May 19, 2020

The future of B2B marketing

B2B
Cloud Computing
Leadspace
Lead Cloud
Bizclik Editor
4 min
The future of B2B marketing

At the core of virtually every well-designed B2B marketing strategy is a highly cultivated, and sometimes hotly debated, lead generation program. Whether the focus is outbound using paid databases, CRMs or marketing automation platforms, inbound with engaging content marketing to capture new prospects, or a mixture of both, lead gen plays a critical role in new customer acquisition.

Unfortunately, it can also be a point of contention between sales and marketing. Disagreement over exactly whom you should be trying to reach, which companies, where to find them and what key messages are most effective can put even the most talented teams at odds.

Even with modern automated systems, marketers still don’t truly understand the B2B buyer in a way that allows them to predict the likelihood that the buyer will actually make a purchase. In some cases, they even lack basic, accurate facts such as the prospect’s employer, title, roles and responsibilities. These missing details make it extremely difficult to deliver the right message to the right person. Why?

  1. Lead databases are by nature outdated and inaccurate. The fact is people change jobs so frequently that keeping these static databases up to date is virtually impossible.
  2. Creative or ambiguous job titles make it difficult to determine exactly what the individual’s roles and responsibilities are.
  3. Basic profile data often doesn’t reveal the individual’s skill set, what products or technology they use, nor what influence they may have over the purchase decision.

But, Big Data was supposed to change all of that, right? By giving marketers the ability to tap into gigabytes of information from across the web, Big Data promised to reveal unprecedented new insight into customer behavior and revolutionize lead gen as we know it.

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Except, it turns out that more data isn’t necessarily better data. The inaccuracies, ambiguities and missing details are simply multiplied exponentially, giving marketers even more clutter to sift through to find the golden nuggets.

What marketers really need is a way to go beyond the stale, structured world of lead databases—a way to tap into the social sphere and listen in on the conversations that reveal what prospective customers really think, feel and need. Imagine having the ability to “eavesdrop” on your ideal buyers—to listen in on their social network and forum postings, tweets and other open interactions across the social web, to see what they’re talking about, concerned about, what products and software they already use and—most importantly—whether your solution fulfills their needs.

Of course, all of this is entirely possible. The data is there, readily accessible and (for the most part) free to access. However, it’s incredibly scattered across dozens of locations for every lead. Manually investigating these sources would take hours for each prospective lead, and that’s merely the qualification process. Sales cultivation might take additional days or weeks—a cumulative process that is far too slow and inefficient to generate a positive ROI.

To overcome the problem, B2B marketing will evolve toward a new model for lead gen in 2014: the Lead Cloud. This new paradigm provides a glimpse into real-time, live data from across the social web to help marketers understand the individual prospect in a way never before possible, revealing what they need, want and are most likely to buy.

By removing conventional data barriers, cutting through the clutter and allowing marketers to tap into the Lead Cloud, this new approach to predictive lead scoring and prospecting will enable sales teams to focus on the most valuable companies—and the individual leads within them—to more accurately align messages and communication tactics to reach the right people at the right time. Perhaps even more important, the Lead Cloud model can be applied to both previously engaged leads and those who have never heard of your company or solution before, allowing you to improve existing and new leadgen efforts.

Meanwhile, this real-time data can finally put marketing and sales on the same page about who to target, putting an end to guessing games, assumptions and hunches with up-to-date, firsthand data derived directly from the source—the leads themselves.

 

About the author

Amnon Mishor is co-founder and VP Products for Leadspace, a pioneer in social lead targeting. With over a decade of experience in the fields of web intelligence, semantic technologies and search, Amnon served in the Israel Defense Force, heading its Intelligence Systems and Data Mining Department for the Technical Intelligence Unit. He has since harnessed this knowledge and experience to design and deploy successful business and competitive intelligence solutions for over a dozen high-tech organizations, including Bezeq (Israel's national telecom company) and ECI Telecom. Since founding Leadspace, Amnon has introduced the power of social lead targeting to some of the most recognized B2B brands in the world, like Marketo, Jive, Badgeville and Stylesight to increase sales and marketing ROI and optimize outbound and inbound lead generation. For more information, visit www.leadspace.com.

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Jun 21, 2021

How AWS helps NASCAR delight its fans

AWS
NASCAR
3 min
Customer obsession and working backwards from the customer is a mantra of Amazon Web Services (AWS), epitomizing its partnership with NASCAR

AWS needs no introduction to readers of Technology Magazine but we rarely get an opportunity to look closely at how it serves the sports sector. All major sports draw in a huge supporter base that they want to nurture and support. Technology is the key to every major sports organization and enabling this is the driving force for AWS, says Matt Hurst, Head of Global Sports Marketing and Communications for AWS. “In sports, as in every industry, machine learning and artificial intelligence and high performance computing are helping to usher in the next wave of technical sports innovation.”

AWS approaches sports in three principal areas. “The first is unlocking data’s potential: leagues and teams hold vast amounts of data and AWS is enabling them to analyze that data at scale and make better, more informed decisions. The second is engaging and delighting fans: with AWS fans are getting deeper insights through visually compelling on-screen graphics and interactive Second Screen experiences. And the third is rapidly improving sports performance: leagues and teams are using AWS to innovate like never before.”

Among the many global brands that partner with AWS are Germany's Bundesliga, the NFL, F1, the NHL, the PGA Tour and of course NASCAR. NASCAR has worked with AWS on its digital transformation (migrating it's 18 petabyte video archive containing 70 years of historical footage to AWS), to optimize its cloud data center operations and to enable its global brand expansion. AWS Media Services powers the NASCAR Drive mobile app, delivering broadcast-quality content for more than 80 million fans worldwide. The platform, including AWS Elemental MediaLive and AWS Elemental MediaStore, helps NASCAR provide fans instant access to the driver’s view of the race track during races, augmented by audio and a continually updated leaderboard. “And NASCAR will use our flagship machine learning service Amazon SageMaker to train deep learning models to enhance metadata and video analytics.”

Using AWS artificial intelligence and machine learning, NASCAR aims to deliver even more fan experiences that they'd never have anticipated. “Just imagine a race between Dale Earnhardt Sr and Dale Jr at Talladega! There's a bright future, and we're looking forward to working with NASCAR, helping them tap into AWS technology to continue to digitally transform, innovate and create even more fan experiences.”

Just as AWS is helping NASCAR bridge that historical gap between the legacy architecture and new technology, more customers are using AWS for machine learning than any other provider. As an example, who would have thought five years ago that NFL would be using  ML to predict and prevent injury to its players? Since 2017, the league has utilized AWS as its official cloud and ML provider for the NFL Next Gen Stats (NGS) platform, which provides real-time location data, speed, and acceleration for every player during every play on every inch of the field. “One of the most potentially revolutionary components of the NFL-AWS partnership,” says Matt Hurst, “is the development of the 'Digital Athlete,' a computer simulation model that can be used to replicate infinite scenarios within the game environment—including variations by position and environmental factors, emphasizing the league's commitment to player safety.”

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