Inspired by Google’s former VP of Brand Solutions, Dan Cobley’s 2010 TED Talk, we realized physics provides some valuable lessons about the field of B2B demand generation, too. While most were introduced hundreds of years ago, these scientific theories can still provide a strong reminder of the foundations of marketing, sales and developing relationships between businesses.
Keep scrolling to see what physics theories can teach us about driving engagement and sales.
Newton’s Law of Force
Newton’s Law of Force, first introduced in 1687, says that the larger the mass, the more force it requires to change its direction. We can apply this same concept to demand generation efforts.
Instead of prospecting a wide range of target accounts, go with the flow of where your business has been most successful (high-fit accounts), as well as which audience behaviors are showing signs they’re ready to purchase (intention to buy). Assessing all these factors before a lead gets qualified, helps reduce potential resistance for sales.
Sales and marketing teams can leverage advanced B2B data sources to feed up-to-date intent search behavior and account fit data on a granular level, with a 100% match rate.
“Rather than fishing in the entire ocean with any lure you can manage, hoping you catch something beyond an old tennis shoe. I’d rather fish where I know there’s going to be great fish that are hunting down what I’m fishing with.”
– Tim Harris, VP of Marketing, DialSource
Heisenberg’s Uncertainty Principle
Heisenberg’s Uncertainty Principle, articulated in 1927 by the German physicist Werner Heisenberg, basically says that it is impossible to measure a particle exactly, because the act of measuring changes it.
Cobley eloquently connected that when sales and marketing teams attempt to understand consumer behavior by measuring it in formal ways (e.g.: focus groups or surveys), the very nature of conducting this formal questioning will change what information gets relayed.
Therefore, the data sets you formally collect are likely to be different from the answers given in the most natural state possible.
So, what’s a marketer to do? Well, instead of formally asking target customers what they want or don’t want, like or dislike, teams should measure what they actually do.
For marketing to remain agile and competitive, it becomes necessary to analyze search patterns and alert you when a target account is performing a coordinated research effort. Insights gained from assessing search behavior is far more realistic than what consumers might admit to, publicly.
“Our researchers identify companies that would possibly be a good fit for our team, giving accurate information about the company for them to write mini reports for the SDRs to use. SDRs prospecting time is massively reduced by having easy access to the information that matters.”
– Fraser Murphy, Sales Team Leader, Workable
The Scientific Method
Overall, the Scientific Method says that regardless of the amount of supporting data, you cannot conclusively prove a scientific hypothesis. But you can disprove it with a single data point that blows the theory.
In other words, you can never conclusively prove a theory without a shadow of a doubt, but it is possible to disprove a theory with one proof point that shows it doesn’t work.
Much like the Scientific Method, lead generation teams can’t expect a foolproof system when it comes to lead or account scoring. There will always be room for some doubt or subjective estimations.
However, with the right information, teams can fine-tune their scoring methods using fit and intent data that is continuously refreshed and integrated in real-time.
“EverString is much more accurate than our traditional demographic / lead scoring method. We get more accurate lead routing to ensure that only the right leads are making their way in front of our sales team. Plus, better target lists for marketing purposes.”
– Administrator in Marketing and Advertising
See How Data Science Is Driving Demand Forward
Watch this overview demo to learn more about data as a service (DaaS), ideal customer profiling (ICP), propensity modeling, data enrichment, matching and other data-driven strategies that help propel your demand generation efforts forward.
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