Welcome back to Funnel Lab Fridays, our weekly LinkedIn Live session, where we tackle some of the trickiest use cases and biggest head-scratchers in data-driven marketing. I’m your host Eric Westerkamp, CEO of CaliberMind.
I’m joined by Jordan Crawford of Blueprint GTM and Doug Bell, our Chief Marketing Officer here at CaliberMind. Today, we’re exploring the critical differences between traditional outbound tactics and data-driven strategies that leverage customer insights, intent signals, and AI tools.
Old-School Outbound vs. the New Reality
The big question I keep hearing is: “Is outbound selling dead, or just in need of a makeover?” Not too long ago, you’d walk into a tech company and see entire rooms of SDRs, cold-calling prospects from a ZoomInfo list.
Doug’s Perspective
Doug says that the old-school “spray-and-pray” approach—where you buy a list and hammer them with generic emails—doesn’t cut it anymore. Buyers are savvier, gatekeepers are stricter, and, let’s face it, cold emails are a great way to get on a block list. But he’s not ready to say “outbound is dead” entirely.
Instead, he envisions a hybrid approach: you still do outbound, but you can’t expect to conjure a deal from nothing. Rather, you want to be “top of mind” when the buyer’s actual need arises. And if you’re ignoring signals from the market—like intent data or first-party engagement—then your outbound motion might be doomed.
Jordan’s Perspective
Jordan echoes that the lazy approach to outbounding is definitely on its way out. In older times, you might pluck a random list from Apollo or ZoomInfo, guess at a title, and send out boilerplate messaging. Today, you have to figure out why someone might buy (based on data about what they’re searching or hiring for) and craft your message around that.
The big shift Jordan sees is in bridging “why they bought” for one customer and applying that context to another. That’s the core of “good outbound” these days: re-creating your best customers’ stories at scale, with actual data to back it up.
Intent Data: Why Does It Feel Like a Black Box?
Our conversation quickly dives into intent data—and the notion that so many intent signal vendors on the market basically say, “We see something—trust us” and stamp their name on your deals. That might help you guess which accounts to call, but it’s often impossible to measure whether it’s truly working or not. As Jordan put it:
“If I can’t see how you derived these signals, or how I should message these accounts, then how is that better than a random cold list?”
We see that frustration all the time at CaliberMind. Companies pay for intent data, but it’s so broad and anonymized that they can’t parse which signals are valuable in their funnel. We all agreed that inbound or outbound, you can’t rely on a black box that won’t let you differentiate why you’re reaching out.
First-Party vs. Third-Party Data: The Blend Is Key
So, it’s clear “black box” intent alone doesn’t cut it. You probably need to combine it with your first-party signals. Let’s define each:
- First-party data: Website visits, form fills, product usage, email engagement—stuff you see in your own systems.
- Third-party data: Insights from Bombora, G2, job postings, or communities that show a company’s external behavior.
Doug hammers home how crucial it is to filter these signals together. If you see surging interest from an account (say, they visited G2 to compare multi-touch attribution tools), but also notice they’re hitting your blog or downloading your content, that’s a strong sign they’re in an active buying cycle. If you see them surging on a competitor, that might be an opening for your outbound team to highlight differences.
“I Like Money. Can I Have Yours?” – Why Intent Doesn’t Guarantee Good Messaging
Jordan’s a big fan of telling cautionary tales about lazy triggers. Take the classic “Hey, I see you raised money—gimme your money.” That was an easy outbound trigger for a while, but guess what? Everyone used it, so now it’s oversaturated and yields poor results. If your approach is basically, “I got your name from an intent feed,” you might not stand out.
Instead, Jordan’s approach is to pinpoint the nuanced reasons an account might be a good fit, then craft a story about how your solution speaks to their situation. That’s not something a black box can do for you, because it doesn’t detail the “why” behind the signal. Just “someone at the account searched your competitor—maybe contact them.” The real question is: Which competitor? Which problem? Is it a developer searching for an engineering solution or a marketer searching for an analytics platform?
If your outbound script can’t answer that, you’re basically rolling the dice.
The Changing Face of Outbound
So where does that leave us? Doug, Jordan, and I all see outbound as being more about creatively weaving data into your approach. Jordan’s tool, for instance, uses job postings at scale. If you notice a company listing a job that references particular challenges, you can guess they might be looking for solutions.
That’s more direct and actionable than “they looked at marketing analytics last week in a big aggregator’s database.” Then you add your first-party data. Are they also on your website? Great sign. Are they ignoring you? Maybe they’re not truly in the market.
Doug warns us to remember that we can’t create demand out of thin air. We can only get in line for a real need. “If you rely on your BDR team to conjure up deals that don’t exist, you’ll burn out your sellers,” he says.
Instead, you want the BDR team to watch for genuine signals. The second they see a relevant trigger—like inbound brand engagement plus an external surge in your product category—that’s a strong nudge to say, “It’s time to pick up the phone.”
The Role of AI in Outbounding
No conversation these days can ignore AI. Jordan showcases how he uses AI to parse job postings, discover relevant signals, and produce email copy that specifically addresses the issues the prospect’s job listing reveals. The real magic is that AI can interpret the data at scale, but you, the marketer or RevOps person, have to define the data you feed it and the constraints. That’s how you avoid generic or spammy messages that bury you in an inbox.
The same logic applies to analyzing which third-party signals matter in your funnel. If you throw them all into a black box, you can’t measure them. If you bring them into a system like CaliberMind, you can see precisely how each intent topic or job posting correlates with pipeline progression.
Then, guess what? You can feed that contextual data into a large language model to produce a bullet-point summary for your sales reps. “Hey rep, here’s why this account might be a perfect candidate, and here’s how to talk about it.”
Tool Time: A Live Demo
We wrap up our session with a short “tool time.” Jordan took the driver’s seat to demo how Blueprint sorts the job postings by “density of problem.” No more sloppy “title and employee size” segmentation. Instead, we see: “They’re looking for an SDR who can do advanced account research, or they specifically mention ‘data-driven outbound motion’ in the job listing.” That’s a bull’s-eye if you sell a data-driven outbound platform.
Jordan shows how he copies the job description context into ChatGPT, then instructs ChatGPT to identify which lines of the job post align with the user’s solution. That yields a dynamic, context-driven reason to reach out, which is leaps and bounds more effective than “Hi, I see you have a new job posting.” That’s open intent—we know exactly how it’s derived. We can measure it, tailor it, and refine it over time.
Compare that to black box signals: “We’re stamping your account as surging—just trust us.” Harder to measure and impossible to message well.
Wrapping Up
At the end of the day, intent doesn’t need to feel like a black box. We have the tools—between advanced data platforms like CaliberMind, generative AI, and imaginative data wranglers like Jordan—to break open the intent signals, figure out how they correlate to your funnel, and produce meaningful, stand-out messaging.
The theme is: “context + data + timing.” If you’re ignoring any piece of that puzzle, your outbound approach might crumble. But if you orchestrate those pieces well, you can transform what used to be a dreaded “dial down a random list” routine into a high-performing, buyer-centric motion that actually meets prospects at their point of need.
Thank you for tuning in to Funnel Lab Fridays. Until next time, keep pushing the boundaries of what’s possible with marketing data. And remember, the best outbound approach doesn’t feel “outbound” at all; it feels like a timely, thoughtful conversation.
