Data Analytics · Singapore
You have the data. You just can't get a straight answer out of it.
Campaign data in one place, CRM data in another, revenue data in a third, and no single view that tells you what's actually driving results. We connect the sources, build the attribution and the reporting, and turn a pile of numbers into decisions you can defend.
The Problem
The B2B Data Analytics Problem
Most marketing teams aren't short of data. They're short of answers. Three patterns are usually why.
You can report activity, but not revenue.
The monthly report is full of impressions, clicks, opens, sessions. None of it connects to pipeline or revenue, so when leadership asks what marketing actually returned, the honest answer is a shrug. You're measuring motion, not outcome.
The data sits in silos.
Ad platforms, the CRM, the website, the email tool — each holds a piece, none of them talk, and stitching them together by hand in a spreadsheet eats a day every month and still doesn't reconcile. There's no single view of performance, so every question becomes a project.
Decisions get made on gut feel.
Because the data is too messy to trust, the big calls — where to spend next quarter, which channel to cut — get made on instinct and the loudest opinion in the room. Not because anyone wants to guess, but because the numbers can't be relied on to settle it.
Data work we've delivered
The Method
How we make data answer the question
Analytics work fails when it starts with a dashboard. We start with the question you need answered, then build back to the data that answers it.
Proven Results
Data Analytics in Numbers
Real outcomes across attribution modelling, data integration, and analytics programs for enterprise B2B clients.
Analysed and restructured for IMDA using a data-led, no-survey approach.
Across six SEA markets via data integration and attribution — $1,000 down to $108 per lead.
Built for IMDA, replacing a team that had no analytics visibility with channel and journey reporting they could act on day to day.
Client Results
Data Work That Performed
Real outcomes across data integration, attribution modelling, and analytics dashboards for enterprise B2B clients in Singapore and Southeast Asia.
IMDA — A data-led information architecture for an 8,000-page government site.
Challenge
IMDA needed to redesign the information architecture of its 8,000-page government website around what users actually need, but with no direct access to those users for research. The usual answer — a survey programme — wasn't available. The decisions still had to be defensible.
Approach
We adopted a data-first approach in place of user research: GA4 traffic and exit patterns, Hotjar scroll depth and rage clicks, and PageSpeed data to identify high-impact pages and priority journeys, with ETL and data management to make 8,000 pages analysable. A Jobs-to-be-Done framework turned the data into user pathways and a new IA model.
Result
A new navigation structure and content model built on evidence, not opinion, and prioritised around what the data showed mattered most to users — delivered without the need for a new survey budget. Data standing in for research, and standing up to scrutiny.
IMDA — Analytics dashboards a government team could actually use.
Challenge
IMDA's team lacked the analytics visibility to understand how visitors engaged with its site, and needed reporting they could read and act on — fast — without a data specialist to interpret it.
Approach
We designed a GA4 tracking framework and built real-time dashboards that surfaced the metrics that mattered — performance by channel and journey — in a format a non-specialist team could use day to day.
Result
Reporting the team described as "way better than what our team could achieve with prototypes, and in a very short time." Analytics visibility where there had been none.
SAP — Data integration that cut cost-per-lead tenfold.
Challenge
SAP's marketing and product teams ran on disparate, siloed systems, with fragmented information flow between marketing and sales and no clean way to measure or compare lead performance across six SEA markets.
Approach
We integrated the campaign data into HubSpot to create a single measurable view, then used it to attribute and optimise lead generation market by market — Indonesia, Malaysia, Singapore, Vietnam, the Philippines, Thailand — on a consistent basis.
Result
A 10× improvement in average cost-per-lead, from around $1,000 to $108, across the six markets. Proof that the gain wasn't a better ad — it was better measurement.
Why Construct Digital
Why Our Analytics Holds Up
Plenty of firms will build you a dashboard. Far fewer connect the data to the decision — and fewer still are a marketing agency that understands what the numbers are for.
Marketers who do data, not data scientists guessing at marketing.
We analyse marketing data as marketers: we know what a CPL, an attribution model, or a channel mix decision actually means for your plan, because we run those plans. The analysis serves a marketing decision, not a statistics exercise.
We connect data to revenue, not just to charts.
The work is built to answer "what's driving pipeline?" — not "what happened last month?" Attribution and ROI analysis tie activity to outcomes, so the report you take to leadership is about contribution, not activity.
Data-led even when there's no user research budget.
The IMDA work is the proof: an 8,000-page IA rebuilt on GA4, Hotjar, and PageSpeed data when direct user research wasn't possible. When you can't run the survey, the data you already have can often answer the question — if someone knows how to read it.
Reporting your team and your leadership both understand.
Dashboards built for the people who use them, not for the analyst who built them. Real-time, plain, and decision-ready — the reason a government team called our dashboards better than what they could build themselves, delivered fast.
You pay for the thinking. The production runs AI-native.
The analytical judgement — what to measure, which model fits, what the numbers mean — is what you're buying. The production: data cleaning, ETL, query, anomaly detection, report generation, runs AI-native, where AI compresses time most. More sources connected, more questions answered, for the same engagement.
One team across data, web, and marketing.
Because we also run the campaigns, build the sites, and manage the CRM, the data work connects to the thing it's measuring. No throwing analysis over a wall to a team that doesn't know the marketing it's meant to inform.
Engagement Model
Start with the question, not the dashboard
Data analytics is bought to answer something specific. We start by finding the real question, then build the data that answers it.
Data Audit & Source Map
A standalone diagnostic: we map your data sources, find what's connected and what can't be trusted, and show you exactly what's stopping your data from answering your questions. You get a data-estate map and a prioritised plan — useful on its own, with us or in-house. The productised entry point that earns the build.
Build the model and the reporting.
Attribution model, ETL and data plumbing, and the dashboards on top. You move from siloed sources to a single, trustworthy view of performance.
Ongoing analysis (retainer).
The part that compounds: a regular read of what's working, what to shift, and what to forecast — so the data drives decisions every month, not just once at setup.
This is marketing and business data analytics — connecting campaign, CRM, and revenue data. If you specifically need website behaviour and conversion tracking, that's Web Analytics. The two often run together.
Testimonials
What Our Clients Say
"At first glance, it is a very pretty format, way better than what our team could achieve with prototypes. And in a very short time. Well-done, awesome!"
"Thank you for your great support over the past year and for sharing these latest insights."
Got Questions
Data Analytics FAQs
Web Analytics is about website behaviour: how visitors move through your site, where they convert, where they drop off (GA4, conversion tracking). Data Analytics is broader: connecting campaign, CRM, and revenue data to answer marketing and business questions like attribution, ROI, and forecasting. The two often run together — website behaviour is one of the sources Data Analytics pulls in.
With a Data Audit and Source Map: we inventory every source, find what's connected and what can't be trusted, and show you what's blocking the answers you need. That diagnostic stands on its own and gives you a prioritised plan — whether you build with us or in-house.
That's the core of the work. We design an attribution model fitted to your sales cycle and data, then build the reporting that ties spend and activity to pipeline and revenue — so you can show contribution, not just activity.
Common, and not a blocker. The data audit handles the "messy data" problem directly. And as the IMDA work showed — an 8,000-page IA rebuilt on existing GA4, Hotjar, and PageSpeed data — the data you already have can often answer the question without new research budget, if it's read properly.
We work across GA4, the major ad platforms, CRMs like HubSpot and Salesforce, and BI/reporting layers. We recommend and build on what fits your stack rather than forcing a single tool — the priority is a trustworthy single view, not a particular logo.
The analytical judgement — what to measure, which model fits, what the numbers mean — is what you're paying for. The production: data cleaning, ETL, querying, anomaly detection, and report generation, runs AI-native, which is where AI compresses time most. You get more sources connected and more questions answered for the same engagement.
Ready to Begin
Stop guessing. Start with the question your data should be answering.
Tell us what you're trying to find out and what data you've got, and we'll map your sources and show you what it would take to get a straight answer. Connected data, real attribution, reporting you can take to leadership.