At a glance
The five-stage email funnel from Sent → Delivered → Opened → Clicked → Converted, surfaced as both absolute counts and conversion rates between stages. The diagnostic decomposition that turns “the programme is under-performing” into “the funnel is breaking at stage X”: each stage has different fixes, different timelines to recovery, and different responsible owners. Brands that monitor only the headline metric (open rate, conversion rate) miss the stage-level drift that the funnel surfaces clearly.
Calculation
Calculated automatically from your Mailchimp data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
A US-based pet supplies brand on Shopify running Mailchimp Standard with a 120,000-contact main Audience. Snapshot for the 30-day window ending Wednesday 15 May 26.
What the funnel is telling us:
- The funnel is healthy at every stage, which is uncommon. Most accounts have at least one stage running below industry baseline; this account is operating at programme-quality consistent with top-third performance. The implication is that further improvement requires moving from “good” to “excellent” rather than fixing weakness.
- The 14.0 percent CTOR is the most diagnostic single number. CTOR is MPP-resilient (both numerator and denominator are MPP-affected, so they cancel) and isolates content effectiveness from open-rate confounds. Brands at 14 percent CTOR are doing genuinely good content work; the message-to-audience fit is right. The lever to push CTOR above 16 percent is typically (a) better personalisation (product recommendations driven by purchase history rather than category browse); (b) shorter, more action-oriented email copy; (c) visual hierarchy that places the primary CTA above the fold.
- The 15.9 percent click-to-conversion rate suggests the on-site experience is converting email-driven traffic well. Email-driven traffic tends to convert higher than paid-social-driven traffic (because the audience already has a relationship with the brand) but lower than direct or organic-search traffic (because email-driven visits are interrupting the customer rather than responding to active intent). 15.9 percent is in the upper band; further improvement here usually requires landing-page optimisation rather than email content changes.
- The 0.62 percent net conversion rate (converted ÷ delivered) is the executive-summary number. It tells “out of every 1,000 emails that land in inboxes, 6 result in purchases”. This translates directly to revenue economics: at the brand’s 260 of revenue per 1,000 delivered emails, against per-send costs of 2.00 depending on plan tier and audience size.
- The funnel surfaces no immediate action item but several monitoring priorities. (a) Deliverability is currently at 98.0 percent; any drop below 96 percent should trigger immediate investigation. (b) Open rate is healthy but Apple Mail share is increasing across the audience over time; the MPP adjustment may need recalibration in 6-12 months. (c) Click-to-conversion rate of 15.9 percent is at the upper end of the band; brands that approach the ceiling here tend to find diminishing returns and should consider whether the marginal effort is better spent on audience growth (lifting the denominator) rather than per-recipient efficiency.
- Identify which stage transition broke. The stage-by-stage rates are the actionable view. A delivery-rate drop is a different fix than an open-rate drop, and an open-rate drop is a different fix than a click-rate drop.
- Delivery-rate drops suggest list-quality or sender-reputation issues. Check
mai_bounce_rateandmc_alert_sender_reputation. The fix involves list hygiene (purging stale addresses), authentication (DKIM, DMARC, SPF), and sender warm-up rather than content changes. - Open-rate drops suggest subject-line, sender-name, or send-time issues. First, normalise for MPP: a “drop” in raw open rate may just reflect a shift in audience Apple Mail share. After MPP normalisation, persistent drops point at subject-line A/B testing and sender-name consistency.
- CTOR drops suggest content-effectiveness issues. Subject lines that over-promise relative to email content drive a wedge between open rate (high, because the subject was compelling) and click rate (low, because the content didn’t deliver on the promise). Audit the subject-to-content alignment for the worst-performing recent campaigns.
- Click-to-conversion drops suggest on-site issues. The email did its job (got the customer to click); the conversion is breaking on landing page, product page, or checkout. Pair with Shopify checkout conversion rate for the commerce-side view.
Sibling cards merchants should reference together
Reconciling against the vendor’s own dashboard
Where to look in Mailchimp’s own dashboard:- Mailchimp → Reports → All campaigns with the per-campaign report showing stage-by-stage figures: Recipients → Successful Deliveries → Opens → Clicks → Orders. The closest 1-to-1 source for the funnel reconciliation.
- Mailchimp → Audience → Engagement for the audience-level engagement breakdown across all sends.
- Mailchimp → Reports → Comparative reports for stage-by-stage comparison across multiple campaigns or automations.
Cross-connector reconciliation:
Quick rule for support tickets: if a merchant says “My Mailchimp campaign report shows 200,000 sent but your funnel shows 150,000”, the most common cause is recipient deduplication (8 sends × 25,000 unique recipients = 200,000 send events but 150,000 unique recipients reached). The next-most-common is the period boundary (campaigns spanning the period start are partially counted). For absolute reconciliation, use a single campaign’s report as the comparison point; the funnel aggregates across multiple campaigns by definition.
Known limitations / merchant FAQs
My open rate dropped 8 percentage points but click rate is steady. Is this a real problem? Probably not, post-MPP. Apple Mail Privacy Protection inflates open rates without affecting click rates; if the Apple Mail share of the audience shifted (more iOS 15+ adoption, or recipient mix shift toward Apple-using cohorts), open rate moves without programme degradation. The check: did CTOR (click-to-open rate) change? CTOR is MPP-resilient. If CTOR is stable, the open-rate drop is MPP-related and not actionable. If CTOR is also dropping, the engagement is genuinely degrading. My delivery rate is 92 percent. Is that bad? Yes. Healthy programmes run 97-99 percent delivery; 92 percent indicates significant list-quality or sender-reputation issues. Common causes: (a) stale list: many addresses are abandoned (former employee email accounts, defunct ISP addresses, role-based addresses for departed roles); (b) sender reputation drop: heavy bounces, spam complaints, or blocklist appearance; (c) list-buying or scraping: purchased or scraped lists generate massive bounce rates and damage reputation persistently. Fix path: pause sends to the lowest-engagement segment (90+ days no opens), run an authentication audit (DKIM, DMARC, SPF), warm up sender reputation by sending only to the most-engaged segment for 14 days, then progressively re-include broader segments. My CTOR dropped from 14 percent to 9 percent. What happened? CTOR drops indicate content-effectiveness degradation. Investigate, in order: (1) subject-line over-promising: if subject lines are getting more aggressive (urgency framing, sale teasers) but the email content is unchanged, openers feel deceived and don’t click; (2) send-frequency increase: subscribers receiving more emails per week become selective about clicking even if they continue opening from habit; (3) content format change: a switch from short-form to long-form emails (or vice versa) often hurts CTOR temporarily as the audience adjusts; (4) product mix shift: featuring products the audience isn’t shopping for currently reduces click intent; (5) CTA placement degradation: hiding the primary CTA below the fold or burying it in dense copy reduces clicks. The fix is rarely a single change; usually a 2-3 element audit and adjustment. My click-to-conversion rate is only 5 percent. Is the email broken or the website? The email is doing its job (delivering clicks); the conversion is breaking on-site. Investigate the landing page first: (a) is the page loading fast on mobile? Email clicks come heavily from mobile devices and slow LCP destroys conversion; (b) is the page targeting the same product or category the email featured? Email-driven traffic to a generic homepage converts 50-70 percent worse than email-driven traffic to a deep-linked product or collection page; (c) is the offer in the email visible on the landing page? If the email promises 20 percent off and the landing page doesn’t display the discount, customers feel they were misled and bounce; (d) is the cart/checkout flow working for the device profile of email traffic? Healthy click-to-conversion is 10-20 percent; below 8 percent is almost always an on-site issue. Why is my Sent stage lower than the sum of my campaign Recipient counts? Recipient deduplication. The funnel shows unique recipients reached across the period; per-campaign reports show recipients per send. A subscriber who received 8 campaigns counts once in the Sent stage but 8 times in the per-campaign sum. The deduplicated view is the right one for “how many unique humans did the programme touch”; the per-send sum is the right one for “how many delivery events happened”. Both are correct for their purposes. Should I optimise for stage rates or for absolute counts? Both, depending on what’s broken. Stage rates surface programme-quality issues (CTOR, conversion rate); absolute counts surface scale issues (audience size, send frequency). A programme with great stage rates but small absolute counts is well-tuned but not contributing meaningfully to revenue; a programme with moderate stage rates and large absolute counts is contributing meaningfully but has room to improve efficiency. The right framing: monitor stage rates for programme-health drift; report absolute counts to the business for revenue contribution. My funnel shows 0 conversions but Mailchimp’s UI shows revenue. What’s wrong? The Mailchimp-Shopify (or other commerce platform) integration is connected but Vortex IQ is not pulling the data correctly. Possible causes: (a) the merchant connected the integration after the period boundary, so historical conversions exist in Mailchimp but not in Vortex IQ; (b) the connector’s OAuth token expired and needs reconnection; (c) the attribution window in Mailchimp was changed and the API is returning different numbers than the UI for historical periods. Reconnect the integration in the Vortex IQ Sources page and check the next refresh; if the issue persists, contact support with a specific campaign ID for trace investigation. My funnel shows healthy stage rates but RPR is dropping. Are these consistent? Yes, in two scenarios. (1) Audience grew faster than revenue: more recipients, same conversion patterns, lower per-recipient revenue (but probably similar absolute revenue). The fix is audience-quality work rather than funnel-quality work. (2) Average order value dropped: the funnel measures conversion counts, not order values. If the same conversion rate is producing smaller orders (mix shift toward lower-priced products, discount-heavy offers), the funnel looks healthy but RPR drops. Pair the funnel withmc_aov_email for the order-value dimension.
Can Vortex IQ improve my engagement funnel automatically?
Read-only by design. Vortex IQ surfaces stage-level patterns and identifies which transitions are degrading; the merchant’s marketing team executes inside Mailchimp. The Vortex Mind Customer Recovery Opportunity report generates merchant-side Actions when funnel patterns suggest specific fixes (e.g. “open rate degraded post-MPP-adjustment, recommend subject-line A/B test rotation”), but the changes themselves sit with the merchant.
Is the funnel the same across email types (campaigns vs automations vs transactional)?
The funnel framework applies to all three but the benchmark bands differ: (a) Campaigns have the lowest stage rates (broad audience, moderate engagement); (b) Automations have higher rates (targeted to specific user states, higher intent); (c) Transactional (order confirmations, shipping updates) have the highest rates (recipients expect and want these emails). The headline funnel blends all three; the per-type breakdown surfaces in mc_automation_revenue_share and individual automation reports.