Tailor Jackson sat down with Aisha Amaira, a MarTech expert known for marrying CRM systems, customer data platforms, and marketing automation into revenue-ready programs. Aisha approaches email audits like a mechanic approaches a high-mileage engine: measure, isolate, and fix what slows performance—then document everything so it scales. In this conversation, she unpacks a full-system approach to email marketing audits: technical deliverability, content rendering, list health, compliance, and performance attribution. She shares candid stories—when Gmail clipped a hero image that tanked clicks, how a failing DMARC record hid behind “fine” averages, and why her fastest wins often come from journey mapping and ruthless prioritization.
When you run a deliverability audit, how do you check SPF, DKIM, DMARC, and sender reputation, and what steps do you take when bounce rates spike? Share a time declining opens signaled inboxing issues, the fixes you applied, and the metrics that recovered.
I start with DNS verification: SPF includes the active ESP and sending IPs; DKIM has a valid selector per domain; DMARC is at least p=none while we test alignment, then I push to quarantine or reject once alignment is clean. I validate all three with MXToolbox, postmaster tools, and our ESP’s authentication checker, then pull IP/domain reputation from Google Postmaster, Microsoft SNDS, and any blocklist monitors. If hard bounces creep above 2% or soft bounces cluster at a specific domain, I segment and pause sends to that domain, run list hygiene (typo fix, role account suppression), throttle volume, and warm back up. One memorable case: opens slid from 33% to 26% in two weeks, and Google Postmaster showed a reputation drop from “medium” to “low.” We discovered a DMARC misalignment after a DNS change and a surge of mail to stale Gmail accounts. After fixing DMARC alignment, suppressing 90-day inactives at Gmail, and reducing daily volume by 30% for a week, inbox placement rebounded by 9 points, opens returned to 32%, spam complaints fell from 0.21% to 0.06%, and hard bounces dropped below 0.8% in 14 days.
Validity reports 65% of email pros say deliverability is getting harder—what recent changes made it harder for you, and how did you adapt? Walk through your detection process, tools used, and the before/after impact on inbox placement and spam complaints.
The bar for authentication rigor and list hygiene has risen, especially at Gmail and Yahoo—tightening on DMARC alignment and spam complaint thresholds. I watch domain-level signals daily: Google Postmaster reputation trend, spam rate, and delivery errors; I pair that with seed tests, folder placement via Validity or a similar tool, and engagement cohort views in our ESP. When we saw a subtle shift—seed tests dropping from 86% inbox to 72% in Gmail Promotions and spam inching up—we built a domain-specific warm-up plan, trimmed frequency for low-engagers, and added an engagement gate to high-risk segments. After four weeks, Gmail inbox placement improved by 11 points, complaint rate stabilized at 0.07%, and read rates in our 30-day engaged cohort jumped 18%. The detection cadence matters: small, daily checks beat monthly surprises.
In a content and design audit, how do you test templates across Gmail and Outlook, and with images blocked? Describe the issues you found, the fixes, and how CTR and conversion moved. Include the steps you use with Litmus or Email on Acid.
I load our top three templates into Litmus and Email on Acid, testing 40+ clients and devices with images off, dark mode, and different DPI settings. I scan for broken buttons in Outlook (VML fallbacks), line-height shifts in Gmail iOS, clipped messages due to heavy HTML, and missing ALT text. We once found that Outlook stripped CSS for a ghost table that positioned our primary CTA—on Windows desktop, the button dropped below the fold. We added bulletproof buttons, reduced HTML weight by 28%, compressed hero images, and set explicit heights to prevent layout jump. CTR rose from 1.3% to 2.1% over two sends, and conversion increased 22% because the button was visible and tappable on first view. The disciplined step-by-step—test, annotate screenshots, fix in code, retest—keeps changes surgical.
When your open rate drops from 35% to 25%, how do you isolate whether it’s content, list health, or deliverability? Outline your diagnostic sequence, the specific reports you pull, and one real example where you reversed the trend, including timelines and key metrics.
First I segment by mailbox provider; if Gmail is disproportionately down, I suspect deliverability. Then I compare opens for 30-day engaged vs. 90-day+ inactive; if the drop is uniform, it’s likely subject lines or send timing. I pull Google Postmaster, seed placement reports, and cohort engagement by acquisition source; I also check recent DNS or template changes. In one case, opens fell from 36% to 24% in 10 days. Gmail seed placement slid, and Postmaster showed rising spam rates—so we paused sends to >90-day inactives and fixed an image-heavy template causing clipping. Within 2 weeks, opens recovered to 31%, then 34% in week three; CTR moved from 1.2% to 1.8%, and spam complaints halved.
For a list health audit, how do you detect bots, inactive accounts, and poor segmentation? Share your criteria for suppression, the reactivation workflow, and how list growth rate, hard bounces (<2%), and unsubscribe rate changed after cleanup.
I flag bots with patterns: instant opens/clicks across multiple sends, identical UAs, and clicks on every link in <1 second. Inactives are tiered: 90, 180, and 365-day no-open tiers, adjusted for Apple MPP by using click and on-site activity as the engagement proxy. Poor segmentation shows up when conversion per send is flat across segments—meaning we’re over-broadcasting. Suppression criterirole accounts, disposable domains, hard bounces at first event, and 180-day pure inactives after a two-step reactivation series. Our reactivation flow is three emails over 10 days with a clear “stay on the list” click, preference center, and a win-back offer. After a cleanup last fall, list size shrank 14%, growth rate stabilized at +3.8% MoM, hard bounces fell to 0.6%, and unsubscribes dropped from 0.34% to 0.18%—and revenue per thousand emails sent (RPME) rose 19%.
During a compliance audit, how do you verify CAN-SPAM basics like a visible unsubscribe link and accurate sender info? Walk through your checklist, a time you found risky wording or broken links, and how complaint rates and deliverability improved post-fix.
My checklist includes: visible unsubscribe in the footer and sometimes the header, one-click opt-out within two clicks, physical mailing address, recognizable From name, accurate subject lines, and consent capture documented in CRM/CDP. I test the unsubscribe endpoint under multiple accounts and ensure the suppression syncs within 24 hours. Once, we discovered a localized footer where the unsubscribe text color matched the background—essentially invisible—and a broken preference center link. We pushed an emergency fix, standardized footer contrast ratios, and added list-unsubscribe headers. Complaint rates fell from 0.29% to 0.09% in three sends, and Gmail reputation moved from “medium” to “high” within a month.
In a performance audit, how do you tie subject lines, send timing, and automations to revenue? Describe your attribution approach, the dashboards you use, and an example where conversion lift came from sequencing changes rather than creative tweaks.
I use a blended attribution model: last non-direct click for paid reporting, plus a 3-day email assist window to capture downstream conversions, and holdout tests for major automations. Dashboards include: cohort revenue by automation, send-time heatmaps, subject line themes tied to CTR/CVR, and journey-level drop-offs. One win: we reordered a browse-abandon sequence—moved social proof to email 1, added UGC in email 2, and delayed discount to email 3. Without changing creative assets, conversion lifted 27%, and revenue per recipient for the flow rose from $1.42 to $1.79. Timing and message order often beat clever copy.
You’re told to gather 6–12 months of data—what exact engagement, deliverability, and list metrics do you pull, and how do you normalize them? Show your trend analysis method, the benchmarks you use, and one insight that changed your roadmap.
I pull opens, clicks, conversions, RPME, revenue per session, bounce types, spam complaints, inbox placement (if available), list growth, churn (unsubs + spam), inactive rates by cohort, and acquisition source performance. I normalize by removing Apple MPP inflated opens (focus on clicks and site behavior), adjust for send volume, and calculate rolling 4-week averages to smooth seasonality. Benchmarks I track: open rate vs. brand baseline, CTR near 1.7% for ecommerce, hard bounces <2%, spam <0.1%, and deliverability >95%. A pivotal insight: new subscribers from giveaways looked “great” on open rate but had 62% lower 60-day LTV and 3x complaint rate. We cut that source by 80% and invested in quiz-driven opt-ins—fewer subs, higher revenue.
“Follow the problems, not a predetermined list.” How do you prioritize when conversions fall but opens look fine? Detail your decision tree, the experiments you run first, and an anecdote where skipping a deliverability deep-dive saved time and boosted revenue.
If opens are steady and CTR dips, I look at content and offer relevance first; if CTR is stable but CVR drops, I examine landing pages, checkout friction, and discount alignment. My first experiments: CTA hierarchy and copy, offer framing, product density, and post-click page speed and relevance. We once bypassed a deliverability rabbit hole and ran a split test on the landing page: we removed an interstitial quiz that added 12 seconds before add-to-cart. CTR stayed flat, but conversion per click climbed from 3.4% to 5.1% in a week, boosting revenue 21% for that campaign. The data said “the email did its job”—so we fixed the page.
What hands-on tests do you run that analytics can’t reveal, like signing up, abandoning a cart, or using multiple clients? Share a case where workflow mapping exposed a broken drip, how you fixed it, and the recovery in customer journey metrics.
I subscribe with a personal Gmail, a corporate Outlook, and an iCloud address, then test: welcome flow, preference center, promo cadence, cart abandonment, and browse triggers. I also test with images off and in dark mode, and I purposely change frequencies in the preference center to confirm updates propagate. A journey map once showed a gap: the second welcome email suppressed users who viewed a product—so they never got the browse-abandon series. We fixed the logic by allowing both tracks with capped frequency. Within two weeks, browse-abandon sends increased 38%, revenue from that flow doubled, and overall day-7 engagement for new subs rose 24%.
When documenting findings, how do you rank a failing authentication vs. weak subject lines? Show your impact/effort matrix, who owns which tasks, and a timeline example where quick compliance fixes preceded bigger creative overhauls.
I score items on a 1–5 scale for impact and effort. Failing SPF/DKIM/DMARC is 5-impact, 2-effort—top priority; weak subject lines might be 2-impact, 3-effort. Ownership: IT or dev for DNS and template code, CRM ops for segmentation and suppressions, marketing for copy/creative, and legal/compliance for consent language. In one sprint, we fixed authentication and broken unsub (48 hours), then cleaned suppressions (one week), and finally redesigned the master template (three weeks). The first two changes alone lifted inbox placement and cut complaints; the template overhaul added a sustained CTR increase.
Which metrics matter most to you—open rate (~31% ecommerce average), CTR (1.74% 2024), conversion rate, spam complaints, deliverability rate—and why? Explain how trends outweigh snapshots, and give an example where “good” benchmarks hid a real problem.
CTR, conversion, revenue per send, spam complaints, and deliverability rate tell me what the audience did and whether mailbox providers are happy. Opens provide directional signal, but with MPP, I weight them lightly. Trends beat snapshots—rolling 4- and 8-week views expose slow leaks that one-off benchmarks mask. We once sat at a “good” 28% open and 1.8% CTR, but deliverability slipped from 97% to 92% over six weeks. Spam complaints were inching up from 0.05% to 0.12%. If we’d stared at the averages, we’d have missed the drift; instead, we cleaned inactives and fixed frequency, restoring deliverability to 96% and complaints to 0.06%.
How do you audit the first 30 days of a new subscriber’s journey across welcome, newsletters, promos, and abandoned cart? Describe overlap conflicts you’ve found, your frequency rules, and the step-by-step changes that raised engagement without spiking unsubscribes.
I map every touch by day: welcome series content, promo cadence, newsletter timing, browse and cart triggers, and SMS overlap if applicable. I look for days with more than two touches or conflicting CTAs (e.g., full-price promo and discount-based welcome). Frequency rules: no more than one automation plus one campaign per day, and cap at 5–6 touches per week for new subs. A conflict we fixed: promo blasts colliding with cart recovery. We slowed promos for active cart sessions and brought social proof earlier in the welcome flow. Engagement (clicks) rose 20% in the first 30 days, unsubscribes held steady, and day-30 LTV improved 15%.
What’s your cadence for annual audits and quarterly mini-audits, and how do you keep them fast over time? Share the living documentation you maintain, the KPIs you revisit each quarter, and one example where early detection avoided a bigger failure.
I run a full audit annually—deliverability, templates, list health, compliance, performance—and quarterly mini-audits focused on KPIs and any new risks. I maintain living docs in a shared workspace: DNS records, IP/domain reputation logs, template versions, automation maps, and an issue/decision register. Quarterly, I revisit spam rate, hard bounces, deliverability, CTR/CVR, list growth vs. unsubscribes, and automation revenue share. Early in Q2, a mini-audit caught a creeping soft bounce rate at Microsoft domains; we found image-heavy sends and tightened retry logic. We avoided a block, and CTR at Outlook devices recovered by 17%.
When mobile rendering breaks but desktop looks fine, how do you pinpoint whether it’s the template, images, or CSS? Walk through your test matrix, the fix, and how mobile CTR and conversion changed after the correction.
I test the template skeleton first in Litmus across iOS/Android mail apps, then swap in plain buttons and placeholder images to isolate CSS from asset issues. If the skeleton passes, I reintroduce images to spot DPI/crop problems; if it fails, I inspect media queries, inline CSS, and min-widths. A common culprit: desktop-first tables forcing 600px widths. We refactored to hybrid fluid with max-width containers, compressed retina images, and added safe line-heights for Gmail iOS. Mobile CTR improved from 0.9% to 1.6%, and mobile conversion climbed 19% over three campaigns.
How do you use Shopify Email or similar tools to create, automate, and track campaigns without code? Outline your automation triggers (e.g., abandoned cart), segmentation rules, and a story where platform analytics guided a profitable pivot.
I lean on native templates, merge tags, and visual automations: welcome, browse, cart, post-purchase review, and replenishment. Segmentation starts with purchase recency/frequency/monetary (RFM), product interest, and channel source; I layer engagement to protect deliverability. Shopify’s analytics make it easy to view revenue attribution by flow and product affinity by segment. We saw replenishment lag at 28 days, but analytics showed a purchase cluster at day 21; moving the reminder up by a week increased flow revenue 31% and reduced discount usage because customers reordered before running out.
When list growth slows, how do you balance acquisition, consent, and segmentation quality? Share the opt-in flows you audit, the content offers that work, and the metrics you watch to ensure growth doesn’t harm deliverability or complaint rate.
I audit every entry point: on-site popups, embedded forms, checkout opt-in, content downloads, quizzes, and loyalty sign-ups. Offers that age well are evergreen: first-order perks, exclusive access, and quiz results that inform personalization. I gate higher-risk sources with double opt-in, throttle new-subscriber sends for 48 hours to monitor complaints, and route them into a welcome-only sandbox. Metrics: confirmed opt-in rate, early complaints (<0.1%), 30-day engagement, and 60-day LTV by source. When we cut low-intent sweepstakes and replaced them with a product quiz, list growth slowed 12% but 60-day LTV rose 29% and spam was halved.
How do you decide when a hard bounce rate near 2% is acceptable versus a warning sign? Describe the thresholds you use for soft bounces, re-try logic, and a real cleanup cycle that restored inbox placement and improved open rate.
I treat 2% hard bounces as the edge of acceptable during list spikes; sustained >1% is a warning. For soft bounces, I allow up to three retries over 72 hours; persistent 4xx at a specific domain signals throttling or filtering. If hard bounces climb, I halt the riskiest segments, validate recent acquisitions, and check for typos and role accounts. After a partnership campaign, hard bounces hit 2.2%; we pruned the source list, ran validation, and suppressed 180-day inactives. Hard bounces fell to 0.7%, deliverability improved from 94% to 97%, and opens ticked up from 27% to 30% within two weeks.
What’s your framework for improving CTAs when CTR lags but opens hold steady? Detail copy, placement, and design tests, your sample sizes, and the best-performing variant you’ve found, including the exact lift and how you validated it.
I test hierarchy first: one primary CTA above the fold, supportive text, and secondary links below. Copy tests include benefit-forward vs. action-first (“Get 20% Off” vs. “Shop New Arrivals”), and urgency framing with real deadlines. Design-wise, I use 44px tap targets, high-contrast buttons, and directional cues near the hero. I run at least 25k recipients per variant to detect a 10–15% relative lift in CTR with 95% confidence. Our best variant changed “Shop the Collection” to “Get Your Size—Limited Stock,” moved the button up 200px, and added inventory microcopy; CTR rose from 1.4% to 2.3% and conversion per click improved 14%, validated over two consecutive tests.
When you inherit a messy program, what’s your first 30-day audit plan across deliverability, content, list health, compliance, and performance? Share your step-by-step sequence, the fastest wins you target, and the measurable outcomes you expect by day 30.
Days 1–3: authenticate domains (SPF/DKIM/DMARC), fix unsub and address in footers, and pull 12 months of data. Days 4–10: list hygiene—suppress hard bounces, role accounts, and 180-day inactives pending reactivation; implement engagement-based throttling. Days 11–15: template QA in Litmus/Email on Acid; fix buttons, image weights, and dark mode; simplify content blocks. Days 16–25: rebuild critical automations (welcome, cart, post-purchase), remove overlap conflicts, and set frequency caps. Days 26–30: run performance tests on subject lines and CTAs, instrument dashboards, and finalize the action plan. By day 30, I expect hard bounces <1%, spam <0.1%, deliverability >95%, CTR +20% from baseline, and revenue per send up 10–15%.
When you run a deliverability audit during a bounce spike, how do you collaborate across teams to fix root causes quickly, and what safeguards do you put in place to prevent regression?
I pull a war-room together: CRM ops handles segment suppression, IT updates DNS, marketing pauses nonessential blasts, and CX flags complaint themes. We deploy send throttling by domain, set temporary caps on inactive cohorts, and add preflight checks to our deployment checklist. Then I implement safeguards: automated alerts for bounce/complaint thresholds, seed list monitoring, and a weekly domain reputation log. Regression drops when you institutionalize the fixes—checklists, roles, and alarms make good habits stick.
What’s your approach to subject line testing in the context of authentication and sender reputation? How do you avoid tests that inflate opens but hurt long-term deliverability?
I avoid clickbait and false urgency; mailbox providers punish misaligned content. Tests center on clarity, value, and brand recognition—preheader synergy and sender name familiarity matter. I require “content congruence”: the email body must fulfill the subject’s promise. We saw a short-term open lift with hypey phrasing, but complaints doubled; we killed the variant. Sustainable wins are boring on paper but bankable over months.
Do you have any advice for our readers?
Treat your email program like infrastructure, not a series of campaigns. Authenticate perfectly, guard your list quality, and map your journeys with the same care you put into creative. Follow the problems: if conversions wobble but opens don’t, fix the page or the offer before you tear apart DNS. And schedule your audits—annual fulls, quarterly minis. The calm you feel when the metrics blip and you already know where to look—that’s the payoff.