September 8, 2008

The layered messaging marketing model as applied to Attensity

My general layered messaging theory survived its first test against an IT vendor example – Netezza. Let’s try another, in this case a company that’s not a Monash Research client.

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

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

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:

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.


3 Responses to “The layered messaging marketing model as applied to Attensity”

  1. Enterprise IT marketing — a layered messaging model | Strategic Messaging on September 8th, 2008 2:54 am

    […] Test the layered messaging model against specific enterprise IT examples such as Netezza and Attensity […]

  2. Neil Hartley on September 18th, 2008 1:14 pm

    I enjoyed the post Curt. I would contend, however, that the reason why text mining has taken off slowly is down to the complexities of the systems themselves and the inordinate amount of set-up time required [rather than a lack of internal data]. These system complexities have always required the vendors themselves to be involved in proof of concepts and prevented the business operationalising the solution post POC.
    The future, as you said, is to enable the business user to easily get to results and, I would add, to make social media actionable. Far from being too large a corpus, the Web is a great source of customer feedback and the techniques already exist to profile social media sources such that you only gather the feedback you’re interest in.

  3. Sid Banerjee on October 16th, 2008 11:58 am

    Curt – as always I enjoy reading your insightful posts (sincerely – even though I am a mere vendor seeking the good graces of an analyst :).

    I agree that the VOC customer space is more interesting because the messaging and actual implementation is more interesting vis-a-vis the pure warranty space – for a mix of business and technical reasons:

    – warranty feedback comes from fewer customers, and can often be sufficiently gleaned from reading, structured data analysis, and interviewing those who actually service a product in the company.
    – warranty support is a back-end function in most companies – meaning it occurs after the hard work and money has been spent to get a prospect to buy and use a product.
    – finally – warranty data is generally used by a smaller audience in a company than VOC data – specifically the support, and product dev teams, limiting its enterprise value.

    VOC data, by comparison:
    – is useful to any customer facing or customer-oriented function in a company, from store ops (helps them figure out how to service their customers better), to merchandisers (helps them figure out what products to stock and promote) to marketers (helps them assess the value and effectiveness of marketing campaigns) to market analysts, to sales executives, to corporate executives who care about the reputations and loyalties of their customers.

    Thus – a well designed VOC solution returns, per $$$ spent on the solution, far more value to far more constituencies in an enterprise. In short – VOC provides a greater ROI to an enterprise than warranty analysis.

    VOC is also a better fit for text analytics due to the nature of the text being analyzed:
    – there’s more text-based customer feedback (surveys, blogs, internal sources likes support sites, support centers, etc), and it cannot be read and acted upon with any degree of accuracy or efficiency without the enabling text analytics technology
    – there’s more of a need for quick response. A product warranty issue, unless it’s causing a company’s customers to get hurt, generally can be addressed in weeks to months. A bad service or selling issue can cause customers to churn and never return, and the decisions require near immediate response. Text mining to find, address, and fix broken customer experiences before they cause broken customer relationships is key.

    Lastly – customer experience/VOC solutions are important for all times economically – good and bad – because in good times companies want to grow customer bases and good experiences are paramount to keeping good customers. In bad economic times (such as the times we’re in now) companies want to keep their customers, particularly their best customers, and VOC analysis helps retailers, banks, airlines, healthcare providers, and others understand, and more quickly act on feedback.

    We’re finding at Clarabridge that our solutions (and I expect those of others in the marketplace) help companies actually lower their market research spend and increase value – because they are able to rely a bit less on manpower intensive collection, reading, collating, and report-producing work done by people and depend more on high performance technology to do the organizing work. As a result a company’s employees can spend their time more strategically analyzing and acting on the insights.

    As to futures – I share your sentiments that there is interest from the bigger companies in this solution space, but I’m not worried about our positioning for the next phase of this market. As long as we keep up the R&D efforts in our products to ensure that the technology remains market-leading, and we keep adding customers, adding value for those customers, and we keep developing synergistic partnerships with technology, market research, and solution providers (which Clarabridge is doing aggressively) – we’ll be well positioned for whatever comes next.

    Take care,

    CEO, Clarabridge

Leave a Reply

Feed including blog about text analytics, text mining, and text search Subscribe to the Monash Research feed via RSS or email:


Search our blogs and white papers

Monash Research blogs

User consulting

Building a short list? Refining your strategic plan? We can help.

Vendor advisory

We tell vendors what's happening -- and, more important, what they should do about it.

Monash Research highlights

Learn about white papers, webcasts, and blog highlights, by RSS or email.