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.
| What it counts | Five sequential stages: (1) Sent = count(emails_sent) accepted into Mailchimp’s send queue; (2) Delivered = sent minus bounces; (3) Opened = unique opens (post-MPP-adjustment); (4) Clicked = unique clickers; (5) Converted = recipients with attributed e-commerce purchase within the attribution window. Each stage carries the absolute count and the conversion rate from the prior stage. |
| Stage 1: Sent | The denominator. emails_sent from the campaign or automation API. Includes anything Mailchimp’s pipeline accepted into the send queue, regardless of whether the receiving server eventually accepted it. |
| Stage 2: Delivered | delivered = sent - hard_bounces - soft_bounces. Soft bounces typically retry up to 3 times before becoming hard bounces; the funnel reflects the final state. Healthy delivery rate is 97-99 percent; below 95 percent indicates list-quality or sender-reputation issues. |
| Stage 3: Opened | unique_opens from the campaign report. Adjusted for Apple Mail Privacy Protection by subtracting an estimated MPP-driven inflation. Without the adjustment, Apple Mail users contribute “opens” they may not have actually performed (MPP pre-fetches images for any Apple Mail user). The MPP-adjusted figure is the comparable-to-pre-MPP open count. |
| Stage 4: Clicked | unique_clicks, recipients who clicked at least one link in the email. Mailchimp tracks every link in the email body via redirect URLs. Click counts are not affected by MPP (clicks require active user action). The Click-to-Open Rate (CTOR) metric clicks ÷ opens is the most MPP-resilient engagement signal and the cleanest measure of content effectiveness. |
| Stage 5: Converted | Recipients whose subsequent ecommerce purchase was attributed to the email click within the attribution window (24 hours default, 5 days for Mailchimp Premium). Requires an active Mailchimp-Shopify (or other commerce platform) integration; without it, converted = 0 by definition. |
| Stage-to-stage conversion rates | Surfaced alongside absolute counts: (a) Delivery rate = delivered ÷ sent; (b) Open rate = opened ÷ delivered; (c) CTOR = clicked ÷ opened; (d) Click-to-conversion rate = converted ÷ clicked. Each rate has its own diagnostic interpretation and benchmark band. |
| Attribution model | Mailchimp uses last-click within the configured window (24 hours Standard, 5 days Premium). Conversions outside the window or attributed to other channels do not count even if email touched the customer earlier in the journey. |
| Currency | n/a, this is a count-based funnel. The currency-denominated impact surfaces in mai_revenue_per_recipient and mai_total_revenue. |
| Bounce handling | Both hard and soft bounces are excluded from the Delivered stage. Soft bounces that recover (the receiving server temporarily rejected, then accepted on retry) move to Delivered; persistent soft bounces become hard bounces. |
| Suppressed members | Excluded from the Sent stage entirely. Suppressed addresses are not sent to and do not enter the funnel. |
| Time window | 30D vsP (30-day rolling vs prior period). Per-campaign funnels are also available via individual campaign reports. |
| Alert trigger | stage-rate drop > 5 percentage points vsP at any stage transition, or delivery_rate < 95 percent (deliverability emergency threshold). |
| Sentiment key | mc_engagement_funnel |
| Roles | owner, marketing |
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.| Funnel stage | Count | Stage rate | Industry baseline | Read |
|---|---|---|---|---|
| Sent | 580,000 | (denominator) | n/a | 6 sends to 96k average recipients per send |
| Delivered | 568,420 | 98.0% (delivery rate) | 97-99% | Healthy |
| Opened (MPP-adjusted) | 158,800 | 27.9% (open rate) | 22-32% | Healthy |
| Clicked | 22,200 | 14.0% (CTOR) | 12-18% | Healthy |
| Converted | 3,540 | 15.9% (click-to-convert) | 10-20% | Healthy |
| Funnel summary | 3,540 conversions on 568,420 delivered | 0.62% net conversion rate | 0.5-1.5% | Healthy |
- 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.
| Time horizon | Action |
|---|---|
| First 1 hour after alert | Identify which stage broke. Stage-level isolation drives the response. |
| First 4 hours | If delivery: investigate sender reputation + list hygiene. If open: investigate subject lines + send times. If click: investigate content + offer. If conversion: investigate landing page + checkout. |
| First 24 hours | Implement the highest-impact change for the affected stage. Avoid changing multiple stages simultaneously; isolate the variable. |
| First week | Measure 7-day-rolling stage rates for early signal. Confirm the fix held before normalising the change. |
Sibling cards merchants should reference together
| Card | Why merchants reach for it |
|---|---|
mai_email_health_kpis | The composite health score that summarises the funnel into a single number. This card is the diagnostic decomposition; that card is the executive summary. |
mai_revenue_per_recipient | The revenue-efficiency view alongside the engagement funnel. RPR is the “outcome”; the funnel is the “how”. |
mai_delivery_rate | Stage 2 drill-down. Deliverability investigation. |
mc_open_rate | Stage 3 drill-down. Open-rate-specific analysis. |
mc_click_rate | Stage 4 drill-down. Click-rate-specific analysis. |
mc_click_to_open_rate | The MPP-resilient engagement signal. |
mc_conversion_rate | Stage 5 drill-down. Conversion-rate-specific analysis. |
mai_bounce_rate | The deliverability counterpart. Bounces remove recipients from stage 2. |
mc_alert_deliverability_drop | Deliverability incident alert. Cascades through the funnel from stage 2 onward. |
mc_alert_bounce_spike | Bounce-volume spike alert. First indicator of list-quality or reputation issues. |
mc_alert_sender_reputation | Sender reputation degradation alert. |
mc_xc_send_to_purchase_lag | The send-to-conversion time distribution. Drives attribution-window decisions. |
Klaviyo klv_engagement_funnel | The Klaviyo parallel for ESP comparison. |
Brevo brv_engagement_funnel | The Brevo parallel. |
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.
| Reason | Direction | What to do |
|---|---|---|
| MPP open-rate adjustment. Vortex IQ subtracts MPP-driven inflation; Mailchimp UI shows raw figures. | Vortex IQ open-stage lower | Use raw open-rate field for direct UI comparison; MPP-adjusted is the trend-comparable view. |
| Recipient deduplication. Vortex IQ deduplicates Sent across the period (a recipient who got 8 emails counts once at the funnel-stage level); Mailchimp’s per-campaign view counts every send. | Vortex IQ Sent lower when recipients overlap across multiple campaigns | The deduplicated view is the right one for “how many unique people did the programme touch”. The per-send view is the right one for “how many delivery events happened”. |
| Attribution window for converted stage. Mailchimp UI uses the configured account window; Vortex IQ surfaces whatever the API returns. | Either direction | Confirm window setting in Mailchimp Account → Settings → Ecommerce. |
| Refresh lag. Mailchimp pipeline refreshes within 30 minutes for engagement events; Vortex IQ refreshes every 6 hours. | Vortex IQ moves slowly for recent activity | Wait for next refresh; check last_synced_at. |
| Time zone. Mailchimp uses account time zone; Vortex IQ uses UTC for period boundaries. | Boundary days differ | Largest impact on T (today) and 7D windows; for 30D the drift averages out. |
| Comparison | Expected relationship | When divergence is legitimate |
|---|---|---|
mai_engagement_funnel ↔ Klaviyo klv_engagement_funnel | Both surface the same five-stage funnel | Different ESPs have different MPP defaults, attribution windows, and click-tracking implementations. Brands running both should expect 2-5 percentage-point differences at each stage that reflect ESP methodology rather than programme differences. |
mai_engagement_funnel ↔ Shopify checkout-and-conversion rate filtered to email source | The Converted stage should align with Shopify’s email-attributed orders | Mailchimp’s last-click attribution differs from Shopify’s UTM-based attribution; gaps of 10-20 percent are typical and reflect cross-attribution effects. The Vortex Mind Customer Recovery Opportunity report quantifies the gap. |
mai_engagement_funnel ↔ Brevo brv_engagement_funnel | Same five-stage decomposition | Same caveats as Klaviyo comparison. |
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.