Key points to keep in mind
The Data Health view helps you understand how reliable your performance results are and whether you can trust your ROAS. It monitors key metrics used in ROAS calculation and gives you insight into the completeness, accuracy, and consistency of your data.
✅ Use Data health to:
Check if data was complete during a specific time period
Spot when and why campaign results might be misleading, due to missing spend or revenue
Identify which issues you can fix to improve accuracy going forward
See how your data quality has changed over time (e.g. after setup changes)
📊 Why Data health matters for ROAS:
Good data = better decisions. Complete, accurate, and consistent data is essential for meaningful ROAS and performance insights.
Broken match rates distort your ROAS.
Low Meta match rate = missing spend → ROAS may appear too high
Low Analytics match rate = missing revenue → ROAS may appear too low
Low Tracking rate = missing DPA revenue → ROAS may appear too high
Improving data health doesn’t boost ROAS directly. It makes your data more reliable so that ROAS is calculated correctly—good or bad.
🧠 What to keep in mind when interpreting ROAS
ROAS changes aren’t always about data. Promotions, seasonality, creative changes, and campaign structure also impact results.
Look at the full picture. No single rate tells the full story—track how metrics influence each other over time.
How to know whether to trust your data
Select a market and a time period (e.g., 90 days, a quarter, or longer) to see how data quality has evolved:
Good Data Health:
Match rate: More than 90% → 🟢
Your data setup was correct and supported your potential.
Poor Data Health:
Match rate: 75% to 89% → 🟠
Match rate: 0% up to 74% → 🔴
Incorrect data hindered the platform’s potential to improve performance and led to inaccurate results.
📝 Data collection
Data form this page only goes back to October 10, 2024 as data wasn't collected before then.
Historical vs current data
The left panel shows the historical average over your selected time range. The right panel shows the current status of your data.
These two views help you understand both how your data looked in the past and whether it’s in good shape now.
Here’s how to read it:
🟢 Green (historical) + 🟢 Green (current): Everything looks good. Your past and current results are based on complete data.
🟢 Green (historical) + 🔴 Red (current): Your past data was reliable, but there are issues now. Check the current panel and fix what’s broken.
🔴 Red (historical) + 🟢 Green (current): Your current data is fine, but keep in mind past results might have been impacted by missing or incomplete data.
🔴 Red (historical) + 🔴 Red (current): The data was incomplete in the past and still is now. You should take action to fix it as soon as possible.
📝 Historical Data
Even when changes are made to improve data quality, you can still see historical data. This lets you track changes over time and understand how the platform worked at specific points in the past.
How to read the data health graph
ROAS is represented by the blue line.
Solid Line: Tracking rate is good and the ROAS value can be trusted. 😐
Dotted Line: One or both metrics used to calculate ROAS was incomplete. 🫥
What metrics can affect ROAS calculation?
Tracking rate: If it’s low, it indicates that some dynamic revenue is missing from ROAS calculation which can make the ROAS appear lower than it really is.
Meta match rate: If it’s low, it indicates that some ad spend is missing from ROAS calculation which can make the ROAS appear higher than it really is.
Analytics match rate: If low, it indicates that some revenue is missing from the ROAS which can make the ROAS appear lower than it really is.
❗️Be careful to draw conclusions too quickly❗️
As you can see, incomplete data can skew ROAS calculations BUT it doesn’t have to be the case always. Sometimes the ROAS ups and downs can be also caused by external factors like promotions, Black Friday, and new creatives etc. However, the main point of this section is 👉 Without complete data, ROIHunter cannot calculate accurate ROAS and do its job effectively.
How to fix Data health issues?
If you spot data issues, click “See more” to open the Markets and Assets section. There, you can check the list of missing products or click the notification in the relevant panel to open a modal to see what steps to take.
If you’re not sure how to proceed, feel free to contact support. We’ll help you figure it out.
How do individual metrics connect and impact each other?
In this section, we’ll explain how different metrics are related and how changes in one can affect the others. BUT:
❗️Be careful not to draw conclusions too quickly!❗️
Improving the Match rates or Tracking rate doesn’t automatically mean the ROAS will go up or down. 🙅♀️ It simply ensures we have complete data, allowing ROIHunter to calculate ROAS more accurately. Any increase or decrease in ROAS will just be a result of incorporating data that was previously missing.
1. Meta Match rate
Incomplete data from Meta can significantly impact the accuracy of ROAS calculations.
Incomplete Data: When Meta match rate is low, it means the inventory product feed given to ROI Hunter is missing products with spend, and it can make ROAS appear higher than it actually is.
🎯 Impact: It's impossible to accurately assess the performance of individual products. So e.g., ROIHunter platform cannot see all the poor performers and do its job effectively.
Complete Data: As the Meta match rate improves and ROI Hunter receives full spend data, you might see a drop in ROAS. This happens because you’re now seeing the true performance of all products—including those that were underperforming but previously not visible.
2. Tracking rate
Tracking rates, Meta match rates, and ROAS are all interconnected. Improving Tracking can positively impact overall performance and revenue.
Low Tracking rate: Can negatively impact product ROAS and segment performance. Generally, a higher tracking rate correlates with a better ROAS.
🎯 Impact: In some situations, low match rates can even impact the identification of best-selling products.
3. Analytics match rate
Analytics match rate shows how many of your selected products in your Inventory feed have Google Analytics 4 and Branch data.
🎯 Impact: A low match rate means your Inventory feed is missing products with historical performance data lowering your results and potential.
4. Data gaps
Missing metric data is displayed as a blank space on line graphs.
This is due to:
Inventory changes
Lack of past data (especially for new clients)
Match rate tooltip
Tip: Move your cursor over the "ℹ️" icon to view the infographic.
This useful Data health tooltip will show you:
How the rates are calculated.
When the rates are considered low.
What metrics are affected if data is missing from either the Meta catalog or the Inventory product feed.
Example: A low Meta match rate indicates that certain products in the Meta catalog are missing so they couldn’t be matched, resulting in lost revenue and a negative impact on metrics such as amount spent and product insights.
7 Key takeaways
A broken Meta match rate = missing DPA spend, which means you cannot see all Poor performers and real product ROAS is lower.
A broken Analytics match rate = missing revenue. You don't see all Bestsellers and real product ROAS can be higher (depending on whether the missing Revenue is from Meta or other channels).
A broken Meta tracking rate = missing specifically DPA Revenue, and real product ROAS is higher.
Data health and its importance: Data health is crucial for accurate results and realizing potential. Metrics like Meta match rate, Analytic match rate, and Tracking rate are key indicators of data health.
However, improving data health doesn’t always = improving ROAS. It means more reliable product-level metrics and better segmentation for better PPM.
ROAS can still fluctuate due to external factors like sales, competition, stock levels, and budget changes, even with improved data health.
Data relationships and the big picture: The relationships between metrics are complex and shouldn't be analyzed in isolation. The big picture is key.
FAQs
What are the potential causes for a sudden increase or decrease in ROAS?
Smaller markets with lower budgets: These can have more ups and downs in ROAS due to their lower spending and larger changes in revenue.
Bulk orders: Big, one-time orders can make revenue look higher than usual, but they might not reflect normal sales.
Fraudulent activity: Competitors might try to mess with the data by making big orders and then canceling them, which can make the numbers look off without actual sales happening.