“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?

The Predictive Benefits of Analyzing Employees’ Communications Networks

Image from Pixabay

Image from Pixabay

In the wake of the destruction left by the Enron scandal and subsequent bankruptcy in the early 2000s, one of the more revelatory and instructive artifacts left behind was the massive trove of approximately 1,600,000 of the company’s corporate emails. Researchers from a variety of fields have performed all manner of extensive analyses on this “corpus” of emails as it known. Of particular interest was the structure and operations of this failed company’s communications network. That is, simply stated, extracting and examining who’s who and what’s what in this failed organization.

No other database of this type, size and depth had ever been previously available for such purposes. What the researchers have learned from this and its subsequent and significant influences in many public and private sectors was the subject of a fascinating article in MIT Technology Review posted on July 2, 2013 entitled The Immortal Life of the Enron E-mails by Jessica Lander. I highly recommend reading this.

[July 18, 2017 Update:  For a new deep and wide analysis of the Enron email database, see What the Enron E-Mails Say About Us, by Nathan Heller, in the 7/24/17 edition of The New Yorker.]

I immediately recalled this piece recently while reading a column posted on the Harvard Business Review blog on February 10, 2016 entitled What Work Email Can Reveal About Performance and Potential by Chantrelle Nielsen. This analytical processes and consulting projects it describes could be of highly practical value to all manners and sizes of organizations. I also suggest reading this in its entirety. I will summarize, annotate and pose some emoji-free questions of my own.

I believe this post will also provide a logical follow-on to the February 15, 2016 Subway Fold post entitled Establishing a Persuasive Digital Footprint for Competing in Today’s Job Market. That post covered the importance a job candidate’s digital presence before being hired while this post covers the predictive potential of an employee’s digital presence after they have become an employee and integrated themselves into an organization.

Data Generation

The author begins by focusing her attention upon the modern tools and platforms used in the workplace for people to communicate and collaborate such as Skype and Slack. More traditionally, there is email. While these modes are important, they can also be a “mixed blessing”. Careful management of these technologies can assist is determining which forms of “digital communications are productive” for both employers and their employees.

Most importantly, these systems produce huge volumes of data. As a result, some firms are developing “next generation products” containing analytical capabilities to deeply dive into these databases and the networks they support.¹,²

The author mentions that her former company, VoloMetrix, is engaged in this field and has been acquired by Microsoft. The examples and her article concern work done for the firm’s clients before it become part of MS. During this time, VoloMetrix worked for years “with executives in large enterprises” to enable them to discern patterns within employees’ digital communications.

Predicting Employee Performance

A “strong network” can be a predictive factor of an employee’s performance. For example, a software company looked at a year’s worth of anonymized employee email data across all job categories. The findings showed that:

  • The best performers were characterized by 36% larger in-house networks, when compared to average performers, where they connected “at least biweekly in small group messages”. (This criterion was used to determine “strong ties”).
  • The lower performers exhibited “6% smaller networks” when compared to average performers.

On an annual basis, the “size and strength” of employees’ networks proved to a better predictor of their performances than managers’ more traditional assessments. Thus, being “intensely engaged” in collaborating with their peers was a driver of their work performance.

This effect was likewise seen at other business-to-business sales concerns. For instance, at a software company the top 10 workers in sales were, on average, connected to 10 or more of their colleagues. Their internal networks proved to be 25% larger than the networks of low performers. When social graph data (used to visualize the structures of networks), was examined it frequently indicated that connections within a company were even more important than those outside of it.

Predicting Employee Potential

Some businesses use “engagement programs” to assist the careers of employees are seen as having high potential to become future leaders. For example, a utility company studied the networks of a few hundreds of these people. They discovered that:

  • Those people who “were often the most connected” were shown to have networks “52% larger than average”.
  • Nonetheless, there were still others within this same group having networks of “below average” dimensions.

Managers surveyed reported that the less connected workers also had “great skills or ideas”, but displayed “potentially less” extroversion³ or emotional intelligence 4 needed to become influential. Still, opportunities are available to assist these people to “gain a broader audience” with better connected “agents” who, in turn, can promote their ideas.

Furthermore, growing a large network only for its own sake is not always the optimal approach. Rather, some networks are “more effective” because of who they include. That is, if they include people who have higher degrees of influence.

Another client, a hardware company, advanced their analysis to examine the “composition and quality” of the networks assembled by their sales reps. Their findings indicated that:

  • The “involvement of certain sales roles” corresponded to a 10X increase in the size of deals with customers.
  • Some sales roles were characterized as “middlemen” and, as such, did not “clearly demonstrate” anyone’s personal leadership potential.

Synthesizing Two Approaches

As described above, two analytical approaches have emerged for examining and leveraging the insights gained from communications networks. Both can work well in conjunction with the other. First is awareness whereby business leaders:

  • Communicate the importance of building networks
  • Provide the network analytical tools
  • Maintain the “faith” that their employees will understand this message and act upon it

The second is the prediction of outcomes, most often by sales organization to determine “which deals will close”. While this currently is applied less often than the awareness approach, this situation is now changing.

The insights gained from studying communications networks which are then applied to help build better working relationships and performance, must be “used thoughtfully” while balancing human and technological factors. Moreover, for these to work properly and “make connections more meaningful and efficient”, effectively gathering sufficient data on how employees do their jobs and communicate with their peers is essential.

My Questions

  • What standards should be established to assess communication and collaboration networks? Should they be the same for all businesses and job types or varied from field to field? Should they be differentiated further from employer to employer within a field and then perhaps for every department and job title within the same firm? (For some excellent new reading on how professional networks compare in their breadth and effectiveness in different professions, I highly recommend reading another new article on The Harvard Business Review blog posted on February 19, 2016 entitled How Having an MBA vs. a Law Degree Shapes Your Network by Adina Sterling.)
  • How should “influential” members of a network be defined in a business environment? Is influencer marketing, where individuals with a significant online presence appear to have more influence upon others in their social networks and are thus given special attention by marketers, the correct model to consider?  If so, should businesses consider developing and applying the equivalent of a Klout score to their employees? (This is an online service that rates one’s relative influence across much of social media.)
  • Would it be helpful to a company’s workforce to make this data and analytics readily available to everyone on their internal network and, if so, what would be the benefits and/or drawbacks of doing so? Would access to one’s network’s shape and reach result in some unintended consequences such as pressuring workers to increase the size of their internal and external contacts?
  • Should rewards systems be piloted to see whether they can positively incentivize employees to nurture their networks? For example, for X amount of new contacts added that support a company’s goals, Y additional days off might be awarded.
  • Can network analytics be used to fairly or unfairly restrict workers with non-competition and non-disclosure clauses when they change jobs?

 


1.   Many of these 26 Subway Fold posts under the Category of Social Media also involve metrics and analytical systems for interpreting the voluminous data generated by a wide range of social media services.

2.  A thriving market exists today in enterprise search products that can index, search and unlock the valuable knowledge embedded deep within corporate email and other data platforms. Here is a list of vendors on Wikipedia.

3.  For a completely different and highly engaging analysis of the virtues of being an introvert in social and business environments, I highly recommend reading a recent bestseller entitled Quiet: The Power of Introverts in a World That Can’t Stop Talking (Broadway Books, 2013), by Susan Cain.

4.  The authoritative and highly regarded work on this subject is Emotional Intelligence: Why It Can Matter More Than IQ (Bantam Books, 2005), by Daniel Goleman.

The Growing Need to Standardize and Validate Online Education Credentials for the Job Market

"Graduation Caps", Image by John Walker

“Graduation Caps”, Image by John Walker

Near the end of The Matrix, right after Neo and Trinity have their epic battle with the agents on the rooftop , he turns to her and asks whether she “can fly that thing”, referring to a nearby helicopter. They need to do this in order proceed to rescue Morpheus. She doesn’t know how to … just quite yet. Then she takes out her mobile phone to call Apoc and ask him to quickly upload a program to her virtual self that will enable her to pilot the chopper.

The very first time I saw this groundbreaking sci-fi film, at the Regal Union Square Stadium 14 on Broadway and 13th Street in Manhattan, the audience laughed at the absurdity of this dialog. While they were utterly dazzled by the rest of the narrative and strikingly original special effects (especially the astonishing and brain-melting sequence known as “bullet time” where Neo fights and clearly proves he’s no neophyte), this was still an awkward moment because people were laughing at this otherwise captivating film.

While I doubt that anyone would still laugh at this line in today’s world of all things networked and digital, we still have not reached anywhere near the point where people can have new skills and knowledge uploaded right to our brains. Well, at least not anytime soon and, to say the least, doing so would redefine the whole notion of an “upgrade”.

Nonetheless, there has been an enormous revolution in the breadth and diversity of webwide learning platforms. These are now available to anyone anywhere anytime with online access and a desire to learn. The benefits and the potential of online education were first taken up here in a Subway Fold Post on February 15, 2015 entitled A Real Class Act: Massive Open Online Courses (MOOCs) are Changing the Learning Process. I have taken MOOCs on everything from content strategy to project management to basic programming and have learned a great deal from them.

Standards Still Lacking for Online Education Credentials

However, in today’s highly competitive economy and job market, employers are just not sure how to evaluate prospective workers when they list online courses on their resumes and discuss them at interviews. There is no standardization yet in the requirements and weighting of these credentials. This critical issue was taken up in a very timely and informative feature in the November 18, 2015 edition of The Wall Street Journal entitled Online Skills Are Hot, But Will They Land You a Job? by Lauren Weber. I will summarize and annotate it, and pose some of my own non-academic questions.

Employers are currently searching for people with latest “technical and digital skills”. As a result, there has been a significant increase in the services rendered by course providers including Udemy and Lynda.com, coding bootcamps, and MOOCs such as Coursera and edX. These online learning platforms aim to assist workers in enhancing their skills or to provide “experience they didn’t get in college”. Nonetheless, many managers still neither trust nor recognize these new providers and their course offerings.

According to Anthony Carnevale, the director of Georgetown University’s Center on Education and the Workforce, there is no central authority setting any standards for these online educational providers. Some of the job seekers who have taken these online classes are likewise frustrated by this situation.

Independent Groups Trying to Create Credential Standards

An effort to create such standards has recently been undertaken by a group of academic researchers with additional assistance from trade groups including the U.S. Chamber of Commerce Foundation. Support for this also includes a $2.25 million grant from the Lumina Foundation, whose stated goal is for 60% of Americans to gain post-high school training by 2025. This project involves creating an online registry for use by both employers and workers to research credentials. This is intended for either group to “see exactly what skills they reflect”.

The creation of this credential registry is currently being done as a joint project by George Washington University, Southern Illinois University and the American National Standards Institute. A pilot of the directory is expected to be rolled out sometime during mid-2016.

The working group plans to assure employers that an online educator’s credentials (or “badges”) are “a sign of rigorous training”, by surveying employers about the credentials held by employees in specific roles. This will be done in an effort to provide validation for particular courses and badges.

(I also searched and found a position paper entitled Connecting Credentials: Making the Case for Reforming the U.S. Credentialing System, published by The Lumina Foundation in June 2015. I highly recommend a click-through and full read of this for the clear and compelling case it makes for this project.)

Similar initiatives have also been developed by:

  • LinkedIn which is engaged in a pilot program in Phoenix and Denver. The company is canvassing area employers about the skills they are seeking and the credentials of the workers they have recently hired. Using this information, the job networking site will permit users to learn the skills they will need for a particular job and the classes and training that “recent hires in that role have had”. This service will launch in early 2016.
  • TechHire which is a new U.S. government venture launched earlier this year by the Obama administration, whose mission is to expedite training and employment opportunities “for people without traditional academic backgrounds”. It is expected to accelerate the validity of the credentials it is offering by persuading “employers to review their skill requirements” and coordinate with training providers of “nontraditional coursework” including coding boot camps and online classes.

Employer Initiative to Test Applicant’s Job-Specific Skills

Employers on their own initiatives may soon be testing job applicants’ tech and marketing skills with simulations. These could be given in conjunction with interviews. During an HR conference in 2014, a number of companies demo-ed such tests for a wide range of specific skills from “basic math to drafting legal contracts”.¹

According to Dennis Yang, the CEO of Udemy, if these gain wide acceptance, college degrees or technical certificates might no longer be relevant. Rather, for him, the two key criteria are the ability and the willingness to learn new things.

Currently, recruiters believe that badges and credentials from online education programs indicate someone’s receptivity to learning. For example, Melkeya McDuffie, the Senior Director of Talent Acquisition recently promoted an employee at Waste Management, Inc. partly because he had taken some relevant MOOCs on Coursera. She was impressed that he had taken the initiative to do so and could demonstrate his knowledge.²

My Questions

  • Would a hybrid of credential standardization and skills simulations be another viable approach? That is, could the groups involved in each of these efforts could inform, influence and shape each others’ work?
  • How would either or both of these processes be affected in jobs requiring state or federal licensing?
  • Should employees in certain jobs be somehow incentivized by their employers to take duly certified online courses in order to remain current in their fields? Should companies factor online courses taken into an employee’s annual performance review?

 


1.  See also a September 12, 2014 post on Lawyerist.com entitled The Legal Tech Audit Proves Lawyers Are Terrible at Technology, by Lisa Needham.

2.  See also an October 23, 2015 article in the Houston Chronicle entitled Waste Management Overhauls Its Recruiting by Sarah Scully, where Ms. McDuffie is also quoted several times.

New Report Finds Ad Blockers are Quickly Spreading and Costing $Billions in Lost Revenue

"Stop Sign", Image by Kt Ann

“Stop Sign”, Image by Kt Ann

The global usage of ad blocking software is rapidly rising and the cost in 2015 so far has been $21.8 billion in lost revenue. This amount is projected to nearly double in 2016. These are the key conclusions of a new 17-page report entitled The Cost of Ad Blocking, co-authored by Adobe and PageFirst (a startup working to analyze and counter ad blocking technology). The report assesses the technological, economic and geographic impacts of this phenomenon.

A concise summary and analysis of this was posted on BusinessInsider.com on August 10, 2015 entitled Ad Blocking Has Grown 41% in the Past Year and It’s Costing Publishers Tens of Billions of Dollars by Lara O’Reilly. I will sum up, annotate, and add a few unblocked questions of my own.

I highly recommend clicking through reading both the actual report and Ms. O’Reilly’s article together for a fuller perspective on this subject.

Other leading data points among the report’s findings include:

  • Ad blocking software usage has increased 41% in the last year, now totaling 198 million active users each month.
  • While this represents only 6% of web-wide activity, it is the dollar equivalent of 14% of the “global ad spend”.
  • In 2016, the revenue lost to ad blocking is expected to reach $41.4 billion.
  • The usage of ad blockers began to rise significantly in 2013 (as shown in the chart on Page 4 of the report).
  • Ad blocker users tend to be “young, technically savvy, and more likely to be male”.
  • The rates of ad blocking varies widely within specific countries (as shown in the graphic on Page 5 of the report), and likewise from country to country (as shown on Page 6 displaying the countries in Europe).

Dr. Johnny Ryan, an executive at PageFirst, views the growth of ad blocking as being “viral” in its characteristics and anticipated continuance. As stated in the 2014 report on ad blocking, this software spreads both by word of mouth and users’ online research.

Currently, most ad blocking activity is on desktops. Despite the 38% of total web browsing occurring on mobile devices, ad blocking is now only present in 1.6% of this traffic. (See Page 10 of the report for the indicators of potential increases turning it into a “mainstream phenomenon”.)

As well, Apple’s pending release of its IOS9 mobile operating system will permit developers to create ad blocking apps. Both Apple and PageFirst stated this could be a “game changer” insofar as Apple’s deep and wide global reach of its mobile products. (See the bottom of Page 11 of the report.)

Regarding users’ motivations for using ad blockers, a survey of 400 US users, displayed on Page 12, found the leading reason was their concern over the handling of their personal data.

In another survey of UK users by the Internet Advertising Bureau, a majority found that ad blockers increased the speed and performance of their browsers (although this was not listed as one of the reasons in the Adobe and PageFirst report). Nonetheless, Mr. Ryan does not consider this to be an important factor is motivating the use of ad blockers.

My own questions are as follows:

  • Are the people motivated enough to install an ad blocker more than likely to not be uninterested in the ads and thus not potential consumers to the degree that the claims of huge lost revenues are not really all that lost?
  • The report’s underlying assumption is that if these blocked ads were otherwise viewed more sales would have been generated. Where’s the actual harm and where’s the real foul if these “lost” users are more unlikely to become paying consumers in the first place?
  • If ad blocking is so pervasive and growing at such a steep rate, are online advertisers now seeing this phenomenon as a just a cost of doing business to be factored into their accounting and reporting systems?
  • How can truly savvy and inventive e-commerce marketers and content strategists possibly use ad blocking their advantage? That is, can they somehow recast their web advertising content and formats to be less intrusive, more informative, and better protective of personal data to incentivize users enough to not use ad blockers?

For additional informative coverage of Adobe’s and PageFirst’s report with further links to useful references, I also suggest clicking through to read a report posted on the Wall Street Journal’s Digits blog  on August 10. 2015 entitled Ad-Blocking Software Will Cost the Ad Industry $22 Billion This Year by Elizabeth Dwoskin.

Watson, is That You? Yes, and I’ve Just Demo-ed My Analytics Skills at IBM’s New York Office

IMAG0082

My photo of the entrance to IBM’s office at 590 Madison Avenue in New York, taken on July 29, 2015.

I don’t know if my heart can take this much excitement. Yesterday morning, on July 29, 2015, I attended a very compelling presentation and demo of IBM’s Watson technology. (This AI-driven platform has been previously covered in these five Subway Fold posts.) Just the night before, I saw I saw a demo of some ultra-cool new augmented reality systems.

These experiences combined to make me think of the evocative line from Supernaut by Black Sabbath with Ozzie belting out “I’ve seen the future and I’ve left it behind”. (Incidentally, this prehistoric metal classic also has, IMHO, one of the most infectious guitar riffs with near warp speed shredding ever recorded.)

Yesterday’s demo of Watson Analytics, one key component among several on the platform, was held at IBM’s office in the heart of midtown Manhattan at 590 Madison Avenue and 57th Street. The company very graciously put this on for free. All three IBM employees who spoke were outstanding in their mastery of the technology, enthusiasm for its capabilities, and informative Q&A interactions with the audience. Massive kudos to everyone involved at the company in making this happen. Thanks, too, for all of attendees who asked such excellent questions.

Here is my summary of the event:

Part 1: What is Watson Analytics?

The first two speakers began with a fundamental truth about all organizations today: They have significant quantities of data that are driving all operations. However, a bottleneck often occurs when business users understand this but do not have the technical skills to fully leverage it while, correspondingly, IT workers do not always understand the business context of the data. As a result, business users have avenues they can explore but not the best or most timely means to do so.

This is where Watson can be introduced because it can make these business users self-sufficient with an accessible, extensible and easier to use analytics platform. It is, as one the speakers said “self-service analytics in the cloud”. Thus, Watson’s constituents can be seen as follows:

  • “What” is how to discover and define business problems.
  • “Why” is to understand the existence and nature of these problems.
  • “How” is to share this process in order to affect change.

However, Watson is specifically not intended to be a replacement for IT in any way.

Also, one of Watson’s key capabilities is enabling users to pursue their questions by using a natural language dialog. This involves querying Watson with questions posed in ordinary spoken terms.

Part 2: A Real World Demo Using Airline Customer Data

Taken directly from the world of commerce, the IBM speakers presented a demo of Watson Analytics’ capabilities by using a hypothetical situation in the airline industry. This involved a business analyst in the marketing department for an airline who was given a compilation of market data prepared by a third-party vendor. The business analyst was then assigned by his manager with researching and planning how to reduce customer churn.

Next, by enlisting Watson Analytics for this project, the two central issues became how the data could be:

  • Better understand, leveraged and applied to increase customers’ positive opinions while simultaneously decreasing the defections to the airline’s competitors.
  • Comprehensively modeled in order to understand the elements of the customer base’s satisfaction, or lack thereof, with the airline’s services.

The speakers then put Watson Analytics through its paces up on large screens for the audience to observe and ask questions. The goal of this was to demonstrate how the business analyst could query Watson Analytics and, in turn, the system would provide alternative paths to explore the data in search of viable solutions.

Included among the variables that were dexterously tested and spun into enlightening interactive visualizations were:

  • Satisfaction levels by other peer airlines and the hypothetical Watson customer airline
  • Why customers are, and are not, satisfied with their travel experience
  • Airline “status” segments such as “platinum” level flyers who pay a premium for additional select services
  • Types of travel including for business and vacation
  • Other customer demographic points

This results of this exercise as they appeared onscreen showed how Watson could, with its unique architecture and tool set:

  • Generate “guided suggestions” using natural language dialogs
  • Identify and test all manner of connections among the population of data
  • Use predictive analytics to make business forecasts¹
  • Calculate a “data quality score” to assess the quality of the data upon which business decisions are based
  • Map out a wide variety of data dashboards and reports to view and continually test the data in an effort to “tell a story”
  • Integrate an extensible set of analytical and graphics tools to sift through large data sets from relevant Twitter streams²

Part 3: The Development Roadmap

The third and final IBM speaker outlined the following paths for Watson Analytics that are currently in beta stage development:

  • User engagement developers are working on an updated visual engine, increased connectivity and capabilities for mobile devices, and social media commentary.
  • Collaboration developers are working on accommodating work groups and administrators, and dashboards that can be filtered and distributed.
  • Data connector developers are working on new data linkages, improving the quality and shape of connections, and increasing the degrees of confidence in predictions. For example, a connection to weather data is underway that would be very helpful to the airline (among other industries), in the above hypothetical.
  • New analytics developers are working on new functionality for business forecasting, time series analyses, optimization, and social media analytics.

Everyone in the audience, judging by the numerous informal conversations that quickly formed in the follow-up networking session, left with much to consider about the potential applications of this technology.


1.  Please see these six Subway Fold posts covering predictive analytics in other markets.

2.  Please see these ten Subway Fold posts for a variety of other applications of Twitter analytics.

 

Eight Proven Factors to Help Make Your Web Content Go Viral

"M31. The Andromeda Galaxy", Image by Adam Evans

“M31. The Andromeda Galaxy”, Image by Adam Evans

On a daily basis, we see news, commentary, videos, photos, tweets, blog posts, podcasts, articles, rumors and memes go viral where they spread rapidly across the web like a propulsive digital wave. From YouTube postings of dogs and cats doing goofy things to in-the-moment hashtags and tweets about late-breaking current events, attention grabbing content now spreads at nearly the speed of light.

All content creators, strategists and distributors want to know how to infuse their offerings with this elusive clickable contagion. Providing eight very useful and scientifically proven elements to, at the very least, increase the probability of new content going viral, is a new article entitled The Science Behind What Content Goes Viral, by Sarah Snow, posted on SocialMediaToday.com on July 6, 2015. I will sum up, annotate, and pose some not entirely scientific questions of my own.

For further reading I also highly recommend clicking through and reading The Secret to Online Success: What Makes Content Go Viral, by Liz Rees-Jones, Katherine L. Milkman and Jonah Berger (the second and third of whom are professors at the University of Pennsylvania – – the “U of P”), posted on ScientificAmerican.com (“SciAm”) on April 14, 2015. Two fully detailed and fascinating reports by Milkman and Berger that underlie their SciAm article are available here and here. Ms. Snow’s article cites many of the findings in the SciAm piece. As well, I suggest checking out a May 22, 2015 blog post by Peter Gasca entitled The 4 Essentials of the Most Read Content posted on Entrepreneur.com for some additionally effective content strategies, not to mention a hilarious picture of a dog wearing glasses.

Ms. Snow organized her article into a series of eight individual hypotheses about online virality that she then proceeds to provide references to support them. I will put each of these in bold and quotes below as she stated them in her text. (My own highlights in orange are explained afterwards.)

  • Long, in-depth posts tend to go viral more than short ones.”: Drawing from the findings of Milkman’s and Berger’s studies that, among other things, examined the data from the feature on the home page of the NYTimes.com called Most Emailed, longer articles had a higher tendency to be shared. As also stated by Carson Ward of the search engine optimization (SEO) consulting firm called Moz, of all possible variables, word count most closely correlate with the breadth of online sharing. Further, he believes this is a directly causal relationship. (The distinctions between correlation and causation have been previously raised in other various contexts in these six Subway Fold posts.) See also, Mr. Ward’s practical and informative January 14, 2013 posting on Moz’s site entitled Why Content Goes Viral: the Theory and Proof.
  • Inspire anger, awe, or anxiety and your post will go viral.”: Evidence shows that “high energy emotions” such as awe and anger, as opposed to “law energy emotions”, are more likely to spur virality.  Among them, anger is the most effective, but it must be, well, tempered without insulting the audience. It is best for content authors to write about something that angers them, which, in return, require “some tolerance” by their readers. In terms of usage data, blog content which engages controversial topics generates twice as many comments in response. Alternatively, awe is a better emotion for those who wish to avoid controversy and instead focuses on the positive effects of brands and heroic acts.
  • Showing a little vulnerability or emotion helps content go viral.”: This is indeed true again according to the U of P studies. Readers respond to emotional content because they “want to feel things when they read”. The author Walter Kirn is quoted recommending that writers should begin with what they feel “most shameful about”. This is where conflict resides and writing about it makes you vulnerable to your readers. For other content creators, rather than shame, writers can start with some other genuine “human emotion”.
  • “Viral content is practically useful, surprising, and interesting.”: Clearly, engaging and practical content beats boring and dull any day of the week. Content that is useful generates the highest levels of online sharing. For example, posting pragmatic suggestions and solutions to “how-to” questions is going to draw many more clicks.
  • “Content written by known authors is more likely to go viral.”: Milkman’s and Berger’s reports further showed that being a known writer had a significant impact on the sharing of a news article. Name recognition translates into credibility and trust.
  • “Content written by women is more likely to go viral.”: The U of P professors also reported that on NYTimes.com, the gender of a writer had an effect insofar as the data showed that articles by female authors had a tendency to be shared more that stories by male authors. 
  • “Posts that spend a lot of time on the home page are more likely to go viral.”: Yes, insofar as the NYTimes.com goes. (The article does not mention whether other sites have been tested or are planning to be tested for this variable.)
  • “Content that is truly and broadly viral is almost always funny“: This quote about humor from Ward’s post (linked above in the first factor about blog post length), is helpful for content authors as it gives all of them an opportunity to be funny. This is particularly so in efforts to make online ads go viral.

I propose the following mnemonic to assist in remembering all of these variables tracking with the key words highlighted above in orange:

Writer + Emotion – – give- – Useful – – content – – Funny + Long + Inspiration + Gender + Homepage Time

That is, WE give U content FLIGHT!

My own questions are as follows:

  • Which of these factors will more likely endure, expand or disappear, especially now that a majority of users access the web on mobile devices? What new factors that have not yet emerged might soon affect the rate(s) of content virality?
  • Is going viral purely an objective and quantifiable matter of the numbers of clicks and visitors, or are there some more qualitative factors involved? For instance, might marketing specialists and content strategists be more interested in reaching a significant percentage of traffic among a particular demographic group or market segment and just attaining X clicks and Y visitors regardless of whether or not they involve identifiable cohorts?
  • Do the above eight factors lend themselves to be transposed into an algorithm? Assuming this is possible, how would it be applied to optimize viral content and, in turn, overall SEO strategic planning?
  • Beside the length of content discussed as the first factor above, how do the other seven factors lend themselves to being evaluated for degrees of correlation and causation of viral results?

Human Resources Management Meets Big Data in Devising Systems to Identify Star Employees

"2009 Leonid Meteor", Image by Ed Sweeney

“2009 Leonid Meteor”, Image by Ed Sweeney

Have you ever seen a clearly talented colleague at your workplace who was not fully recognized for his or her potential?

Today there is a raft of sophisticated data-driven software products being marketed to Human Resources departments (HR) to assist companies in finding possible star employees. However, some of these systems are not living up to their own, well, potential. Employers are still struggling to identify people on their staffs who have might be likely to excel in their future career paths.

This modern workplace quandary was the subject of a very interesting and informative feature in the June 17, 2015 edition of The Wall Street Journal entitled Are Companies Any Good at Picking Stars?, by Rachel Feintzeig.  I will sum up some of the main points, annotate, and ask some additional questions.

Businesses today have a wealth of data about their employees’ performances and productivity. Nonetheless, identifying who among them have the greatest potential to assume leadership roles in the future is still “more art than science”.  Assessments by humans as well as software algorithms are both still lacking in some respects.

As a result, companies including Nokia, American Express and SAP are turning to new means to measure employee potential. These include new forms of metrics and classifications, as well as games to identify leadership characteristics.

No firm has yet constructed a truly breakthrough HR system to accomplish this. Furthermore, a survey entitled Potential: Who’s Doing What to Identify Their Best? to conducted by Talent Strategy Group LLC indicates, among its other findings, that much of the approximately $70B to $75B US spent on corporate training has been “misspent”.

Tom Rauzi, Dell’s Director of Global Talent, will soon be launching a research project to assess employee data including “education, trajectories and performance” in an effort to identify candidates who might be best qualified to move up in the company.

Generally, when managers have workers with high potential, they have a tendency to choose people “who are like them”.  In another survey, this one by US-based management and advisory company CEB Inc., 25% of 9,500 manager surveyed reported that they “reply on gut instinct” when choosing potential future leaders. This might suggest why some businesses are so challenged in locating “fresh thinkers and diverse hires”.

Christopher Collins, “an associate professor at Cornell University’s School of Industrial and Labor Relations and director of its Center for Advanced Human Resource Studies“, reports that workers who sensed their work is being tracked and evaluated for advancement, often stay with their companies longer and work harder.

Conversely, those workers who are not tracked for future leadership may become resentful. As a result of this, SAP North America ended its high potential categorization.

Carie Davis, who until March 2015 was Coca-Cola’s Director of Innovation and Entrepreneurship, sensed that the company’s high potential program was made up mostly of “Type A employees” with common backgrounds. During some meetings, she found that the discussion ended up being more about “jostling for power” than the intended purpose of innovation.

At a management consulting company called Development Dimensions International Inc., a vice president named Matt Paese reported that companies are now using executive level assessment tools to test thousands of employees throughout their companies. His firm is set to soon start offering a “cheaper, lighter version” of their existing executive-level products for this purpose.

Some HR software vendors are devising their own new tools to illuminate potential. Their algorithms draw from a series of metrics including, among others, an employee’s 401(k) contributions, promotions and network connections within their firms.

For example, a system called UltiPro High Performance Predictor from Ultimate Software Group Inc., measures workers on the probability of their performing well, as distinguished from their potential, into future months. Currently, they are extending their research on “predictors of potential”.

Another suppressant of potential leadership in the workplace, rude and disrespectful behavior by management, was covered in a very insightful opinion piece in the June 25, 2015 edition of The New York Times entitled No Time to Be Nice at Work, by Christine Porath. I highly recommend reading this for its many piercing analytical insights as well as an adjunct to this terrific WSJ article by Ms. Feintzeig. I found that these articles overlapped on some points and can be seen as two sides of the same coin in their effects upon today’s workplaces.

My own questions are as follows:

  • In addition to all of the testing, training, metrics collection and analysis that goes on by HR departments, what if any role does the opinion of an employee’s peers have in spotting potential? While there are many businesses that engage in peer evaluations, I wonder whether on a more informal basis, are co-workers also asked to identify which of their colleagues could be future stars?
  • What are the results of follow-up validation studies in those who were promoted along a path to leadership? While the WSJ article explores the faults in these systems, what about the successes? If John and Mary have been vetted for a leadership track, do they more often than not meet such expectations? Are they more or less inclined to change jobs or departments along the way?
  • As companies, consultants and academics continue to experiment with and fine tune their algorithms, what is the relationship between and among data establishing a correlation as opposed to actual causation in identifying leaders? (This issue has also previously been visited in these five Subway Fold posts.)

Finally, for a hilarious take on a completely unqualified and unmotivated fictional employee failing his way up the corporate ladder, I very highly recommend checking out Season 2 of Silicon Valley on HBO. Here is an interview on Tumblr with the actor Josh Brenner, discussing his role as this character named “Big Head”.