Prospecting signals are overwhelming. Make them work for you.

Dave Kellogg called it a pipeline crisis in his post from last winter, and it hasn’t let up.
Even as a hypergrowth startup, the targets keep climbing and theres always pressure to do more: launch another campaign, try another channel, run another event. But even when something works, Andrew Chen's Law of Shitty Clickthroughs kicks in and whatever channel or tactic is working today gets saturated within weeks as everyone else piles in. So even though you’re doing more, it still feels like nothing is landing.

All of that effort generates activity — website visits, content downloads, event registrations, and trial signups — but knowing what to do with those signals is it own challenge. Kyle Poyar describes teams still experimenting with signals, without agreeing on which ones really matter

We felt this too, even when demo requests grew. When I dug in, the tactics weren’t the problem. The signals from them lived in different places. Marketing knew who downloaded the whitepaper, sales knew who replied to an email, product knew who was active in the trial. But those signals lived in different places, leaving reps to piece together which accounts actually deserved their attention.

An overload of lead signals, in different places

Our AEs were drowning in signals and didn’t have one place to look to give them the aggregate view of which account they should prioritize. They’d receive Slack notifications when someone signed up for our product, registered for an event, or even just visited our website. Salesforce reports showed one piece of this picture, multiple dashboards in Hex showing different slices, and Notion docs with meeting summaries. The information overload made it impossible to focus.

Every signal felt urgent, even when it wasn’t. Each rep had to piece together their own view and figure out who to prioritize. They could chase everything and waste time on cold leads, or tune out the alerts and miss real opportunities.

Being on the Marketing side for years, I’ve grown a lot of empathy for the team knowing they are doing everything they can to drive pipeline. The real bottleneck isn’t their production or event planning, it is the way that Sales is presented the information. While leadership debates attribution models and marketers scramble for budget, there's an opportunity sitting right there if you're willing to evolve how you use your data.

You probably have the pipeline — just solve for the signal overload

We stopped trying to send better alerts. Instead, we built one data app that brought every signal about each lead together in one place. This new data app became a live, prioritized view of accounts and the specific reasons they matter right now.

The app pulls together:

• Product engagement: what leads are active in trial, what activities are they doing
• Website visits: repeat visitors, pages viewed, depth of engagement
• Campaign engagement: event registrations, content downloads, webinar attendance
• 3rd party signals: new data leader hires, existing user job changers, and fundraising signals
• Opportunity data: past deals and who's involved

Then it runs everything through a scoring model and prioritizes the accounts that reps should work. While the summaries show why an account is at the top of the list, the actual detail views help the AEs really understand the account and what they are interested in.

Instead of chasing 100 different contacts every day, reps can commit to a set of high-priority accounts for the week and understand why they’re worth pursuing.

Why we built this instead of buying it

There are plenty of tools that promise to do this but I consistently run into two issues:

1. We have so much data in our warehouse that would be difficult to get into a third party tool.
2. Our reps don’t need another tool to go to in their workflow.

The question wasn't whether we could buy a solution. It was whether we should wait for a vendor to understand our specific use case, live within their platform’s confines, or build exactly what we needed ourselves.

Here's the other thing: I built this app on my own and I’m not a data scientist. With Hex's Agent, I built my first version in a day! The agent helped me write the scoring logic in SQL and troubleshoot along the way. I’ve refined it through many iterations and feedback rounds with sales reps since then.

You don't need to wait for another product demo, security review, or extensive onboarding to get your team the insights they need. You can ask just Hex (or whatever tool you use for agentic analytics) and learn as you build. The point isn't to start doing more — it's to start consolidating what you have and coordinating how you use it.

The results speak for themselves

The impact showed up fast: our lead-to-pipeline conversion climbed, which meant we were getting more pipeline from the same lead volume. Combined with other improvements across the sales process, the compounding effect was significant. We saw:

• 54% higher inbound conversion to sales opportunity in the first full quarter after launch, using data apps that surface warm signals in real time.
• Follow-up time on inbound leads cut roughly in half (median 39 hours down to 20), driven by a clear prioritization framework that tells reps exactly who to call first.
• We just hit our highest week of opportunity generation ever in the first week of October!

But beyond the metrics, there were some more fundamental shifts. The data team stopped being a service desk and started being strategic partners, our marketing team could be more prescriptive, and sales stopped chasing cold leads.

The most unexpected payoff? Instant credibility from industry-leading prospects

Aside from internal results with tangible pipeline velocity, we also saw an advantage that no one had seen coming: competitive, top-tier prospects were impressed with how quickly we followed up with them and told us that it increased their trust with us.

To directly quote a prospect:

This is the type of action that will help you cut through the noise.

Why this worked when everything else didn't

We'd tried a lot of things before this:

• Better Slack notifications (just more noise)
• Salesforce Reports (no strong way to summarize and prioritize)
• Training reps on how to use all the different tools (they didn't have time, and we couldn't get consistent adoption across AEs, especially as we were hiring quickly)

What made this new data app and its inbox notifications work, was combining consolidation with intelligence. We stopped expecting reps to be data analysts and instead gave them AI-powered recommendations they could act on immediately. Because everyone works from the same app, those improvements don’t stay in a one-off analysis —they become part of the prioritization the whole team uses.

Instead of 47 Slack notifications, reps open one app in the morning and know exactly where to focus. Instead of guessing which account to call first, they have a priority list with reasoning behind it. And because it's the same coordinated view that marketing sees, everyone's finally working from the same playbook.

Now I maintain the scoring model and tune it based on what actually predicts conversions. I get feedback from the reps on what is helpful and what's not, and then I can easily make adjustments to improve the experience for everyone. The reps don't have to understand how it works — they just need to know who to call and why.

What you actually need to do this

You don't need to be a data platform company. But you do need three things:

1. Consolidated data in one place

Stop making reps check 7 tools. Pull your signals together — product data, intent signals, CRM data, engagement data. If you can't build it yourself, there are tools that can do this (we obviously use Hex).

2. A scoring model and interactive visual, powered by AI

If you want to make this actually useful, you need more than a simple sort by highest to lowest activities. You need an interactive experience with sophisticated logic built based on how your company operates and which signals are most important for you. While this is not impossible to code by hand, it is far faster with an agent.

3. Iterate and evolve it overtime

Your first version won't be perfect. What matters is building a feedback loop with your reps and then refining the model based on what actually drives conversions. I use Hex's Agent to analyze the app's performance — asking questions like "which signals are most predictive of closed-won deals?" or "are there engagement patterns we're undervaluing?" The agent helps me spot trends I'd miss manually and keeps the scoring model sharp without rebuilding from scratch.

What this means for the pipeline crisis

Dave Kellogg's recommendations are solid: focus on ABM, run better events, optimize for AEO, leverage partners. All of that works.

But every single one of those tactics requires knowing which accounts to target, when to reach out, and what message will resonate. That's where pipeline intelligence comes in — not as a replacement for good GTM tactics, but as the foundation that makes them actually work.

Because in a world where everyone's fighting for the same constrained pipeline, where every tactic gets saturated within weeks, and where buyers' inboxes are completely overwhelmed, the teams that win will be the ones that know exactly where to focus.

We made our signals talk to each other. We made our teams work from the same truth. We went from feeling like nothing was working to having a coordinated system that actually drives results.

And that makes all the difference.