Before launching any ABM campaign, prioritize data hygiene. In large enterprises, it's common for a single account to exist under multiple names. This 'dirty data' can make 40-50% of an uploaded account list unmatchable in ad platforms, wasting significant budget and effort.

Contrary to the 'always be closing' mindset, the goal of early-stage qualification should be disqualification. Advancing deals based on mere 'interest' rather than true 'intent' leads to bloated pipelines and low win rates. Getting to 'no' quickly is more efficient than chasing unqualified leads.

Friction between sales and marketing often stems from using separate definitions for a Marketing Qualified Lead (MQL) and a Sales Qualified Lead (SQL). The most effective approach is to have one unified definition: a potential customer that sales can realistically close. This focuses both teams on the ultimate goal of revenue generation.

AI models for campaign creation are only as good as the data they ingest. Inaccurate or siloed data on accounts, contacts, and ad performance prevents AI from developing optimal strategies, rendering the technology ineffective for scalable, high-quality output.

True problem agreement isn't a prospect's excitement; it's their explicit acknowledgment of an issue that matters to the organization. Move beyond sentiment by using data, process audits, or reports to quantify the problem's existence and scale, turning a vague feeling into an undeniable business case.

CMO Ben Schechter argues that tracking raw lead count is a dangerous metric. A marketing leader can easily manipulate lead scoring to hit a volume target, flooding sales with low-quality prospects. This erodes sales team trust and causes them to stop following up on all marketing-generated leads.

In B2B sales with multiple decision-makers, tracking individual MQLs is a "lazy metric" that misrepresents buying intent. Success depends on identifying and engaging the entire buying group. Marketing's goal should be to qualify the group, not just a single lead.

While a performance dashboard is important, a data-driven culture bakes analytics into every step of the marketing system. Data should inform foundational decisions like defining the ideal client profile and core messaging, not just measure the results of campaigns.

The company had a significant 'prospecting black box.' For 40% of all opportunities, there was no traceable sales trigger or activity log, such as logged calls. This meant they couldn't measure or optimize a huge portion of their pipeline creation process, particularly SDR outbound efforts.