Signal Stacking Strategies for Better Results

Recently I shared that there is a laundry list of considerations for successful signal plays, and someone wanted to know: “So where would you start on that "laundry list" of considerations?” If the signal volume is large enough, I'd start with one signal for one sales department (eg SDRs). That way you don't have to think about lead routing, ownership, or even whether it's a customer account. Outreach messaging can be standardized, as well. But proving a signal-based play works is only the first step. How do you expand from there? At this point, you have two options. 1/ Signal expansion by department - Evaluate the existing signal for ways other departments can plug that signal into their motions Example: → SDRs run a New Hire signal playbook to target prospects who have recently joined target accounts, and it is successful. → AEs then begin to run a similar playbook using the same signal to trigger multithreading efforts. → CSMs recognize that new hires joining customer accounts is a churn risk, so they develop a playbook with the New Hire signal to minimize the risk. This approach is somewhat complex because now you need to get different people involved, and you need to train them. 2/ Stacking signals - Build on your wins by adding more signals for a single department to iterate on existing workflows. Example: → SDRs run a New Hire signal playbook to target prospects who have recently joined target accounts, and it is successful. → When the play generates meetings, they build a new playbook for Customer Job Change signals that follows existing workflows. → When this new signal is validated, they add another signal, Website Visitors, to their signal stack. This approach is easier because the team is already convinced of this play. They understand the fundamental framework, and you don’t need cross-departmental training. Which strategy is the “right” one? Like everything in B2B, it depends. Ultimately, there is only one wrong way to use signals: not actioning them.

Everyone else is chasing home runs but you’re over here playing Moneyball with buying signals. That’s what signal stacking is all about. Instead of waiting for the perfect lead to fill out a demo form, you start piecing together early clues — intent, hiring, tech, even who’s in their network. Alone, these signals might not say much. But when they show up together it can help you move from guesswork to confidence. Here’s what it looks like in action: 🏥 Healthcare Tech - Someone on the clinical team is Googling patient portal integrations - The hospital announces a digital transformation initiative - There’s a mutual connection to the CIO 🏗️ Construction SaaS - A GC downloads a cost control whitepaper - They hire a VP of Ops from a tech-savvy firm - They just landed a 7-figure municipal contract 🔐 Cybersecurity - They’re using Auth0 - Hiring a Product Manager for Identity & Trust - Engineers are posting in GitHub about identity workflows Signal stacking helps you get in earlier, with more context, and a way better message. One signal is interesting but multiple signals is a story worth chasing.

More of the same signal is often a strong buying signal in itself. Examples when this is the case: - When many job openings > a single open role - When many web visits > a single homepage visit - When many roles recently hired > a single new hire - When many product users > a single new user signup - When many LinkedIn interactions > a single reaction to a post - When many integrations connected > a single integration connected When many signals > a single signal in isolation, I turn to a simple yet powerful feature of Common Room: Calculated Fields➕ What does it do? Essentially, it aggregates the frequency of a single signal—think open headcount, web visits, keywords in a 10-K, product activity, etc.—into a structured, filterable, and sortable field. I like to think of it as more signal for your signals (or 🥞 signal stacking). 📓 Playbooks you can unlock with calculated signals: - Reach out to an economic buyer when # of product users > one - Prioritize accounts with > 10 (or any number) of open eng headcount - Alert a rep when 2 or more visitors from a target account visit the site Peep the Loom to follow along and build your own plays…

How to avoid "spray and pray" while still automating (steal this workflow) ↓ Context: Automation and efficiency are NOT mutually exclusive. In other words, it's possible to touch fewer prospects *and* drastically increase results. While automating work that humans no longer need to do thanks to the LLM gods. ↓ Here's an example of an automated system Andreas Wernicke and I set up for a Series B company that helps them send *fewer* emails, while still automating 90% of the work: Step 1 → [Signal] “Engaged w/ recent marketing email" (Iterable) pulled from database (Postgres) Step 2 → Postgres pushes engaged leads to Clay Step 3 → Clay enriches with "rich data" (not just out-of-the-box data like employee count, location, industry, etc.)—this is where AI is magic Step 4 → Clay tiers the leads (custom-built logic as a single "row" using 6 columns—the rich datapoints—in the Clay table) Step 5 → Clay pushes "Tier 1-3" leads to Attio (and the enriched data into custom fields) Step 6 → Clay pushes tiered leads to Apollo Step 7 → Apollo Workflow automatically adds+routes leads based on tiers (Tier 1 leads are put into a sequence—from Rep 1—that has emails+calls+LinkedIn steps; Tier 2 and 3 leads are put into an email-only sequence from Reps 2 and 3 based on their respective segments) ↓ Now that this system is in place, the fun begins 😈 We can now replace Step 1 with any signal (or multiple signals... aka stacking signals). This system lets the company "operationalize experimentation™." Basically, a magical playground for a GTM Engineer to play. :) But again, the goal of all of this automation is not to send MORE emails. Actually, the opposite. This system finds ~150 Tier 1 leads for every ~10,000 leads processed through it. So, we're effectively decreasing the volume of outreach by 98.5%. Automation 🤝 efficiency.