“Technographics” – A New Approach for B2B Marketers to Profile Their Customers’ Tech Systems

"Gold Rings - Sphere 1" Image by Linda K

“Gold Rings – Sphere 1” Image by Linda K

Today’s marketing and business development professionals use a wide array of big data collection and analytical tools to create and refine sophisticated profiles of market segments and their customer bases. These are deployed in order to systematically and scientifically target and sell their goods and services in steadily changing marketplaces.

These processes can include, among a multitude of other vast data sets and methodologies, demographics, web user metrics and econometrics. Businesses are always looking for a data-driven edge in highly competitive sectors and such profiling, when done correctly, can be very helpful in detecting and interpreting market trends, and consistently keeping ahead of their rivals. (The Subway Fold category of Big Data and Analytics now contains 50 posts about a variety of trends and applications in this field.)

I will briefly to this add my own long-term yet totally unscientific study of office-mess-ographics. Here I have been looking for any correlation between the relative states of organization – – or entropy – – in people’s offices and their work’s quality and output.  The results still remain inconclusive after years of study.

One of the most brilliant and accomplished people I have ever known had an office that resembled a cave deep in the earth with piles of paper resembling stalagmites all over it. Even more remarkably, he could reach into any one of those piles and pull out exactly the documents he wanted. His work space was so chaotic that there was a long-standing joke that Jimmy Hoffa’s and Judge Crater’s long-lost remains would be found whenever ever he retired and his office was cleaned out.

Speaking of office-focused analytics, an article posted on VentureBeat.com on March 5, 2016, entitled CMOs: ‘Technographics’ is the New Demographics, by Sean Zinsmeister, brought news of a most interesting new trend. I highly recommend reading this in its entirety. I will summarize and add some context to it, and then pose a few question-ographics of my own.

New Analytical Tool for B2B Marketers

Marketers are now using a new methodology call technography to analyze their customers’ “tech stack“, a term of art for the composition of their supporting systems and platforms. The objective of this approach is to deeply understand what this says about them as a company and, moreover, how can this be used in business-to-business (B2B) marketing campaigns. Thus applied, technography can identify “pain points” in products and alleviate them for current and prospective customers.

Using established consumer marketing methods, there is much to be learned and leveraged on how technology is being used by very granular segments of users bases.  For example:

By virtue of this type of technographic data, retailers can target their ads in anticipation of “which customers are most likely to shop in store, online, or via mobile”.

Next, by transposing this form of well-established marketing approach next upon B2B commerce, the objective is to carefully examine the tech stacks of current and future customers in order to gain a marketing advantage. That is, to “inform” a business’s strategy and identify potential new roles and needs to be met. These corporate tech stacks can include systems for:

  • Office productivity
  • Project management
  • Customer relationship management (CRM)
  • Marketing

Gathering and Interpreting Technographic Signals and Nuances

Technographics can provide unique and valuable insights into assessing, for example, whether a customer values scalability or ease-of-use more, and then act upon this.

As well, some of these technographic signals can be indicative of other factors not, per se, directly related to technology. This was the case at Eloqua, a financial technology concern. They noticed their marketing systems have predictive value in determining the company’s best prospects. Furthermore, they determined that companies running their software were inclined “to have a certain level of technological sophistication”, and were often large enough to have the capacity to purchase higher-end systems.

As business systems continually grow in their numbers and complexity, interpreting technographic nuances has also become more of a challenge. Hence, the application of artificial intelligence (AI) can be helpful in detecting additional useful patterns and trends. In a July 2011 TED Talk by Ted Slavin, directly on point here, entitled How Algorithms Shape Our World, he discussed how algorithms and machine learning are needed today to help make sense out of the massive and constantly growing amounts of data. (The Subway Fold category of Smart Systems contains 15 posts covering recent development and applications involving AI and machine learning.)

Technographic Resources and Use Cases

Currently, technographic signals are readily available from various data providers including:

They parse data using such factors as “web hosting, analytics, e-commerce, advertising, or content management platforms”. Another firm called Ghostery has a Chrome browser extension illuminating the technologies upon which any company’s website is built.

The next key considerations are to “define technographic profiles and determine next-best actions” for specific potential customers. For instance, an analytics company called Looker creates “highly targeted campaigns” aimed at businesses who use Amazon Web Services (AWS). The greater the number of marketers who undertake similar pursuits, the more they raise the value of their marketing programs.

Technographics can likewise be applied for competitive leverage in the following use cases:

  • Sales reps prospecting for new leads can be supported with more focused messages for potential new customers. These are shaped by understanding their particular motivations and business challenges.
  • Locating opportunities in new markets can be achieved by assessing the tech stacks of prospective customers. Such analytics can further be used for expanding business development and product development. An example is the online training platform by Mindflash. They detected a potential “demand for a Salesforce training program”. Once it became available, they employed technographic signals to pinpoint customers to whom they could present it.
  • Enterprise wide decision-making benefits can be achieved by adding “value in areas like cultural alignment”. Familiarity with such data for current employees and job seekers can aid businesses with understanding the “technology disposition” of their workers. Thereafter, its alignment with the “customers or partners” can be pursued.  Furthermore, identifying areas where additional training might be needed can help to alleviate productivity issues resulting from “technology disconnects between employees”.

Many businesses are not yet using technographic signals to their full advantage. By increasing such initiatives, businesses can acquire a much deeper understanding of their inherent values. In turn, the resulting insights can have a significant effect on the experiences of their customers and, in turn, elevate their resulting levels of loyalty, retention and revenue, as well as the magnitude of deals done.

My Questions

  • Would professional service industries such as law, medicine and accounting, and the vendors selling within these industries, benefit from integrating technographics into their own business development and marketing efforts?
  • Could there be, now or in the future, an emerging role for dedicated technographics specialists, trainers and consultants? Alternatively, should these new analytics just be treated as another new tool to be learned and implemented by marketers in their existing roles?
  • If a company identifies some of their own employees who might benefit from additional training, how can they be incentivized to participate in it? Could gamification techniques also be applied in creating these training programs?
  • What, if any, privacy concerns might surface in using technographics on potential customer leads and/or a company’s own internal staff?

IBM’s Watson is Now Data Mining TED Talks to Extract New Forms of Knowledge

"sydneytocairns_385", Image by Daniel Dimarco

“sydneytocairns_385”, Image by Daniel Dimarco

Who really benefited from the California Gold Rush of 1849? Was it the miners, only some of whom were successfully, or the merchants who sold them their equipment? Historians have differed as to the relative degree, but they largely believe it was the merchants.

Today, it seems we have somewhat of a modern analog to this in our very digital world: The gold rush of 2015 is populated by data miners and IBM is providing them with access to its innovative Watson technology in order for these contemporary prospectors to discover new forms of knowledge.

So then, what happens when Watson is deployed to sift through the thousands of incredibly original and inspiring videos of online TED Talks? Can the results be such that TED can really talk and, when processed by Watson, yield genuine knowledge with meaning and context?

Last week, the extraordinary results of this were on display at the four-day World of Watson exposition here in New York. A fascinating report on it entitled How IBM Watson Can Mine Knowledge from TED Talks by Jeffrey Coveyduc, Director, IBM Watson, and Emily McManus, Editor, TED.com was posted on the TED Blog on May 5, 2015. This was the same day that the newfangled Watson + TED system was introduced at the event. The story also includes a captivating video of a prior 2014 TED Talk by Dario Gil of IBM entitled Cognitive Systems and the Future of Expertise that came to play a critical role in launching this undertaking.

Let’s have a look and see what we can learn from the initial results. I will sum up and annotate this report, and then ask a few additional questions.

One of the key objectives of this new system is to enable users to query it in natural language. An example given in the article is “Will new innovations give me a longer life?”. Thus, users can ask questions about ideas expressed among the full database of TED talks and, for the results, view video excerpts where such ideas have been explored. Watson’s results are further accompanied by a “timeline” of related concepts contained in a particular video clip permitting users to “tunnel sideways” if they wish and explore other topics that are “contextually related”.

The rest of the article is a dialog between the project’s leaders Jeffrey Coveyduc from IBM and TED.com editor Emily McManus that took place at Watson World.  They discussed how this new idea was transformed into a “prototype” of a fresh new means to extract “insights” from within “unstructured video”.

Ms. McManus began by recounting how she had attended Mr. Dario’s TED Talk about cognitive computing. Her admiration of his presentation led her to wonder whether Watson could be applied to TED Talks’ full content whereby users would be able to pose their own questions to it in natural language. She asked Mr. Dario if this might be possible.

Mr. Coveyduc said that Mr. Dario then approached him to discuss the proposed project. They agreed that it was not just the content per se, but rather, that TED’s mission of spreading ideas was so compelling. Because one of Watson’s key objectives is to “extract knowledge” that’s meaningful to the user, it thus appeared to be “a great match”.

Ms. McManus mentioned that TED Talks maintains an application programming interface (API) to assist developers in accessing their nearly 2,000 videos and transcripts. She agreed to provide access to TED’s voluminous content to IBM. The company assembled its multidisciplinary project team in about eight weeks.

They began with no preconceptions as to where their efforts would lead. Mr. Coveyduc said they “needed the freedom to be creative”. They drew from a wide range of Watson’s existing technical services. In early iterations of their work they found that “ideas began to group themselves”. In turn, this led them to “new insights” within TED’s vast content base.

Ms. McManus recently received a call from Mr. Dario asking her to stop by his office in New York. He demo-ed the new system which had completely indexed the TED content. Moreover, he showed how it could display, according to her “a universe of concepts extracted” from the content’s core. Next, using the all important natural language capabilities to pose questions, they demonstrated how the results in the form of numerous short clips which, taken altogether, were compiling “a nuanced and complex answer to a big question”, as she described it.

Mr. Coveyduc believes this new system simplifies how users can inspect and inquire about “diverse expertise and viewpoints” expressed in video. He cited other potential areas of exploration such as broadcast journalism and online courses (also known as MOOCs*). Furthermore, the larger concept underlying this project is that Watson can distill the major “ideas and concepts” of each TED Talk and thus give users the knowledge they are seeking.

Going beyond Watson + TED’s accomplishments, he believes that video search remains quite challenging but this project demonstrates it can indeed be done. As a result, he thinks that mining such deep and wide knowledge within massive video libraries may turn into “a shared source of creativity and innovation”.

My questions are as follows:

  • What if Watson was similarly applied to the vast troves of video classes used by professionals to maintain their ongoing license certifications in, among others, law, medicine and accounting? Would new forms of potentially applicable and actionable knowledge emerge that would benefit these professionals as well as the consumers of their services? Rather than restricting Watson to processing the video classes of each profession separately, what might be the results of instead processing them together in various combinations and permutations?
  • What if Watson was configured to process the video repositories of today’s popular MOOC providers  such as Coursera or edX? The same as well for universities around the world who are putting their classes online. Their missions are more or less the same in enabling remote learning across the web in a multitude of subjects. The results could possibly hold new revelations about subjects that no one can presently discern.

Two other recent Subway Fold posts that can provide additional information, resources and questions that I suggest checking out include Artificial Intelligence Apps for Business are Approaching a Tipping Point posted on March 31, 2015, and Three New Perspectives on Whether Artificial Intelligence Threatens or Benefits the World posted on December 27, 2014.


*  See the September 18, 2014 Subway Fold post entitled A Real Class Act: Massive Open Online Courses (MOOCs) are Changing the Learning Process for the full details and some supporting links.