Here are some static examples (screen-shots) of customer feedback analysis for E-Readers and Tablets. The reason I mixed them together is because I have found that customers often investigate one before they purchase another. It appears that this category “divide” is largely historic and the two may eventually merge into one, just like laptops and notebooks have done.
This is the result of automated analysis of customer reviews published online. Opinions are measured on the scale from “0″ (unacceptable) to “2″ (delighted), with “1″ indicating satisfaction (expectation=experience). The importance is expressed by the line across bars and measured by a weighted average of total number of opinions about specific attribute of a specific product.
List of Attributes is extracted from customer’s conversations, not preconceived by any individual.
The dashboard controls allows a user to select and filter subsets of data for more targeted analysis. Below is an example of one-to-one comparison between Amazon Kindle and B&N Nook Color
Further “drilling” into the 5 (for example) most “important” to the E-Readers customers attributes may be achieved by selecting these attributes at the top left list of Attributes. The screen-shot below reveals that while both e-readers exceed their customers Usability expectations by a wide margin, Amazon holds 3% lead over the Nook Color which is within margin of error (+/- 7.2%). However its Reliability (second most important attribute) is 12% higher than Amazon Kindle. Both e-readers offer their customers a very good value exceeding their expectation by a wide margin, but the Nook pulls ahead again when it comes to Quality of Display.
It turns out that more than half of the large companies had a formal VoC program in place. And when we asked about the results, the data was amazing — 83% of companies reported that they had positive results from those efforts.
I am surprised and encouraged that large companies are so interested and invested in this large and not very clearly defined area of a Customer Experience Management. Mostly I am surprised to learn that these companies claim they are actually act on their findings. I am not challenging Bruce’s findings, I just would like to see it as a consumer more often.
Meanwhile I would like to muse on one of my favorite subject – how to make your customers to market your products – i.e. Word of Mouth Marketing. The WOM became an important part of VOC enterprise strategy as social media enabled consumers to become much more influential than we used to be. However WOM is the original form of advertising and it played an important role in every day life on issues of much more importance than selection of a product purchase or store preference for millennium. People have, and still do, share information about our experiences with each other across dinner tables, camp fires or customer reviews websites because WOM is much more relevant to an individual needs, concerns or desires than a broad advertising messaging addressed to the market place. Marketers try to improve relevancy of the advertising through segmentation, but their assumptions and biases limit effectiveness of their messages, while WOM is ultimately more effective as its recipients control the message they decide to accept or trust, and then reinforce it by sharing their own experience.
People decide intellectually but buy emotionally. Selling based on features and benefits won’t close the sale for the customer, but emotional messages from WOM (“This product is great because it met this need for me”) will do it. All people make decisions intellectually but will buy emotionally – particularly if the purchase resolves a ” pain “for them as recommended WOM
37 years of being in the brand building business has made me realise that the CONSUMER IS THE MEDIUM. Sumit Roy
Here are few more quotes from the same conversation
1- CONSUMER is MEDIUM – implies there are great opportunities to tap into for better and faster business results. The medium, which is in the process of being discovered and integrated into strategies and business models.
2- WOM is AMPLIFIER, i.e. ADs x WOM – implies that WOM can be that multiplier improving the result beyond your “traditional” mediums diminishing returns curve. In fact, WOM is the enabler to make that curve steeper (you get more with every additional dollar invested) and higher (you can enjoy that for longer than usual).
However this is my favorite – “Trust is what helps social intercourse breed healthy brands”. My deepest thanks to everyone who contributed to that discussion.
This video demonstrates how a registered user can extract a “Deep Dive” attribute analysis for a specific product.
This is an example of how our algorithms translate qualitative data (Word of Mouth, Customer Feedback, Voice of Customer) into quantitative, structured information. Our customers are using this tool to do pre-survey research, to identify the questions the subsequent survey validation.
This week we analyzed Digital Picture Frames. As of this date we monitor 192 products in this category and analyzed 18,427 reviews written by their customers. However some of these products have not accumulated enough reviews to produce statistically representative and accurate metrics, so we filtered them out of the competition. The second round disqualified any product that failed to meet Customer Expectations with its Functionality, Reliability or Support.
Portable USA PU-10W 2010 Piplzchoice Award winner 26.8% above average Customer Satisfaction in its Category The winners are chosen by their customers
Clay Shirky once said in on of his presentations – “There is no information overload – it is filters failure”
Some people complain that the Internet has created overwhelming volumes of information. Is there really too much information about objects of interest or is the perception of overwhelming volume actually misstated? Perhaps the issue is not quantity but level of quality. It is a matter of perception and focus; the ability to discriminate signal from background noise. Both producers and consumers care about what is said about a product or service equates to dollars or pounds or yen because positive statements will usually translate into higher demand. It is ironic how growing numbers of sophisticated product producers and consumers are tapping into the same information stream that has only recently come out of emerging social networks; a kind of digital crowdwisdom.
Whether consumers are overwhelmed by the amount of product information or just lazy, many consumers apparently prefer the conversation threads shared by digital “friends” in their social network over search engine result pages generated by a product’s keywords and metadata tags. There is a very human tendency to seek out the opinion or advice of a “social herd” of like-minded people with similar values, interests, and needs. It is more than just a contemporary cynicism of Madison Avenue hype and infomercial verbiage. Following the “virtual herd” may at first sound like a derogatory statement but it is in fact fair and descriptive. Herding is an adaptive trait that fosters very important social behaviors. Though it can, if carried to an extreme like lemmings jumping off a cliff appear pointless, following a “digital” herd saves time and minimizes personal risk. Whether inexperienced or as mentioned above, overwhelmed by too much information, “attending” to what the other member’s of one’s social circle say, do, or prefer is like a filtering device. Some people feel that the wider their circle and the greater the consensus toward a selection, the less risky their final choice. This filtering is especially cost-efficient. A consumer, after finding a common and comfortable social niche, has to neither spend additional time nor effort to select objects of value or need; they just follow the Word-of-Mouth recommendations of their trusted circle and their satisfaction is guaranteed.
Sophisticated product producers recognize that tapping into these social niches, if they can find them, provide free and truthful evaluations of what is right and wrong with their product line. Crowdwisdom would appear to reflect unsolicited, and therefore one hopes, unbiased evaluations of many different facets of a product. If postings in some niche social network discuss a product, its reputation, and its brand over some reasonable time frame, a producer could conclude the data is accurate rather than misrepresented, for example, by a competitor’s planted remarks or their own staff trying to “market” company goods. They could conclude it is balanced rather than atypical and biased when, for example, a single irate customer monopolizes bandwidth with redundant rants. Producers who cast their virtual nets over social networks to catch real-time comments must follow the best practices in statistical sampling and testing of experienced psychologists and trained sociologist. Crowdwisdom is not necessarily wise but it is, when collected carefully, extremely relevant. Especially in this digital age where many people struggle to find the signal in all the noise, it is cost-effective and an adaptive trait that minimizes personal risk. It doesn’t matter whether or not you trust or even like everyone in your social circle, if the group hangs out at a particular water hole, it must be safe to go there to drink.
This week we analyzed Customer Reviews for Computer Speakers. As of this date we monitor 136 products in this category and analyzed 10,265 reviews written by their customers. However some of these products have not accumulated enough reviews to produce statistically representative and accurate metrics, so we filtered them out of the competition. The second round disqualified any product that failed to meet Customer Expectations with its Functionality, Reliability or Support.
Logitech Z-3 Wood Grained 2.1 Speakers
2010 Piplzchoice Award winner
20.3% above average Customer Satisfaction in its Category
The winners are chosen by their customers
For full list of products in this category and Customer Reviews used for this research, select “Computers & Accessories > Computer Speakers ” Category in Product Reputation Market Intelligence Report.
Say you’re a product manager responsible for a line of MP3 players. One of the players is not selling well, in spite of various promotional activities including two price reductions within the last six months. You still can’t find lift.
As with any product development cycle, you conducted focus groups and researched the market to determine the optimal feature set for your target audience, at a compelling price point. The research didn’t yield any unexpected or actionable results.
In addition to handling your regular workload, you have several hundred online customer reviews collected over the last 60 days to plow through. It’s vital to read these reviews but you simply don’t have the bandwidth to go over them all with an attention they require. You need an easy way to filter out relevant customer themes that provide quick, current, actionable insights from customers.
Competitive products offer almost the exact same features as your MP3 player at a similar price. You’re now working on a next- generation player but aren’t clear on what the “must have” features should be for this version. Not only is your market data ambiguous, but also it’s now stale after all this time.
Sound familiar?
Enter Amplified Analytics. Using AAI’s Product Reputation Market Intelligence Reporter (PRMIR), which is based on semantic analysis of customer reviews and behavioral economics models, product managers and key decision makers can quickly segregate and analyze key performance indicators (KPIs) like Customer Satisfaction (CSI) with a product functionality, reliability and a quality of support.
Top category selections, such as MP3 players, on the PRMIR data entry screen are, easily identifiable and simple to find. Users can see the ratio of reviews to products, using a significant product sampling (in the case of the MP3 player, 75:1). All listings are date stamped, so that users know precisely when data has been updated. In just four mouse clicks, a product manager is able to generate meaningful functionality rates for his or her product;
The PRMIR interface allows customers to make multiple selections of competing manufacturers and filter the number of reviews and ranges for several performance indicators, including Customer Satisfaction Index (CSI), Product Functionality Score (PFS), Product Reliability Score (PRS) and Product Support Score (PSS).
When recalculating the CSI factoring out this specific design issue, the MP3 player in question outscores the competition by 4.2%. An up-to-date analysis with easily importable data is available in less than 15 minutes; the entire process takes less time than a normal lunch hour. Most important, stakeholders walk away with accurate data and tangible feedback to ensure customer satisfaction and profitability of future products.
The positive effects of Word of Mouth references in customer acquisition (btw I hate that term) are very well documented. Often I see the term “peer to peer” marketing being used in the same context interchangeably, however not being a marketing professional I am not sure if there is a difference or if they are really synonymous. Wikipedia defines WoM Marketing as
Word-of-mouth marketing, which encompasses a variety of subcategories, including buzz, blog, viral, grassroots, cause influencers and social media marketing, as well as ambassador programs, work with consumer-generated media and more, can be highly valued by product marketers. Because of the personal nature of the communications between individuals, it is believed that product information communicated in this way has an added layer of credibility. Research points to individuals being more inclined to believe WOMM than more formal forms of promotion methods; the receiver of word-of-mouth referrals tends to believe that the communicator is speaking honestly and is unlikely to have an ulterior motive (i.e. they are not receiving an incentive for their referrals).[2]
Customer Reviews, describing personal experiences, opinions and recommendations of individual customers, are one of the best examples of WOMM. Amazon pioneered the approach and now there are many retailers like NewEgg, Best Buy and others, with technologies from BazaarVoice and PowerReviews that collect, manage and publish Customer Reviews. I have both contributed and used them as guidance for my purchases for many years, and even though I understand that the reviews sometimes tell more about the reviewer than the product reviewed, I still find them the best tool for reduction of purchasing decision uncertainty. I know some tech pros and gadget mavens, who’s advice is sought and respected by many, to use customer reviews as an important part of their product evaluation process.
Consumers have no squabbles over paying for independent advice and recommendation, often called unbiased which is incorrect as such a thing does not exist IMHO. The Consumer Report was a very successful example and provided great service to generations of shoppers who subscribed to their magazine and its online version, however their model has difficulties to cover an ever expanding breadth of the products offered, and it does not really deal with customer experiences. The point however is that their approach is not misleading – you, the customer, pay them to learn their opinion and recommendation and thus the only incentive is to provide you with good and honest information.
I want to make very clear that I am not attacking profit motives or the marketing profession. I love profits when they are honestly earned by providing quality customer experiences, and I love marketing that helps me find providers of such experiences. The problems arise when some people or companies decide to focus on deception instead. Many years ago, one of my good friends shared with me her great admiration for Amway products. I was very grateful for her zeal to “help” me find a good product, until I realized the concealed motivation. Needless to say, we are no longer good friends, just acquaintances and I would never buy anything associated with the Amway brand. That is not to say that Word Of Mouth Marketing cannot be incentivised, just that marketers have to understand that it could become a double-edged sword and can easily create unintended adverse consequences. It also creates a challenge for us, at Amplified Analytics, to develop an effective approach to weigh authenticity of reviews we analyze for producing Product Reputation metrics.
There has been a lot written lately about the rising power of customer recommendation within the Marketing paradigm. Here is just one of many examples and a reference to an interesting study:
Advertisers are courting social-networking users because their opinions matter. More than 65 percent of 112,000 people surveyed said they were more likely to purchase products or services that they learned about in social-networking services, according to Powered Inc., an Austin-based company that helps Sony Corp. and Hewlett-Packard Co. with their social-media strategies.
Edelman Trust Report finds that trust in a recommendation, based on a personal experience of “a person like me”, has grown from 22% to 58% in just 6 years. AdAge reports that
So what is the meaning of “peer” or “a person like me” in an environment where most recommendations are anonymous, and the privacy of the recommenders is carefully protected? We all are too well aware of unscrupulous, and not too smart, marketers who tried to game the system with widely publicized failures. However that very publicity seems to give us even more confidence in our “peers”, as it makes us believe that the sheer number of reviews and recommendations of the authors, and the transparency of the Internet, will protect us from being manipulated.
Sometimes positive recommendations of people I know, will cause me not to buy the recommended product, as I am aware in our taste or skills difference. So how can we rely on the experiences of people we don’t know at all? I suppose there is a lesser of the two “evils” compared to the traditional advertising or “unbiased” review by paid experts.
As we have been working on mining Consumer Insight from unstructured and untagged data, I have been thinking of ways to algorithmically weight and/or score the “Authenticity” and “Authority” of authors in context of their product reviews and recommendations.
I believe that when someone (I hope that is me) manages to figure how to do it, it would bring even more value and meaning to the market. It would enable us to make more personalized choices.
Another thought, related to peer2peer marketing, came to me while I was exploring Cloud Expo grounds of the Dreamforce 2009. Not a single Dreamforce exhibitor with “Marketing” in their name, was demonstrating any functionality or service focused on learning and/or managing Customer Experience. I suppose to most people “Marketing” is still “Shouting”.
I have encountered some mixed emotions among some Market Research and Customer Experience Management practitioners about the usefulness of Customers Reviews as a source of real business intelligence, as opposed to their use as marketing gimmicks. I do not fancy myself as a true professional in these fields as I lack true hands-on, hard core operational experience; however, I doubt these mixed emotions and remain determined to develop technology that “listens” to the stories of customers to “learn” and measure how a product experience meets customer’s expectations. I ran across this post today from ClearAction that clarifies some of these doubts:
What’s the difference between the way customers volunteer feedback versus the way they’re requested to give feedback? One revolves around outcomes in the customer’s world, whereas the other revolves around customer satisfaction enablers in the company’s world. True customer-centricity requires primary focus and decision motivations be centered on the customer’s world, rather than the company’s.
It is easy to imagine that politics, real or perceived loyalties and conflicts of interest can easily skew the results of customer satisfaction research. However biases, mistakes and algorithmic-imperfections can also result in low quality output. The method is less important than the intent.
customers “hire” a product or service to get something done for them. When we understand the circumstances motivating the customer to hire a product or service, then we gain insight into the customer’s jobs-to-be-done. A great way to identify customers’ desired outcomes throughout the customer experience is to scan customer-generated inputs on your brand category. Good sources of customer-generated inputs include contact center and sales call logs and social media.
Ethnography, or observation research, is also instrumental in understanding outcomes in the customer’s world. What value does your organization place on these customer outcomes sources relative to your formal research that is typically organized from a customer satisfaction enabler viewpoint? Why not consider revising formal research to focus on customer outcomes rather than enablers?By really understanding customers’ jobs-to-be done, constraints, work-arounds, hassles, and other elements of their world, new insights emerge for superior alignment with customers. Adopt the customers’ jargon — don’t make them adopt yours. Cater to the customers’ world — don’t make them cater to yours. Your jargon and world are customer satisfaction enablers, or a means-to-an-end toward customers’ desired outcomes. The outcomes are the direct link to re-purchase behavior and propensity to recommend a brand. In the end, it’s only the outcomes that matter.
The important point is that no single source of data, or method by which such data is acquired, produces viable knowledge. At this point I need to channel Chance, “The Gardener” from “Being There” by relying on my sailing experience – you cannot navigate by less than 3 points of reference; that is why the word “triangulation” was introduced. Our technological approach does not change this any more than the invention of GPS.
