The Intelligence Quotient

Intelligence, Stephen Hawking famously opined, is the ability to adapt to change. At a building in Manhattan’s Flatiron District and at a stealth location in Silicon Valley, a team of innovators is working on a cutting-edge enterprise network that could reverberate across the sales industry.

Hunched over laptop, Stephen Messer reflected on the fall of the Ottoman Empire.

As PhD-certified engineers hammered away just beyond a glass door, Messer, the cofounder of the Manhattan-based data analytics firm Collective[i], deliberated on the 625-year reign of Turkish rule and the assassination of Franz Ferdinand in Sarajevo before jettisoning to the U.S. space program and its relevance to the technological renaissance. Later in his discourse, Messer would reference colonial empires, the industrial revolution and early technology.

“Change is a constant, even though the status quo feels like it will last forever,” said Messer. “In the late 1800s, the majority of the population worked in farming jobs.  Less than 100 years later, that number was less than 2 percent. The same goes for empires throughout history—Egyptian, Mesopotamian, Roman, Ottoman, Mongolian, Soviet, French or British.  In the end, they all had stagnated while the world around them advanced, until one day, the seemingly impossible happened, and a new empire was born.”

A history buff to be sure, Messer connects the dots between the toppling of empires unable to adapt and the enterprise-software companies that have been the predominant providers of technology designed to manage sales and revenue. Messer shifts from political history to the stultifying 40-year influence of customer relationship management (CRM) technology on enterprise revenue management and the inevitability of its collapse. is among a cutting-edge wave of consumer and enterprise applications that seamlessly thread automation platforms with artificial intelligence, thereby unleashing highly strategic insights without the pain of manual data entry.

For decades, vendors like Salesforce, Oracle, Microsoft and SAP have sought to illuminate the sales process by offering CRM software under the premise that logging sales activity would improve outcomes. Producing revenue was viewed as a manufacturing process that, if broken into stages, could be managed to grow revenue. The fundamental flaw in assuming that all buyers shared a similar process is the primary reason why not one of those multi-billion technology companies have been able to demonstrate the correlation between using CRM and growing revenue, according to Messer. “Unlike in other areas, technology applied to B2B sales has reduced productivity or hasn’t increased revenue, even after billions have been spent.”

Indeed, as buying complexity increases in correlation with the number of decision makers involved in the B2B sales process, and the plethora of information available grows exponentially, the traditional playbook, with its antiquated approach to one-size-fits-all salesmanship, has become inadequate as a tool to cut through the fog.

As impressive as the growth of CRM has been—Salesforce, for example, is now valued at $66 billion, with more than 150,000 customers—the adoption rates for using the technology are abysmal. The requirement that sales professionals manually enter accurate and comprehensive data describing every interaction they have with prospective customers is virtually impossible, for reasons like human bias, self-preservation and beyond. Aside from being inaccurate and universally despised, however, using the CRM technology is also tedious and time consuming, devouring up to 20 percent of each sales professional’s workweek, according to one estimate. “Sales professionals hate CRM for good reason. It seems almost inconceivable that, in this day and age, any technology would require people to do manual data entry when the rest of the world stopped doing that 10 years ago,” says Messer. “It would be like Waze asking you to log your location and drive at the same time. The beauty of us all being on a network is that the machines can do that work and simply provide guidance”.

“It was astonishing to us that companies like IBM could miss or lose revenue for quarter after quarter—21 quarters to date—while touting AI in the form of Watson as the panacea for any company searching for growth,” said Stephen Messer.

“The data entry is frustrating,” said Mary Shea, a sales analyst at market research firm Forrester, who said that, beyond manual input, prohibitive costs and limited sales enablement capabilities are among the perennial complaints raised by customers who utilize CRM software. “People can’t get sellers to enter in data. They never have been able to. But I think those days will come to an end pretty quickly.”

And despite limited adoption, lost productivity and flawed inputs, CRM data is often the main source informing a range of decisions, like sales commission structures and forecasts that impact how companies predict and manage revenue.

At Collective[i], Messer’s cofounders—Tad Martin and his sister and long-term business partner, Heidi—are no strangers to disruption, having previously founded and managed several technology companies to massive exits, including LinkShare and Beginning in 2007, they began recruiting a global team of engineers and scientists to study and solve the problem of why B2B sales was plagued with forecasting challenges, high attrition and unpredictability.

“It was astonishing to us that companies like IBM could miss or lose revenue for quarter after quarter—21 quarters to date—while touting AI in the form of Watson as the panacea for any company searching for growth,” said Stephen Messer.

The light bulb went on when Messer and his cofounders realized that the technology companies that were actually growing—Facebook, Google, Netflix and Amazon—shared a singular trait, a massive network of customer data. That epiphany explained why companies like IBM and Salesforce, and solutions like Watson and Einstein, failed to produce revenue growth and, worse, diminished productivity because they lacked a network and used flawed internal data to understand external buyers, he said.

The stakes were high, given the $8 trillion B2B sales market. The idea behind Collective[i] was to shift the paradigm from analyzing the seller’s behavior to understand buying patterns to analyzing the interactions of buyers and sellers via a vast network of data spanning multiple industries. Using deep learning, a highly sophisticated form of AI, that data would be converted to reverse-engineer buying behaviors so that sellers could map their activities to outcomes.

Their network,, is among a cutting-edge wave of consumer and enterprise applications that seamlessly thread automation platforms with artificial intelligence, thereby unleashing highly strategic insights without the pain of manual data entry. Calendar, e-mail, voice and signature functions, for example, are all tapped as resources from which sales professionals can easily glean powerful insights on, for example, when to abandon an opportunity or whether they are talking to a decision maker, influencer or gatekeeper, Messer explained.

“This, we believe, is the first true sales network and application,” said Messer, sitting in a conference room overlooking Madison Avenue, in New York City. “Think about how much easier it is to get a ride via Uber, or for companies like Netflix and Amazon to sell you movies and products. It’s because they know you as a customer. offers B2B sales professionals insights about their customers. So instead of spending their time inputting data, or in meeting after meeting looking in the rearview mirror of the past, they can focus on the road and the fastest way to win.”

As Messer wound down his history lesson, he turned his laptop to reveal the Intelligence interface, which is startling in its presentation for the similarity to consumer social networks like Facebook and LinkedIn rather than the mind-numbing dashboards of traditional workplace software.  Like its consumer cousins, is part social network, part collaboration software and part insights engine that allows members to connect and collaborate. By combining the brainpower of millions of members, the aptly named Intelligence provides insights far superior to CRM or other sales tools that log individual and company behavior.

To make his point, Messer clicked on an account one of his salespeople was negotiating, a deal with a banking conglomerate valued at about $940,000. With another click, the latest news and research reports associated with the bank appeared on his screen, pulling in feeds from across the web. Without leaving the application, he scrolled down his employee’s sales pipeline, noting the employee has a personal relationship with the CEO, listed as a decision maker among a network of colleagues involved in the purchasing process. Remarking that Intelligence had calculated the odds of closing the deal at approximately 90 percent, Messer decided to intervene in the opportunity, sending an e-mail from the interface.

“With CRM, I was always asking, ‘what happened?’ and focusing on the past. I was constantly questioning my team and the accuracy of its predictions. With Intelligence I’m operating in the present aided by AI driven insights. The best part is that I can do something impactful with my time, talent and connections.  Every time I open the application I’m thinking, ‘What can I do to optimize the outcome and ROI on this opportunity?’” said Messer. “Having odds based on predictive analytics rather than people—looking at everything we know about the buyer, the seller, the interactions, the company, the macroeconomic environment, the news and social media. I can see this salesperson has done a phenomenal job of positioning us, and I know I have a connection to the person who can help us bring this to the finish line. That’s the beauty of I know the value of my actions and how I can impact outcomes.”

Especially lacking from CRM, such transparency equips sales teams with the ability to be truly agile and customer-focused. Thanks to applications like Intelligence, sales managers faced with dwindling odds can adjust their strategies, perhaps by offering additional training or by collaborating with the market. Indeed, Messer believes the network will, eventually, eliminate the tendency by some sales professionals to paint a rosier picture of an opportunity in an effort to please their managers by abandoning faulty internal metrics in favor of artificial intelligence-driven insights.

As companies increasingly turn to artificial intelligence to become data driven, they compete based on their value to customers rather than size or pricing power. Just as streaming applications like Spotify have replaced the radio for many consumers, and Amazon has rivaled Walmart, the technology that fuels consumer-facing networks like Facebook and LinkedIn is heading for enterprise, said Messer.

“Winston Churchill said it best: ‘The empires of the future are the empires of the mind,’” added Messer, returning to the lessons of history to make his point about antiquated CRM software. In the sales world, he reflects, “You have to ask yourself, Why have we invested all of this time in studying the past rather than using the present to make a more intelligent future? If I had to build my empire on CRM versus Intelligence, I’d pick the latter every time.”  

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