Vault Mark helps Thai and APAC teams connect outcomes, metrics, tracking, AI and people—so decisions are based on shared truth, not screenshot battles.
Built for Business Owners, Managing Directors, C-level leaders and Marketing Directors who need a measurement operating system—not another isolated dashboard project.
Treat data as a decision system—not a reporting factory.
An AI-Data & Measurement OS defines how the organisation collects, interprets and uses signals across marketing, sales, ecommerce, CX and operations.
The problem is usually not a lack of tools. It is a lack of shared definitions, connected outcomes and a dependable rhythm for turning numbers into decisions.
When every function has its own dashboard, the organisation spends more time reconciling evidence than deciding what should change.
What outcomes actually matter—revenue, margin, LTV, category or market?
Which signals must be visible, at what level, and at what cadence?
How will every function see one version of the truth?
Where should AI help—and how will it remain explainable and trusted?
Different tools and teams show different numbers for the same business question.
Meetings are consumed by reconciliation instead of action.
Impressions, clicks, GMV or NPS are reported without a clear link to profit, LTV or strategic outcomes.
A small group understands the model; everyone else is asked to trust it.
Privacy, consent, access and risk are addressed after the system is already in use.
The operating model changes the conversation from “Which number is right?” to “Which action do we take?”
Data everywhere is not the goal. The goal is signals everyone agrees on—and can act on.
AI-Data & Measurement OS runs horizontally across every module. It turns separate activities into shared signals, dashboards and decision forums.
The engagement defines the system around the tools: outcome logic, signal architecture, governance, review cadence and the role of AI.
Connect top-level outcomes to module- and channel-level metrics.
Define events, attributes and derived metrics across web, app, Line OA, media, CRM, marketplaces, stores and service channels.
Clarify what to measure well now, what to improve over time and what to stop measuring.
Set roles, responsibilities, request flows and cadences across business, data, analytics and IT.
Define where AI can assist with anomaly detection, forecasting, clustering and narrative summaries—and how it will be validated.
Establish practical expectations for quality, documentation, access, PDPA and responsible use.
Design views for C-level, module owners and operating teams around specific decisions.
Set weekly, monthly and quarterly agendas that turn signals into action.
Support AI-GrowthLab with success metrics, instrumentation, samples and analysis patterns.
The first 90 days move the organisation from fragmented reporting to an outcome map, signal architecture and operating model grounded in current reality.
Impact is designed to be felt internally within the first 1–3 months through clearer definitions, fewer report conflicts and better conversations. Deeper decision and experimentation quality builds as the operating model is implemented.
The strongest fit is not determined by company size alone. It is determined by the number of systems, teams and commercial decisions that must rely on the same evidence.
Already uses multiple analytics, ad, CRM, CDP, BI or warehouse tools
Operates across several channels, brands or markets
Experiences conflicting reports, misaligned KPIs or slow decision-making
Wants AI in measurement, but needs it to remain understandable and trusted
Has very little digital data and a simple single-channel model
Only wants a one-off GA4 setup or dashboard build
Is not ready to align leadership on outcomes, metrics and trade-offs
Still treats data as an IT problem rather than a shared business asset
Vault Mark does not take sides between functions. We connect the people who own P&L and customers with the teams who own analytics, BI, engineering and governance.
Outcomes and metrics are defined with the people accountable for customers and commercial results.
Existing investment and constraints are respected. The goal is to prioritise change, not demand a rebuild from scratch.
Measurement is grounded in the needs of Search, Social, Paid, Influencer, Lead, Ecom, CX, GrowthLab and Ops.
Data and AI concepts are translated into operating decisions instead of remaining a specialist department secret.
The difference is not another visual layer. It is an agreed operating system that connects outcomes, signals, ownership and decisions.
Dashboards and reports requested ad hoc
KPIs defined separately by each team
Data teams overloaded with one-off asks
AI and models used in isolated projects
Little change in how decisions are actually made
Outcomes and metrics defined across leadership and OS modules
Signals mapped to decisions and review cadence
Data teams work inside an agreed operating model
AI and models used transparently with clear limits
Measurement becomes an input to strategy and prioritisation
The OS begins with decisions, outcomes and current reality—not with a requirement to buy or rebuild every platform.
We will review the metrics, dashboards and decision forums that matter most—and show how they can connect into one trusted signal system.
