We've run MarTech audits across EdTech, SaaS, HealthTech, B2B services, and e-commerce. The companies range from $20M EBITDA platforms to $400M enterprise software businesses. Their stacks are different. Their teams are different. Their markets are different.
But the failure patterns are the same.
After enough audits, you stop being surprised by what you find and start looking for it systematically. Five patterns appear in nearly every engagement — sometimes all five at once, sometimes three or four, but always at least two. Knowing them in advance lets you structure diligence more efficiently and scope the remediation cost more accurately.
Here's what we find, how to spot it, and what it actually costs.
The company's revenue attribution model is broken in ways that have become invisible through habituation. UTM parameters are inconsistently applied — some campaigns have them, some don't. Offline conversions (sales calls, demo completions, contract signatures) are not mapped back to marketing sources. Multi-touch attribution models are running on incomplete data and producing numbers that feel roughly right but cannot be validated.
How we spot it: We pull the last 12 months of pipeline data and ask the CMO to attribute each won deal to a marketing source. Then we pull the same data from the CRM. The gap between the two is the attribution collapse — revenue that "happened" from the CMO's perspective but can't be traced to a channel, campaign, or spend line.
What it costs: Attribution collapse makes every marketing investment decision unreliable. The company is spending based on a map that doesn't reflect the terrain. The remediation cost — rebaselining attribution, implementing consistent UTM governance, and mapping offline conversions — runs $40K–$120K depending on stack complexity. The opportunity cost of the decisions made on bad attribution data is typically 5–15% of marketing spend.
The MarTech stack contains two to four tools doing materially the same job. A MAP and a separate email platform. Two CDPs or data enrichment tools. CRM and a separate sales engagement platform with overlapping contact databases. The redundancy is almost never intentional — it accumulates through team growth, acquisitions, and the path of least resistance when a new problem needs solving.
How we spot it: We map every tool in the stack against its stated function and its actual usage. Then we look for functional overlap: tools that perform the same action on the same data for the same users. When two tools both "own" contact records, you have stack redundancy — and every integration between them is a potential data consistency failure point.
What it costs: Direct redundancy costs (duplicate licensing) average $80K–$200K annually for mid-market companies. Indirect costs (integration maintenance, data consistency overhead, training time for two systems doing one job) typically double that number. Stack consolidation is almost always a positive ROI project — but it requires a sequenced migration plan, not just canceling licenses.
Customer data lives in three or more systems with no authoritative source of record. The CRM has one version of a customer's account. The MAP has another. The product analytics tool has a third. When the same customer has three different records across three systems — with three different activity histories, different segment assignments, and different contact information — reporting is a manual reconciliation exercise disguised as analysis.
How we spot it: We ask: "What is the single source of truth for a customer account?" If the answer is a person's name rather than a system, there are silos. We then trace a sample of accounts across systems and count the discrepancies. More than 15% discrepancy rate is our threshold for flagging a silo problem.
What it costs: Data silos create reporting overhead (manual reconciliation), customer experience failures (inconsistent personalization), and downstream AI/ML problems (models trained on silo data produce silo-specific predictions that don't generalize). The fix requires a CDP implementation or a governed CRM consolidation — an 8–16 week project that competes directly with 100-day value creation initiatives if not scoped pre-close.
Personal data is distributed across more tools than anyone has documented, with inconsistent consent records, retention schedules, and deletion workflows. The company has a privacy policy. It may even have a consent management platform. But when you trace where a contact's personal data actually lives — CRM, MAP, advertising platforms, analytics tools, third-party enrichment services, data warehouse — the picture looks nothing like what the privacy policy describes.
How we spot it: We conduct a data flow mapping exercise: for each tool in the stack, we document what personal data it receives, from what source, under what consent framework, with what retention policy, and with what deletion capability. The gaps between what should exist and what does exist constitute PII sprawl.
What it costs: Under GDPR, non-compliant processing carries fines of up to 4% of global annual turnover. Under CPRA, each intentional violation carries a $7,500 civil penalty. The practical cost in a PE context is the liability that transfers on acquisition — including the historical exposure for violations that occurred before the deal closed. Remediating PII sprawl post-close is a 6–18 month program depending on stack complexity.
The marketing organization's structure doesn't match the company's growth stage or the PE value creation thesis. A company that needs demand generation velocity has a brand-heavy org. A company that needs ABM precision has a volume-focused team. A company entering a new segment doesn't have segment expertise. The CMO's background and incentives are optimized for a stage the company left two years ago.
How we spot it: We map the current org structure against the skills required for the 100-day plan. Where the plan requires skills the org doesn't have — performance marketing, RevOps, product marketing for a new segment — we flag the gap. We also review CMO incentive structures against value creation KPIs to check alignment.
What it costs: Misaligned org design delays value creation plan execution by an average of 45–90 days while the acquirer diagnoses the gap and begins a leadership or team restructuring process. At typical PE-backed growth targets, 90 days of execution delay costs 8–15% of the year's value creation. This is the most expensive failure pattern — and the most preventable, if identified pre-LOI.
What to Do With This Information
If you're conducting pre-LOI diligence, these five patterns give you a structured audit framework. You don't need to find all five to justify concern — finding two or three at meaningful severity is enough to warrant a formal MarTech audit before exclusivity.
If you're post-close and recognizing these patterns in a portfolio company, the sequencing matters. Attribution collapse and data silos block everything else — fix those first. Stack redundancy and PII sprawl can run in parallel as workstreams 2 and 3. Org design assessment should inform the 100-day blueprint from day one, not emerge from it.
The patterns are consistent because the underlying cause is consistent: growth outpacing governance. The remediation is also consistent — not because every company needs the same solution, but because the diagnostic framework is the same. Find the pattern, measure its severity, quantify the cost, sequence the fix.