The average enterprise now runs more than 120 marketing technology tools and spends upwards of $3 million annually on its MarTech stack. Yet most organisations use less than half of what they’ve already paid for.
This is not a tooling problem.
It’s a value realisation problem.
For the last decade, MarTech strategy has focused on what to buy: platforms, suites, clouds, and capabilities. But as stacks mature and budgets tighten, a new question dominates executive conversations:
How do we extract materially more value from what we already have?
The answer is not more technology.
It is a new discipline.
Enter the MarTech Value Engineer.
The MarTech Value Gap
Most MarTech investments underperform for the same structural reasons, regardless of industry, region, or platform choice. Three persistent disconnects sit at the heart of the problem.
Individually, these disconnects are costly.
Together, they compound – widening the value gap year after year.
What Is a MarTech Value Engineer?
MarTech Value Engineering is a capability first, a role second.
Organisations do not fail to extract value from MarTech because they lack job titles. They fail because no one is explicitly accountable for value realisation.
Value Engineering names that accountability, regardless of where it initially sits in the organisation. A MarTech Value Engineer is not a rebranded marketing operations manager, systems administrator, or platform specialist. Rather, it represents a discipline focused on engineering measurable business value from existing MarTech investments…
Where traditional roles ask:
- Is the platform live?
- Are teams trained?
- Is the system stable?
Value Engineers ask:
- Which capabilities materially move business outcomes?
- Why aren’t they being used?
- How do we redesign adoption so value becomes inevitable
The role exists to turn MarTech from a cost centre into a compounding value engine.
Table: Capability – Outcome (Dexata Value Engineering Lens)
How MarTech Value Engineering differs from Marketing Operations and Revenue Operations – and why value realisation requires a distinct discipline.
| Dimension | Marketing Operations | Revenue Operations | MarTech Value Engineering |
|---|---|---|---|
| Primary focus | Platform execution & enablement | Revenue alignment & reporting | Value realisation & ROI optimisation |
| Core responsibility | Make systems work | Align teams & data | Ensure MarTech pays off |
| Success measured by | Uptime, delivery, adoption | Forecast accuracy, efficiency | Business outcomes & return on spend |
| Time horizon | Short–medium | Medium | Continuous, compounding |
| Typical outputs | Campaigns, workflows, training | Dashboards, processes | Value maps, optimisation loops, outcome lift |
| Core question | “Is it running?” | “Is it aligned?” | “Is it creating value?” |
Source: MarTech Value Engineering framework
The Three Core Competencies of Value Engineering
1. Value Architecture
Value Architecture connects specific platform capabilities to explicit business outcomes.
This goes far beyond feature inventories. A Value Engineer maps how each capability – segmentation logic, scoring models, integrations, workflows – contributes to outcomes such as pipeline acceleration, conversion lift, retention improvement, or cost reduction.
Crucially, this discipline forces prioritisation. Not every feature matters. Some capabilities deliver disproportionate impact; others create marginal gains. Value Architecture identifies which is which and designs around the highest-value paths.
2. Adoption Engineering
Technology creates no value until behaviour changes.
Adoption Engineering applies principles from behavioural design and change management to make high-value usage the path of least resistance. This means:
Reducing friction for valuable behaviours
Embedding capabilities directly into existing workflows
Designing feedback loops that show users the impact of their actions
Enabling progressive sophistication rather than overwhelming teams upfront
The best adoption engineering feels invisible. Users don’t feel “trained” – they simply find the system easier, faster, and more effective to use.
3. Continuous Value Optimisation
Value is not static. Markets shift, teams evolve, and platforms release new capabilities continuously.
The Value Engineer builds operating rhythms that:
Monitor value realisation over time
Identify underperforming capabilities
Test hypotheses through structured experimentation
Scale what works and retire what doesn’t
This transforms MarTech optimisation from a one-off initiative into an ongoing discipline.
The Value Engineering Loop
Effective MarTech value engineering follows a repeatable five-stage loop.
1. Discover
Audit what you own, what it can do, and how it’s actually used. Establish baseline business metrics – not platform activity metrics – to anchor future measurement.
Key question: Where is the gap between potential and reality largest?
2. Architect
Map capabilities to business outcomes and prioritise ruthlessly. Focus on the small number of capabilities that could materially move strategic goals.
Key question: If we fully leveraged just three capabilities, which would deliver the greatest impact?
3. Activate
Design adoption intentionally. Embed capabilities into workflows, remove friction, and guide users toward higher-value behaviour through system design.
Key question: What behaviour is the system currently encouraging, intentionally or not?
4. Prove
Measure impact rigorously. Use experiments, cohort analysis, and attribution to connect MarTech usage to business results. Make value visible to both executives and frontline teams.
Key question: Can we demonstrate causation, not just correlation?
5. Compound
Review, refine, and expand. Incorporate new platform capabilities selectively, guided by the value map, not vendor hype.
Key question: What should we double down on, and what should we stop doing?
This loop repeats continuously, compounding returns over time.
Value Engineering in Practice
Dexata applies value engineering principles to align MarTech capabilities with business outcomes through architecture, adoption design, and continuous optimisation.
Table: Capability – Outcome (Dexata Value Engineering Lens)
| MarTech Capability | Common Usage | Value-Engineered Usage (Dexata) | Primary Business Outcome |
|---|---|---|---|
| CDP / Data Unification | Data consolidation and identity resolution | Activation-first data architecture enabling real-time decisioning and outcome measurement | Improved data usability and personalisation impact |
| Marketing Automation | Batch campaigns and linear journeys | Orchestrated, lifecycle-based journeys continuously tested and optimised | Pipeline velocity |
| Integrations | Basic data synchronisation | Workflow and process orchestration across MarTech, SalesTech, and CX platforms | Reduced manual effort and operational cost |
| Personalisation | Rules-based or static personalisation | Contextual, behaviour-led personalisation across channels and touchpoints | Engagement and revenue lift |
| SEO | Tactical optimisation and reporting | Search-led demand and intent optimisation integrated with content and CX strategy | Sustainable organic growth |
Source: MarTech Value Engineering framework
A B2B technology company was spending $2.5 million annually on a combined marketing automation and CRM platform. Three years in, utilisation hovered around 35%. Sales leaders complained about lead quality. Marketing teams felt constrained by system complexity. Executives questioned whether they needed more technology.
A Value Engineering assessment revealed something different: the stack wasn’t lacking capability – it was lacking focus.
Rather than activating everything, the Value Engineer identified three capabilities with outsized potential:
Predictive lead scoring
Automated sales alerting
Progressive profiling
Early resistance came from sales teams wary of “another scoring model.” Instead of forcing adoption, the implementation embedded insights directly into existing workflows. Reps received timely alerts without logging into new dashboards. Profiling happened gradually through natural interactions. Complexity disappeared behind the scenes.
Six months later:
Sales cycle length dropped by 22%
Qualified lead volume increased by 40%
Platform value realisation nearly doubled, without additional tech spend
The technology didn’t change. The approach did.
Building Value Engineering Capability
In practice, MarTech Value Engineering can begin within existing functions such as Marketing Operations, Revenue Operations, or enterprise transformation teams, or be introduced through specialist external support.
As the need for sustained optimisation grows, leading organisations formalise the capability into a dedicated MarTech Value Engineer role. The shift is not about adding headcount for its own sake, but about protecting accountability for value realisation.
How Value Engineering is delivered varies by organisational scale, maturity, and complexity.
Large enterprises benefit from dedicated Value Engineer roles with authority across marketing, sales, IT, and finance.
Mid-market organisations can embed the discipline within MarOps or RevOps – but only with protected time and explicit ownership.
The critical risk is treating value engineering as “extra work.” When optimisation competes with operational firefighting, optimisation always loses.
Successful Value Engineers combine:
Deep MarTech fluency
Business and financial acumen
Change management expertise
Analytical rigour
Executive-level communication skills
Table: MarTech Value Engineering Delivery Models
Common operating models for delivering MarTech Value Engineering, and the trade-offs associated with each.
| Delivery Model | When It Fits Best | Key Strength | Key Risk |
|---|---|---|---|
| Embedded (MarOps / RevOps) | Early maturity, smaller stacks | Speed & proximity to execution | Optimisation crowded out by delivery |
| Consultancy-led | Need for acceleration or objectivity | Rapid value discovery & prioritisation | Value fades if capability isn’t internalised |
| Dedicated Value Engineer role | Large, complex MarTech estates | Sustained optimisation & accountability | Requires exec sponsorship |
| Hybrid (recommended) | Most enterprises | External acceleration + internal ownership | Requires clear handover plan |
Source: MarTech Value Engineering framework
Measuring Success
Value Engineering success shows up across three metric categories:
Utilisation: breadth and depth of high-value capability usage
Efficiency: reduced manual work, faster cycles, lower operational cost
Outcomes: revenue impact, pipeline velocity, retention, ROI improvement
The most compelling signal is simple:
when incremental value delivered exceeds the cost of the function itself.
At that point, value engineering becomes self-funding.
Table: MarTech Value Engineering Measurement Framework
The core metric categories required to measure MarTech value realisation beyond platform usage and activity.
| Metric Category | What to Measure | Example Indicators |
|---|---|---|
| Utilisation | Depth of capability adoption | % advanced features in use |
| Efficiency | Operational improvement | Time saved, automation rate |
| Outcomes | Commercial impact | Revenue lift, cycle time reduction |
| ROI | Financial return | Value delivered vs MarTech spend |
Source: MarTech Value Engineering framework
The Way Forward
The MarTech arms race is over. The optimisation era has begun.
Competitive advantage no longer comes from owning the most tools – but from extracting the most value from the tools you already own. Organisations that master this discipline will outperform peers with larger budgets, leaner stacks, and faster learning cycles.
The MarTech Value Engineer represents a shift in mindset: from buying capability to engineering outcomes.
The untapped value already exists inside your stack. The question is whether you have the discipline to unlock it.
MarTech Value Realisation Diagnostic
Try this simple diagnostic to assess whether your organisation has clear accountability for MarTech value realisation.
| Diagnostic Question | Yes | No |
|---|---|---|
| Can we clearly link MarTech usage to business outcomes? |  | |
| Do we know which 3–5 capabilities drive most value? | | |
| Is someone accountable for value realisation? | | |
Source: MarTech Value Engineering framework
MarTech Value Assessment
Identify underutilised capabilities and high-impact optimisation opportunities.
About The Author
Charlie Nicholls
