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
Below is the list of the runner up products
2010 Piplzchoice Award for Digital Frames
and a sample of the Customer Satisfaction Index distribution from average for the best and the worst performers
Digital Frames Reputation – Deviation from average CSI
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
Below is the list of the top runner up products:
Logitech Z-3 Wood Grained 2.1 Speakers wins 2010 Piplzchoice Award
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).
The Product Satisfaction Analysis report is generated in less than a minute. Within five minutes, in this particular scenario the product manager would discover that 5% of customers were reporting design issues with the battery compartment latch design.
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
77% of U.S. consumers trust businesses less than they did a year ago; consumers trust their peers’ opinions online more than any other source.
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.
Very interesting results of the survey:
A Social Media Survey conducted on behalf of PRWeek and MS&L by PRWeek and CA Walker found that marketers don’t make changes to their products based on customer feedback, despite monitoring feedback being one of the most common business uses of social media in the first place.
The survey found that 70% of marketers say they’ve never made a change to a product or marketing efforts based on feedback from consumers on social media sites.
I have to second Larry Malloy’s comment.
I believe there’s two reasons for this.First, we are still in the early stages of social media as a marketing tool. I believe as the technology matures, potentials are stretched, metrics are determined, and processes are developed this will change.
Second, there could be a disconnect between marketing and product management (you said the survey polled senior level marketers). As a product manager, I often used social media throughout the product lifecycle, and the executives I reported to often did not know where the new product ideas came from. And, what I learned through social media, I often further tested through more traditional marketing technologies like surveys, customer visits, interviews, etc.
Most Product Management and Marketing executives I have talked to are interested in listening, but have no strategy, processes, methodologies or best practices to act on customer feedback. Most tools available today are not providing particularly actionable data either. I am not sure what would or should come first, but without these elements you cannot produce any ROI. I attempted to come up with a “calculator” to measure an impact of customer feedback on product profitability, but it is just a rudimentary attempt for discussion and anybody who wants a copy can find it here.
It seems that the major difference between product management practices in the software business and the consumer electronics business lies in the perception of how, or even whether, a life cycle of a product can be managed after its release.
In software, alpha and beta testing by actual users are a common practice, that results in multiple releases based on actual user experiences learned or observed during these processes. In other words the product is actively managed throughout its life-cycle.
In contrast CE product management practices do not appear to be very pro-active after product launch, and are limited primarily to promotional functions. If a product is expected to have an 18-24 month life, focus groups are organized 12-14 months after product launch, to learn how to design and market its next generation. These exercises are very expensive in that they require a lot of effort to organize, and a lot of special skills to produce truly valuable results, hence they are often contracted to specialists.
There are multiple channels available for finding this data, and multiple technology offerings to process it into a meaningful source of business intelligence, however I am not aware of many processes that use this intelligence to pro-actively manage launched CE products profitability.
The example above is a very positive one, but keep in mind it only addresses the issue of the “next” product design – not how to improve profitability of the “current” product. However I suggest that it can be done and I would love to learn about people and companies who are already do, before starting to speculate how I would approach doing it myself.
As usual, your comments, opinions and experiences are greatly appreciated.
Amplified Analytics Blog Updates