To overcome internal assumptions that only the "head of payroll" mattered, the marketing team analyzed past wins. The data showed that 17 different job roles were involved in the customer's buying process, providing definitive proof to justify broadening their account targeting.
Related Insights
The next frontier in B2B marketing, enabled by AI-powered segmentation, is identifying the specific 'buying group' within an account relevant to each product. This granular focus moves beyond traditional Account-Based Marketing (ABM) to more directly correlate efforts with pipeline generation.
Research shows half the buying committee consists of "invisible buyers" (e.g., C-suite, procurement) that sales can't access but who hold veto power. Marketing's primary ABM role is to build brand trust and familiarity with this hidden cohort to prevent them from killing a deal due to unfamiliarity with your solution.
CloudPay stopped using the word "lead" and adopted "signal" instead. This semantic shift prevents sales reps from chasing a single junior contact and encourages them to research and target the entire buying committee (CFO, CHRO) at the interested account.
The key to justifying brand marketing isn't a perfect dashboard, but internal education. A marketing leader's primary job is to explain to the CFO and sales team that buying decisions are not linear and are influenced by multiple, often unmeasurable touchpoints over time.
A modern data model revealed marketing influenced over 90% of closed-won revenue, a fact completely obscured by a last-touch attribution system that overwhelmingly credited sales AEs. This shows the 'credit battle' is often a symptom of broken measurement, not just misaligned teams.
Stop defining your Ideal Customer Profile with abstract firmographics. Instead, feed context from your best closed-won deals into an AI and ask it to find public data that signaled their specific pain *before* they engaged you. This reverse-engineers a truly effective, data-driven targeting model.
Executive teams often create an ICP based on a 'wishlist' of big logos. The most accurate ICP is actually found by analyzing your first-party CRM data. Examining patterns across both close-won and close-lost deals reveals surprising truths about which customer segments are actually the best fit for your solution.
Many founders operate on flawed assumptions about how they acquire customers. Analyzing marketing data often shatters these myths, revealing that sales and traffic come from unexpected sources. This discovery points to untapped growth opportunities and where marketing energy is best spent.
A social media tool found its users were trying to either "grow an audience" or "automate processes." They had marketed to both as one group. By identifying and focusing messaging on the higher-value "automators," they increased trial-to-paid conversions by 40%.
CloudPay stopped attributing opportunities to single sources like "marketing" or "sales." Analysis showed multiple departments influenced every deal, rendering attribution a source of pointless internal arguments. They still use multi-touch attribution at the campaign level, but not to assign inter-departmental credit.