Every send is a bet on your sender reputation.
We make it a calculated one. Sendnexa runs churn-risk, predicted-CLV, and next-order-date models on your ESP data, so campaigns reach people likely to buy and skip the ones about to complain.
*Representative engagement results; client identities anonymized.
Built for brands where purchase timing is the whole game
Predictive email pays off where buying happens on a cycle. These are the four verticals our models are tuned for.
Cart abandonment averages 70.2% across Baymard's 50-study meta-analysis. Timed recovery and predictive suppression claw revenue back without burning the list.
1.9% → 3.1%cart conversion, 8 weeksCoffee, supplements, pet food: expected-date-of-next-order triggers replace the guesswork of fixed timers on every replenishment cycle.
1.1% → 2.4%replenishment conversion, 90 daysOnboarding tied to product behavior, complaint rates held under Google's 0.1% guidance so lifecycle mail never poisons transactional sending.
3.1% → 4.6%trial-to-paid, 10 weeksLocation-aware segments and CLV tiers on the same predictive stack, once POS data reaches the ESP daily instead of weekly.
9% → 16%email share of revenue, 12 weeksRepresentative engagement results; client identities anonymized. Full stories in thecase studies.
Six services, one predictive backbone
AI audience analysis
Churn-risk suppression, predicted-CLV tiers, and next-order-date triggers built from your ESP's own predictive data. This is where every engagement starts.
Lifecycle automation
Welcome, abandoned-cart, browse-abandon, replenishment, and win-back flows. Klaviyo data shows flows drive ~41% of email revenue from just 5.3% of sends.
Deliverability & compliance
SPF, DKIM, DMARC, RFC 8058 one-click unsubscribe, GlockApps seed tests, and a CASL consent audit. The unglamorous work that keeps you in the inbox.
Campaign intelligence
AI-assisted campaign analysis and honest A/B test reads, reported in placed-order rate and revenue per recipient rather than opens.
ESP migration
Platform switches with a 2–4 week warm-up and order-history import, so reputation and predictive data survive the move.
List growth & consent
CASL-compliant capture with double opt-in and a consent register. Quality measured by 30-day engagement, not signup counts.
Results we can walk you through
Interview-style case studies with the numbers, the misses, and the design parameters included. Client identities anonymized; figures are representative engagement data.
Cart conversion 1.9% → 3.1% while cutting sends 22%
Churn-risk suppression plus a retimed 4-touch cart sequence, with the final touch held back for low-risk profiles only. 8 weeks, complaints down to 0.06%.
Replenishment conversion 1.1% → 2.4% in 90 days
Expected-date-of-next-order triggers replaced a fixed 30-day timer. Revenue per recipient rose from $1.60 to $2.75 across the flow.
From audit to model-driven program in four steps
Audit the list and the consent base
Two weeks of analysis: engagement recency, complaint rates against the 0.1% Gmail guidance, CASL consent records, and where sends are going that they should not.
Build the predictive segments
Churn-risk suppression lists, CLV tiers, and next-order-date cohorts, built inside your ESP so you own everything we make.
Rebuild the flows around timing
Cart, browse, replenishment, and win-back sequences triggered by model scores rather than fixed timers.
Report in revenue, then iterate
Placed-order rate and revenue per recipient, reviewed in a monthly working session. Read the full engagement process for the details.
Guides we maintain in public
The playbooks behind the services, with the numbers and sources attached. Each is owned by the team member who runs that area.
How to fix email deliverability
The 7-step repair sequence, from failing DMARC to a monitored volume recovery.
Using Klaviyo's predictive analytics
The 500-customer threshold, and the first three segments worth building.
CASL for email marketers
Express vs implied consent, the two-year clock, and the consent register.
What founders ask us first
What does "AI-driven" mean at Sendnexa?
It means decisions come from predictive models, not hunches. Your ESP already scores churn risk, predicted customer lifetime value, and expected date of next order; Klaviyo retrains these models weekly. We build the segments, suppressions, and flow triggers that act on those scores, then report the result in placed-order rate and revenue per recipient.
Do you work with our existing ESP?
Yes. We work inside Klaviyo, Omnisend, Customer.io, ActiveCampaign, and Brevo, so there is no forced migration. Note that Klaviyo unlocks predictive analytics once an account has 500+ customers with orders and 180 days of history. Below those thresholds we start with lifecycle flows plus recency-based proxy segments, and build toward the predictive layer as history accumulates.
Is this compliant with CASL?
CASL is an opt-in law: you need express or implied consent before a commercial email goes out, and penalties reach $10 million per violation for corporations. Every program we run starts with a consent audit, keeps identification and unsubscribe mechanics in each message, and documents the consent basis for every segment we mail.
How quickly do results show up?
Suppression and timing changes move numbers within weeks because they touch every send. A DTC coffee-subscription client saw abandoned-cart placed-order rate climb from 1.9% to 3.1% over 8 weeks, with 22% fewer sends and complaints down from 0.19% to 0.06%. Flow rebuilds take longer; 90 days is a fair horizon.
Bring your list. We'll bring the models.
A working session is 45 minutes on your actual ESP data: where the sends are leaking, what the predictive layer would change first, and what it costs.
Book a working session