For marketing executives, a simple diagnostic to reveal deep integration problems is measuring how long it takes a lead from an event to reach the sales team. If the process—which involves cleaning, importing, and checking for duplicates—takes days instead of minutes, it signals a critical failure in automation and data connectivity.
Related Insights
Many marketing teams invest in attribution tools hoping to justify spend, but these platforms can't provide clear answers if the underlying engine is inefficient. You must first diagnose and fix how your leads convert into meetings before attribution data becomes meaningful.
Most go-to-market challenges, from low conversion rates to departmental friction, can be traced to the handoff process between marketing and sales. Start your diagnosis here to find the root cause of issues like low-quality leads or poor pipeline velocity, not just the symptoms.
Focusing on successful conversions misses the much larger story. Digging into the reasons for the 85% of rejected leads uncovers systemic issues in targeting, messaging, sales process, and data hygiene, offering a far greater opportunity for funnel improvement than simply optimizing wins.
Your CRM's lead rejection data is a goldmine, but only if you scrutinize it. Vague reasons like "not a fit" often conceal systemic GTM flaws. Interviewing SDRs to understand what this label actually means can reveal critical disconnects between marketing's targeting and sales's enablement.
Your GTM process is a factory that turns raw materials (leads) into a product (pipeline). Just as a car factory rejects faulty parts, you must analyze your process to stop feeding it low-quality leads that SDRs discard, thereby eliminating massive marketing and sales waste.
Companies struggle to get value from AI because their data is fragmented across different systems (ERP, CRM, finance) with poor integrity. The primary challenge isn't the AI models themselves, but integrating these disparate data sets into a unified platform that agents can act upon.
For marketers running time-sensitive promotions, the traditional ETL process of moving data to a lakehouse for analysis is too slow. By the time insights on campaign performance are available, the opportunity to adjust tactics (like changing a discount for the second half of a day-long sale) has already passed, directly impacting revenue and customer experience.
Businesses often misdiagnose a lead quality problem when the real issue is a slow internal response process. A lead that waits hours or days for a callback has likely already found another provider. The lead wasn't bad; the company's speed-to-lead process failed, making the opportunity appear worthless.
One company discovered that while MQLs were plentiful, they took 130 days to convert. In contrast, "hand-raiser" leads converted in just 12 days at a much higher rate. Focusing on conversion velocity reveals where to allocate resources for efficient growth.
MQLs should function as internal signals for the marketing team to orchestrate the next step in the buyer's journey, such as triggering a new automation. They are a delivery system within marketing, not a basket of leads to be handed to sales, which prevents sales from chasing low-quality signals.
