Services · 02
Data & AI — from data pipelines to dashboards and AI prediction models
Stop compiling channel reports by hand. Funnel metrics from visit to conversion to repeat purchase show you what to fix next.
EnterNext's data & AI service designs tracking with GA4 and GTM, pulls ad-platform, revenue, and customer data into one pipeline, and refreshes it daily on Looker Studio, Metabase, or Tableau dashboards. Dashboards are built around funnel metrics rather than vanity metrics such as views, and extend to AI prediction and recommendation models when the data supports it. The first dashboard typically takes 2–4 weeks.
At a glance
- 01
GA4/GTM tracking design and data pipeline build from one team
- 02
Naver, Google, Meta and other ad-platform data on one screen, refreshed daily
- 03
Funnel-metric dashboards that extend to AI prediction and recommendation models
What's included
Data · AI
Tracking design (GA4 · GTM)
We start by defining what counts as a conversion. Event, conversion, and UTM rules are designed and implemented in the data layer so every later analysis rests on the same base.
Data pipelines
Ad platforms, commerce, CRM, and internal databases are collected automatically. Manual compilation disappears and the whole company looks at the same numbers.
Dashboards & metric design
Decision screens in Looker Studio, Metabase, or Tableau, structured so the visit → conversion → repeat-purchase funnel can be read step by step.
AI prediction & recommendation
Demand forecasting, churn prediction, product recommendation — models that turn accumulated data into a next action and plug into the real workflow.
Data operations
Monthly data-quality checks so metric definitions don't drift, and pipeline extensions whenever a new channel or product appears.
How it runs
- 011–2 weeks
Metric diagnosis
We check which numbers your meetings run on and where they come from, filter out vanity metrics, and settle on five funnel metrics.
- 022–4 weeks
Pipeline & dashboard demo
A dashboard running on your real data, shown first. Metric definitions get corrected together here, and only then do you decide on the full build.
- 03Ongoing
Build & operate
The auto-refreshing pipeline is built out, data quality and metric definitions are reviewed monthly, and AI models are added when justified.
Comparison
| Criterion | Manual per-channel compilation | BI tool only | EnterNext Data & AI |
|---|---|---|---|
| Refresh cycle | Weekly or monthly, by a person | Automatic only for connected sources | All sources refreshed daily |
| Metric definitions | Differ by owner | Tool defaults | Defined against the funnel first |
| Ad-platform integration | Separate report per channel | Connector cost and setup each | Naver, Google, Meta and more on one screen |
| Next action | Interpreted in the meeting | Charts only | The bottleneck step is visible on screen |
| AI extension | Not possible | Separate project | Prediction and recommendation on the same pipeline |
Data & AI is the right fit if
- Someone compiles channel reports by hand and the night before the weekly meeting is always busy
- GA4 is switched on but nobody can say precisely what a conversion is
- Data keeps piling up but it isn't clear what to fix next
- Demand, churn, and recommendations run on gut feel and you want a model behind them
What changes — the numbers tell you the next move
Structure starts with five numbers, not a grand dashboard: visits, sign-up (or add-to-cart) rate, purchase rate, repeat-purchase rate, and revenue per customer. Track those weekly and the location of the problem shows itself.
Ten thousand visits and 100 purchases isn't one number, 1% — it's 40% reaching the product page × 10% adding to cart × 25% completing checkout. Read it step by step and you see which stair customers fall off. Dashboards are designed so that stair is visible.
Tooling — GA4, GTM, Looker Studio, Metabase, Tableau
Tracking is designed in GA4 and GTM; dashboards use Looker Studio (free), Metabase, or Tableau depending on team size and budget. Ad-platform data from Naver, Google, Meta and others lands on one screen refreshed daily, so nobody compiles channel reports by hand.
AI models are proposed only when enough data has accumulated. Starting with prediction while data is thin produces results nobody can trust, so the pipeline and metric definitions are stabilized first.
Related projects
View the full portfolio →
FAQ
Frequently asked questions
- Which tools do you use for analytics?
- GA4 and GTM for tracking design, and Looker Studio, Metabase, or Tableau for dashboards. Ad-platform data is collected automatically through connectors and APIs; prediction and recommendation models are built in Python and attached to the dashboard or the business system.
- We don't have much data yet — is this still worth it?
- Yes. With little data, the first job isn't a model but tracking design. Record conversions and events properly from now and in 3–6 months you'll have an asset to analyze. Prediction models come after that.
- What do a dashboard build's cost and timeline look like?
- Metric diagnosis takes 1–2 weeks and the demo dashboard 2–4 weeks; nothing is billed until you approve the demo. After that a monthly contract is set by the number of sources connected and the operating scope.
- When do AI prediction models actually pay off?
- When there's enough history and the prediction drives a real action — demand forecasting, reorder timing, churn prediction, product recommendation that changes orders, coupons, or placement. We don't recommend prediction where data is thin or nothing acts on the output.
- We already use GA4 — what more is needed?
- Turning GA4 on and defining conversions are different things. In most accounts event names vary by owner or platform conversions don't reconcile with GA4, so we start by auditing the existing setup and re-establishing event, conversion, and UTM rules.
We'll start by tracing where the numbers in your meetings actually come from, then build a dashboard on your real data and show it to you before you decide anything.
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