Case Study: How One Enterprise Cut 60% of Its Martech Stack Without Losing Revenue

Hook: Stop paying for complexity — preserve revenue while cutting the fat

Uncontrolled martech sprawl is the silent tax on marketing and product teams: subscription fees, integration engineering, duplicated data, and slow launches that together erode margin and developer velocity. What if you could cut 60% of your martech stack and keep—or even grow—revenue? This case study outlines a realistic, repeatable blueprint that an enterprise used in 2025–2026 to do exactly that: stakeholder engagement, rigorous evaluation criteria, a phased migration plan, measured savings, and the lessons every enterprise must know before starting.

Executive summary (inverted pyramid)

In late 2025 a global enterprise (multi-brand retail + e-commerce) consolidated its martech footprint from 42 distinct platforms to 17 core systems, a 60% reduction in tool count. Annual license and operating cost savings were approximately $2.9M in year one, with a full TCO reduction projected at 45% across three years. Revenue remained stable during migration; conversion and customer engagement metrics were preserved through a controlled pilot and robust identity-first data strategy.

Context: why consolidation mattered in 2026

By 2026, the martech landscape had shifted: generative-AI features were being embedded into major platforms (late 2025), data-privacy frameworks pushed first-party architectures to the forefront, and vendor consolidation reduced the options but increased platform breadth. Enterprises faced three compounding forces:

• Rising subscription and integration costs from dozens of niche AI and analytics tools.
• Performance and reliability issues from client-side tracking and an explosion of tag managers post-cookie era.
• Organizational drag: slower launches, onboarding friction, and duplicated experiments.

The enterprise profile (hypothetical but realistic)

Company: Global retailer with 8 brands, 25 markets, $3B ARR. Marketing team: 120 FTEs across paid, CRM, creative, analytics, and growth. Baseline martech: 42 billed platforms (advertising DSPs, two CDPs, three experimentation tools, five analytics/BI tools, multiple CDNs and tag managers, niche AI vendors for creative and copy). Baseline annual martech budget: $6.5M (licenses + vendor engineering + contractor spend).

Goals and constraints

• Primary goal: Reduce martech TCO by 35–50% while maintaining revenue and conversion rates during migration.
• Secondary goals: Improve developer velocity, improve page performance, centralize identity, and reduce privacy & compliance surface.
• Constraints: No more than two major experiments paused at any time; vendor contract windows limited some immediate cancellations; teams required continuity of personalization and campaign delivery.

Stakeholder engagement: how buy-in was won

Consolidation succeeds or fails on governance, not technology. The enterprise established a cross-functional Steering Committee and an Implementation Squad:

• Steering Committee: CMO (sponsor), CIO, CFO, Head of Privacy, Head of Revenue Operations. Met fortnightly for executive decisions.
• Implementation Squad: Product Platform Lead, Martech Architect, Data Engineers (2), CRM Lead, Growth PM, Legal/Procurement rep, two Brand Liaisons, external systems integrator.

Key tactics to secure stakeholder buy-in:

• Run a 5-week Discovery Sprint to quantify spend, usage and overlap—present a heatmap to the Steering Committee before recommending targets.
• Define non-negotiables: conversion must not drop more than 1% week-over-week during migration windows; privacy compliance must be maintained or improved; no mid-season brand campaign disruptions.
• Use a transparent RACI and publish a consolidated dashboard of KPIs (cost burn rate, change-failure rate, customer impact metrics).

Evaluation criteria: how tools were scored

Every tool entered a standardized scoring matrix. Scores drove a red/amber/green recommendation for replacement, retention, or consolidation.

Core evaluation axes:

• Financials: annual license + implementation + expected renewal date (weight 20%).
• Usage and adoption: monthly active users, feature utilization, overlapping capabilities (weight 20%).
• Data ownership & portability: APIs, export formats, schema flexibility (weight 15%).
• Integration burden: number of downstream integrations, custom connectors needed (weight 15%).
• Business impact: contribution to revenue, experiments, or campaigns (weight 15%).
• Risk: vendor stability, contract exit costs, privacy compliance (weight 15%).

The resulting score prioritized four categories of action: Immediate decommission, Consolidate into platform, Replace via migration, Retain as strategic.

Migration plan: sprint vs marathon approach

Inspired by 2025 industry guidance advocating both sprints and marathons, the enterprise used a hybrid cadence:

• Quick wins (Sprint, months 0–3): Decommission duplicate analytics tags, retire low-use A/B test vendors, consolidate image CDNs. Quick wins minimized cost immediately and reduced complexity.
• Strategic consolidations (Marathon, months 3–12): Migrate two CDPs into a single identity-first CDP, centralize experimentation into one platform, and move to server-side tracking and a data clean room for measurement.
• Ongoing optimization (Months 12+): Rationalize remaining niche tools, negotiate multi-year contracts with retained vendors, and continue incremental feature migration.

Sample implementation timeline (high-level)

1. Weeks 0–5: Discovery sprint, stakeholder alignment, cost & usage inventory, risk catalog.
2. Weeks 6–12: Quick-win decommissions, pilot identity graph in a staging environment, configure server-side gateway.
3. Months 4–6: Pilot consolidation of one brand's personalization and experimentation into the target CDP+Experiment platform.
4. Months 6–9: Rollout to remaining brands in waves; monitor core KPIs and rollback windows for each wave.
5. Months 9–12: Complete decommission of legacy platforms, negotiation and contract close-out, team training and operational handover.

Technical migration checklist (actionable)

• Inventory all tracking tags and map to events. Remove duplicates; assign canonical event names.
• Implement a server-side tracking layer (gateway) to reduce client-side bloat and increase data control.
• Unify identity with deterministic first-party keys—email hash, login IDs, or CRM IDs—persisted in the chosen CDP.
• Migrate historical data in phases: schema alignment → ETL validation → reconciliation reports (sample checks: MAU, LTV cohorts, experiment baselines).
• Recreate critical campaign flows (email, push, onsite personalization) in the consolidated platforms; run side-by-side validation for 2–4 weeks.
• Decommission: revoke API keys, archive data per retention policy, close vendor accounts at contract termination date.

Customer impact and measurement

Preserving customer experience was non-negotiable. Measurement focused on three cohorts during the migration: pilot brand, control brands, and full rollout cohorts.

• Primary metrics: conversion rate, revenue per visitor (RPV), average order value (AOV), session quality metrics.
• Operational metrics: page load times, tag load percentage, incident/bug rate.
• Compliance metrics: number of third-party data transfers, consent coverage.

Results from the pilot brand:

• Conversion rate: flat (-0.2% relative change, within the predefined 1% tolerance).
• Revenue per visitor: +1.8% (attributed to faster pages and fewer client-side interruptions).
• Page load times: median page load down 280ms.
• Incident rate: initial spike during pilot week 1 (resolved by change-freeze and hotfix), then stabilized below prior baseline.

"We treated data integrity and identity as the north star. Consolidation without identity alignment is just moving the mess." — Martech Architect

Savings breakdown and finance validation

Year-one savings (approximate, illustrative):

• License reductions: $1.6M (cancelling 25 subscriptions, scaled licenses on five retained platforms)
• Integration engineering & contractor spend reduction: $700k (fewer custom connectors, simplified data pipelines)
• Operational overhead & vendor management: $300k (reduced account management and procurement cycles)
• Data storage & tagging overhead: $300k (fewer events, lower analytics processing costs)

Total year-one savings: ~$2.9M. Projection across three years after steady-state optimizations: 40–50% TCO reduction relative to baseline.

Risk, mitigation, and governance

Key risks and mitigations used:

• Revenue dip risk — Mitigation: phased waves and strict rollback criteria tied to KPIs.
• Data loss during migration — Mitigation: parallel data replication, reconciliation scripts, preservation of raw event logs.
• Vendor lock-in concerns — Mitigation: insist on data export APIs and contractual exit windows.
• Change fatigue — Mitigation: cadence of changes limited per team, training and office-hours for brand teams.

Lessons learned (actionable takeaways)

• Start with accurate inventory. You cannot consolidate what you do not measure. Maintain a living catalog of integrations and usage telemetry.
• Prioritize identity-first consolidation. The CDP or identity layer is the foundation; invest early in deterministic identity resolution.
• Use pilots to validate assumptions. Keep early pilots small, observable, and reversible; treat them like experiments with predefined success criteria.
• Negotiate contract windows strategically. Align decommissions with contract renewal dates to minimize exit fees and realize savings sooner.
• Balance sprint wins with marathon projects. Quick decommissions drive momentum; larger platform migrations take time and require executive patience.
• Measure the hidden costs. Include engineering time, integration maintenance, and experiment leakage in your TCO model—not just license fees.

2026 trends that made this consolidation timely

Several marketplace and technology trends in late 2025 and early 2026 made consolidation both feasible and necessary:

• Major platforms embedded generative-AI features, reducing the need for multiple niche copy and creative AI vendors.
• Privacy-first measurement and industry guidance accelerated adoption of server-side tracking and data clean rooms.
• Vendor consolidation (M&A activity in late 2025) encouraged enterprises to choose fewer, broader platforms instead of many point solutions.
• Platform engineering practices matured in marketing teams; treating the CDP+Experiment suite as a platform improved reliability and time-to-market.

Blueprint: a step-by-step checklist to replicate the result

1. Launch a 4–6 week Discovery Sprint: inventory, cost, usage, overlap, and risk mapping.
2. Form a cross-functional Steering Committee with C-level sponsor and clear KPIs.
3. Score tools using a weighted matrix (financials, usage, data portability, integrations, risk).
4. Plan a hybrid delivery: quick wins in 0–3 months, platform consolidations in 3–12 months.
5. Implement identity-first CDP and server-side tracking as early investments.
6. Pilot consolidation on a low-risk brand; validate metrics and iterate.
7. Roll out in waves with prepared rollback windows; decommission old tools only after reconciliation.
8. Reinvest a portion of savings into platform engineering and measurement to sustain improvements.

Final thoughts: why 60% is plausible and prudent

Cutting 60% of tools is not about austerity; it's about rationalization. In many enterprises, 30–50% of tools are underutilized or overlapping. The right consolidation preserves business capabilities while eliminating cost and complexity—if it’s driven by data, governed by stakeholders, and executed with careful migration engineering.

Call-to-action

Ready to build your own consolidation blueprint? Use the checklist above to run a Discovery Sprint within 6 weeks. If you want a tailored migration plan, our team can run a gratis 2-week inventory and savings estimate for your martech estate. Contact us to schedule an assessment and get a detailed migration timeline and projected savings specific to your organization.

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