One of the interesting challenges Marketers are charged with is to make their product or service stand out in minds of the potential customers. Those who are not blessed with analytical talents commonly slide into well bitten path to differentiate by specifications or price. These approaches do not really require any expense and/or curiosity to seek deep understanding of the customers, but they are ultimately led to erosion of profit margins and brand equity. If you, brand “owner,” don’t care about the customers, the customers don’t care about your brand. Advertising alone could carry the brands a great distance in the “good, old days” but in the age of Social Customer, an advertising message is expected to resonate with customer needs or it will cause more harm to the brand image and product sales than good. When it comes to a product reputation or brand equity, the notion that “Any publicity is good publicity” is not the best strategy.

None of it is new to most marketers and some companies are spending serious money to develop processes for discovery of consumer/customer insights. However, most are struggling to convert the findings into specific actions. Measuring financial impact of these actions seems to be an even more formidable challenge. I would like to explore these challenges and perhaps offer some ideas for dealing with them.

Many marketers today are too insulated from their customers to develop a true, genuine understanding and empathy of customer experience with the products or services they market. One of the reasons is the use of outdated market segmentation methodologies based on demographic data that was developed to help marketers to quantify and forecast, but do not provide much help in understanding the needs and discovering opportunities for differentiation. More evolved methodologies that attempt to develop customer “personas” are much more helpful in learning needs of the specific, pre-defined groups of customers. Scott Sehlhorst of Tyner Blain offered a wonderful explanation of how such methodology can be used.

Use of both abovementioned approaches together will likely to improve your product traction, but will fall short of true understanding you need to differentiate your product because everything you have learned so far is based on your own original assumptions. You start with a hypothesis of who your potential customers are, what functions and features they would like in your product, and how much they will pay for it. Then you proceed with a number of potential customers’ validation and advisory activities that confirmed or cancelled your assumptions with various degrees of certainty. However, you still don’t know if the group and personas (within the group) are your best potential customers since you cannot possibly validate that with every potential segment. Additionally, I don’t think it is possible to effectively differentiate – by design, packaging or message – without ultimate understanding how the customers experience the product. All the steps you have taken so far cannot give you this knowledge for 2 reasons:

• You have started at the “wrong” place – i.e., demographic segmentation of market is a wrong starting point. Much better starting question is – what products/services my future best customers are hiring today to do the job they need done. I use here terminology and concepts developed by Clayton Christensen. Check this video where he explains why the basic thinking taught in business schools and promulgated by consultants is killing innovation and the US economy if you are not familiar with his work.

•  Any knowledge of customer preference you have gained so far is company-biased because it was obtained by methods of inquiry and/or moderation. The one who forms a question or selects the subject of discussion ultimately influences any possible outcome. I do not believe that there is such a thing as an unbiased research, and I prefer customer’s bias to a company’s bias for the purpose of learning how a customer experiences a product or a service. This is my preference because regardless of our opinion, that is what they are going to use while selecting to purchase your product or a product of your competitor.

I am not dismissing the value of traditional methodologies off hand, but I am suggesting that substantially better results can be achieved by using triangulation of these with true insights of customer experience. There are plenty of customer-generated content available online for aggregation and analysis; however, even if you find difficult to find good data, we had very good results by asking customers wide open questions designed not for validation and easy tabulations, but to help them tell their stories:

What made you interested in a product XYZ?

• How and where do you use it?
• What was your experience so far?

Let them know that you asking because you want to learn how to make their experience better and promise that you will let them know the results of the study. Most people are motivated and willing to help. These types of questions are traditionally reserved for qualitative research that in the past was considered expensive, and the results are often dismissed as statistically not representative as they are normally reserved for a small number of customers. Those who try to find insights manually in large volumes of data are quickly get overwhelmed by “drinking from a fire hose.” However, advances in opinion mining technologies significantly reduced cost of high volume content analysis and can offer benefits of qualitative research and statistically representative numbers to back up the value of insights. In the words of Clay Shirky, “There is no information overflow-it is a filter failure.”

Good use of right technology can provide a marketer with a substantial and representative number of clues and hints to how customers think and feel about their experience with a given product or a group of products. However, no automation or outsourcing can replace your creative power of interpreting these clues into actionable insight. You can see examples regularly published on our Google+ feed.

The language customers who used to describe their experience will also provide the source of how to communicate with the market in the way the message will resonate and connect on the emotional level.

Every great product starts with a great idea. Unfortunately, many mediocre products and outright flops have also started with an idea that seemed great at the time. I would like to evoke a memory of the patron-saint of Product Managers and quote:

“Deciding what not to do is as important as deciding what to do. That is true for the companies, and it’s true for products.” – Steve Jobs

Unfortunately, we fall in love with our great ideas all too often and tend to overvalue creativity at the expense of critical thinking. Let’s face it – we are paid to create great products, not to engage in “paralysis through analysis.” The cost and effort required to conduct market research is most frequently invested into finding evidence to support our great idea, not to challenge it; hence the survey questions and focus group discussions often default to a pro bias.

I do not believe that human beings are capable of processing information without a bias; however, customer bias is much more valuable than a company/product bias to support critical GO/NO GO decisions. Insights found in experience of consumers, who are most likely to become customers for your proposed product, are in my opinion the best information to help us make that decision.

Insight is a tricky concept that is often used without clear definition of its meaning. I would like to suggest a few ideas on how to define it:

Insight is…

• Penetrating understanding of consumers

• An undiscovered truth that suggests an unmet need

• Something that makes you go, “AHA!”

These came from member contributions to a Customer Intelligence LinkedIn Group discussion and do not pretend to be an exhaustive list. The classic example I have come across is:

“People don’t want quarter-inch drills. They want quarter-inch holes.” – Theodore Levitt, Harvard Business School

Here is a “best practice” used by some of our clients to make this decision:

•  Identify and articulate a “job” the proposed product (or service) is going to be purchased by its customers to do. If you are not familiar with this product/job correlation concept, you want to learn about research of Clayton Christensen.

• Identify the most successful products currently available on the market that people buy to do that “job.” These products may or may not have anything to do with the technology, features and specifications of your proposed product.

• Find and aggregate customer-generated content describing their experience with products identified in the previous step.

• Analyze this content to identify shortcomings or inadequacies of currently available products to fulfill customer needs from their experience and perspective. Our clients use Market Intelligence Analysis reporting service to save time and effort, but it can be done manually as well.

• “Deep Dive” into those elements of customer experience that score below customer expectations as a group to generate an insight.

• Ask yourself if your proposed product can improve customer experience based on the insights you have discovered in previous steps. If the answer is ‘No, but…’ follow the advice of Ron White who said, “If you have got an idea… let it go.” The best hope for this product is to become one of many options available to consumers “to-do-the-job” and no fancy marketing communications would be able to differentiate it in their mind.

The cost of error can be relatively low in an agile software development business, but it quickly escalates into millions if product has to be manufactured in volume and brought into consumer market to be tested. On that scale, even a small reduction of product failure ratio will generate outstanding return on investment in this methodology and effort.

We have recently created  our new Google+ page to publish regular updates of Customer Intelligence Analysis and Market Intelligence Analysis reports for specific products and market segments contained in our data base. These reports are the samples we produce for our own research and training of our Opinion Mining software and based on the analysis of the data subsets, which may or may not be statistically representative of all Customer Generated Content (CGC) available online. The reports will be published a few times per week as long as there is sufficient interest, i.e. visitors who have Amplified Analytics in their circles.

Please visit Amplified Analytics Google+ page, join Google+ if you are not yet on it, and include it into your circle to get updates every time new content is published.

It is an inescapable part of our existence that we have to make important decisions in our private and professional lives without sufficient amount of information. In fact, in most cases, there is no amount of information that can make an output of a decision to be certain. We can only improve our odds (i.e., increase a probability of a decision to produce the desired outcome) by making a “good” decision. The process includes gathering relevant information, analyzing it and making good judgment in an effort to reduce economic uncertainty.

“Good judgment comes from experience, and a lot of that comes from bad judgment.” — Will Rogers

Presumably management decisions are made by experienced people, and the more important these decisions are, the more experienced are the people who suppose to make it. However, people who make it to the higher ranks in management hierarchy are often those who avoided making mistakes before or at least managed not to be caught making them. In other words, most career-minded people often display a risk-averting behavior. If this is true, then the management decision-making process is irrational according to Dan Ariely, the author of best-selling books Predictably Irrational and The Upside of Irrationality. In his blog post, Dan wrote:

”Companies pay amazing amounts of money to get answers from consultants with overdeveloped confidence in their own intuition. Managers rely on focus groups—a dozen people riffing on something they know little about—to set strategies. And yet, companies won’t experiment to find evidence of the right way forward.”

One needs to make uncommon decisions to achieve uncommon results, even though the consequences of being wrong can be devastating. Just ask Tony Hsieh, CEO of Zappo.com, who decided to allocate most of his marketing budget to fund customer support call center and to turn upside down the most common Customer Service Reps’ performance measurements. Their call center has become their marketing growth engine that generates customer retention, Word of Mouth and revenue per customer returns that outpace common marketing investment performance by a wide margin.

We often rely on the “knowledge” of the past, while the rate of change around us seems to escalate relentlessly, and traditional approaches to gathering, analyzing and judging the relevance of information we consume to make decisions today seem to be less than adequate. Big help could be found in uncommon sources of empirical information and market intelligence, particularly if it challenges your institutional orthodoxy. Change requires courage and conviction according to Jim Farley, Group VP, Global Marketing, Sales and Service at Ford Motor Company. Here is what he said in a recent interview with Brian Solis:

This article was originally published at CustomerThink.com and being re-posted with some updates and modifications.

Marketers used market segmentation methods for a very long time. However, as our abilities to collect and manage information continues to improve, the new methods of segmentation become available to enable more targeted marketing efforts for marketers and better products and services for consumers. One of the most commonly accepted strategies utilized is demographic segmentation based on an assumption that a specific group (based on age, gender, etc) is a primary consumer of your product or service. Sometimes this assumption is based on the product purchase history. Regardless of the validity of an assumption, it does not often provide an insight on “WHY” this demographic segment would select the product in question or “HOW” they would use it. In other words, there is a lot of guessing that has to take place or additional segmentation strategies to be deployed. In my opinion, the popularity of demographic strategy lay mostly in its low cost and ease of access as behavioral and psychographic segmentation requires a lot of research that translates into high cost and time-to-market constraints.

The advances in technology start to offer new opportunities for market segmentation based on automated analysis of customer-generated content which is becoming available with the proliferation of social media and the rise of Social Consumer. Essentially instead of assuming what demographic group would be the ideal target for our marketing efforts, we could look at a group that already expressed their interest by purchasing specific types of products or services and learn “WHAT” elements of their experience were important to them.

Joel Rubinson, one of my favorite authorities in the field,  posted this on Google+ ”as I review materials for the NYU social media class I am about to teach, I believe that Facebook will lead to the end of demographic targeting for media. Of course, content consumption and sharing behavior also enable this but Facebook will be the catalyst. Why not target on interests and actions? Thoughts?”

Most companies of any size use online survey techniques in an attempt to engage their customers, but the method does not support discovery of customer perspective; it validates assumptions of the company based on questions posed and deemed important. Again, the primary driver of survey method popularity is not the quality of the output and ability to provide better market intelligence, but the cost of implementation. I would suggest there are better alternatives today to learn unbiased market segment knowledge in applications of Opinion Mining technology to unsolicited customer-generated content.

The Opinion Mining approach offers much better quality of market segment intelligence and often rivals Survey approach in terms of implementation complexity and cost. I would like to offer an example to illustrate my point. Let’s look at tablets market segment defined by a few popular products in this category; however, non-like products that compete for the same wallet share can be used to get valuable insights:

• Apple iPad2 (666 customer “stories”)
• Blackberry Playbook (255)
• HP TouchPad (650)
• Motorola Xoom (576)
• Samsung Galaxy Tab 10.1 inch and (502)
• Toshiba Thrive (433)

These products were selected based on their popularity that manifested itself in a number of their customer-generated content references available online in a form of customer reviews, forum comments or social networks product page messages.

The first level of Customer Intelligence gained by Opinion Mining of this customer content is a list of customer experience attributes, sorted by their importance. The importance is measured as a percentage of total number of unsolicited opinions expressed by the customers. This answers the questions – WHAT is important to the customers and HOW important that is.

The next level allows the measuring of the difference between customer expectations and their experience and measures HOW well the customers’ needs are met. We use a two-point scale to visualize that difference (0=unacceptable, 1=experience meets expectations, 2=delighted); however, the measurements can easily be converted to any scale of choice without losing their meaning or accuracy. The chart below focuses on the top four attributes of customer experience by their importance to illustrate the approach.

There are practical implications of these measurements as they reflect on marcom messaging that have created customer expectations the product needs to meet. In the example above, most of the products exceeded the expectations of their customers in attributes most important to them by a significant margin. As an illustration, I would suggest that perhaps messaging about usability of these products could leverage customer sentiment to assure consumers who are hesitant to make a purchase and increase their products market adoption. That calls for a next level of intelligence that provides an answer to WHY customers feel this way and provide a context in which they express their opinions.

Above is a very small sample to illustrate the use of words and expressions (in square brackets) people to describe their opinions, and how they are attributed to a specific element of customer experience. These words, expressions ad even quotes can be used to fortify marketing messaging. Think of the very successful marketing campaign by Tempur-Pedic.

The flip side of the coin – early understanding of root causes of customer disappointment – can help to alleviate larger problems, turn the problem situation around or even present an opportunity for differentiation as illustrated below.

Looking deeper reveals a lot of unhappiness about compatibility:

And even deeper analysis will provide a context that is invaluable for taking an advantage of the opportunity (click on the image below to make it larger):

To sum it up – this type of market intelligence can be produced within a few hours at cost of a few hundred dollars without any installation, implementation or training investment which makes it difficult to ignore as an alternative or addition to survey and panels approach. As GPS technology thought us – multiplicity of signal sources results in better decision quality.

This analysis is based on 82,620 customer reviews of 318 smart phones published online by December 15th 2011.

To insure statistical representation and accuracy of results, we have focused on 42 smart phones that were reviewed at least 100 times this year. That may mean that some phones that were introduced toward the end of the year did not qualify for this report.

We have studied before the correlation between number of reviews published online and a number of units shipped, and therefore found it important to use it for comparison.

The most customer-reviewed phones of 2011 are HTC Thunderbolt (5,579), Apple iPhone 4-16GB (4,106) and LG Ally (2,514).

HTC got a hold on the position of the most reviewed brand in the smart phones category largely based on popularity of the Thunderbolt.

The customer’s enthusiasm for Android smart phones and the availability of a large number of models from multiple brands produced very unbalanced distribution of  reviews (75%).  

However, the Android OS enthusiasm did not translate into customer satisfaction lead as Windows phone customers’ expectations were exceeded by their experience with a wider margin. One of the possible reasons is the relatively weaker support of Android by the developer’s community that translates into the availability of applications.

It appears that Nokia’s decision to migrate their phones to Window OS is a wise one considering Symbian satisfaction scores.

Our Market Intelligence Analysis of the smart phone segment indicates that the following Attributes of customer experience are most important to them:

1. Reliability – 14.76% of all opinions expressed
2. Usability – 7.23% of all opinions expressed
3. Battery Life – 6.42% of all opinions expressed
4. Display – 5.82% of all opinions expressed
5. Camera & Video – 4.91% of all opinions expressed
6. Reception/Call Quality – 2.57% of all opinions expressed
7. Customer Support – 2.27% of all opinions expressed
8. Keyboard – 2.27% of all opinions expressed
9. Design (style) – 1.57% of all opinions expressed
10. Price – 1.23% of all opinions expressed
11. Music Player – 1.00% of all opinions expressed

In terms of overall satisfaction, Blackberry Style 9670 has earned the top customer satisfaction rating (1.60) and HTC Rhyme (1.59) came within a statistical tie, while Motorola Citrus (0.72) and Droid 2 Global (0.82) are on the very bottom of the list.

To get more specific insights into the dynamics of the smart phone customer perceptions, we sampled a market segment by analyzing the most experienced (i.e., most reviewed) models representing different operating systems. We picked the models that are close to each other in a number of customer reports to make it more comparable.

1. Apple iPhone 4S – 542 customers
2. Blackberry Torch 9800 – 550 customers
3. HTC Trophy – 236 customers
4. Nokia N8 – 523 customers
5. Samsung Continuum Galaxy S – 444 customers

More details and customer feedback verbatim are available via access to the dynamic dashboard for this segment on request.

We are proud to announce the a new release of Opinion Mining software is now available to our customers and registered users. The release includes the following functions, features and enhancements:

• New format of Customer Intelligence analysis report along with short videos to help quick adoption

• New pages on our website describing how our customers using Customer Feedback Analysis to outsell their competition.

• New Trending report

I would like to thank our customers who provided valuable feedback that helped us develop these new capabilities.

I keep struggling with the definition of what is an innovative blockbuster product (or service), and this is yet another attempt: A truly innovative product is the one that delights its customers by anticipating their needs before they knew they have them. In other words, if you want to develop a blockbuster product, you should stop trying to better serve the existing needs of your customers and instead try to discover needs that customers may not realize they have and address them.

Traditionally, companies use customer feedback to assess satisfaction with existing products and to validate product developer’s ideas for the improvements. One of the most popular methods used for collecting customer feedback are survey and panels, where the questions asked or topics moderated tend to reflect interests of product development team and focus on how customers experience their product.

I would like to pose that truly innovative product developers use a different perspective to discover the needs customers cannot articulate in controlled or moderated environment – the perspective of holistic experience of a job the customer “hired” the product in question to do.

The journey starts with the understanding of what the “job” they want to do is and what a desirable outcome is. The next step is to imagine how this whole experience can be simplified in its entirety, which may or may not involve your product. I use the word “simplified” because it is an ultimate description of improvement in a context of “desirable outcome.” Terms we usually use to describe improvements – Better, Faster, Cheaper – are traps anchoring us to the incremental changes of status quo.

The complete customer experience starts with a notion that the desired outcome can be achieved, and goes through discovery of components required, acquisition of the components and/or materials and skills all the way through a process of applying them. Your product may be just one of many in that process, but if you can make it easier to find at the conception stage, simpler to understand that it is the best alternative to get the job done at the acquisition stage, and require less skill and/or effort to operate, that will make your product a lot more successful. However, truly innovative products do often have an element of disruption that does not easily fit into organizational structures. If you are a drill product manager, and survey satisfaction of a drill purchasers, the ideas of alternative wall anchoring to hung pictures will not likely come up. However, even if it does, how does it help you or your department?  I wonder if a celebrated genius of Steve Jobs could only manifest itself because he operated from above of organizational hierarchy.

The question is, “Can Customer Feedback help to create innovative products?” If you define Customer Feedback as the results of survey or other structured information-gathering method, the answer is NO. The best outcome of these exercises is reduced uncertainty about your assumptions (i.e., confirmation of what you already know). The probability of discovering an idea that could lead to the conceptualization of an innovative product is extremely low, but could be improved somewhat by allowing open-ended questions and a lot of unstructured comments.

I define Customer Feedback as any and all customer-generated content available about a product/service in any form customers chose to communicate it. That includes company and public forums, customer support notes and call transcripts, company sales notes, customer’s Facebook comments, and customer videos and reviews published online. The wider Customer Feedback “fishing” net is cast, the higher probability of innovative ideas discovery. Combine it with the right analysis methodology that does not tie you up with pre-conceived keywords and ontology, and your chances are looking even better.

This is a new analysis of customer feedback, which is available online, about their smart phones. At this time we are tracking and analyzing comments from 37,110 customers on 136 mobile smart phones. I decided to filter out the phones which were not updated with new customer comments during the last 30 days to insure that these phones are still available on the market. The resulting Product Reputation report is available at Market Intelligence.

I selected the most reviewed phones for each operating system to take a close look at what attributes are important to the smart phone customers. As customers keep posting their reviews and forum comments about their experience with the phones they chose,  Reliability remains the most important specific attribute that dominate the conversation as 15.22% of all opinions mined is focused on it.

Let’s face it, anybody who buys a smart phone and pays for the service expects to be able to use their phone every time they want to. Apple clearly outshines competition by exceeding customers’ expectations of Reliability by 10%. HTC-HD7 (Win7 OS “representative”) meets their customers’ expectations: however Android (HTC Thunderbolt), Symbian (NokiaN8) and RIM’s Blackberry Curve 9330 are a disappointment to customers who selected to purchase these phones.

Overall customers are satisfied with their decisions to various degrees, but Apple iPhone users are reporting that the phone exceeded their expectations by 42%. Not surprisingly they also are the most satisfied with the choice and quality of the Applications available to them, their Usability and Web Browsing Experience. Since I personally have never purchased an Apple product, nobody can accuse me in the Apple bias: however this phone has earned a remarkable reputation by managing not to disappoint its user in a single attribute of customer experience.

Nokia N8 leads customer satisfaction in Battery Life, exceeding expectations by 14%, Call Quality, Music Player experience, Sound and Video Quality. However, it also disappoints their customers where it really counts – poor Customer Support, inadequate Keyboard, Operating System experience and Web Browsing.

Below is the list of top 19 Customer Experience Attributes by their importance to the customers as they opined in their comments and reviews. Our methodology does not utilize surveys, focus groups, panels or other forms of leading questions/bias forming market research tools. I have filtered out any Attribute with importance below 0.35% that may be very valuable for Product Marketing analysis, but not very meaningful for general consumption. The complete list is available on request:

1. General Satisfaction (~CSI) – 16.96%
2. Reliability – 15.22%
3. Usability – 8.97%
4. Battery Life – 5.42%
5. Screen/Display experience – 4.44%
6. Call Quality – 3.78%
7. Customer Support – 3.39%
8. Style/Design – 3.19%
9. Picture Quality – 1.94%
10. Feature Set – 1.92

The selection of phones for the comparative analysis would vary based on criteria important to a person who conducts the research – I wanted to compare a single representative phone per operating system and you may want to find the best Android phone for example. The Attributes and their importance may vary based on such choice as customers “conversations” could yield substantially different results.

Last week, the announcement of Amazon Fire line of products created a sizable splash in social media, Consumer Electronics, business and IT publications. While this new device does not have specs of a tablet, most observers immediately started to pin it against iPad. Is it a fair comparison? The answer to this question depends on your definition of what a product is. If you define a product by its functions and features, the answer may be – No. However, if your understanding of a product agrees with Clay Christensen’s definition as the “jobs-to-be-done,” the iPad and Fire will most definitely compete for the same share of consumer wallet, as most customers of these devices use them for web browsing/entertainment most of the time.

Since the Amazon Fire is not yet shipping to the customers, I would like to offer a comparison between Amazon Kindle and Apple iPad 2 from the perspective of their customers. Online marketing research produced this analysis of 7,706 customers feedback published in social media.

The image above highlights the attributes of customer’s experience most important to them as they have articulated in their feedback. No keywords were used during the analysis to identify these attributes, and no questions were asked to influence the answers, as surveys are not our business or part of our opinion mining methodology.

You can click on this link to access the dynamic dashboard and verbatim (by clicking on a specific bar).

Given such a high perception of value users of relatively primitive but extremely functional Kindle give to their experience, the Fire is poised to make a sizable bite out of current iPad tablet growth prospects.