Why 78% of Martech Stacks Fail Business Goals, and How to Fix It: A Q&A With eClerx’s Scott Houchin

Key Takeaways

• eClerx’s Scott Houchin explains that most martech stacks fail business goals not because of missing tools, but because disconnected systems create an activation gap between insight and action.
• To close that gap, B2B leaders need trusted data foundations, connected workflows, stronger measurement practices, and AI embedded into operations.

You’ve invested in mature martech platforms, hired the talent, and built the dashboards. Yet 78% of organizations say their stacks still don’t support their business goals. The disconnect isn’t a tooling problem. Most stacks were assembled one tool at a time, each solving a single issue, and never streamlined to work as one system.

The 2026 eClerx Marketing Data Report: Mind the Gap names the dominant culprit: the activation gap, the distance between having data and being able to act on it consistently, at scale. The answer comes down to connected workflows, measurement built into the operating rhythm, and data leadership actually trusts.

In this Q&A, eClerx’s Scott Houchin breaks down how to close that gap with the discipline of a practitioner who works in regulated industries where data quality is non-negotiable. He covers three subjects every B2B marketing leader is wrestling with right now: how to rethink ROI measurement when cross-channel attribution will never be perfect, what role AI should play in collapsing the distance between insight and action, and the foundational capabilities, from unified identity to a decisioning layer, that personalization needs before it can scale.

Demand Gen Report (DGR): Scott, thanks for taking tine to talk to us today. In your survey, 78% said their martech stacks do not support business goals despite heavy investment. What do you see as the biggest root cause behind that disconnect?

Scott Houchin: Thanks for having me. Most stacks were assembled tool by tool, each solving a point problem. The result is mature platforms that perform individually, but were never streamlined to work as one system. Insights stay siloed within the team that owns the tool, and analytics never get embedded into day-to-day workflows. In theory, the stack technically works, but the business never feels it— decisions stay slow, customer experiences stay generic, and the investment doesn’t show up in outcomes.

DGR: How does eClerx define the “activation gap,” and how is it different from general martech underperformance?

Houchin: Martech underperformance has many causes— low adoption, skills gaps, budget constraints, poor data quality. The activation gap is one specific, and in our view, dominant, cause: the disconnect between having data and being able to act on it consistently, at scale. It shifts the diagnosis from “do we have the right tools?” to “can we operationalize what we already own?” Most organizations we surveyed struggle with the second question, not the first.

DGR: What does “activation maturity” look like in practice for a B2B enterprise with complex buying journeys and multiple stakeholders? What separates the 25% of organizations that describe themselves as fully data-driven from the rest of the market?

Houchin: In the report, we lay out three stages of activation maturity, each with a specific “unlock” to reach the next. The practical test: do insights from the stack reach every function, beyond marketing — sales, product, service, etc. — fast enough to change a decision in flight? In a complex B2B buying journey, that means account-level signals being visible to every stakeholder-facing team in real or near-real time.

The fully data-driven 25% share a design philosophy: they build stacks for action, not reporting. Workflows are connected end to end, they’re deliberate about how data is collected and where it flows, they measure performance continuously, and they’re laying the data foundations that operational AI will require. We also break this down in detail in the report.

DGR: How should B2B marketing leaders rethink ROI measurement when cross-channel attribution is still incomplete or unreliable?

Houchin: Stop treating attribution as a source of truth and start treating it as one directional input. Perfect cross-channel attribution in B2B — long cycles, buying committees, dark-funnel research — is not coming. Three practical shifts:

1. Triangulate. Combine attribution data with incrementality testing (geo or audience holdouts) and lightweight marketing mix modelling. Where all three point the same way, you can invest with confidence.
2. Measure at the account and pipeline level, not the touch level. Pipeline velocity, stage conversion rates, and account engagement scoring are more defensible to a CFO than last-touch channel credit.
3. Agree the business case scorecard upfront. Pick a small set of metrics both functions accept, and report against those consistently. ROI debates usually fail on disputed definitions, not disputed math.

DGR: What practical steps can marketing teams take to move from fragmented insights to real-time action across channels?

Houchin: Start with diagnosis — you can’t unblock data flows you can’t see. Map where data stagnates: which insights exist, who can access them, and how long they take to reach a decision. Then run an honest maturity assessment; we included a scorecard in the report that is built to kick-start those conversations and locate actual inefficiencies.

From there it’s execution: redesign workflows so insights flow to the teams that act on them, build measurement into the operating rhythm rather than treating it as a quarterly exercise, and assign explicit ownership and governance. Activation fails most often not from missing technology but from nobody owning the flow.

DGR: How can B2B organizations improve trust in marketing data across leadership teams, especially with CFOs and revenue stakeholders?

Houchin: We work extensively in regulated industries, insurance, finance, healthcare, where data quality is a crucial consideration, so we know the importance of reliable data. The trust problem is the same everywhere: if leaders believe marketing data is incomplete, stale, or inconsistent with their own systems, they will discount it. Two areas build confidence fastest:

• Pipeline discipline. The right data, from agreed sources, delivered at a predictable cadence to the teams that need it. Treat it like an SLA. When numbers reconcile with what finance sees in its own systems, scepticism drops quickly.
• Visible proof. Once the pipeline is reliable, show it. Regular reporting, trend alerts, documented data lineage — in whatever format your stakeholders will actually read. Trust compounds through repetition, not through one good dashboard.

DGR: How should MarTech leaders prioritize investments between data unification, workflow redesign, measurement, and AI enablement?

Houchin: Data integrity first, without exception. Organizations are generating more data than ever but often can’t share it, trust it, or use it. On a weak data foundation, redesigned workflows move bad data faster, measurement produces numbers nobody believes, and AI confidently automates errors.

Workflow redesign, measurement, and AI enablement all matter — but they compound on top of strong data pipelines that deliver trusted data to all teams in real time. Sequence them that way.

DGR: What role should AI play in closing the activation gap, and what mistakes do organizations make when adopting AI too early?

Houchin: AI can collapse the distance between insight and action; but only inside the right frameworks and guardrails. Many organizations are building significant AI capability now and will struggle to justify the ROI in coming quarters, because the AI sits beside workflows rather than inside them.

The most common mistake we address with clients is the absence of a 6–12 month post-adoption plan. The debate centers on selecting the right platform, while the factors that determine success are often neglected: upskilling the teams who’ll use it, the data foundations that feed it, and the governance and ownership that control it. AI is not a deployment event — it has to be embedded across teams, systems redesigned around it, and visibly backed by leadership so adoption percolates all the way down.

DGR: For B2B marketers focused on personalization, what foundational capabilities need to be in place before personalization can scale effectively?

Houchin: Four things, in order:

1. Unified identity. In B2B organizations, that means resolving identity at both the contact and account level, so engagement across channels and stakeholders rolls up into one coherent account view.
2. Connected, governed data. Behavioral, firmographic, and intent data flowing into one accessible profile — with consent and privacy governance built in from the start, not retrofitted.
3. Content operations that can keep up. Personalization at scale demands variant volume. If content production is a bottleneck, the decisioning engine has nothing to serve.
4. A decisioning layer with feedback loops. Something must choose the next-best message per account in real time, and learn from the response.

Without these, “personalization” is really just segmented batch campaigns with better labels. The foundations are unglamorous, which is exactly why the organizations that build them pull ahead.

DGR: If a MarTech executive could focus on just one change in the next 6–12 months to close the activation gap, what would you recommend first?

Houchin: A full activation audit — locate where data stagnates and where the highest-value opportunities sit. Follow that with a focused effort to strengthen the data foundation, so the inflow is high quality and usable, then reimagine workflows so insights reach the right teams at the right time. One audit, honestly done, typically reorders the entire investment roadmap.