1. Web search

2. Public-facing site search

3. Enterprise search and knowledge management

4. Custom publishing

5. Text mining and extraction

Three are more standalone:

6. Spam filtering

7. Voice recognition

8. Machine translation

This list comes from a talk I gave Monday at the Text Analytics Summit called The Text Analytics Marketplace: Competitive landscape and trends. In half an hour, I covered the first five areas (in Sue Feldman’s word, at a “gallop”). The slide deck has been uploaded to the link below. I plan to break out the material from the talk into a series of blog posts over the next few (or perhaps not-so-few) weeks.

Slides:

• The Text Analytics Marketplace: Competitive landscape and trends

Other posts based on those slides:

• Three specialized markets for text analytics (based on Slide 2)
• 6 trends that could shake up the text analytics market (based on Slide 19)
• Why search technologies are going to recombine (in A World of Bytes, based on Slide 19)
  

And that’s an outstanding result. Material Girl is obviously a direct reference, conceptually and visually, to Diamonds Are A Girl’s Best Friend. So my question is: How does YouTube know that? Are there favorite videos lists on which they co-exist? Did somebody hand-enter the connection? Is it inferred from their comment threads (which I definitely have not paged through)? Or — by far the least likely but most interesting of all — is there some sort of direct visual comparison?

Other than popularity presumably having something to do with it (both videos are, deservedly, very often watched and commented on), I haven’t figured out which it is.

• Coveo spun out of Copernic a few years ago. The only relationship between the companies now is that Coveo licenses Copernic’s desktop search product.
• Coveo has 60 employees.
• Coveo has 5-600 customers, including lots of big-name companies.
• Coveo’s pitch boils down to “inexpensive, easy to install, and no-apologies functionality.” Actually, Coveo also claims superior relevance and performance, but I’m not going to comment much on those until I have a chance for a more technical discussion.
• Example of ease of set-up: Coveo says Factiva downloaded the product on a Monday, called up and bought it on Thursday, and deployed it in production that Friday. This may be a growing industry trend. Attivio also features a “download first, talk to us second” distribution model. So do vendors of other kinds of “platform” software such as database management systems, application servers, or complex event/stream processing.
• Average selling price: $50K. Everything is included for one price unless it requires bundled third-party software (as is the case for audio, video, and OCR search).
• Coveo claims 90% head-to-head win rates vs. Google OneBox and Microsoft Sharepoint search. Generally, customers have other search products too (I guess that’s obvious, since Coveo has only been around 2-3 years). Sometimes they even have all-you-can-eat licenses to competitive products, but buy from Coveo anyway. Rule of thumb: Nobody’s head-to-head win rate is truly as high as they like to think, but companies that think their rate is 90% generally are doing quite well.
• Coveo cites a strong demand for text search of relational databases. Based on specific examples cited, this seems to mean text fields such as call center notes.
• Coveo offers audio/video search. Really, it’s just an audio search technology; what’s being searched on in videos is the audio part. And the audio search boils down to a speech-to-text transcription, with a search of the resulting text. Coveo’s key claim is that the error-laden text you get from speech-to-text conversion is sufficient for useful searching. Specifically, you do best searching for unusual words, such as proper names. In the case of telephone calls, which are low quality – perhaps 32 kb/sec – Coveo says there’s only 10-20% accuracy in word transcription. However, Coveo also says that the words that do come through are exactly the unusual ones most usefully searched on.
• Coveo also says that its speech-to-text lexicon is initially strengthened by text crawls. In general, while I didn’t ask, I would guess that the easy-installation story involves a fair amount of automated lexicon enhancement.