How to Use Signal-Based Analytics in GTM Strategy

I honestly can’t believe he shared this. But Tyler Calder (PartnerStack CMO) shared their whole GTM strategy with me. And it’s not influencer fluff, it works. Over the last year+ they’ve been able to: -- Increase pipeline value by 58%+ -- While DECREASING cost per dollar of pipe by 35%. -- And improving NRR, & ACV So… getting MORE efficient as they scaled, not less. As a marketer I’m always looking for real playbooks that I can actually use, because they come from another practitioner. Proven. No incentives. This is one. His playbook is simple but beautiful: (full breakdown here: https://lnkd.in/e6qJcsx7) 1️⃣ 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻: 𝗨𝘀𝗶𝗻𝗴 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 & 𝗜𝗖𝗣 𝗠𝗼𝗱𝗲𝗹 𝘁𝗼 𝘄𝗼𝗿𝗸 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝘀 (& 𝗻𝗼 𝗺𝗼𝗿𝗲 𝗻𝗼𝗻-𝗜𝗖𝗣 𝘀𝗽𝗲𝗻𝗱) -- They built an AI-powered ICP Model (with Keyplay) that lets them hyper-focus on accounts that are showing fit signals. Built on real modern fit signals like: 1. Are they using a PartnerStack competitor? 2. Are they actively hiring for partnerships? 3. Do they have multiple partner motions live (affiliate, referral, agency)? 4. Are they growing? Recently funded? Product-led? Employee count? 5. Are they investing into areas that partnerships could either compliment or displace because it’s more efficient? etc. 2️⃣ 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁: 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝗽𝗹𝗮𝘆𝘀 𝘁𝗼 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝘀 𝘄. 𝗺𝗼𝗱𝗲𝗿𝗻 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 & 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 -- Prioritize accounts by fit (Tier A, B, C, D) -- Tailor plays to accounts by segment & tier -- Use AI signals to segment deeper and hyper-personalize 3️⃣ 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁: 𝗣𝗿𝗼𝘃𝗶𝗻𝗴 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴'𝘀 𝗶𝗺𝗽𝗮𝗰𝘁 𝘁𝗼 𝗸𝗲𝗲𝗽 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯. Imagine what you could do if you knew every account in your market… You’d build a report that shows every account and their engagement. Then you’d report on how that changes weekly, monthly, quarterly… They do exactly that. This isn't a shiny tactic. But I guarantee if take this seriously you’ll get something out of it that will work. It's fundamentals done right. And a perfect reminder for any marketing leader. Read the in-depth breakdown here: https://lnkd.in/e6qJcsx7

Controversial take - I believe Signal-based prospecting should sit with your marketing/growth team. The reality is prospecting currently sits with your sales team who is adopting a 1960s style of static prospecting. What is Static OR Dynamic Prospecting? Static → They build a static list of 1,000 leads, blast them with messages, and hope for the best. ⚠️ That’s the old way. Here’s what smart marketing & growth teams are doing instead → Dynamic prospecting. What’s the difference? ❌ Static prospecting: You create a one-time list based on generic criteria (job title, company size, industry) and send mass outreach messages. ✅ Dynamic prospecting: You reach out when something happens— silent signals like engaging with content X number of times, engaging with a competitor, posting in a WhatsApp group etc. It’s based on real-time triggers, not stale data. Why does this work better? - Relevant outreach - Higher response rates - No list decay So how do you get started with Dynamic Prospecting? 1. Identify what triggers indicate buying intent in your industry 2. Use tools like Trigify.io, Vector 👻, Keyplay, Clay to monitor these signals and automatically surface leads. 3. Instead of generic outreach, tie your message to the pain created by the trigger 4. Keep iterating (Change your signals) If you’re still relying on static lists, you’re leaving a ton of deals on the table.

It's wild to see the the role "GTM Engineering" is starting to blow up via companies such as Clay, Apollo, etc.. Something I see missing from the discussion though is how "engineering" is involved with the role. I've seen multiple job description, blogs, and LI posts who essentially equate the "engineering" in the role to Zapier integrations and being "data driven." I think this is a huge mistake and misrepresents what a GTM Engineer can actually do. Yes, knowing how to integrate various GTM tools together via integrations and Zapier is important. My first year at Gable was focused heavily on this job component... but this doesn't set GTM Engineering up for success. Specifically, the power in GTM Engineering is the ability to ingest GTM signals specific to your business and then use these signals to optimize your GTM funnel. The key phrase here is "specific to your business." THIS IS 100% A DATA ENGINEERING AND DATA SCIENCE PROBLEM. Anyone can quickly setup a CRM and start integrating tools and throwing Zapier on top. But this is what an engineer focuses on: 1. What are the unique signals that are specific to the business? 2. Given these unique signals, what does the underlying data model need to be. 3. For this data model, what is the architecture diagram across databases (hint: your CRM is database managed by a 3rd party tool) 4. For key signals not associated with a native integration, what data pipelines do you need to build? 5. How do we maintain this architecture, version control code associated with it, and keep the cost of running these pipelines low? For context, say I want to add the signal of "email opens" to a tool. Many companies will push you to use Zapier and thus push the cost to run the pipeline on you. One zap workflow costs around $0.06, but you send 10k email a week with a 10% open rate-- in other words, one zap workflow will cost you ~$3k/year minimum (not accounting for growth). Multiply that by multiple workflows and it gets expensive FAST. In comparison, a AWS lambda call (cloud compute) is $0.20 per 1 MILLION requests. Zapier and vibes will get you by until you actually need engineering. Hopefully you hired engineers in practice and not engineers in title alone. #gtm #GTMengineering #data #ai

Demandbase has used AI to score 38B accounts, predict 4M opportunities, and launch 20k outcome-based advertising campaigns. Here are 3 best practices for using AI in your account-based GTM: 1. Start with data AI strategy should start with data cleansing and enrichment. Not all data is equal, it’s important to understand what signal matters most and to focus on quality over quantity – you don’t need 150M contacts weighing down CRM, you need 100k highly accurate contacts from your ICP. 2. Build healthy models There are three best practices here too: (i) Know what the strongest signals are. For example, for tech companies generally technographics, industry, and revenue ranges are strong signals for ICP models, while campaign responses, sales activities, website engagement, and intent are strong signals for pipeline prediction models. (ii) Build specialized models for different products, regions, and aspects of your GTM. For example, models focused on acquisitions of new logos, models focused on customer retention, and models focused on gross retention. (iii) Models need to be re-trained frequently to avoid following behind your GTM evolution. 3. Avoid black boxes AI models have to be transparent. Without transparency you can’t tell if the AI model is making a recommendation that you know for obvious reasons is flawed. Transparency enables Marketing and Sales to improve their messaging and activation by learning directly from model recommendations. And transparency is critical for data science teams at your company driving AI strategy across the enterprise. There’s a lot of hype and promise in AI. What’s working best for account-based GTM’s is focusing on the strongest signal, prioritizing quality of data over quantity, using specialized models, re-training models frequently, and making sure AI is transparent.

Every team talks about intent, triggers & signals Very few know how to act on it.   The truth? 👉 Most "intent signals" are just passive data exhaust. 👉 And most GTM teams aren't wired to act on them fast enough to matter.   Intent without infrastructure = no replies. Cold Email without the right timing = ignored. LinkedIn engagement that no one follows up on = wasted.   So here’s what we do instead 👇   We treat intent like infrastructure ☑ If someone hits a pricing page → RB2B flags it → Clay auto-enrichs → lemlist sends an email ☑ If someone engages with a competitor → Apify triggers an “alt vendor” play ☑ If a company raises $10M → Clay surfaces the jobs they're hiring for → If they hire for GTM roles, then lemlist sends an email   Intent is only useful when your system knows: 1. What happened 2. Who to send it to 3. What to say 4. When to say it 5. And how to follow up   Most teams never get past #1. Intent isn’t insight. Intent is activation.   If your GTM isn’t built to react in <24 hours, you don’t need more signals. You need better wiring.   Want to build the system? Let’s talk.   #coldemail #gtm #intentdata #salescaptain #clay #signals