Generated marketing analysis

The weekly report nobody has to build

CheckDataNow runs the analysis, writes the narrative, and updates it when the numbers move. You read it. You don't make it.

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For D2C and ecommerce teams who own more analysis than they've ever looked at.

Weekly growth report · Northwindrun 12

Third consecutive week paid CAC has risen. The lift is concentrated on two Meta creatives — efficiency drift, not demand collapse.

find_01Landing-page concentration4 URLs drive 61% of paid sessions. Two were never opened in GA4 last quarter.trend

find_02Meta vs Shopify gapPurchases disagree by 18%. View-through windows — not a broken pixel.stable

nextSuggested nextPull GA4 behaviour for those 4 URLs before raising Meta spend.next

The problem

More marketing data. Less idea what to do with it.

D2C brands notice Meta does not match Shopify, cannot say what the numbers mean, never open the reports that would help, and still rebuild the same deck by hand every week.

Discrepancy

Meta, Shopify, Ads, and GA4 disagree. Teams lose confidence and pick the convenient number.

Interpretation

Every platform uses its own attribution and conversion definition. Extracting a defensible insight is a skill most teams lack.

Discovery

Significant-revenue brands have never opened GA4 channel or funnel reports — not misread them, never opened them.

Assembly

Someone still has to pull the data, choose the cuts, make the charts, write what it means, and do it again next week.

P3 and P4 compound. A team that does not know a report exists cannot ask for it. A team that does know cannot afford to rebuild it weekly. Assembly is the layer this product removes.

What they do today

Export. Paste into a chat. Start again cold.

A general model does not know what the data is, does not ask the next question, and remembers nothing from last week. The most valuable sentence in marketing analysis — “this is a trend, not a blip” — is structurally impossible.

Meta reported purchasesShopify orders

The model does not know the source

Upload GSC and it sees strings and numbers. It does not know the canonical organic questions or that period-over-period is the point.

It never asks the next question

It will not say: this looks like a landing-page problem — want GA4 behaviour for these URLs? The follow-up is where analysis happens.

Nothing persists

Every session starts cold. No comparison to last month. No awareness that a metric has moved for three consecutive weeks.

What CheckDataNow is

A living report. Not a dashboard. Not a chat window.

The output is a report whose narrative regenerates as the data moves — because the analysis re-runs, not because a number refreshed inside a chart somebody built once.

Source-aware ingestion

Recognises GA4, GSC, Meta Ads, Google Ads, and Shopify — their semantics, quirks, and canonical questions.

Canonical analyses

Encoded analyses your team already performs by hand: landing pages, funnel drop-off, channel contribution, incrementality, CRO.

Narrative memory

Each report knows what the last one said. Continuity instead of a series of unrelated observations.

Weekly growth · funnel & channeljust now

findWhat changedPaid CAC up for the third week. New-customer share flat.trend

caveatCaveatMeta and Shopify still disagree by 18% — explained, not ignored.stable

nextWhat to look at nextGA4 path analysis on the four concentrating landing pages.next

In the product

The analysis refreshes. Not just the chart.

A persistent link per client. Evidence attached. Suggested next analyses. Memory of prior runs so the report can say what actually changed.

CheckDataNow living report workspace: chat prompt, narrative finding, paid CAC chart, continuity memory, and evidence. — Living report workspace — ask a question, get a finding with evidence and memory.

Why this is different

Interfaces are commodity. Recurring analysis with memory is not.

Category	Who decides what to show	What refreshes
BI tools	A human builds it once	Data inside fixed charts
Ecommerce dashboards	Vendor pre-builds	Data inside fixed charts
LLM chat over CSV	The user, if they know what to ask	Nothing persists
CheckDataNow	Canonical analyses + memory	The analysis itself, with memory

Reconciliation — why Meta and Shopify disagree — is table stakes for trust, not the value proposition. The value is removing the assembly-and-interpretation layer.

How a run works

Upload or pull. Ask the question. Get a living report.

Bring the data

Upload an export, or trigger a GA4 pull. CheckDataNow recognises the source and states what it can do.

Ask the question

Name the cut in plain language — landing pages, channel mix, incrementality. Ambiguous requests get a clarifying question, not a guess.

Read the finding

Narrative-first: the finding, the evidence, the caveat — and explicitly what changed since last time.

Come back next run

The report persists at a stable link. The next run regenerates against retained history.

GA4Meta AdsGoogle AdsGSCShopify

The hard problem

Insight stability — or trust dies in a month.

If the narrative regenerates, it will happily contradict itself every Monday on noise. CheckDataNow has to distinguish signal from churn, and remember what it previously claimed. That is the central engineering problem — more than charts, more than ingestion.

Stop rebuilding the same report every week.

We are building living marketing analysis for teams who already paste CSVs into a chat and get a shallow one-shot answer. Talk to us if that loop is your week.

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