Before we start, an honest disclosure. If you’re looking for some high-level nerdery on math, this is not gonna be that. Math is not my jam – but luckily, it is the jam of our Chief Data Scientist, Steve Biafore, so if you’re looking for that, here’s a video series on B2B targeting vectors that he put together for you to enjoy. It’s quite comprehensive. Don’t say I didn’t warn you.
Now, for those of you who would like to learn about vectors without melting your brains, then you’re in the right place.
Still with me? Let’s do this.
First, let’s set the scene.
It’s the fall of 1970 and a man and a woman are standing outside of a fenced pasture looking at two foals. For the purpose of this exercise, we’ll call them Horse 1 and Horse 2. At the end of this meeting, they’ll both walk away with ownership of 1(one) horse.
Having won an earlier coin toss, the man gets to choose first. The woman gets the horse he doesn’t want.
The man is basing his choice on two pieces of information.
- he wants a horse
- he knows which horse the woman wants
With that information, he chooses Horse 1. This leaves the woman with Horse 2, and given that she had voiced her interest in Horse 1, we’d understand if she was disappointed. The odds were not in her favor.
However, the woman isn’t disappointed at all.
The woman knows the following pieces of information.
- she wants a horse
- Horse 2’s “dam” (it’s mom) was the daughter of an Irish racehorse celebrated for his endurance in long-distance races
- Horse 2’s “sire” (it’s dad) was known for possessing speed and stamina
- that those combined traits – endurance and speed – would likely make for a hell of a racehorse
But that’s not all. The woman also knows the personality of the man she’s dealing with. She knows that if he thinks she wants Horse 1, that’s the horse he’ll pick.
And that’s exactly what happened.
This is how the woman, Penny Chenery, ended up with Horse 2, more commonly known as Secretariat. Secretariat became the Triple Crown Champion of 1973 and one of the greatest thoroughbred racehorses to have ever lived. The man, Ogden Phipps, scion of one of horse racing’s most successful family dynasties, received Horse 1, aka The Bride – who never won a race.
You’re probably thinking, “Hey, Emma, what the heck does all of this have to do with demand gen?” Well, a lot. Stick with me, I’m getting there. But first I have to let you know this is not a true story. (I mean the coin toss stuff is true, but the rest of it was made up for Disney’s 2010 Secretariat movie. It makes for a good scene, but most horse racing historians are absolutely bonkers over how untrue this is.)
True or not, this story does make a good point in terms of the value of information. Chenery had better information than Phipps had. And with that information, she was able to make a better choice.
So what does any of this have to do with B2B targeting and vectors?
Complicated math aside, vectors and dimensions are just pieces of information.
(At this moment, Steve’s face is turning a very alarming shade of red and he’s wondering if I even bothered to read the very thorough explanation he wrote up in the patent description. I did, and WHOA you guys. I’ll be the first to admit that this is a wild oversimplification of an incredibly complex process. But I’m a marketer, not a data scientist, so here we are. Apologies to Steve and his team.)
Why Information Quality Matters in B2B Targeting
When it comes to B2B targeting, information is an important piece of the puzzle. In traditional targeting, the amount and quality of the information that is available is incredibly limited. Most B2B marketers rely on Industry Codes, economic reporting data that is the equivalent of just wanting ‘a horse’.
LeadCrunch vectors, on the other hand, are custom built by our data science team to uncover truly meaningful insights about a company. Not a broad overview, not simply ‘a horse’ or ‘not a horse’, but if a horse is, say, blessed with really specific and desired qualities.
That’s not all though. The other key piece of the puzzle here is in the relationships of the information. To use our example, Chenery didn’t just need information on a horse, she needed to know what kind of a man she was dealing with. If she’d let it slip that she really wanted Horse 2, that’s the one Phipps would have chosen, she’d have lost Secretariat and the Disney movie wouldn’t have been nearly as dramatic.
In pairing the hard facts with these softer information relationships, Chenery was able to make a Really Good Decision that cemented her place in history as a woman with a gift for recognizing a spectacular horse. And Phipps? In basing his choice on limited, inaccurate information, he became the guy that lost out on Secretariat.
Damn, that had to have hurt.
To succeed in horse racing, or B2B targeting, you’ve got to have high-quality information, and a lot of it. You also have to know how those pieces of information connect.
B2B marketers are tired of targeting that sucks and delivering leads that the Sales team hates.
Lucky for us then that Steve and his team have created vectors and dimensions that are designed for massive computing. (This computing is performed by artificial intelligence, because ain’t nobody on our data science team got time for that.)
LeadCrunch provides savvy B2B marketers with the information that really matters for making quality business decisions, and filling the pipeline with leads that are ready and willing to buy. We do this by creating B2B Lookalike Audiences that look a whole lot like your best customers, and nurturing that audience with your content – white papers, ebooks, webinars and the like. They know who you are and what you do before your Sales team ever picks up the phone.
We run these lookalikes through a rigorous 13-point validation process, so your Sales team only gets verified leads with accurate, current contact information. (Backed by a lead quality guarantee, bam!) And we do all this on a cost per lead basis.
And that, my friends, is how you win a horse race.