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Archive for January, 2012

Kindle Fire vs iPad2 – new intelligence report

This statement published on Forbes blog inspired me to take another look at customer satisfaction with tablets in question.

If there was ever a chance for a tablet contender to take a run atApple’s dominance in the tablet market, it was Amazon’s Kindle Fire. Not the myriad Android-based products. And certainly not RIM’s Playbook. No, the job was up to the Kindle Fire and the moment it needed to go in for the kill was the holidays. Amazon tried and failed. Now it’s over.

The complete post can be found here.

The premise of this article is based on assumption that people who were planning to buy iPad, would purchase Kindle Fire instead. I think this is a false assumption. In fact analysis of reviews from 7,897 customers shows that both tablets exceeded expectations of their customers, albeit by a different margin. I included the analysis of Samsung Galaxy Tab (10.1″) to make it more interesting, because it is probably the distant 3rd in this race for customer’s affinity. Apple clearly beats Amazon and Samsung Tab in Customer Support (1.03), Design (1.33), Screen size (1.75) and Usability (1.82). However Kindle Fire leads in General Satisfaction (1.35), Price (1.31), and Reliability (1.07).

 

I will publish the link to the interactive dashboard on our Google+ page along with the video on How to Navigate Market Intelligence Dashboard.

 

Customer Intelligence on Google+ page

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.

Rationality of Management Decisions

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:

 

Amazon Kindle Fire vs Asus Transformer Prime

This analysis of customer reviews for Kindle Fire vs Asus Transformer Prime shows the attributes of customer experience that are the most important to the customers and how their experience meets their expectations. Click on the image of dashboard to make it larger.

Market Segmentation from Customer Perspective

Opinion Mining

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.

Attribute

Importance

usability

12.02%

reliability

10.28%

quality of construction

8.92%

display

6.21%

specifications

3.58%

portability

3.49%

audio quality

3.08%

price/value

2.64%

applications

2.18%

battery

2.17%

video and camera

1.73%

customer support

1.53%

performance

1.51%

operating system

1.29%

web experience

0.87%

flash

0.86%

connectivity

0.27%

build quality

0.24%

screen resolution

0.2%

replaceable battery

0.19%

color quality

0.15%

 

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.