Big Data

The Age of Distraction and Rise of Predictive Analytics

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SalesChoice is a predictive and prescriptive SaaS analytics company and has focused for the past three years on advanced artificial intelligence (AI), analytics and cognitive computing methods to help businesses cope with the declining rates of attention skills impacting productivity levels of sales professionals. Using these technologies has huge consequences to CEOs and board members. This article offers facts and insights to encourage leaders to invest in predictive analytics and cognitive computing solutions to take advantage of machine-learning approaches to solve some of our world’s toughest challenges. 

A few facts about today’s realities help position this article effectively for leaders. 

1. Did you know that the average attention span for the notoriously ill-focused goldfish is nine seconds, but according to a new Microsoft study [1], people now generally lose concentration after eight seconds, highlighting the effects of an increasingly digitalized lifestyle on the brain? Some key findings: 

  • Since the year 2000 (or about when the mobile revolution began), the average attention span dropped from 12 seconds to eight seconds.
  • Heavy multi-screeners find it difficult to filter out irrelevant stimuli and they are more easily distracted by multiple streams of media.
  • Over the past 10 years, our attention spans have dropped by 50 percent. Clearly the mobile always-connected environment brings consequences impacting productivity. 

2. North Americans are not leading in the productivity global race anymore. Germany recently topped the rankings of the most productive employees according to new data from OECD [2]. Americans work the longest, but this is not correlating to productivity outcomes. 

3. A 2012 Time-Warner Study on the media consumption habits of “Digital Natives” (Millennials in their 20s who grew up with digital) showed that young people flipped between content across different devices on average 27 times per non-working hour. That’s roughly 13 times in 30 minutes. This is in contrast to older generations now adapted to digital, who switched media venues 17 times per non-working hour. Experts find that focus and concentration have similarly become seemingly impossible in the workplace [3]. 

4. David Hallowell, author of Harvard Business Review Press book, “Drive to Distraction at Work,” coined the resulting condition “Attention Deficit Trait,” or “ADT.” For Hallowell, ADT is not a medical condition like ADHT but, rather, a set of traits, habits and coping mechanisms induced by modern life – the advent of the Internet [4]. Key traits of ADT are:

  • Heightened distractibility and a persistent feeling of being rushed
  • Inability to sustain lengthy and full attention to any task or activity even if you want to
  • Growing tendency towards impatience, boredom, dissatisfaction, restlessness, irritability, frustration or frenzy, sometimes approaching panic
  • Tendency to hop from task to task, idea to idea
  • Growing, compulsive need for frequent electronic hits (i.e., checking emails, speaking on a smartphone) 

5. The price of this distracted-time reality is lost time and money. A 2011 survey by Harmon.ie estimated that companies with more than 1,000 employees lose roughly $10 million per year to time spent on smartphone communication exchanges. Estimates of the loss of productivity in the workplace due to screen sucking, time wasted online or in front of a screen, as well as other distractions vary widely, but all are in big numbers. A study published in Inc. magazine in 2006 estimated that $282 billion was lost annually in the United Stated to screen sucking. 

What do these study findings tell us? 

It tells us a number of board-imperative realities. Focus and attention-management approaches must be top of mind to counter the drops in productivity levels. No one will throw their mobile devices away; perhaps shutting them off to focus more can be a leadership discipline, but these devices are addictive. 

Now the Gen X or Y’ers, or Gen Virtuals are rapidly moving into our workforces and their focus levels will be less than what we now have. 

Strategies to survive productivity loss from digital distraction 

One strategy SalesChoice advocates is to invest in predictive and prescriptive analytics or cognitive computing solutions that learn about your business practices and processes by ingesting mass data sources and use data science and machine learning methods to guide your employees to the best course of actions giving acute insights to aid in decision making. 

McKinsey has already gone on record advocating big data and predictive analytics approaches can impact growth by over 10 percent CAGR [5]. The value is there if you know what problem or challenge you are trying to solve, and of course data quality is a factor. But with the volumes of data now available in both structured and unstructured formats, there are enough connections or associations to be mined that give companies a significant competitive edge. 

Making sales a science. One important survival strategy is to make sales a science. Tremendous strides have been made in relationships selling methods and most recently challenger selling methods. But despite these solution methodologies, no one can deny focus is increasingly impacting our productivity and sales results. The area SalesChoice is pioneering in is building SaaS advanced data science tools to aid sales professionals to win more. With 30-60 percent of Fortune 2000 companies with middle-tier sales professionals not meeting quotas, there needs to be some major breakthroughs. 

One of the reasons that many middle-tier sales professionals have an inability to focus or maintain attention on sales cycles is to keep moving the “best cycles” forward versus more often chasing the “duds.” They often forget what their best pathways are, especially in handling multiple accounts in their sales funnel (usually over 30+ new account pursuits vs. named accounts). We also know sales attracts professionals with ADHT who are prone to ADT behaviors. Even more reason to build solutions to counter these forces. 

To help solve this tough problem, SalesChoice partnered with the University of Toronto Math Faculty in a NSERC research grant, and our company filed a patent for our integrated ALGO methods using AI, sentiment mining, text mining, etc. to create a unique blended-methods algorithm that guides sales professionals to win more. 

We like to call our tech the world’s first SalesGPS using machine-learning approaches to mimic and learn from “trusted” process win-or-loss patterns in “how people work” and then correlate the win patterns to guide professionals on what to do to win more. We are rapidly advancing to the real patterns and shapes of behavioral interactions with these methods. 

Companies using our SaaS predictive and prescriptive analytics tools have increased their revenue growth by 10-30 percent and reduced their cost of sales by 10-30 percent. Our forecasting is running at 80-85 percent accuracy in the accounts that we are working in. As we ingest more data sets from different sources, internal and external, into our Insight Engine platform, we will be able to guide sales professionals even more efficiently. We also know our methods can also be used to help advance health sciences to predict health patterns. So although we are focused on solving a sales acute problem, the underlying platform can analyze all types of challenges to make our world a better place.

Another company to watch because it has a vision for the new world of sales is InsideSales. Its recent acquisition of C9 signaled technologies like we have been pioneering and building are on the right trajectory. But unlike C9, which only analyzes a fixed number of predictive factors to compute its prediction(s), SalesChoice analyzes all the attributes across multiple objects in SalesForce.com to get to a unique predictive model for each company. 

As external data sources start to propagate like Infer is doing, the race is on for making sales a science. Despite over three generations of CRM, the adoption rates in using CRM are still plagued with issues in making sense of the data. We have found that prescriptive analytics that guide sales opportunities with milestone intelligence feedback loops are very powerful ways to remind people what to do and in what sequence, and all of this happens with very limited human intervention. Deployed in days vs. months or years, agile SaaS models now bring advanced sciences to the masses. 

Value of machine learning and sense-making tech 

The adaptive nature of machine-learning approaches is uncannily smart and self-correcting. I have come to realize that everything in life can be distilled down to a mathematical formula. With sufficient and accurate data and diverse sense-making methods, we can reliably predict future outcomes before they happen with accuracy rates that no human can consistently achieve. 

Professor Stephen Hawking, one of Britain’s pre-eminent scientists, said that efforts to create thinking machines pose a threat to our very existence. He recently told the BBC, “The development of full artificial intelligence could spell the end of the human race.” His warning came in response to a question about a revamp of the technology he uses to communicate, which involves a basic form of AI. Prof. Hawking says the primitive forms of artificial intelligence developed so far have already proved very useful, but he fears the consequences of creating something that can match or surpass humans [6]. 

Honestly, I don’t like knowing we have less attention spans than a goldfish, and I don’t like to see North Americans, who work far too hard, not being productive against global benchmarks. Although a Canadian company, SalesChoice is inextricably tied to the economic forces of the U.S. economy simply due to geographic proximity. With advanced analytics and advanced sciences using machine-learning, organizations now have an opportunity to improve sales productivity issues that have been systemic for many years. 

Evolving with data sciences 

Making sales a science has arrived, along with the value of establishing data science centers of excellence (CoEs). What is your organization doing to evolve to this world? 

When I was a partner at Accenture, we used to say that “collaborate or die” – sharing knowledge – was critical to the global ecosystem’s success. I now believe the new mantra that will increase in tempo will be “hire data scientists or die.” 

With data exploding everywhere, the world’s total knowledge base being duplicated in less than five months and attention dropping to less than 50 percent from just 10 years ago, surely every CEO or board director must ensure a data sciences competency center is positioned to tackle some of these enormous challenges around data and inability to focus. More importantly, the resources predicated on growing revenues in a company are in sales careers. This is a real problem that deserves advanced sciences attention.

If you want to read a more detailed eBook on this topic, and join the Predictive World webinar series, click here.   

Dr. Cindy Gordon is co-founder and CEO of SalesChoice Inc., a SaaS predictive and prescriptive analytics company headquartered in Canada. Earlier, she was a venture capitalist, an Accenture partner, a Xerox Director/GM and VP with Citibank. She has authored over 14 books on business and tech: SaaS, portals, eCommerce, collaboration commerce, big data and social media. The company is currently interested in an early-stage VC round to advance its commercialization process and is focused on developing stronger U.S. partnerships. Contact her at cindy@saleschoice.com

References:

[1] Microsoft Study (2015). Source: advertising.microsoft.com/en/cl/31966/how-does-digital-affect-canadian-attention-spans

[2] OECD results worker Productivity Infographic (2015). Source: http://blog.pgi.com/2014/07/winding-work-week-infographic/

[3] Time Warner Digital Native Study (2012). Source: http://www.businesswire.com/news/home/20120409005536/en/Time-Study-Reveals-“Digital-Natives”-Switch-Devices#.VbA7eUsbpH0

[4] Hallowell. Driven to Distraction at Work. Source: http://www.totalpicture.com/career-podcast-interview-channels/big-picture-interviews/1495-driven-to-distraction-at-work-an-interview-with-dr-ned-hallowell.html

[5] McKinsey. Customer Journey Analytics and Big Data (2013). Source: http://www.slideshare.net/McK_CMSOForum/customer-journey-analytics-and-big-data

[6] BBC News. Cellan – Jones, R. (December, 2014) Stephen Hawking warns artificial intelligence could end mankind Source: http://www.bbc.com/news/technology-30290540

 

 

 

 

 

 

 

 

 

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