating impact on the revenue and profitability of in-force term blocks. In addition, shock lapses do not help establish a positive customer experience. When the actuaries originally designed and priced these term products, they usually predicted (and often planned and designed for) high lapse rates, but not necessarily the 80-90%-plus levels they are experiencing today. When these products were first designed and priced, actuaries focused on the desired financial results over the level term period, not maximizing results during the post-level term period. These problems cannot be ignored and they appropriately find their way into the C-suite where they get attention from the CEO, CFO and other top execs.

How Advanced Market Research and Predictive Analytics Can Maximize Profit (and the Customer Experience) from Your In-force Term Block.

Thankfully, there are a number of ways to successfully address this vexing problem. And, each solution relies on some combination of advanced market research and predictive analytics for answers. Let's take a look at a few solutions that every life insurance carrier should consider as they evaluate their in-force term blocks.

Solution I - Reduce the Shock by Altering the EOLP Premium Scales

As I've previously mentioned, the end of level period (EOLP) pricing on in-force term blocks is the culprit behind the customer's shock and lapse when reaching PLT. By adjusting the EOLP premium scales, carriers can retain some policyholders a few years longer, which can drive greater revenue/earnings from the block. At a typical EOLP price increase of 6-7x the level premium, the policyholder lapse rate is 82% (source: 2010 SOA Report on Lapse and Mortality Experience of PLT Premium Plans). However, when the EOLP increase is reduced to 3-4x, the lapse rates fall to 51%. Finding the right new EOLP premium scales requires market research and advanced analytics to help settle on pricing that attracts enough healthy customers to cover the increased claims that will be incurred. These adjusted scales should be thought of as a new "concept" to be tested and then targeted to the most appropriate current policyholders. As such, best practices market research can accurately test the concept and predict which customers (by health cohort) will remain at varying price levels. Predictive analytics can then be used to score and target the very best customers (the healthiest customers who are most likely to adopt the product) for this offer. So, if the carrier finds the right blend of price/adoption/health, they can successfully execute the EOLP changes.

Solution II - Cannibalize Your Current Policies with Your New Policies

If the EOLP scale adjustment solution is not favored and/or the carrier would like a multi-solution approach, they should consider offering alternative products before the shock lapse occurs. Instead of sitting back and letting your customers walk into your competitor's arms, smart carriers will proactively offer desirable alternatives that are easy to understand and buy. In other words, they will cannibalize their own policies. For example, a Simplified Issue (SI) product can be offered. Requiring very little paperwork and no body fluids, the SI can be an attractive option to many policyholders. Like the EOLP price change solution, this is a product concept that requires testing and targeting to get maximum results. Again, market research confirms the product is desired (and which features and price points are the key drivers) while predictive analytics precisely identifies the very best customers (the healthiest customers who are most likely to adopt the product) for this offer.

Solution III - Deliver Targeted and Timely Communications

Regardless of the offer (revised EOLP pricing, SI product, or other offers), carriers simply must improve the targeting, timeliness and type of communications as their customers approach PLT. After all, is the "expected" shock lapse an acceptable component of a well-oiled customer experience (CX) strategy? Often, the default communication is a highly impersonal 60-day notice to customers indicating their level term policy is ending and their new premium is 6-7x higher. Carriers that rely on this "CX approach" are creating self-inflicted shock lapses among their policyholders. It's at this point that it's worth pointing out my strong preference for the term "paying customers" vs. "current policyholders." While the latter moniker is most prevalent, I believe it only serves to depersonalize the customer. And, it certainly doesn't encourage carriers to think about the best possible CX.

"Is the expected shock lapse an acceptable component of a well-oiled customer experience (CX) strategy?"

In any event, these paying customers deserve the right communications (personal call, email, direct mail or all three) at the right times (in general, sooner and more frequently) to produce a more than satisfactory CX. As you might guess by now, market research and predictive analytics play a huge role for this solution. Market research can tell you exactly which messages will motivate different customers for different needs and solutions. Predictive analytics will identify which customers are most likely to lapse, which are most likely to adopt specific offers, which are healthier, and which to target with certain offers and messages.

A PLT Policyholder Story With a Profitable Outcome and a Positive Customer Experience.

Ok, we've covered a lot of ground on this blog post. Now, let's wrap it up with a story and some next steps. First, the story. Imagine that YOU are the paying customer. Instead of getting a 60-day notice in the mail, your carrier...as part of its comprehensive CX strategy...has determined that you are likely to appreciate a different EOLP price or an alternative product that fits your needs. Furthermore, they take the proactive step of notifying your agent to contact you personally. You get a call six months before you reach PLT. This gives you time to evaluate your options and make the best decision for you. You avoid the unnecessary shock and you happily remain with your carrier. What's more, you actually recommend your carrier to your co-workers at the office (and maybe even a few friends on Facebook). Now, how did you like that market research and predictive analytics-enabled story? That's the power of research and analytics to maximize profits and the CX.

"That's the power of research and analytics to maximize profits and the Customer Experience."

Three Next Steps for You to Get Started

So, what can you do to improve the CX of your term life paying customers in order to earn more of their business and drive greater revenue and profit from your in-force blocks?

1) Step one: Assemble a small team that includes the term block owner, an actuary, someone from marketing/sales, someone from CX, and a few market research and analytics pros.

2) Step two: Calculate the financial impact of the annual lapses. I call this quantifying your pain and your opportunity.

3) Step three: Brainstorm the three solutions above, among others. Then, test something. Do something. Try something. And, steadfastly refuse to sit by and wring your hands as your customers and revenue quietly walk out your back door.

Click here now or click the box below to discuss this blog post with FGI and explore how it applies to your current business. We look forward to discussing it with you.

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Key sources and further reading:

1) The Financial Reporter, Society of Actuaries (SOA), Dec 2013

2) SOA Annual Meeting, Oct 2013

3) The Messenger (Risk Management Newsletter)

Topics: performance improvement tips, market research, big data, data analysis for business success, predictive analytics, customer experience, life insurance, term life, churn, retention, CX

Retaining and Growing Super Consumers

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Many companies aspire to grow and maintain their brand loyalists—the customers who purchase their products time and time again. After all, this small segment of heavy users often accounts for the majority of a company’s sales. But within this group of loyalists, there’s an even smaller—and more powerful—segment of consumers that is often overlooked. This group, known as “Super Consumers,” might just hold the secret to success for your company. 

The Harvard Business Review recently published an article from the Cambridge Group and Nielsen about the power of Super Consumers. We’ve broken down some of the main points and explained how you can use research to effectively retain and grow your Super Consumer segment.

Who are “Super Consumers?”

Although they both are known for their big spending habits, Super Consumers and traditional Heavy Users are not the same. Heavy Users are simply identified by how much they spend. Super Consumers, on the other hand, are a segment of Heavy Users that are highly engaged with a category and a brand beyond their high purchasing levels.1Super Consumers might have a special connection or association with a brand, or they may value certain aspects of a product’s usage more than other buyers. Whatever the reason is, it helps drive Super Consumers to purchase your product more often than the average Heavy User. 

Why are they important?

Super Consumers represent a large portion of sales for most brands and products. And according to HBR's article, Super Consumers are usually willing to buy even more of a product than they already do if they can find uses for it, giving this group a high potential for sales growth.

Beyond that, the usage habits and brand associations that set Super Consumers apart from traditional Heavy Users provide valuable insight into what companies should and should not to do increase sales. While Super Consumers are more resilient than other customer segments and can withstand things like the occasional stale product or a slight price increase, it’s vital to protect the brand attributes that make these people true Super Consumers. Attempting to change essential qualities or brand attributes can drive these and other valuable customers away.

But, if you uncover these purchase drivers, you can find out how to increase sales to existing Super Consumers and convert more of your regular customers into future Super Consumers.

How can you pursue this strategy?

Identifying your Super Consumers is easy with consumer purchase data and shopper panels. Once you’ve identified this group, we recommend a two-step research process to uncover what drives them to purchase your product:

1. Use qualitative research to create a catalog of brand associations and usage that drive Super Consumers to purchase in high volumes

2. Conduct quantitative research to identify the most critical brand values for Super Consumers

With this data in hand, you can protect what Super Consumers consider to be your brand or product's most important attributes, use these insights to create new Super Consumers, and sell even more to your existing Super Consumers. 


Learn more about FGI's  marketing solutions


[1] Make Your Best Customers Even Better, Harvard Business Review, 2014

Topics: Product Marketing, segmentation, consumer research, customer loyalty

Measuring Customer Experience with Net Promoter Score

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. 

NPS Infographic

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. 



[1] Under the Influence: Consumer Trust in Advertising, Nielsen, 2013

[2] Net Promoter SystemSM, Bain & Company, 2013

Topics: customer satisfaction, market research, consumer research, customer loyalty, customer experience

A Look Ahead for the CPG Industry: Is Digital Marketing Really Needed?

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.

New Product Innovation, Pricing & Marketing: Maximize Revenue, Share & Profits

David Wilson, CEO

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 [1]

  • 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:

Product Innovation

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.

Product Pricing

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.

[1] Stage-Gate International.


View Case Study

Topics: Manufacturing, Consulting, Product Marketing, consumer packaged goods

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