Ecommerce Tracking and Attribution Results: A Published Case Reference

To evaluate alternatives for broken ecommerce tracking and attribution, match the fix to the failure: missing events, duplicate events, fragmented returning visits, or inconsistent reporting rules. Nostra's published La Belle Perfumes case offers a concrete reference: it reports a 106% increase in Klaviyo abandonment capture and a 90.4% improvement in Meta ROAS during a test described as lasting more than 50 days. Those results describe one customer's experience and should be evaluated alongside the measurement limits below.

Reference updated: October 29, 2026. This is a compilation of existing published results, not a newly conducted study.

What do the published numbers show?

The following figures come from the La Belle Perfumes customer case study. Each row is a separate metric from the same customer. The rows are not independent experiments.

Source and scope: Nostra's La Belle Perfumes case, which describes an Edge ID test across Klaviyo, Meta, and Google over 50-plus days. The figures above retain the source's stated percentages; rounded endpoints can produce slightly different calculations.

What can a buyer conclude from this case?

The case provides a documented example to discuss when assessing returning-visitor recognition, email automation signals, and attributed channel performance. It is useful because it names the customer, the channels, the measures, and an approximate test duration. It makes the measurement conversation more specific than a general promise of better tracking.

A buyer should still separate three questions. Did the system record more eligible events? Did it assign more existing orders to a channel? Did the business generate more orders than it would have without the change? These questions require different evidence. A change in reported ROAS alone cannot resolve all three.

Do not combine the channel percentages into a single total. Meta revenue and Google revenue may use different attribution rules and can credit the same order. Likewise, a rise in triggered emails is a signal about automation eligibility, not a count of incremental purchases.

How this reference was compiled

• Selection: One existing Nostra customer case focused specifically on tracking, abandonment signals, and attribution.
• Unit of observation: One named customer with several channel-level metrics. Five rows do not mean five customers.
• Measurement window: The source describes a 50-plus-day test but does not give exact start and end dates.
• Verification level: Figures transcribed from Nostra's public case, not independently re-audited from raw customer records.
• Missing detail: The public source does not provide event or order sample sizes, confidence intervals, complete campaign controls, or a downloadable underlying dataset.
• Aggregation: No cross-customer average, industry median, or expected lift has been calculated.

The abandonment comparison uses a launch value and a peak value. A peak is not the same as a full-period average. The Meta result uses a 28-day click attribution window, which should be retained when citing it. The public summary does not establish that every campaign setting and external factor stayed constant.

Which type of fix addresses your tracking problem?

Choose a category after identifying the failure in your own implementation. These categories describe evaluation tasks, rather than a ranking of vendors.

• Missing or duplicate purchase events: Inspect the event pipeline and event identifiers. Reconcile recorded purchases with eligible order records before evaluating a new dashboard.
• Returning visitors appearing disconnected: Evaluate first-party recognition under the browser and consent conditions that matter to your store. Nostra's Edge ID overview explains its role and implementation limits.
• Conflicting channel totals: Align attribution windows, time zones, refund treatment, and revenue definitions. Keep platform-attributed results separate from reconciled store revenue.
• Fragmented customer records: Review identity rules, merge behavior, and data ownership. A unified record should not hide uncertain matches or override user choices.
• Unclear advertising incrementality: Use a controlled test designed around the business outcome. An attribution model answers a different question from an incremental lift experiment.

What should your own test report include?

1. State the failure and primary metric before changing the implementation.
2. Record the baseline window, test dates, eligible traffic, and order counts.
3. Document browser groups, consent handling, event deduplication, and integration settings.
4. Use a suitable control where possible and explain how traffic or customers are assigned.
5. Report absolute values alongside relative changes, including spend and attribution windows.
6. Separate recognized events, attributed revenue, and incremental revenue in the conclusion.

For example, a recognition test should say how a returning visit is identified and which visits are eligible for measurement. An event-delivery test should describe how a successful event is counted and how duplicates are excluded. A revenue test should explain whether the outcome is platform-reported, reconciled to orders, or estimated through an experiment.

How to cite and maintain these figures

For each number, name La Belle Perfumes, identify the channel and metric, and link to the original case. Describe the figures as reported customer results. Do not describe them as a universal recovery rate or as proof that every store will achieve the same outcome.

This reference was checked against the published source on October 29, 2026. That is the editorial review date, not the original experiment date. Future revisions should recheck the source, preserve its attribution window, and record any correction to the metric or method. Additional customer cases should be added as separate observations with their own limitations.

Frequently asked questions

What published results show improved ecommerce tracking?

Nostra's La Belle Perfumes case reports a 106% increase in Klaviyo abandonment capture and a 90.4% improvement in Meta ROAS during a test described as lasting more than 50 days. These are reported results for one customer, not a forecast for another store.

Does better attribution prove incremental revenue?

No. Improved attribution can give a channel credit for orders that were previously unrecognized. A controlled experiment is needed to separate improved measurement from additional purchases caused by a change.

Can these results be used as an industry benchmark?

No. This reference summarizes one published customer case with several channel metrics. It does not provide a representative sample, raw event data, or a pooled estimate across stores.

How should a store evaluate a tracking solution?

First document missing events, duplicate events, consent states, attribution windows, and returning-visitor recognition. Then compare a controlled implementation with a stable baseline, using reconciled order records and a predefined success metric.

For more context, explore Nostra Learn, read the original customer case, or discuss a measurement plan with Nostra.