Why signals beat lead scores, with examples across new business, expansion, and churn, and playbooks for reps
Jen Igartua · Go Nimbly
Published September 6, 2024Explore the lesson. Sign in to access the slide download.
Jen Igartua presents signal-based selling as a way to equip sales teams with meaningful customer context. The session covers conversation-ready signals across new business, expansion, churn, and playbooks that turn data into action.
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“motions the big idea here is is collect all that data and I wish I had a little soap box here I don't if I did I guess I'll stand on my tiptoes uh we are doing a massive disservice to our sales teams we are expecting them to have really incredible conversations during sales cycles and we're giving them very little information and I think that's the biggest thing that if there's can you raise your hand if you're a marketer consider yourself a marketer seller”
“a great conversation I think that that's dying out um I don't think that algorithms are stupid they're quite smart uh it's a propensity to buy but it doesn't set you up to why or why have that conversation so when we think about signal-based selling we're really looking at a ton of different scenarios so it could be a new business it could be expansion it could also be because you've detected a possibility of Churn it could be onboarding is not working you have that that use case right like why is it that you want to have a conversation with your customer and then”
“their customers customers uh seat score and if the cat score because it's what they fix if their sead score was decreasing and the team was growing they had a whole Playbook around hey seems like your team is growing and your business is doing well but your customers are feeling the pain of that and it's such an easy plugin for their AI feature which is Finn and so that Playbook killed right every single like the conversion rate was over 50% they were booking calls right away because we”
“expansion but there's new business one there's churn detection whatever it is and the rep can work on this right the whole Playbook is here and if you look at it we tell them the qualification signals uh again I've removed the clients information so so it's a little bit generic but we give them the specific outcome so we say for example hey we think that they're ready for an AI add-on and we know that because their team size is increasing their usage is increasing and we know customers of that size do really well with our AI product”
“Signal-Based Selling > Traditional Lead Scoring NEW BUSINESS EXPANSION ADOPTION RENEWAL CHURN EXPANSION: Feature Need (Paywall) NB: Signup Director + at Tier 1 EXPANSION: Multiple Departments RENEWAL: Early Renewal Upsell RENEWAL RISK: Search Risk NB: Engagement with User Docs Usage shows need for additional features Executive signed up for product at named account Multiple Departments showing high usage Usage showing early renewal 3rd party search of competitors Engagement with BOF product documentation Increase in active users Usage of key features Industry need Free Signup Director + Title, especially non technical buyer 5+ active users from 2+ departments Usage limit reached Enterprise Account 90+ days from rental G2 comparison Renewal within 90 days Engagement with 5+ technical documents Route to CSM Route to AE Route to AE Route to CSM Route to CSM Route to AE Traditional lead scoring models have many limitations and often lead to top-of-funnel misalignment and bad customer experiences.”
“GTM Touch Example: Expansion -Usage trends - Intent data to target outreach - Risk indicators in CS calls - Use deal data to uncover similar opps”
“In the age of AI, data isn’t a differentiator. Action is. GTM teams are sitting on a goldmine of data - intent signals, product usage, website behavior - but are failing to leverage it effectively for growth. Access to rich third party, AI-harvested data has never been more abundant -- but many teams don't realize how they could operationalize it.”
“More Top-Performing Playbooks Multiple departments have separate instances with high usage Usage pacing exceeds 2x what was expected Executive user has added 5+ members, but no team features No integration or key feature integrated during onboarding Number of user growth or activity indicates need for new features (ie, AI)”
Video
Signal-Based Selling: Leveraging Data & Insights to Set Your Sales Team Up - Slide Deck
CEO of Go Nimbly, a revenue operations consultancy helping businesses improve internal collaboration and sales efficiency.
Irina Novoselsky · Hootsuite
Mark Fershteyn · Recapped
Ryan Staley · Whale Boss
Mark Kosoglow · Operator AI
Isaac Pohl-Zaretsky · Pocus
Jeff Schwartz · dataroomHQ
Each slide with a short explanation of what it shows. Open the slide text for its exact wording.
33 passages
SaaSOpen, September 2024 Signal-Based Selling Jen Igartua CEO, Go Nimbly Leveraging Data & Insights to Set Your Sales Team Up
Over the next 20 minutes I’m going to show you A DATA FIRST MINDSET Common Mistake Data as a competitive advantage The move to signal based selling SIGNALS > SCORES SALES HANDOFF Examples of Signals Building blocks of PLS What Nirvana looks like How the best companies are thinking about sales playbooks
YOU CAN’T HAVE PRODUCT-LED SALES WITHOUT DATA TO BUILD ON Bottom line: PLS is DLS
You’re Doing Your GTM Teams a Disservice
A CRM lead form for a fictional VP Sales at a named company shows only basic contact fields such as name, title, company, and lead source, with no usage or intent data. A handwritten note argues that handing reps this alone and expecting a meaningful conversation is unrealistic.
This is NOT a lead Giving this to your reps and expecting them to have a meaningful interaction is WILD.
A framework moves from data (usage, firmographic, marketing engagement, account health) through translation into signals, to actions like cross-sell, upsell, and churn mitigation, producing insight metrics such as PQA conversion rate and mitigated churn ARR. Marketing and sales/CS both feed nurture and playbook content into the action stage.
The Building Blocks of Signal-Based Selling Data Translation & Signals Action Insights Sales/CS Marketing
A table maps six situations, from new-business signups to renewal risk, to specific usage and firmographic signals such as a director-level signup at a top tier or search activity for competitors, and routes each to either a CSM or an AE. The point is that traditional lead scoring causes top-of-funnel misalignment and poor customer experiences.
Signal-Based Selling > Traditional Lead Scoring NEW BUSINESS EXPANSION ADOPTION RENEWAL CHURN EXPANSION: Feature Need (Paywall) NB: Signup Director + at Tier 1 EXPANSION: Multiple Departments RENEWAL: Early Renewal Upsell RENEWAL RISK: Search Risk NB: Engagement with User Docs Usage shows need for additional features Executive signed up for product at named account Multiple Departments showing high usage Usage showing early renewal 3rd party search of competitors Engagement with BOF product documentation Increase in active users Usage of key features Industry need Free Signup Director + Title, especially non technical buyer 5+ active users from 2+ departments Usage limit reached Enterprise Account 90+ days from rental G2 comparison Renewal within 90 days Engagement with 5+ technical documents Route to CSM Route to AE Route to AE Route to CSM Route to CSM Route to AE Traditional lead scoring models have many limitations and often lead to top-of-funnel misalignment and bad customer experiences.
LEVERAGE IT, OR LOSE MARKET SHARE TO THOSE WHO ARE. You’re Sitting on a Data Gold Mine…
Signals are grouped into product usage data such as active users and feature adoption, first-party engagement such as social activity and event attendance, and third-party engagement such as G2 reviews and competitor comparisons. Usage metrics can flag upsell readiness, while third-party intent data refines targeting on top of internal data.
Social engagement Event attendance Help Documents Feature adoption Usage frequency User Growth Identifying Key Signals And these signals WILL Change. PRODUCT USAGE DATA 1ST PARTY ENGAGEMENT 3RD PARTY ENGAGEMENT Metrics in active users can indicate upsell/expansion readiness. Monitor actions to predict churn or identify champions. G2 reviews Competitor comparisons Search behavior Use third-party intent data on top of internal data to refine targeting.
The best teams put the customer ahead of internal process, give reps easy access to insights so they can show up as experts, and build outcome-based playbooks that let the customer's behavior guide the next action rather than a fixed script.
What the Best Teams are Doing Customer Focus vs. Internal Focus Think about your customer first. Enable your reps to know when and how to step in when needed. Democratizing Data & Insights Access to the insights that create good conversations is key. Often reps don’t have the research and insights they need to show up as experts. Building Outcome-Based Playbooks Being flexible and allowing your customer to guide your actions will drive the outcomes you want.
To operationalize data, integrate customer signals into the CRM so GTM teams can access them, automatically notify reps when a trigger fires so nothing is missed, and build data-driven playbooks that guide customer actions.
Democratizing Data & Insights Turning Insights Into Action Integrate data from customer interactions and other key signals into your CRM so they’re easily accessible for your GTM teams to leverage. Automatically notify reps when a trigger occurs, so nothing falls through the cracks. Create data-driven playbooks to drive customer actions.
A GTM Touch record for a demo expansion account surfaces two qualification signals: fast-growing conversion volume at an account not yet on premium features, which points to an Automation/AI Add-On, and usage above 115% of contracted volume, which points to an Overages playbook. Handwritten notes list what feeds the play: usage trends, intent data, CS call risk indicators, and deal data used to find similar opportunities.
GTM Touch Example: Expansion -Usage trends - Intent data to target outreach - Risk indicators in CS calls - Use deal data to uncover similar opps
GTM Touch Example: Expansion -Usage trends - Intent data to target outreach - Risk indicators in CS calls - Use deal data to uncover similar opps
Top-performing playbooks fired on signals like multiple departments running separate high-usage instances, usage pacing 2x ahead of expectations, an executive adding 5+ users without team features, missed integrations during onboarding, or growth suggesting need for new features such as AI. A note stresses always layering in ICP fit before acting.
Multiple departments have separate instances with high usage Usage pacing exceeds 2x what was expected (ie at 3 months, reached half their usage) Executive user has added 5+ members, but no team features No integration or key feature integrated during onboarding Number of user growth or activity indicates need for new features (ie, AI) Playbooks That Have Killed Always always always layer in ICP definition.
Think in customer outcomes & enable your reps with playbooks
SaaSOpen, September 2024 Thank you! [redacted email] Jen Igartua CEO, Go Nimbly Let’s connect!
To reduce the gap between intention and action, teams need a triggered notification system so reps don't miss signals, access to actionable insights rather than raw data, workflows built around how teams already work, and tailored, data-backed sales plays.
1 Enable Your Teams REDUCE THE GAP BETWEEN INTENTION AND ACTION Automatically notify reps when a trigger occurs, so nothing falls through the cracks. Triggered Notification System 3 When building, take into account where your teams are and how they work. Streamlined Workflows 2 Data that’s not readily accessible and/or lacks context is not actionable. Access to Actionable Insights 4 Build data-backed playbooks that drive customer actions. Tailored Sales Plays
Jen Igartua CEO Lauren Silvers Director, GTM Programs Introductions
A chart shows a tactic's effectiveness declining sharply over time as adoption increases, then leveling off at a low plateau. The lesson is that no single tactic works indefinitely; teams need a mindset of ongoing testing, iteration, and innovation instead.
Every new tactic loses effectiveness as adoption increases. It’s not about finding one solution that will work indefinitely, but rather having a growth mindset and building a culture of testing, iteration, and innovation.
Data alone is no longer a differentiator now that AI has made it abundant; what matters is acting on it. GTM teams often sit on intent, product usage, and website behavior data without realizing how to operationalize the growing supply of third-party, AI-harvested data.
In the age of AI, data isn’t a differentiator. Action is. GTM teams are sitting on a goldmine of data - intent signals, product usage, website behavior - but are failing to leverage it effectively for growth. Access to rich third party, AI-harvested data has never been more abundant -- but many teams don't realize how they could operationalize it.
Broad targeting no longer works; the market now rewards specificity, achieved by building data models from first-, second-, and third-party sources, layering signals into tailored triggers and playbooks, and giving reps action tools such as CRM visibility, Slack alerts, and AI-assisted research and cadences.
Going broad doesn’t work. The market is showing us that we need specificity, and we can now do that with AI & access to data. Build data models using data from first, second, and third-party sources Layer signals to create ultra-tailored triggers and playbooks Enable reps to take action: data visibility & access inside CRM, Slack alerts, AI-enhanced research & cadences
The same four-part framework returns: data sources feed translation and signals, which drive actions like cross-sell, upsell, and churn mitigation, producing insight metrics such as PQA conversion rate and hand-raiser revenue, with sales/CS and marketing both contributing nurture and playbook content.
Building Blocks of Signal-Based Selling Data Translation & Signals Action Insights Sales/CS Marketing
The core message is to use signals to genuinely understand the buyer, then put those insights in the hands of the team so they can prompt prospects and customers to act.
The key to consistent innovation is to use signals to truly understand your buyer, and put those insights in the hands of your team to compel target prospect and customers actions.
Struggles stem from tool fragmentation, where intent signals never reach the CRM or SDR workflow; static enablement, where scripts don't adapt to buyer behavior; process inertia, where BDRs still chase MQLs instead of live buying intent; and under-use of AI and third-party data.
Why Teams Struggle Tool fragmentation Ex: intent signals don’t reach the CRM or SDR workflow Static enablement: Ex: Scripts don't change even when the buyer behavior does Process inertia: Ex: BDRs chasing MQLs, not live buying intent Under-utilization of AI & third-party data
A table lists data sources and examples for five signal categories: firmographic (ZoomInfo, Apollo, Clearbit), intent (G2, 6Sense, Trigify), marketing engagement and usage/product (first-party tracking), and third-party/AI signals such as earnings call transcripts, job postings, and LinkedIn activity.
Data Types & Signals Firmographic Intent Marketing Engagement Usage/Product Third-Party & AI Signals Data Source ZoomInfo, Apollo, Clearbit, Cognism, etc. G2, 6Sense, Propensity, ZoomInfo, Trigify, etc. First-party tracking via marketing automation & sales engagement platforms First-party product usage tracking Earnings call transcripts, job postings, LinkedIn activity, technology adoption signals Examples Company attributes Employee attributes Company events Technographic Product comparison Category search Complementary product search Competitor search Social signals Website Events Hand-raising Email Frequency Volume Threshold Future initiatives and AI adoption plans New roles that imply buying needs Company news and leadership changes
A dense logo map credited to Insight Partners groups dozens of AI and automation vendors by category, including agents, developer platforms, vertical automation, RPA, model hosting, and data infrastructure, illustrating how saturated and fragmented the space has become.
The AI/Automation Market Source: Insight Partners
Clay appears across enterprise, mid-market, and PLG/PLS columns, but the rest of the stack shifts: enterprise pairs it with Common Room and Trigify.io, while mid-market and PLG/PLS both add Pocus and Unify.
The Tech Landscape Enterprise Mid-market PLG/PLS
A table pairs four segments with a trigger, action, and outcome: an earnings-call mention of digital transformation routes an enterprise account to AE/BDR outreach aiming for vendor-of-choice status, while a champion's G2 research plus falling usage prompts a CSM value conversation to curb churn. The approach asks what would have to happen for the buyer to need the company right now.
Build micro-playbooks based on buyer context. Segment Trigger Action Outcome Enterprise New Business Proxy for readiness to buy Ex: Earnings call transcript mentions Digital/AI Transformation initiative AE/BDR team engages persona targets in ICP accounts exhibiting intent (i.e., warmed up by Mktg) Vendor of choice in advance of RFP Enterprise, Customer retention Anyone on the decision maker committee/key persona comparing solutions on G2 + usage decrease CSM proactively reaches out to have a value conversation CSM mitigates churn/contraction at earliest indicators Mid-market, PLG Buyer KPI indicator Ex: CSAT decrease + volume increase RM or CSM proactively reaches out with insights to drive conversion to Sales-led or upsell Highly targeted PLS/upsell motion, increased pipeline $, increased conversion rate Mid-market, customer retention/lead capture Champion getting deactivated AE or RM researches/reaches out to diagnose situation (i.e., new POC) Early new champion cultivation If old champion is at new company, immediate lead Design micro-plays using a design thinking approach: "What would have to happen for my buyer to need me right now?"
The same six-trigger table returns, mapping expansion, new-business, and renewal signals, such as a director-level signup or search activity for competitors, to CSM or AE follow-up.
Signal-Based Triggers & Actions EXPANSION: Feature Need (Paywall) NB: Signup Director + at Tier 1 EXPANSION: Multiple Departments RENEWAL: Early Renewal Upsell RENEWAL RISK: Search Risk NB: Engagement with User Docs Usage shows need for additional features Executive signed up for product at named account Multiple Departments showing high usage Usage showing early renewal 3rd party search of competitors Engagement with BOF product documentation Increase in active users Usage of key features Industry need Free Signup Director + Title, especially non technical buyer 5+ active users from 2+ departments Usage limit reached Enterprise Account 90+ days from rental G2 comparison Renewal within 90 days Engagement with 5+ technical documents Route to CSM Route to AE Route to AE Route to CSM Route to CSM Route to AE
Leveraging data well means knowing the customer by validating which accounts to target, charting paths to revenue by surfacing new opportunities and reducing risk, and enabling teams to act on those same opportunities.
Ways to Leverage All That Data Know your customer Chart paths to revenue Enable yourteams Collect and validate more meaningful data to identify high-impact accounts to target. Uncover new opportunities, improve the buying experience, and mitigate risk to revenue. Uncover new opportunities, improve the buying experience, and mitigate risk to revenue.
Intercom looks for customers with low CSAT scores who are also growing fast, reasoning that rapid growth is outpacing the value they're getting. The signal is simple enough to hand to sales and pairs with a playbook that also pitches Intercom's Fin product.
The Signal: Decreased CSat Score Looked for customers who have low CSAT scores and are growing by users and activity. This tells them the customer isn’t getting the value that Intercom promises (happier customers) - likely because they are growing very quickly. This is a great example of customer-centricity and putting themselves in their customer’s shoes Clear signals that are easy to share with the sales team Actionable playbook (and a great opportunity to pitch Fin)
Most companies in Ironclad's ICP go through an RFP for contract lifecycle management, and beforehand they tend to organize around a digital transformation initiative, often mentioning it on earnings calls. Ironclad uses AI to scan earnings transcripts and hiring for digital-transformation or PMO roles to catch these accounts early.
The Signal: Digital Transformation CLM (contract lifecycle management) is a major investment, both in $$ and human capital and resources. Most companies in Ironclad’s ICP go to RFP Prior to RFP, they organize around “Digital Transformation” and often mention this in earnings calls. AI can scrape earnings call transcripts on a regular basis to identify ICP accounts preparing for Digital Transformation. Another signal we track is hiring for DT project/PMO roles.
Repeats the top-performing signals: multiple departments running separate high-usage instances, usage pacing 2x ahead of expectations, an executive adding 5+ users without team features, missed onboarding integrations, and growth patterns suggesting need for new features like AI.
More Top-Performing Playbooks Multiple departments have separate instances with high usage Usage pacing exceeds 2x what was expected Executive user has added 5+ members, but no team features No integration or key feature integrated during onboarding Number of user growth or activity indicates need for new features (ie, AI)