Learn: The Difference Between Meh & WOW in B2B Artificial Intelligence

July 17, 2019

You’ve been in B2B for a hot minute. You know this story. 

The hype cycle. A new methodology or CRM or automation software or what-have-you appears on the scene, and it’s going to fix what ails us as B2B marketers. It’s going to be that thing that finally lifts conversion rates over the industry average of 1-3%. So we’ve thrown all these new things at our campaigns, hoping they will be the fix. And things – maybe – marginally improve. 

We can’t help ourselves in falling for the hype cycle – because we know there are potentially-great prospects out there that we’re not reaching effectively. I’ve had this conversation with so many B2B marketers – incredibly talented ones – that I’m starting to suspect that we all feel this way. 

That there is some enigma that we have yet to crack. 

And that buzz you’ve been hearing lately? The hype cycle, gearing up to full throttle on the promise of artificial intelligence in B2B. On the surface, this makes a whole lot of sense. We’re collecting terabytes of data on prospect behavior, email engagement, funnel velocity, conversions – why wouldn’t we use AI-powered software that can read all this data and provide us with meaningful insights? Insights we can use to make progressive campaigns even more successful and effective. 

There’s a catch though. Not all B2B artificial intelligence for marketing is created equal. Because … data quality. 

The insights an artificial intelligence engine can discover are only as good as the quality of data you’re feeding into the engine. Poor data equals poor insights. AI is complicated, but that math is simple. A lot of companies currently hyping their artificial intelligence are using the same targeting datasets that already have us spinning our wheels. Firmographics, NAICS codes, headcount – and most recently, intent data. The Usual Suspects of B2B targeting. 



It’s still roughly the same targeting you’ve been doing manually, only fed into a software program that crunches the numbers faster. Meaning, you’ll spend a lot to get the same information you were getting before – but you’ll get it faster. It’s buying a Corvette with a Ford Edsel motor under the hood. Looks good, doesn’t perform. 


For AI to deliver the insights that actually make a difference, it needs better data. 


b2b data


For artificial intelligence to deliver B2B targeting insights that are better than what we’ve been able to come up with ourselves, we need a completely new AI approach to determining what data is actually valuable. In building the LeadCrunch engine, our data science team started from square one. They tossed out the assumption that the data the industry has relied on for decades is inherently valuable – if it was, our targeting would already be better. 

LeadCrunch AI doesn’t deliver the same targeting as other AI, because we use profoundly different data. And a lot of it. This fundamental, philosophical difference enables us to provide insights that are unmatched by our competitors. And as we work with our clients, providing them with leads that consistently outperform what they’re getting from other vendors, we feed those results back into the engine – and successive campaigns get even better. 

From Bryce Roberts, Senior Demand Generation Manager at Oracle + Bronto and LeadCrunch client for 2+ years: 

Speaking about KPIs with LeadCrunch, right out of the gate, the MQL cost went down 30% and we’re able to get these immediate savings … then down the funnel, as we work the leads more, as we nurture them, brought them forward as sales discovered them, we saw increases in the SQL rate for us.

We saw opportunity pipeline grow and be at least 30% better than the next best vendor in the space and at times way better than the average or some of the underperforming vendors that we have in the space. All the way across the board, the impact just really goes through the full revenue cycle for us.

Read the full interview here.

The benefits of this philosophy goes beyond simply lead delivery – though, admittedly, that piece of the puzzle is incredibly important. Improving targeting has profound implications across the entire B2B sales cycle, and within the organizations we work with. 

Sales teams are happier because they’re getting better leads, so conversations and alignment between Sales and Marketing gets a whole lot smoother. Pipeline starts to fill faster. Revenue starts to climb. And all the previous hype-cycle processes and software that had been implemented and failed to show a measurable ROI – suddenly those things start working better than ever before. 


It’s not magic, it’s just better data science.

Further Reading

Olin Hyde
April 8, 2020
. 5 min read
Lainey Mebust
November 4, 2019
. 15 min read
Lainey Mebust
October 9, 2019
. 5 min read