B2B & Lead Generation · Growth problem
Lead volume is up, but quality is down
A rising lead count is often presented as a marketing success, but if sales acceptance and conversion rates are falling at the same time, the business is simply generating more work for the same or worse pipeline outcome. The cause is rarely one campaign; it is usually a combination of loose lead definitions, ICP drift and channel incentives that reward volume over fit.
Symptoms
What this usually looks like
- Lead count is rising month on month, but SQL or opportunity count is flat or falling
- Sales reports that a growing share of leads are unresponsive or clearly not a fit
- MQL-to-SQL conversion rate is declining even as MQL volume grows
- A widening share of leads come from channels with weak intent signals (generic content downloads, broad paid audiences)
- Sales acceptance rate of marketing-qualified leads is falling
- Form completions are up but phone or email contactability is down
Diagnostic questions
What we would test first
- Pull lead-to-MQL and MQL-to-SQL conversion rates by source and campaign for the last two to four quarters
- Compare current MQL scoring rules against the current ICP definition to check for drift
- Interview sales on reasons for lead rejection and categorise the pattern
- Check time-to-first-contact against lead volume trend to isolate a follow-up speed effect
- Review any changes to lead-gen form fields or qualifying questions over the same period
- Audit agency or channel KPIs to see what is actually being optimised for
Root causes
Why it happens
- 01
Lead definition has drifted from ICP fit
When lead scoring or campaign targeting is defined by activity (a download, a webinar signup) rather than by fit against the ideal customer profile, volume grows by including prospects who were never likely to buy. This is often a gradual drift as campaigns are optimised for cost per lead rather than for fit.
- 02
Source mix has shifted toward low-intent channels
Broad paid social, generic content syndication and gated-content plays typically produce high volume and low buying intent compared with search, referral or high-intent content. If the channel mix has shifted to hit a volume target, quality decline is a predictable consequence, not a mystery.
- 03
Form friction and qualification questions were removed to boost conversion rate
Simplifying forms increases completion rate but removes the self-selection that used to filter out poor-fit prospects, so the same campaign spend now produces more leads that fail qualification later in the funnel.
- 04
MQL rules are too permissive or too old
Scoring models set up when the ICP or proposition were different continue to mark activity as qualifying long after they stopped correlating with sales-readiness, quietly inflating MQL counts without improving downstream conversion.
- 05
Sales follow-up speed has fallen behind lead volume growth
Contactability and conversion both fall sharply as response time to a new lead increases; if sales capacity hasn't grown alongside lead volume, some of the 'quality' problem is actually a speed-to-lead problem.
- 06
Channel and agency incentives reward volume, not qualified pipeline
Where paid media or content agencies are measured and paid against lead or MQL volume rather than SQL or opportunity contribution, campaign optimisation will continue to prioritise the metric being paid for.
Evidence
The numbers we would look at
These are the metrics that make the constraint visible, and the cuts that stop them being reassuring by accident.
| Metric | What it tells you | How we read it |
|---|---|---|
| Lead-to-MQL rate by source | Isolates which channels are producing leads that meet basic qualification.A blended rate across all sources hides which specific channels are dragging the average down. | A blended rate across all sources hides which specific channels are dragging the average down. |
| MQL-to-SQL rate | Tests whether marketing qualification actually predicts sales-readiness.Compare against historical baseline; a sudden drop usually coincides with a scoring or targeting change. | Compare against historical baseline; a sudden drop usually coincides with a scoring or targeting change. |
| Sales acceptance rate | Direct sales feedback on lead fit, independent of marketing's own scoring.Ask sales for reasons for rejection, not just the rate — the pattern in reasons is diagnostic. | Ask sales for reasons for rejection, not just the rate — the pattern in reasons is diagnostic. |
| Contact/connect rate on new leads | Distinguishes a genuine fit problem from a follow-up speed or data-quality problem.Cross-reference against time-to-first-contact; slow follow-up depresses this independent of lead quality. | Cross-reference against time-to-first-contact; slow follow-up depresses this independent of lead quality. |
| ICP fit rate (firmographic match) | Measures whether leads match target company size, sector and role criteria.Segment by campaign and channel to see which are attracting off-ICP volume. | Segment by campaign and channel to see which are attracting off-ICP volume. |
| Cost per SQL by source | The efficiency metric that actually reflects quality, not just volume.A source with a low CPL and high cost per SQL is a quality, not efficiency, problem. | A source with a low CPL and high cost per SQL is a quality, not efficiency, problem. |
Measurement traps
What can mislead you
- Looks fineOverall MQL volume hit target this quarter
- An aggregate volume target can be hit while quality collapses in the channels driving the increase; always view volume alongside MQL-to-SQL rate by source.
- Looks fineLead form conversion rate has improved
- Higher form conversion after removing qualifying questions is a top-of-funnel efficiency gain that can directly cause a downstream quality decline; check whether SQL volume moved in the same direction.
- Looks fineSales says leads are 'bad' this quarter
- This can reflect genuine fit problems, but is equally often a symptom of slow follow-up or a change in sales capacity; contact-rate and time-to-first-contact data will show which it is.
Outcome
What better looks like
Not a promised number. A clearer basis for the next investment decision.
- Marketing and sales agree a single, current ICP-based lead definition rather than two competing ones
- Volume targets are set alongside MQL-to-SQL rate targets, so growth in one cannot be reported as success while the other declines
- Channel and agency performance is judged on cost per SQL or opportunity, not cost per lead
- Sales rejection reasons are tracked systematically and fed back into targeting and scoring
Where a Growth Diagnostic would start
A three to four week senior review across demand, discovery, acquisition, conversion, retention, measurement and capability — sequenced so this problem is either confirmed as the constraint or ruled out early. Read alongside the b2b & lead generation model page for how we frame the wider system.
Related problems
B2B & Lead Generation
MQL-to-SQL conversion is low
Marketing is hitting its MQL numbers, but few of them are becoming sales-qualified leads.
B2B & Lead Generation
Cost per lead keeps rising
CPL keeps climbing, but the real question is what's happening to cost per qualified opportunity.
B2B & Lead Generation
The sales and marketing handoff is broken
Leads are generated and leads are worked, but a lot goes missing or gets misqualified in between.
Questions about this problem
- Is this a marketing problem or a sales problem?
- Usually both, in different proportions — marketing controls targeting and scoring, sales controls follow-up speed and acceptance criteria, and the diagnostic separates the two contributions rather than assuming either is solely responsible.
- Do we need to change our CRM to fix this?
- Not usually. Most of this analysis can be done with existing CRM and marketing platform data; the fix is more often a definitional and process change than a systems change.
- How quickly can we see if a fix is working?
- MQL-to-SQL rate changes are usually visible within one sales cycle of a scoring or targeting change; SQL-to-close impact takes longer given the sales cycle length in most B2B models.