Call, click, or visit! Genius.

Mr. famous fabulous Seth what’s his name marketing genius does not have a way to comment on his blog – he is probably encouraging us mortals to post and trackback (still one of the most unintuitive things in the blog world).

Mr. fabulous says that we are ready to possibly drop the spoken, ‘dot com’ when referencing our sites in radio and TV ads – or just in general. He is probably right, as he makes money.

I have noticed several ads where the website URL is not spoken at all, but the words, ‘Call, Click, or Visit”, is used instead. I never gave it much thought, and blind community aside, the construct of the phrase is almost stealth. Yeah, man:

Call click or visit. So Economical. Trite yet plain.


Open Bid to the Brand Monitoring Industry

What causes the wholesale mis targeting of an entire emerging technical discipline?
Who Knows!

I can guess, however that the dozen or so companies pitching brand monitoring are  making  brand owners their initial target, because there seems to be roughly equivalent services being offered by classical agencies.

The problem with the head to head comparison is that classical brand equity consultants are inherently a decision support and steering service, while new age text mining for brand monitoring falls far, far short of steering anything.

But take heart, all who invested equity in these young and promising companies, for there is a sector that has been completely overlooked – the brand intermediaries. How do we re target this badly gone astray brand monitoring via CGM sector and get them on track to a market that will grow and pay?

Why, my dear Watson, simply hire the author of this weblog to engage in a six month product redesign cycle  and most of the investment made in your text mining product for brand monitoring can be re targeted at incremental cost towards the brand intermediaries and multi-line retail dealer market!

Download intermed.pdf

There is nothing inherently wrong with text mining of the wild corpus, but the slow uptake and faltering of the sector must be addressed via a serious re targeting towards customer that care.

Limited Time – 30 day Product Strategy Critical Review

I am offering two 30 day review periods to company’s desiring an outside, critical review of their product strategies. I have worked for some very prestigious clients, including France Telecom, and have several recommendations on my Linkedin profile (see my Linkedin badge on this page).

Why would you want me to take an outside look and generate an analysis of your sector and strategy? Well, let me tell you:

  1. Your product strategy might have alternate channels that you may already be aware of. I can provide cost effective methodology to exploit and evangelize new markets with a small or zero incremental cost.
  2. I almost always find alternate channels or vertical strategies that my clients were unaware of.
  3. I deliver a complete section on exploiting these alternate channels. This has proven especially fecund in the Web Applications arena – many horizontal web services have vertical and B2B potential.
  4. You may feel that your sector is in slower uptake for a new product or service than originally expected; I can often generate avenues for OEM, Partnership, and Licensing Opportunities.
  5. You may have considered alternate channels and ran out of time, steam or resources. I can supply a road map that may be used for planning the appropriate delegation of a market evangelism campaign.

I am currently negotiating several long term contacts, and until these major engagements strike, I am offering these two 30 day engagements for the highly discounted rate of $7,500.00, I include all costs of materials and communications.

This offer might be withdrawn with my next call or courier; however, once I accept your engagement, it will take priority of order and will be completed on schedule. To see an example of my work, please see:

Brand Decisions are Grand Decisions: Product Perceptions and Interaction Outcomes

Brand Decisions are Grand Decisions.

So, I often ask folks in the text mining industry, particularly the linguists and applied mathematicians that specialize in language detection, “have you ever read 100,000 customer service records, complaints, etc.?”. I have.

Of course not. These folks, who are toiling in their applied sciences, must attack the challenges of computational linguistics with an expansive and intentionally non-specialized approach to text harvesting. Likewise, the well developed BI community has a certain orientation towards data visualization and raw statistics. Neither of these camps has the requisite immersion in customer relations and the language of ‘consumer perceptions’ that occurs after a product is released into the hands of the public.

Don’t get me started on the ersatz ‘brand speak’ of the current early market entrants specializing in brand monitoring via sentiment analysis – I have made my opinions known, and those with far more august credentials have slapped me down good’o. This is all as it should be in the open media.

The brand intermediaries (multi-line dealers and retailers) that are the victims of branding decisions and the blunders of the brand owners are the fat part of a potentially untapped market being completely ignored by the brand monitoring leaders, both old school, as well as new age text harvesters. This is predictable, as the technology adoption curve has shown this myopia will occur across numerous innovation categories – the telephone (never envisioned to become a residential device), the computer (“might be useful for up to five of our largest corporations”, Thomas J. Watson., the PC, (what do people want a computer in their home for?).

Whereas the old school brand equity and monitoring agencies have made a conscious and informed choice for the moment, not to take the mid-market into their plans for brand monitoring, the new entrants are just skating by the opportunity due to arrogance, hubris, and let’s say, a lack of vision. Plus, it’s harder to serve the mid-market with an affordable decision support service, than it is to deliver the current sentiment scoring clap-trap.

Enough already, my position is known! What am I going to do about it as an independent analyst? Why, Dear Watson, I’m going to analyze, and then find a friendly technology leader to hire me as a consultant to implement these systems. Let us now break down the dynamics of Customer Perceptions and Interaction Outcomes in a semi-formal manner that is somewhat long for the blog format, however, I will condense the specifics to a few pages, and add a link at the bottom to a full white paper. Read On Babes:

Continue reading

Brand Monitoring and Text Mining Saved by the New Science of Consumer Perceptions and Interaction Outcomes

The brand intermediaries throw a curve ball while the early users of today’s immature text mining services are non-plussed.

Although I’m generally classified as a self-employed ‘business analyst’, my sub specialty is the critical review of technology-based product and services strategies. It’s been a problem getting the recruiter corps to wrap around my specialty, and I have to, by necessity, do my own marketing and outreach. Word of mouth, satisfied clients, and blog articles all help. I dwell on this because my latest madcap idea to re-focus brand monitoring as a decision support web hosted service for brand intermediaries toward the mid-market, and away from the brand owners, had stirred some ire in the industry.

“What do you know? What are your credentials? How did you come to this conclusion? What a conflagration.

Just because you put the word, ‘analyst’, after your name on a business card, does that make currency of your surmises and adjudications in themselves valuable? Of course not. Unless you carry the dubious distinction of working for the de facto pimps of the analyst industry – Gartner, Forrester, Giga, etc – you have to analyze and find the kernel of truth that is non-obvious, and oftentimes disruptive to the status quo and common beliefs  prevailing in the industry. Don’t get me wrong, I use the word pimps advisedly and gingerly; while these august agencies turn out a lot of paid for whore crap, you can’t beat their non-commissioned, self-sponsored reports and quantitative research that no lone-wolf analyst can match.

What do I know? Nothing special – I just try and call it as I see it. I have had a relatively undistinguished technology career with a few notable home runs. I write well, I express myself clearly, I can outline why I come to my conclusions and methods that led to those surmises and adjudications. I never graduated university, although I have one of the best interdisciplinary skill sets covering hardware and actual software systems and standards. This was all accidental, fate intervened.

What makes someone retain a bozo like me and say, ‘study this sector, look at what we are doing, or are proposing to do, and make a critical review’. Moi? Yes, moi. If a client has internal technical leadership calling a product play, I can be a cheap insurance policy to get some critical perspective on the pitfalls, or a clear headed referee for the go-ahead. Since I am not in the management or review chain, and my engagement is closed ended, I am no threat and my advice has often been disregarded. That’s my twinkle. I continue to get work because I more often than not catch a critical issue that was missed by those with an emotional or political attachment to a product or strategy or R&D path. It may be a mess when the client is in the midst of upheaval, but people go to other jobs and remember the good advice Wilensky  gave them back at old company X. The institutional investors and VC’s have been good to me, too. Continue reading

Show me the code !

Can you make it do this, can we give you sample text? Show me your code, you mutha….

I recently ran into this common response, as I do from time to time, in the course of meeting and writing to people about the work on advanced outcomes text mining and public corpus monitoring that grew out the work that me and my soon to be friend, Doc Martin, were performing at an EU telecom R&D lab.

As an analyst looking solely at the market dynamics and product strategies of the brand monitoring industry, I was a virgin, I knew nothing, I was coming in clean. Way over on the other side of the lab, was Charles Martin the outrageous, working on an unrelated machine learning project. It was pure chance that we started talking about the limitations of sentiment analysis, from both a market perspective (me) and machine processing and computational linguistic perspective (Charles). Two Jewish guys from good, East Coast and Mid-West stock, unrelated projects, working in an indifferent R&D environment that wouldn’t know a hot topic if it fell in their pants.

Show me your code!

My job was to suss out the large industry issues from users perspectives – what would a good, sustainable, grow-able, web-based services look like in the brand monitoring world, 6 months, a year, five years?

I surveyed the users, the brand owners, several hundred who had partaken of the bounty that is today’s state of the art in text mining and brand monitoring services. Talk about the corpus of the unimpressed! Still, these folks of the fortune 1000 brand owners, many of them visionaries, knew that this was a field of endeavor that needed to be cultivated.

It was an accidental left turn that led my research into the mid-market brand intermediaries that are the target of our product research. Very briefly, the mid-market is where decisions are made to add or drop product lines at the distributor or retail level. This is all covered in my other articles. The set of problems faced by the mid-market is completely different than the brand owners, who are the current target of sentiment scoring and brand monitoring; the market encompassed by these brand intermediaries is far larger and more fecund (from the POV of web based monitoring services) than the current brand monitoring sector could ever guess.

So, this complex web of issues regarding the scientific and strategic product issues that shall ultimately serve this mid-market, the true arbiters of commerce, is the starting point of our study – what do they want, what functional framework serves their needs to make decisions?

Show me your code…..does it do this?

In the world of what we might call, ‘pre-product management’, the analyst needs to be a bit more technical in order to understand the possible. We have here an entirely virgin decision support service territory that has been missed by the text mining industry – unless they are keeping their efforts secret.

The issues that are hinted at in my article are just the tip of the iceberg, and the merest crystallization of the larger body of work that has been done regarding the scientific underpinnings that need to be made, in a step by step way, practical, in……code!

But before that can happen, we need to move beyond the revelation that this very important market exists and is waiting, is not being served by the current industry leadership, formalize the product functional issues, re investigate the tools issues, ratify the academic issues and establish alliances with the academicians that are working on advanced linguistics.

A grand edifice of a product architecture needs to be built on the sound foundation of understanding the sector. That’s the kind of research that I am proposing, which will lead to functional test in about six months….or we shall find that the issues are vexing and that new tools are required to perform these complex extractions…or…..

We found the market, we have engaged with it from a product research perspective, we have divined the opening issues, we have covered the potential technical challenges that need further study, now we seek comity with a sponsoring organization.

This issue is too grand for vencap, it needs to be nurtured, not harvested.

CGM Steered Wrong – Problems in the Brand Monitoring Industry

Breaking it down to the bare bones:

1) All of the text mining leaders that cater to brand monitoring (not the BI or BAM guys), are, without exception, focused on sentiment scoring – the weakest of all metrics. Other than vendors like Buzzlogic, which takes an interventionist approach to marketing support via the ‘influence’ channel, sentiment only got a foothold because it is deployable via HSD’s, hand scored dictionaries.

2) To understand why this is so, we must realize that these very early sector leaders specializing in Brand Monitoring of public corpora are all venture backed. This private equity binding imposes a ‘time to market’ condition. Therefore, the earliest ventures went with the best understood methods that seemed demonstrable. Other technologies for issue detection would have taken a longer development life cycle exceeding the ‘time to harvest’ most Series A rounds demand.

3) When one peels back the covers from sentiment analysis, it shows not only linguistic scoring weaknesses (even across large samples, although this helps), but it hardly provides the fine grained kind of qualitative analysis that would allow some type of steering or advisory when co-integrated with other variables – sales, stock levels, incentive program offers, etc.

4) Good words, bad words, neutral words – blah…..all of the HSD’s used by the current ‘leaders’ (the term is used advisedly), are non-standard. Even test query sets run against the same data show non-repeatable results…the text scoring engines are not mature. Surprisingly, the Open Source tools are very robust (lingPipe for example.)

5) Compare this to the giants of brand monitoring, even for modest samples, and Arbitron, ACN, and Gallup always get repeatable results. The brand owners know this, and having tried some of the Buzzmetrics and Cymfony campaigns, few have opted for continuing campaigns. The returned data is just not compelling.

6) The TNS acquisition of Cymfony was a repudiation of the brand monitoring model, and the two rounds of private capital that Cymfony raised were barely covered by the sale. TNS is an advertising reporting service bureau, not a brand consultancy – it came from the opposite side of the spectrum. Cymfony could not find a buyer amongst the brand equity giants.

7) Like lemmings, all of the sector entrants went towards this model of butting heads with the brand equity giants – creating a ‘recurring campaign model’, using long leads, non-reusable reporting dashboards, etc. Unsustainable.

8) The fat part of the market is in the intermediaries – the mid-market that acts as recipients of the brand decisions as they play out in the market. Retailers, distributors, small and medium multi line durable goods purveyors.

9) These brand intermediaries were never approached by the text-mining sector entrants, because they could not be served with the type of technology that these companies had available, that is to say, sentiment scoring using non-standard dictionaries.

10) These mid-market intermediaries can be served by providing fine-grained guidance of the consumer’s perception of product performance and interaction outcomes. There are technologies that can provide this type of detection – eBay is rumored to be investigating modular dynamics to create a system to detect redress issues.

11) The noise reduced (via statistical algorithms) and scored outcomes can be co-integrated with the retail program incentives (floor plan financing, coop advertising) and be placed within a ‘decision support’ framework.

12) A web-based, moderately-priced, service architecture, can be innovated with care. Such a system can generate many streams of revenue beyond subscription fees, for the use by mid-market intermediary distributors, retailers,. and someday…..consumers.

Text Mining for the Brand Intermediaries – a CGM Whodunnit

A Colloquial Treatment of the Product Performance and Outcomes Aspect of Mining of the Blogosphere for Brand Services Monitoring.
Alan Wilensky
The author’s recently posted monograph, “ Employing Advanced Natural Language Text Processing to Provide Guidance to Mid-Market Multiline Dealers and Product Distributors”, has generated a great deal of blog traffic, but some have asked for a more brief and colloquial treatment.
The following is a basic description of where the industry is, or has, led itself, and where the author thinks the industry should be going, based on research recently conducted for a telecom industry client. The resulting conclusions do not betray any verbatim strategic conclusions that the author provided to the client.
The Text Mining Industry, and in particular those specializing in brand monitoring services, have come to focus on ‘sentiment’ as the metric du jour. We all know the typical meaning of sentiment, it’s how we feel, or how we express how we feel. The problem occurs when we create machine scored metrics of a very human thing, such as sentiment, and expect the mined results from the blogosphere to make sense.
Other than the linguistic problems of generation (That is so bad [in one generation] means bad, in another it means really great – Pimp, is (a derogatory) in one generation and quite amazingly, good in another [ we be each other’s pimp] (advocates), [pimp my ride] (make fancy)). One can find even more subtle examples of how linguistic subtleties might confound an algorithm. There are many more robust social media metrics that far exceed the reliability of sentiment, or that can augment sentiment in order to strengthen the ultimate guidance that is being sought via the mining of the corpus in question, i.e., the blogosphere and/or public user forums.
But, linguistic problems aside, it is the role that the early text mining sector entrants have cast themselves in as ‘brand monitoring surrogates’, that is really a point of contention. Who is concerned with monitoring brand? Why, it’s the brand owners of the Fortune 1000. These large companies, mostly in the durable goods sector (for who blogs about toothpaste and other consumer packaged goods?), have had unfettered access to the best brand monitoring and consulting practices. AC Neilsen, Arbitron, and Gallup are the giants of this industry, but there are others.
Creating text mining services for the Fortune 1000 has been a high latency, fussy business. Whereas professional brand monitoring is based on actuarially sound models and standardized methods of sampling, these recently innovated ‘social media text mining services’, often have account reps, and sometimes, computational linguists (gasp), work with clients to identify verbiage that that does, or does not, express sentiments concerning products of interest and brand issues of concern. But there is a problem:
Catering to these brand owners is, as previously stated, a high latency business; contracts from the leaders sometimes take 2-4 weeks in the sales cycle, and 2+ weeks to setup the query and dashboard reporting. Furthermore, brand ownership is limited to the relatively small group of brand owners who are used to sampling brand awareness, and regularly avail themselves of brand equity practices, such as consulting. Such brand consulting businesses often make their nut by getting a percentage of the ad buy. This brave new world of ‘text mining of the blogosphere’ is a curiosity that has not made significant inroads into the brand monitoring business – maybe to the tune of a very optimistic $100M, compared to the entrenched brand services billions. There is also ample evidence that the clientele served, and the investors at equity in such new age ventures, are tiring quickly of the model and results.
So, what is needed for text mining of the public corpus to succeed? First of all, to turn the attention of these services from the brand equity owners, to the branding recipients – those who must deal with the customer’s perception of branding, product performance outcomes, and interactions with the entire spectrum of the product’s touch points – service, warranty, dealerships, etc.
Who are these prime recipients of brand decisions? Customers, certainly, but from the point of view of a web based service to analyze the public corpus, the true targets are the brand intermediaries. These intermediaries are multiline retailers and distributors that span the gamut of local shops, regional retailers, and national department stores and distributors. These are the true recipients of branding decisions, and have had very little guidance to steer their decisions as to whether or not to add or drop product lines, take advantage of ‘spiffs’ (incentives that cover cooperative advertising and floor-plan financing), or any decision affecting what brands to carry and promote.

Continue reading

A New Service for CGM

Consumer Generated Media Metrics Services:

Employing Advanced Natural Language Text Processing to Provide Guidance to Mid-Market Multiline Dealers and Product Distributors.

Alan Wilensky
Executive Summary
The present rage over such issues as ‘sentiment analysis’ and text mining of the public corpora is fertile ground for a fresh and focused analysis of the state of the CGM industry; such an analysis was recently undertaken by the author.  The author’s privately commissioned report answered various questions, such as:

what is the state of the industry?
are early entrants employing a sustainable business model?
what is the traditional / entrenched competition?
what has been missed, where is the prime the opportunity?

Without betraying the verbatim text of the 90 day analysis compiled at the behest of the previous client, the author would indeed like to share the non-proprietary conclusions leading to strategies for future products and services.

The most abstract and brief statement of the analysis is that current CGM services being proffered to the marketplace by the early entrants (Cymfony, Buzzmetrics) are purely focused on Brand Owners, occupying positions chiefly within the fortune 1000. The services provided by these leaders are positioned against traditional brand equity services (provided by AC Nielsen, Arbitron, and Gallup). Cloning the established recurring campaign model, and offering such services to a limited cadre of potential clients (that already have access to repeatable data from entrenched leaders) is not a formula for success.

The author’s commissioned analysis for the previous client bears this out by verifying statements from some of the equity investors involved in early CGM ventures. Such bitter regrets led to the sale of Cymfony to TNS – the very nature of the acquiring entity1 is a repudiation of the brand equity campaign model being applied to Consumer Generated Media metrics.

Sentiment is the weakest of CGM metrics, and the notion that highly customized campaigns that are iteratively refined by the client and CGM agency, is simply unsustainable when compared to services provided by the multi-billion dollar brand equity leaders deriving solid, actionable data from surveys, focus groups, and other statistical sources.

Furthermore, the attribute  of brand ‘equity’, or ownership, is truly limited to a very elite few, when viewed within the total business opportunity matrix. Therefore, the few high latency campaigns that have been sampled by the brand owners, as replacements or adjuncts to existing brand equity services, are simply not making a sustainable impression.

The author believes that the real, sustainable market for CGM analysis lies in the mid-market – those companies that are recipients of branding decisions and who make the daily decisions as to what lines to carry, and which products to drop; we are truly speaking of the multi-line retailers and product distributors that most of us deal with regularly in our personal and professional lives.

The mid-market makes up the lion share of commerce decisions; while the fortune 1000 CPG and durable goods markets toy with Sentiment Analysis, the mid-market struggles with allocation of limited dollars in actual cash and lines of finite credit. Which line shall we carry or drop, what action shall we take in light of incentives taking the form of cooperative advertising and floor-plan financing contributed by manufacturers and up-stream distributors? In short, what is the actionable intelligence we can gain from any service that can steer the ship of commerce?

The answer is to create this actionable market intelligence from multiple text corpora (for statistical accuracy), and to employ a greatly extended model of linguistic metrics based on phrasal Ontologies which detect consumer issues, outcomes, and declarations. Such issue detection methods result in real, actionable metrics. These metrics lend themselves to statistical scrutiny, and may be charted against offers from the brand owners – such as the aforementioned contributions of advertising cooperative program dollars, as well as seemingly advantageous adjustments to a multi-line dealer’s floor planning finance carrying charges.

With such a service in place, dealers and distributors who must make constant corrections to their product catalogs and inventory levels, etc., may avail-themselves of a more dispassionate decision support methodology. Such a hosted services solution can be monetized in many ways, i.e., by subscriptions, at varying service levels as a free or ad supported service, or as an up-sell generator for  hosted CRM services.

By Linking the aforementioned perceptions and “statements of outcome” with variables that are derived from the decision matrix data important to the mid-market (such as cooperative advertising, floor-planning, and inventory financing incentive programs), such a subscriber based system can be created to offer guidance to multi-line dealers/distributors in regards to purchasing decisions, and weighing program incentives against overall market perceptions.

The component technologies that must be developed for this system are sufficiently novel, such that revenues from licensing, syndication style Web 2.0 widget embedding, and turnkey system via VAR are entirely possible as ancillary revenue streams in addition to offering a comprehensive, hosted solution.

The author is of the firm opinion that such a system is within reach, given the appropriate project management, sound architectural principles, and the application of creative thinking diligently applied to crystalizing superior solutions.

Continue reading

Predicting Advanced Outcomes

Balanced Services Rationale of the Universal Taxonomy of Customer Services and Product Performance Outcomes

A bi-corporal stochastic method of customer service interactions and product performance outcomes.

Alan Wilensky, Analyst, vCastprofiles
Charles H. Martin, Ph D.

Analyst’s Steering Notes

As if marching not only in lockstep, but in apparent synchrony, the seed players of repute within the very young CGM metrics industry have all entered the virgin sector as brand service aspirants. Such an obvious strategy seems logical on its face, but upon further examination, fails in thought and deed. As explained in the accompanying report, the brand services industry is well established, provides repeatable metrics, and is the province and preference of internal brand managers. Although classical brand services are certainly not barred from extending CGM services to their proven sampling sets, and may be doing so, the performance of the early sector leaders has hardly made a dent in current practices.

The balanced services offering for CGM seeks to redress the major flaws of current CGM practice by providing a bridge between Business Intelligence mined from call centers, warranty, support systems, and surveys, against in-the-wild metrics from the public corpora. By reconciling the topic chains between these two data sources, we are able to create a more rationally scored set, and derive (over time), modular ontological chains that remove the very ambiguities found in the current state of CSA. Further expansion of the metrics beyond sentiment (the weakest and most subjective of the variables), will enhance the evolving accuracy of the product’s output.

In the industry’s short history, there have been hints of a developing interest in cross verified linguistic metrics. Some of these early methods are purely statistically based, while others are based on the wide body of research using monolithic ontological models; applying any research, either adapted from academic research or one that is wholly original will be a non-trivial undertaking, as the final offering must deliver an actionable service across the value chain from the top brand owners, through the mid-market, and even to the smallest dealers in a product network. Catering to the mutually overlapping desires of the entire value chain is an application well suited to modular ontological models, and the hybrid statistical verification of multiple sources.

Fancy words aside, the problems faced by early sector entrants have been manifold, in regard to the extension of the measurement model, metrics, and especially, reconciliation against corporate BI data warehouses. As most of the visible leaders are venture backed, they have had to ‘stick to the knitting’, making CSA pay no matter how weak or unsustainable the model. But this missive is not a competitive or sector commentary, it is a rationale – however, it is important to point out that the evolution of a balanced services model is a resource intensive undertaking, requiring fairly extensive test regimes that employ CS and CRM system mining, as well as sophisticated linguistic modeling. Such a research prerogative is the domain of the well-resourced, not venture backed entities with a time to harvest bias.

Telco’s and media conglomerates are entities well suited to evolving the balanced model; the size of the company, services portfolios, and global relationships open the door for a full vetting of the concepts of reconciliation, concomitants, and the evolution of modular Ontologies for cross value chain issue detection. Such a product offering is deep, expansive, and could sweep the sector as the normative method of providing real-time, actionable market intelligence. Continue reading