Bayesian causal Marketing Mix Models on Google Meridian, paired with model calibration and a hypothesis engine. Growth becomes predictable, privacy-proof, and ready to answer the what-if questions you can't ask today.
Customers cross devices, channels and apps. Last-click and platform-reported ROAS double-count the same conversions.
Cookies, ATT and walled gardens have broken deterministic attribution. The signal you used to lean on is gone.
Incrementality and what-if questions are treated as unanswerable, so budgets get set with obsolete heuristics.
Four engines that work together, with humans firmly in the loop. Software does the heavy compute. People do the judgement, calibration, and quality control that turn a model into a decision your CFO will sign.
Sophisticated Bayesian causal inference that reveals the true, holistic impact of every investment, with full uncertainty quantification, at zero software cost.
Continuous geo-holdout experiments feed measured causal lift back into the model as priors, so accuracy compounds instead of decaying.
Privacy-safe clean-room signals from Ads Data Hub. Cross-device journeys and data-driven attribution generate the hypotheses every experiment then proves.
Senior operators sit at every step. They tune priors, vet experiments, sense-check outputs, and translate posteriors into recommendations a CFO can defend.
The platform is built around three questions every growth team has and very few can answer.
Which spends are driving true business lift, separate from the customers we would have won anyway?
How does the system respond to a change in spend, mix, or channel before we commit a single dollar?
Where are we under-investing in channels that work, and over-investing in channels that don't?
Most growth losses live in one of these two buckets. Trifecta is engineered to spot both.
Spending on channels that don't drive the business. Money wasted where we should not have spent. Last-click attribution loves these channels. The geo-holdout test exposes them. Trifecta surfaces them inside the operator console so they can be defunded with conviction.
Failing to invest in channels that do drive the business. Money never spent where we should have spent it. Often the slowest-burning, hardest-to-attribute channels like upper-funnel video, OOH, and podcast. They compound but never close a session.
Three loops that compose into a single engine. Each one feeds the next, and each one improves the last.
The single most important feature for your team. Anyone in the organisation can ask the model anything, in plain language, and get a defensible answer in seconds.
Five stages, repeated per client. The first run takes weeks. Subsequent runs are an operator job, not a project.
Audit the spend, audit the data. Assess every investment and every data stream natively available in BigQuery.
Capture, clean, inject every input — API or upload — into BigQuery. Signal separated from noise, mapped per source.
Continuously run geo-holdout incrementality experiments and inject results into the model as deterministic priors.
Mine Ads Data Hub DDA data for the hypotheses worth testing. Every experiment closes a loop the model needs closed.
Connect Meridian to an MCP server. Anyone in the org can query the MMM in plain language, then act on what it says.
Growth is the lifeblood of the business. It cannot live with consultants or third-party platforms. It has to be managed and diffused from inside the organisation, with democratic access to all.
Per-brand model archetype, prior catalogue, channel taxonomy and experiment cadence. Designed around your business, not a generic template.
Two operators run the data pipelines, calibration loops and training runs while the engine matures inside your cloud, from month one to month twelve.
The model, the data, the priors, the playbook. All in your GCP project. We hand the operator console and Signal to your team. The engine is yours.
If you've tried MMM before and it didn't stick, one of these is usually why.
Engaging MMM once a year. The world is dynamic. The planning cycle must be quarters or weeks, not seasons.
Letting data ingestion and Bayesian priors decay after the consultant report ships. A static MMM is a dead MMM.
Not feeding priors systematically keeps models unintelligent. Regular updates compound into real knowledge.
Overlooking the only privacy-safe clean-room for cross-device customer journey insights, out of the box and at scale.
MMM has to be accessible via conversational AI. Without that, it never becomes part of the company's growth DNA.
Trifecta is built for serious growth budgets. Here's what we look for and what we charge.
Partners who run the engagement, supported by domain advisors with deep MMM and category expertise. HQ Singapore · Indonesia.
30+ years in performance media, globally. Most recently VP of Performance Marketing at Tokopedia. Previously Global Managing Director of Havas Performance, leading the performance media practice across the network.
Measurement · Havas · Tokopedia15+ years in data & analytics. Leads Midpoint Global in Indonesia and the wider archipelago. Deep data and analytics expertise across South-East Asian media, retail and consumer businesses.
South-East Asia · Midpoint Global · MD20+ years in performance media, marketing & analytics. An entrepreneur who bootstrapped his own performance agency from zero, then exited it to Dentsu. Brings the commercial and technology lens.
Commercial · Entrepreneur · Exit to DentsuLeads solutions architecture across data platform and AI delivery engagements, bridging business requirements with scalable technical design.
Architecture · Data Platform · AISenior analyst with deep expertise in data modelling, analytics engineering, and translating complex datasets into actionable business intelligence.
Analytics · Data Modelling · BIBuilds production data pipelines, orchestration, and lakehouse foundations across GCP and AWS for Midpoint's enterprise client deliveries.
Cloud · Data · PipelinesLeads technical delivery across all client engagements, ensuring engineering quality, sprint velocity, and on-time production releases across the Midpoint project portfolio.
Delivery · Engineering · SprintsLeads cloud infrastructure and engineering across AWS and GCP environments, building the data pipelines, orchestration layers, and platform foundations that underpin every delivery.
AWS · GCP · Infrastructure30+ years of technology leadership experience spanning enterprise platforms, data architecture, and large-scale digital transformation programmes across Asia and globally.
CTO · Enterprise Tech · 30+ Years30+ years in FMCG and media, with deep expertise in marketing mix modeling and econometrics. Brings category depth across consumer goods, media planning, and brand measurement for Asia's leading companies.
MMM · FMCG · Media · 30+ YearsTwo operators, one platform, your brand. Bayesian causal MMM with calibration and hypothesis loops baked in, and a conversational decision layer for everyone who touches budget.
