Case studies usually end with a testimonial and a logo. This one doesn't, because the three B2B engagements below are anonymised — and because the interesting part isn't the quote. It's the before-and-after.
Three companies, three different problems: leads answering nobody for two days, a CRM quietly rotting from the inside, and a marketing stack with no owner. Three transformations, each following the same rough arc — diagnose, fix, measure over 90 days. Here are the numbers.
1. Lead Response Time: 48 Hours → 15 Minutes
The company: a B2B services firm, mid-market, small sales team.
The problem: leads arrived through forms, ads, and referrals, then sat in a shared inbox. Median first response: 48 hours. A meaningful share of those leads had already spoken to a competitor — sometimes signed — before anyone made contact.
What changed: routing automation. Every inbound lead assigned, notified, and escalated on a timer instead of on someone's memory. No new headcount, no new CRM — the stack they had, wired together properly.
The numbers:
- Median lead response time: 48 hours → 15 minutes
- Contact-to-meeting rate: roughly doubled within 8 weeks (directional)
- Leads never contacted at all: effectively zero — the escalation path made silence impossible to hide
The full story is here: how the firm cut lead response time from 48 hours to 15 minutes, including what broke first and what they'd do differently.
2. Data Hygiene: The Quiet Project That Moved Every Downstream Metric
The company: a B2B software company with a fast-growing database and a slow-growing sense of trust in it.
The problem: duplicate records, dead domains, missing fields gating the workflows that depended on them. Nothing dramatic — just a database where roughly a third of what sales touched was wrong, stale, or duplicated.
What changed: a four-week data hygiene sprint. Deduplication rules, decay monitoring, and a decision for every critical field: enrich it, infer it, or stop pretending you use it.
The numbers:
- Duplicate records: reduced by ~30% in the sprint window
- Dead/unreachable domains: purged from active sequences
- Campaign deliverability and segmentation accuracy: improved measurably — every downstream metric moved once the input data stopped lying
- Conversion impact: the firm credited clean data as the unlock behind conversion improvements it had previously chased with more spend (directional — full detail pending client data sign-off)
The unglamorous details are in data hygiene: the unsexy project that doubles conversion.
3. Governance: Who Owns What, and Why That Fixed Everything Else
The company: a manufacturing group with a marketing stack assembled over five years by three different teams.
The problem: orphaned tools, unclear ownership, and reporting nobody trusted. Nothing was broken individually; everything was broken collectively, because no one could answer "who owns this?" for more than half the stack.
What changed: a RACI-style ownership model applied to the entire stack — platforms, data, content, reporting. Every tool got an owner. Every recurring decision got a forum.
The numbers:
- Tools with a named owner: ~50% → 100%
- Orphaned/redundant tools retired: a double-digit percentage of the stack, with the licence savings as a side benefit
- Reporting trust: monthly reviews went from skipped to attended — because the numbers finally had owners behind them
- Downstream effect: response-time and data-quality fixes (like the two above) stayed fixed, because governance is what keeps improvements from decaying back
The ownership model itself is documented in who owns what in your marketing stack.
What All Three Had in Common
Strip away the specifics and the pattern is identical:
- A named internal owner. Not a committee — a person whose name was on the outcome.
- A diagnosis before a purchase. All three fixed what they had before buying anything new.
- A 90-day arc with measurement built in. Executive patience for one quarter, not one sprint.
- Boring fundamentals, done completely. Routing, data, ownership. No AI, no re-platform, no heroics.
The transformations that last are rarely the dramatic ones. They're the ones where somebody finally owned the boring part.
Honest Caveats
These results are directional: real engagements, anonymised, with figures kept deliberately approximate pending client data sign-off. Your baseline determines your ceiling — a firm already answering leads in an hour has less to gain from response-time automation than one answering in two days. Treat these numbers as evidence that the levers exist and which order to pull them in, not as a forecast for your P&L.