How to Analyze Email Marketing Data
Knowing how to analyze email marketing data starts with a campaign-level export that includes send volume, opens, clicks, conversions, and unsubscribes organized by send date and list segment — without segmentation, aggregate open rates hide which audiences and subject lines are actually driving results. Most ESP exports provide these columns but need reshaping before meaningful comparison is possible.
See exactly which export columns to pull, how to calculate deliverability and engagement rates correctly, and how to compare campaigns across send dates and segments.
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What this export contains
| campaign_id |
| send_date |
| subject_line |
| list_segment |
| sent_count |
| delivered_count |
| open_count |
| click_count |
| unsubscribe_count |
| conversion_count |
What usually goes wrong with it
Open rate calculated on sent vs delivered
Some ESPs calculate open_rate as opens divided by sent_count; others use delivered_count as the denominator — comparing open rates from two platforms without checking the denominator produces misleading benchmarks.
DataMimi Datamimi calculates open rate on delivered_count by default and flags any row where the denominator appears to be sent_count so mixed calculations are visible.
Aggregate metrics hide segment performance
A campaign sent to five list segments reports one blended open rate in the summary export — the segment that performed best and worst are invisible without a segment-level breakdown.
DataMimi It breaks aggregate campaign metrics into per-segment rows when list_segment is present so segment-level performance is visible alongside the blended total.
Apple Mail Privacy Protection inflates open counts
Since iOS 15, Apple Mail pre-loads tracking pixels, registering opens for recipients who may never have actually opened the email — overall open rates in exports are overstated for lists with high Apple Mail usage.
DataMimi It flags high Apple Mail open inflation risk based on open_rate outliers and cross-references click_count to identify campaigns where opens are likely overstated.
Click-to-open rate not exported directly
click_count and open_count are available, but click-to-open rate — the best engagement quality metric — is rarely included as a column and must be calculated from those two fields.
DataMimi It calculates click-to-open rate as click_count divided by open_count for every campaign row without requiring a manual formula column.
Common questions
How do you analyze email marketing data in a spreadsheet?
Load your campaign export with sent_count, delivered_count, open_count, click_count, and conversion_count; calculate open rate as opens divided by delivered; calculate CTOR as clicks divided by opens; pivot by campaign_id or list_segment to compare.
What are the most important email marketing metrics?
The core metrics are delivery rate, open rate (on delivered), click rate, click-to-open rate, unsubscribe rate, and conversion rate — CTOR is the best indicator of content quality independent of subject line performance.
How do I compare email campaign performance over time?
Create a time-series table with send_date, open rate, click rate, and conversion rate per campaign; plot the trend to see whether engagement is improving or declining across sends.
What is a good email open rate benchmark?
Average open rates vary widely by industry and list quality; B2B SaaS typically sees 20–30% open rates on delivered; consumer e-commerce averages 15–25%, with significant variation by segment and subject line.
How much does Datamimi cost?
Datamimi offers a Free plan at $0/month (40 credits, no credit card required), Lite at $9/month (400 credits), Starter at $24/month (1,500 credits, up to 3 simultaneous files), Pro at $59/month (5,000 credits with rollover), and Team at $199/month (20,000 credits, 5 users).
Try it with your own file
DataMimi reads the file you actually have — merged cells, headers below row one, totals pasted at the bottom — and shows which rows and columns every number came from.
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