The theory can’t be denied. What’s more, bad actors have other motives to pollute the Web. For example, if they plant favorable automated comments about their own products or unfavorable about the competition’s, Voice of the Customer/Market applications will naturally be confused. And if automated reputation-checkers get more prominent, there will be a major incentive to game them, just as there has been for Google’s PageRank. So VOTC/VOTM market research tools could polluted as a side effect.

Similarly, if somebody wants to test your e-commerce site by throwing a ton of searches at it, your search logs will lose value.

But disinformation of competitors for the sake of disinformation? Or, as the article suggestions, vandalism/extortion? Off the top of my head, I’m not thinking of a serious near-term threat scenario.

David’s thoughts as to why the SaaS share isn’t even higher – as it seems to be for Clarabridge* – centered on the point that some customers want to blend internal and external data, and may not want to ship the internal part out to a SaaS provider. Besides, if it’s tabular data, I suspect Attensity isn’t the right place to ship it anyway.

*Speaking of Clarabridge, CEO Sid Banerjee recently posted a thoughtful company update in this comment thread.

When I challenged him on ease of use, David said that Attensity is readying a Microstrategy-based offering, which is obviously meant to compete with Clarabridge and any of its perceived advantages head-on.

Attensity is a text mining vendor with a lot of cool technology. Like other text mining vendors, it’s had mixed market success at best. However, sales activity suggests that Attensity recently put together it’s strongest marketing story ever, specifically in its new Voice of the Customer / Voice of the Market (VotC/VotM) focus.

Attensity Voice of the Market messaging stack

• Know what real consumers think about your products/services, how they react to your marketing, and what stories are being told about you
• The only way to listen in on actual consumer conversations. Humans can’t begin to to do this.
• Mine the Web to find out what’s being said about you; easy SaaS install
• See – here are real, usable results
• Extraction of the essence from any kind of text, as exhibited via proofs-of-concept

That’s a good story. The technology works. Prospects can see that it works. The benefits are self-evident, because the technology gives unique access to highly desirable information. (Obviously, you can’t have employees sit at their screens and try to read the whole Web on your behalf.) The cost, time to installation, and so on are attractive. All is good.

Let’s now compare that to what probably was Attensity’s prior commercial focus, warranty analysis, for products like automobiles, other vehicles, and consumer electronics. In this market, the story was something like:

Attensity warranty messaging stack

• Faster, more accurate warning of product problems
• Human reading of the warranty claims is too slow or costly
• Mine your warranty claims to see why your products break
• See – here are real, usable results
• Extraction of the essence from warranty claims, as exhibited via proofs-of-concept

That worked up to a point, which is a big part of why Attensity remained in business. But in fact, there were relatively few customers for whom the assertion “Human reading of the warranty claims is too slow or costly” was true. So relatively few sales on that basis were ever made.

Now, as a market-success-prediction tool, this kind of analysis may seem like overkill. In essence, all I’ve done is reiterate:

• Text mining has shown slow growth because too few customers had internal corpuses large enough to need it.
• If you’re mining the whole Web, however, your corpus is enormous.

But this analysis has another point. There’s a text mining industry consensus saying, more or less:

The text mining industry used to be too focused on the minutiae of technology and especially semantics, but now we’ve seen the light and are selling straight to business users who don’t really care about how the stuff works.

As with most views held by a broad consensus of smart people, that one contains a lot of truth. But it’s missing a next act. Whether or not Attensity, Clarabridge, and TEMIS get acquired soon – as most industry participants seem to expect – it seems inevitable that there will be large, technology-rich contenders in the text mining market. SAP/Business Objects/Inxight? Oracle/somebody? The enterprise search players? Dow Jones/Factiva? One way or another, there will eventually be big companies in the text mining market. Attensity (and the same goes for Clarabridge) isn’t doing much these days to position itself in advance of such an onslaught.

Anyhow, whatever you think of my market-evolution views, it sure seems as if the layered-messaging template works in this example as well.

]]> http://www.texttechnologies.com/2008/09/08/attensit-layered-messaging-marketing-model/feed/ 3 http://www.texttechnologies.com/2008/06/17/temis-tidbits/ http://www.texttechnologies.com/2008/06/17/temis-tidbits/#comments Tue, 17 Jun 2008 05:27:59 +0000 Curt Monash http://www.texttechnologies.com/?p=244 The usual TEMIS execs didn’t make the trip to the Text Analytics Summit this year. But cofounder Alessandro Zanasi did come, and I chatted with him for a bit. Alessandro is also author of a recent book on text mining, and pretty much a one-man Italian operation for France-based TEMIS. Despite his nominal 100:1 manpower disadvantage vs. Italian national-champion text anayltics vendor Expert System S.p.A., Alessandro proudly rattled off four different Italian government accounts he’d won vs. Expert System, all of them apparently in the government area.

Beyond that, Alessandro denies all the rumors that have grown out of TEMIS being hard to reach recently. He reports that pharma is still TEMIS’s big market, but stresses that this covers a range of apps, from research to Voice of the Market. I do get the sense that TEMIS’s sentiment extraction capabilities are less sophisticated than some of the other vendors’ — but the other vendors I’m thinking of are pretty focused on English, SPSS aside. If you need sentiment analysis in non-English languages — e.g., French or Italian — TEMIS should definitely be on your vendor shortlist.

Lexalytics’ main business is OEMing sentiment extraction, e.g. to the many blog-analysis/reputation-management (i.e., Voice of the Market) companies that recently started up and in some cases have been bought by big market analysis firms. Lexalytics can and sometimes does extract the more basic stuff as well, but sentiment analysis is the heart of its business. A partial customer list can be found on the Lexalytics site. Lexalytics extracts in the English language only.

One feature Lexalytics is proud of is that it doesn’t just assess sentiment from a phrase; it also gives a confidence (“evidence”) weighting. In such a fuzzy area as sentiment, I think that’s a good idea.

Lexalytics has a demo site, PoliticalTrends.info. The links on the left show some of the charts and reports they offer. But the bar charts in the middle inadvertently show the limitations of an approach that overweights some kinds of linguistic analysis at the expense of others. As I write this, the top 5 “Breaking themes in the last 3 days” are

• last week
• court decision
• web site
• nuclear program
• front page

I think that particular part of the app might work better if a little more restriction were placed on what is or isn’t counted as a “theme.”

*In reality, the bar opened early, and I took a Sam Adams into the roundtable room.

Now, it’s possible this reflected a certain vendor bias. Most of the show’s attendees are either vendors or users whose attendance the vendors pay for, and many of the rest are prospects the vendors encourage to come. The show’s program is also heavily influenced by what the vendors think is important. Still, this is confirming evidence that the text mining industry’s center of gravity has shifted emphatically to the CRM area.

• Voice of the Customer/Market applications require less linguistic sophistication than other text mining applications.
  
• Hence, Voice of the Customer/Market apps are easier to get running than other text mining applications, which he conjectures is a big part of the reason for burgeoning sales.

I’m guessing most text mining vendors would agree with those views, although they might not agree with his elaborations, which include:

• Attensity’s knowledge extraction technology is more sophisticated than Clarabridge’s or most other competitors’.
• In particular, Clarabridge’s extraction is little more than bag-of-words.
• There’s a good match between companies he thinks have less-sophisticated extraction (e.g., Clarabridge, SAS, SPSS) and companies whose text mining sales are heavily concentrated in Voice of the Customer/Market applications.

So the question arises: Just how much linguistic sophistication is needed in these market-trend-oriented text mining applications?

I actually got onto this subject not just because of what David said, but also via a conversation an hour earlier with Brooke Aker of Expert System, who proposed linguistic sophistication as a key reason for beating the competition (which, however, didn’t include Attensity or Clarabridge) at two accounts. The point Brooke was stressing is that it’s important to be able to extract multiple facts or indicators of sentiment from the same sentence. E.g., “I just had a crummy Chevy, but at least the seats were comfortable” is both a negative indicator about Chevrolet and a positive indicator about Chevrolet’s seats. Attensity captures both of those too, and I think Clarabridge would as well. (If you do comprehensive/ exhaustive extraction, you extract — well, you should extract comprehensively.)

Anyhow, my first-best answer to the question I posed is:

• Sentiment analysis is hard, at least in venues where you have to deal with slang, metaphor, or irony (the real biggie). The more sophisticated, the better.
• Otherwise, the linguistics of customer/marketing applications is pretty straightforward. Just put together the right list of wacky synonyms, and you’re good to go.

But what do you think?

• Expert System now has 145 employees.
• 2 of the employees are in the US (plus at least one more full-time equivalent on a contract basis). Brooke believes the US operation will eventually be the biggest part of the company.
• Expert System has sold its market intelligence SaaS offering to two global auto manufacturers. Competitors were Nielsen BuzzMetrics, somebody whose name sounded like “flexilytics” (I presume that would be Lexalytics Edit: But see Lexalytics CEO Jeff Catlin’s comment below), and somebody whose named sounded like “Truecast” (I haven’t yet guessed who that is).
• If I understood correctly, Expert System acquired that product by picking up Brooke’s tiny company Acuity Software. Acuity was/is a user of Expert System’s technology, having replaced Coveo’s with it so as to get better semantics.
• Brooke is optimistic about Expert System’s prospects in the intelligence market. New semantic networks in Arabic and English (joining one Expert System already had in Italian) are a big part of the reason. Brooke says the intelligence community is now actively interested in technology that’s been validated by the commercial market, on the theory it’s apt to be more complete than research/government-only products. Expert System is also working on a semantic network in another undisclosed Middle Eastern language; Brooke stoically refrained from confirming the blindingly obvious guess that this would be Farsi.
• Expert System’s third effort in the US market, coming soon, will be a semantic ad platform.

Once again, however, I made it through an Expert System briefing without gaining a real understanding of its technology. I gather they’re proud of their in-memory data structure for their semantic network, but I haven’t a clue (beyond the obvious guesses) as to what that data structure is. Similarly, Brooke said that a distinguishing feature of Expert Systems semantic network is that words have lots of attributes, which are the same thing as categories, and supplied a list of the 11 top-level categories: Objects, animals, plants, people, concepts, places, time, natural phenomena, state, quantity, group. But it’s easy to come up with a lot of things that don’t seem to fit that list very well (especially events, such as numerous different word-senses of “strike”), so absent further elucidation I didn’t find that particularly instructive either.

1. Attensity is the older company than Clarabridge, and is good at more things. Is Clarabridge really good at everything you want them to be?

2. In particular, Attensity has more overall sophistication at linguistic extraction. Do any of the differences matter to you?

3. Both companies are working hard on ease of use, for multiple kinds of user (business user tweaking linguistic rules, IT user, etc.). Whose approach and feature set do you like better?

4. Usually, buying one of these products involves some professional services. Whose organization do you like better?

5. Attensity’s default database schema for its exhaustive extraction is pretty flat and normalized, as befits a happy Teradata partner. Clarabridge’s is more of a star schema, as befits a bunch of ex-Microstrategy guys. Either can be straightforwardly translated into the other, so you may not care — but do you?