A high number of leads can make campaigns look successful while few contacts actually become opportunities. When targeting stays too broad, forms attract out-of-market profiles, companies with no project or contacts with no decision-making power.
Cost per lead then looks fine, but cost per opportunity keeps rising. Performance must therefore be measured all the way to meetings, opportunities and revenue generated, not just to the number of contacts collected.
Our view
A cheap lead is not necessarily a good lead. We recommend steering first on cost per MQL, per SQL and per opportunity. These KPIs give a truer reading of marketing's real contribution to the sales pipeline.
Marketing and sales do not always use the same criteria to recognize a qualified prospect. A download, an event registration or several visits may be read as maturity signals by marketing, while sales reps expect an explicit need, a timeline or a budget.
This gap in reading reduces trust in the leads handed over and makes reports hard to use.
A shared definition of MQLs and SQLs clarifies responsibilities and lets you analyze conversions at each stage.
Our position
Qualification must not be imposed by a single team. It must be built from marketing data, sales feedback and the traits of customers actually won. Without this cross-check, scoring stays theoretical.
A prospect who views several strategic pages, attends an event or keeps coming back to the website shows an interest that must be caught at the right time. When this information stays invisible or assignment takes several days, the company lets a workable intent go cold.
Workflows, alerts and assignment rules trigger the right action for each maturity level. Not every lead needs an immediate callback, but no priority signal should go unhandled.
What we see
The main obstacle is not always a lack of leads, but the delay between the signal and the action. The best-performing companies clearly define which behaviors trigger an alert, a nurturing track or a sales handoff.
Lead generation relies on data that can identify prospects, their companies, their behavior and their history. When this information is incomplete, outdated or scattered, scoring loses precision and teams get only a partial view of the journey.
Duplicates, free-text fields and poorly filled sources also complicate campaign attribution. Structured CRM data improves segmentation, content personalization, recognition of priority accounts and measurement of each channel's real contribution.
Our recommendation
Before adding new campaigns or predictive scoring, check the quality of the data available. Artificial intelligence and automation improve a reliable model; they do not automatically fix a poorly structured database.
A high-performing lead generation strategy starts with organizational choices.
We help teams define their target, their qualification criteria, their MQL/SQL model and the KPIs that will measure marketing's contribution to revenue.
Tools then support this strategy, never replace it.
HubSpot centralizes acquisition, CRM, lead scoring, marketing automation, reporting and now artificial intelligence on a single platform. As a HubSpot Elite Partner consulting firm, we configure these features to meet concrete business goals: qualify faster, prioritize better for sales reps and improve conversion rates.
Reliable scoring depends directly on the quality of the data available. We structure CRM properties, acquisition sources, governance rules and dashboards so every decision rests on usable data.
Reliable data improves personalization, segmentation, reporting and performance analysis.
Stop counting your leads.
Count what they bring in.
Effy wanted to support strong growth in acquisition while improving prospect qualification and handoff to sales teams. The goal was to build a setup able to move each contact forward based on its maturity, with minimal manual work.
See the Effy case study →
