“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?

Artificial Swarm Intelligence: There Will be An Answer, Let it Bee

Honey Bee on Willow Catkin", Image by Bob Peterson

“Honey Bee on Willow Catkin”, Image by Bob Peterson

In almost any field involving new trends and developments, anything attracting rapidly increasing media attention is often referred to in terms of “generating a lot of buzz”. Well, here’s a quite different sort of story that adds a whole new meaning to this notion.

A truly fascinating post appeared on TechRepublic.com this week on January 22, 2016 entitled How ‘Artificial Swarm Intelligence’ Uses People to Make Smarter Predictions Than Experts by Hope Reese. It is about a development where technology and humanity intersect in a highly specialized manner to produce a new means to improve predictions by groups of people. I highly recommend reading it in its entirety. I will summarize and annotate it, and then pose a few of my own bug-free questions.

A New Prediction Platform

In a recent switching of roles, while artificial intelligence (AI) concerns itself with machines executing human tasks¹, a newly developed and highly accurate algorithm “harnesses the power” of crowds to generate predictions of “real world events”. This approach is called “artificial swarm intelligence“.

A new software platform called UNU has being developed by a startup called Unanimous AI. The firm’s CEO is Dr. Louis Rosenberg. UNU facilitates the gathering of people online in order to “make collective decisions”. This is being done, according to Dr. Rosenberg “to amplify human intelligence”. Thus far, the platform has been “remarkably accurate” in its predictions of the Academy Awards, the Super Bowl² and elections.

UNU is predicated upon the concept of the wisdom of the crowds which states that larger groups of people make better decisions collectively than even the single smartest person within that group.³  Dr. Roman Yampolskiy, the Director of the Cybersecurity Lab at the University of Louisville, has also created a comparable algorithm known as “Wisdom of Artificial Crowds“. (The first time this phenomenon was covered on The Subway Fold, in the context of entertainment, was in the December 10, 2014 post entitled Is Big Data Calling and Calculating the Tune in Today’s Global Music Market?)

The Birds and the Bees

Swarm intelligence learns from events and systems occurring in nature such as the formation of swarms by bees and flocks by birds. These groups collectively make better choices than their single members. Dr. Rosenberg believes that, in his view there is “a vast amount of intelligence in groups” that, in turn generates “intelligence that amplifies their natural abilities”. He has transposed the rules of these natural systems onto the predictive abilities of humans in groups.

He cites honeybees as being “remarkable” decision-makers in their environment. On a yearly basis, the divide their colonies and “send out scout bees” by the hundreds for many miles around to check out locations for a new home. When these scouts return to the main hive they perform a “waggle dance” to “convey information to the group” and next decide about the intended location. For the entire colony, this is a “complex decision” composed of “conflicting variables”. On average, bee colonies choose the optimal location by more than 80%.

Facilitating Human Bee-hive-ior

However, humans display a much lesser accuracy rate when making their own predictions. Most commonly, polling and voting is used. Dr. Rosenberg finds such methods “primitive” and often incorrect as they tend to be “polarizing”. In effect, they make it difficult to assess the “best answer for the group”.

UNU is his firm’s attempt to facilitate humans with making the best decisions for an entire group. Users log onto it and respond to questions with a series of possible choices displayed. It was modeled upon such behavior occurring in nature among “bees, fish and birds”. This is distinguished from individuals just casting a single vote. Here are two videos of the system in action involving choosing the most competitive Republican presidential candidate and selecting the most beloved sidekick from Star Wars4. As groups of users make their selections on UNU and are influenced by the visible onscreen behavior of others, this movement is the online manifestation of the group’s swarming activity.

Another instance of UNU’s effectiveness and accuracy involved 50 users trying to predict the winners of the Academy Awards. On an individual basis, they each averaged six out of 15 correct. This test swarm was able to get a significantly better nine out of the 15.  Beyond movies, the implications may be further significant if applied in areas such as strategic business decision-making.

My Questions

  • Does UNU lend itself to being turned into a scalable mobile app for much larger groups of users on a multitude of predictions? If so, should users be able to develop their own questions and choices for the swarm to decide? Should all predictions posed be open to all users?
  • Might UNU find some sort of application in guiding the decision process of juries while they are resolving a series of factual issues?
  • Could UNU be used to supplement reviews for books, movies, music and other forms of entertainment? Perhaps some form of “UNU Score” or “UNU Rating”?

 


1.  One of the leading proponents and developers of AI for many decades was MIT Professor Marvin Minsky who passed away on Sunday, January 24, 2016. Here is his obituary from the January 25, 2015 edition of The New York Times entitled Marvin Minsky, Pioneer in Artificial Intelligence, Dies at 88, by Glenn Rifkin.

2.  For an alternative report on whether the wisdom of the crowds appears to have little or no effect on the Super Bowl, one not involving UNU in any way, see an article in the January 28, 2016 edition of The New York Times entitled Super Bowl Challenges Wisdom of Crowds and Oddsmakers, by Victor Mather.

3.  An outstanding and comprehensive treatment of this phenomenon I highly recommend reading The Wisdom of the Crowds, by James Surowiecki (Doubleday, 2004).

4.  I would really enjoy seeing a mash-up of these two demos to see how the group would swarm among the Star Wars sidekicks to select which one of these science fiction characters might have the best chance to win the 2016 election.