From signals to pipeline: 7 lessons for delivering efficient growth
Efficient growth—prioritizing revenue quality over volume, maximizing returns from existing resources, and scaling profitably—has become the defining mandate for go-to-market leaders. In a recent conversation, Manosh Anani, Founder and CEO of SalesIntel, and Mike Burton, Co-founder of Bombora, shared lessons from organizations implementing AI and building efficient growth strategies in a noisy marketplace.
Here are seven key takeaways every revenue leader should consider when building a data-driven GTM strategy.
1. AI only delivers value when built on quality data
AI success hinges on one factor: data quality. Anani captured this reality clearly: “AI is only as good as the data and the context that you provide to it.” Poor-quality inputs send agents and teams in the wrong direction and waste resources. Regardless of approach, before any scoring model or AI agent can deliver value, foundational data must be clean, complete, and current.
2. Your ICP definition needs validation from market signals
Efficient growth has made ICP analysis mission-critical. With greater emphasis on metrics like LTV to CAC and sales efficiency, precision targeting is essential.
Modern ICP analysis examines dozens of traits across closed-won and lost opportunities to define your ideal customer profile. Yet only a small portion of accounts in your TAM or ICP are in market at any given time. This is where Intent data is essential—not to define your ICP, but to reveal which accounts are actively researching and should be prioritized now.
Accounts you believe are ideal should exhibit research behavior indicative of real buying intent. If little or no intent is evident in a vertical despite a few wins, redirect resources to markets with stronger demand.
3. Data “recipes” outperform single signals
Top GTM teams develop formulas that combine multiple signals rather than relying on single data points. Burton described the approach: “First things first, identifying data recipes that get you into deals you wouldn’t have had otherwise.”
A typical recipe might include:
- Strong ICP fit based on firmographics and technographics
- First-party signals like website visits in the last 60 days
- Third-party intent showing research on relevant topics
- Technology install data indicating compatibility or displacement opportunity
Anani outlined SalesIntel’s three-step approach: “Make sure the data is clean. Get your ICP in place, and then …try with a handful of those [signals] and see what works for you.”
Signals vary by business model:
- Job changes – Tracking champions who move companies reveals warm opportunities
- Website visits – Indicate prospects already know your brand
- Technology changes – Companies adding complementary tech signal readiness for deeper integrations
Anani described a workflow combining signals with advertising: “Put ads in front of them, use AdsIntel, or if you have a LinkedIn account, put ads on LinkedIn, and then drive that traffic back to your website. We’ll anonymize it. Now you know that this prospect already knows about your brand, and then give it to the BDRs – because otherwise the BDRs are gonna send emails to thousands of accounts without seeing much of a result.”
The process requires experimentation, with the goal of identifying signal combinations that consistently lead to deals, then aligning sales and marketing around them.
4. Expansion is usually more efficient than acquisition
Winning new accounts can cost five times more than expanding within existing accounts.* That’s why driving more revenue from current customers is at the core of efficient growth.
While Intent data adopters once focused on applying the data to acquisition, expansion and retention use cases have doubled. Burton noted: “I’d say over the last couple of years we’ve seen a doubling of the first use case out of the gate. It is more about how I get more out of the base I already have.”
Intent signals identify cross-sell and upsell opportunities before they’re obvious. When customers start researching topics related to other products in your portfolio (or your competitors), it’s a clear signal to begin expansion conversations.
5. Privacy-first data collection protects your GTM investments
As Intent data becomes central to GTM strategies, privacy and compliance have become differentiators. Durable Intent data requires two layers of consent: from publishers who create content and from end users who consume it.
Burton outlined Bombora’s approach: “We collect our data with a proprietary tag placed on participating sites” governed by “persistent privacy-first, consent-driven data collection protocols.”
Because Bombora’s Data Co-op was built on formal relationships with publishers and transparent consent, the resulting data withstands regulatory changes and evolving privacy standards—protecting both providers and customers.
6. Measure at scale to see true signal effectiveness
A key lesson from practitioners: measure at scale, not in small batches.
Burton explained: “Sometimes we’ll get this: I reached out to 10 of these accounts, but 4 of them were already too far along in their buying journey. And when I hear that I’m like, wow! 4 out of 10 were buying something. I think that’s really good.” He continued, “The right question is:’Did this signal improve our conversion percentage across hundreds or thousands of attempts?’ rather than ‘Did every account convert immediately?’”
7. Not all intent data is created equal—quality and scale both matter
Understanding the difference between sources of Intent data is essential. Without quality data at scale, even the best programs will underperform.
Low-quality Intent data creates false signals, resulting in wasted time on accounts that aren’t truly in-market. For example, bidstream-only sources lack stability for accurate modeling. Without visibility into an account’s normal research behavior, meaningful intent spikes are impossible to detect.
Limited coverage means missing active buyers. If your provider monitors only a fraction of the B2B web, you’re operating with major blind spots.
Intent providers differ widely in accuracy, detail, and privacy practices. Narrow sources—like bidstream data or review sites—miss the full buyer journey.
- Inconsistent baselines: Unstable data obscures real intent.
- Keyword-only models: Without AI or NLP, they misread topics and create false positives.
- Unconsented data: Non-compliant collection creates legal and operational risk.
- Limited visibility: Providers without direct publisher relationships can’t see where real B2B research happens.
- No engagement metrics: Without site tags, they miss scroll depth, dwell time, and other true engagement signals.
Building for durability, not just speed
Efficient growth requires more than new tools or data sources. Winning organizations build durable foundations—powered by quality data at scale—where:
- Clean data feeds proven recipes
- AI extends team capabilities without replacing judgment
- Market signals validate ICP definitions
- Privacy-first practices withstand scrutiny
- Measurement at scale reveals real effectiveness
This shift moves organizations from being busy with data to driving outcomes with it—from data volume to data value, from activity to impact, and from growth at any cost to growth that’s truly efficient.
Partnerships like Bombora and SalesIntel make this possible by combining high-quality Intent data with clean, enriched datasets and AI-driven activation. To learn more about building data recipes with trusted, actionable Intent signals, reach out to us.