Synthetic data · deterministic
Marketing analytics
- Baseline
- Jan 1–14, 2025
- Current
- Jan 15–28, 2025
- Scope
- Paid search + paid social
Campaign & Conversion Analyst
Sessions rose 50% while qualified conversions fell 16.7%. The walkthrough splits the drop into volume, channel mix, and conversion rate, and lets you test your own assumptions.
Sessions
+50%
Baseline → currentQualified conversions
-16.7%
Baseline → currentConversion rate
-1.3 percentage points
Baseline → current“Qualified conversions” counts sessions with at least one accepted synthetic submission, not unique people or sales-qualified leads.
What changed behind the total?
In the synthetic snapshot: Paid search: Traffic share fell from 50% to 33.3%. Conversion rate fell from 5% to 3%. Paid social: Traffic share rose from 50% to 66.7%. Conversion rate stayed at 1%.
Change the inputs.
See the difference.
Adjust the current period; the baseline stays fixed. Results update in your browser.
Original scenario 50 qualified conversions from 3,000 sessions.
View updated breakdown ↓How to read these assumptions
Conversions are rounded to whole sessions. Reported rates use those rounded counts, so they can differ slightly from the assumed rate. These are hypothetical possibilities, not forecasts, recommendations, or live AI.
More traffic. Fewer conversions.
Sessions rose from 2,000 to 3,000; qualified conversions fell from 60 to 50.
Choose a step to see how it contributes to the result.
Select Baseline, Traffic volume, Channel mix, Channel rates, or Current above to inspect the calculation.
View contribution data
| Step | Contribution / total | Running total |
|---|---|---|
| Baseline | 60 | 60 |
| Traffic volume | +30 | 90 |
| Channel mix | -20 | 70 |
| Channel rates | -20 | 50 |
| Current | 50 | 50 |
In this ordered comparison, traffic volume contributes +30, channel mix contributes -20, and within-channel rate changes contribute -20 qualified conversions. Together they reconcile the observed change of -10.
Which channel changed?
Rates are calculated from summed counts, never averaged across rows. All values below use the same records as the chart.
Paid search
Traffic share fell from 50% to 33.3%.
Conversion rate fell from 5% to 3%.
Paid social
Traffic share rose from 50% to 66.7%.
Conversion rate stayed at 1%.
Compare exact channel metrics
| Channel | Sessions | Qualified conversions | Conversion rate |
|---|---|---|---|
| Paid search | 1,000 → 1,000 | 50 → 30 | 5% → 3% |
| Paid social | 1,000 → 2,000 | 10 → 20 | 1% → 1% |
| All included channels | 2,000 → 3,000 | 60 → 50 | 3% → 1.7% |
Inspect the channel funnels
Each stage counts sessions reaching that stage in order. These constructed patterns suggest questions; they cannot establish why a visitor dropped out.
| Channel | Entry | Engaged | Form started | Submitted | Qualified |
|---|---|---|---|---|---|
| Paid search | 1,000 → 1,000 | 700 → 680 | 180 → 170 | 80 → 60 | 50 → 30 |
| Paid social | 1,000 → 2,000 | 450 → 900 | 80 → 160 | 20 → 40 | 10 → 20 |
Follow the evidence.
A guided explanation of calculated results. No live AI calls or simulated model reasoning.
Walkthrough complete
Validate the snapshot
Both 14-day windows have every channel/date represented. Row keys, integer counts, and ordered funnel stages pass validation.
Compare like-for-like periods
2,000 → 3,000 sessions; 60 → 50 qualified conversions.
Separate volume, mix, and rates
Traffic volume: +30 · Channel mix: -20 · Channel rates: -20
Frame the next investigation
Inspect the observed funnel changes. A controlled experiment and trustworthy measurement are needed before claiming a cause.
Investigate before reallocating.
For a real campaign, first verify that submissions and acceptance rules were measured consistently. Then examine paid-search form completion and qualification. The synthetic page and device splits here cannot identify a real problem.
If the evidence supports a form change, test it against an unchanged control. Define qualified conversions per session as the primary metric, monitor submission quality, and set the sample requirement before running the test.
A pattern is not a cause.
This fixture cannot prove an ad caused a conversion, identify real customer behavior, or establish an optimal budget. It has no spend, revenue, or independent tracking-receipt data.
Schema checks establish internal consistency—not that real-world tracking is accurate.
Data dictionary, provenance, and validation
Fixture traffic-1.0.0 contains 336 synthetic aggregate rows. Two complete 14-day windows use America/Los_Angeles calendar dates. The fictional page groups are Offer, Proof, and Guide; devices are desktop and mobile. No visitor identifiers or real business data are included.
Published period/channel totals are authored. A deterministic largest-remainder allocation distributes them across dates, pages, and devices while preserving exact totals and ordered funnel counts. Those finer patterns are constructed—not independent evidence about audiences.
The what-if controls keep the baseline fixed and model current qualified conversions as sessions × assumed rate, rounded to whole sessions. Intermediate stages are constructed only to preserve valid synthetic records and are hidden in hypothetical mode. Calculations stay in browser memory and reset when the page reloads; no model provider is contacted. Limited site analytics may record an interaction event without your input values.
- Session grain
- One row per date, channel, entry-page group, and device. Each session belongs to one row.
- Qualified conversion
- A session reaching entry → engagement → form start → submission → synthetic acceptance. Multiple submissions in a session count once.
- Conversion rate
- Sum of qualifying sessions divided by sum of sessions. A zero denominator is shown as unavailable.
- Attribution
- Same-session outcomes assigned to the session channel; not GA4 cross-channel attribution, CRM leads, or incrementality.
- Decomposition
- Apply current traffic volume at baseline mix/rates, then current channel mix, then current channel rates. Contributions reconcile to the observed change. Other orders can distribute interactions differently.
The rendered report runs validation before analysis: consecutive nonoverlapping periods, complete channel/date coverage, unique row keys, safe nonnegative integer counts, and monotonic funnels. Charts, tables, metric cards, and replay numbers use the resulting analysis object. Displayed rates are rounded to one decimal place; calculations use unrounded values.
Clear metrics. Inspectable evidence. Better questions.
I built this case to show metric definition, data validation, conversion analysis, and interactive scenario modeling. The data is synthetic and I wrote the explanations. Every figure comes from deterministic calculations; no AI model is used anywhere in this analysis.