Next Best Channel Prediction
Predict whether each customer is most likely to engage via email, SMS, push notification, or in-app message. EkamFlow's next best channel prediction improves response rates and reduces opt-outs.
Next best channel is an AI decisioning approach that predicts which channel — email, SMS, push, in-app, direct mail, or paid retargeting — is most likely to drive a response from each customer at each moment. Instead of routing customers by declared preference or a fixed channel priority list, next best channel evaluates per-customer engagement patterns, cost per channel, deliverability signals, and fatigue state to return the single optimal outreach surface. Modern channel decisioning systems replace static channel priority rules with per-customer probabilities that feed directly into marketing automation and campaign orchestration.
What is Next Best Channel Prediction?
Next best channel prediction uses machine learning to determine which communication channel is most effective for each individual customer. Instead of sending the same campaign across all channels, or letting customers self-select, ML predicts which channel each person is most likely to engage with — email, SMS, push notification, in-app, or direct mail.
Channel fatigue is one of the fastest ways to lose customers. Over-messaging on the wrong channel drives opt-outs and unsubscribes. Under-utilizing a customer's preferred channel means missed engagement. Most marketing teams have no data-driven way to make this decision at scale.
EkamFlow predicts the next best channel for every customer as part of a unified prediction. The model learns from engagement history — opens, clicks, responses, and opt-outs across channels — to recommend the channel most likely to drive action for each individual.
How it works, step by step
- 1
Connect your warehouse
Snowflake, BigQuery, Databricks, Redshift, or Postgres. Read-only access to customer, engagement event, and channel-delivery tables.
- 2
Unify channel engagement history
EkamFlow inspects email open/click history, SMS reply rates, push open rates, in-app interactions, and ad-response data — no schema mapping required.
- 3
Private model trains on your response data
The model learns how each customer engages across every channel available to your brand, factoring in deliverability, opt-outs, and cost per channel.
- 4
One API call per customer × channel
The API returns a per-channel score plus the top-ranked channel with expected engagement lift. Sub-60ms latency for real-time orchestration.
- 5
Continuous re-scoring
As new channels launch, engagement rates shift, and customers opt out or resubscribe, the model reallocates automatically. No priority-list rewrites.
Signals the model uses
- Historical open rate by channel (email, SMS, push)
- Click-through and reply rates per channel
- Deliverability signals (bounces, opt-outs, suppressions)
- Device and app-install patterns
- Time-of-day engagement patterns per channel
- Cost per channel (email vs SMS vs paid social)
- Recent channel fatigue signals
- Declared preferences vs. actual behavior gap
- Cross-channel cannibalization patterns
- Regulatory constraints per channel (CAN-SPAM, TCPA, GDPR)
Where it fits in your stack
- Marketing automation: Braze, Iterable, Customer.io, Klaviyo, MailChimp
- SMS/messaging: Attentive, Postscript, Twilio, Bird
- Push & in-app: OneSignal, Airship, MoEngage
- CDPs: Segment, Rudderstack, mParticle, Hightouch
- CRM: Salesforce, HubSpot, Zoho
- Ad platforms: Meta CAPI, Google Enhanced Conversions, TikTok Events API
- Data warehouse: Snowflake, BigQuery, Databricks, Redshift
Build in-house vs. EkamFlow
| Dimension | Build in-house | EkamFlow |
|---|---|---|
| Personalization depth | Fixed channel priority per segment | 1:1 per customer × per channel |
| Cost awareness | Not factored | Channel cost baked into ranking |
| Fatigue prevention | Global frequency caps | Per-customer × per-channel fatigue modeling |
| New-channel onboarding | Manual priority-list update | Automatic — model incorporates as data flows |
| Cross-channel cannibalization | Blanket suppression rules | Learned from actual response data |
| Latency | Depends on orchestration layer | <60ms at send-time |
| Team required | Channel leads + marketing ops + data eng | None |
What it looks like in different industries
A meal-kit brand had a hard-coded channel order: email first, then SMS 48 hours later, then push. Next best channel per customer surfaced that ~30% of subscribers had actually opted out of email but engaged 4× on SMS. Reversing the order for that segment lifted reactivation 22%.
A neobank routes account-alerts and offers through the channel each customer actually engages with — while respecting regulatory carve-outs (fraud alerts must reach the customer via multiple channels). NBC handles the ranking; compliance rules stay as hard overrides.
A news app decides per user whether the morning breaking-news story goes to push or email digest. Push-fatigued readers who ignore alerts get moved to the digest automatically, cutting opt-outs 40% without losing engagement.
A DTC apparel brand had a policy of firing SMS for every abandoned cart. NBC ranked SMS below email for ~40% of the base — customers who had ignored the last three SMS pings but reliably opened emails. Shifting them cut per-cart outreach cost 60% without touching conversion.
A B2B SaaS growth team ran identical lifecycle emails across their whole trial cohort. NBC surfaced that senior-buyer contacts preferred LinkedIn InMail response over email, while implementer contacts preferred in-app. Splitting the outbound by ranked channel improved trial-to-paid by 14%.
A digital-health platform routes appointment reminders, medication nudges, and educational content through the channel each patient actually opens — SMS for older patients, push for millennials, secure-message for a small chronic-care segment. Adherence metrics improved without adding to the compliance-review workload.
How EkamFlow does it
Per-customer channel preference
Each customer gets a channel recommendation based on their individual engagement history — not segment-level assumptions or one-size-fits-all rules.
Reduces opt-outs and fatigue
By sending on the right channel, you reduce unsubscribes and opt-outs. Customers engage more when you reach them where they actually respond.
Combined with timing and offer
Next best channel comes back with next best time and next best offer in one API call — so you send the right message on the right channel at the right time.
Related predictions
Frequently asked about next best channel prediction
Declared preferences (a user picking 'email only' in a preference center) are treated as hard constraints, not signals — the model will never route away from a customer's declared preference. What it does is optimize within the channels they've opted into. If a customer picks 'email + SMS', NBC will decide when each is right.
Yes. Global and per-channel caps configured in your marketing automation platform stay authoritative. NBC scores which channel would be most effective if a customer is eligible for outreach; your cap-management layer decides whether to send at all.
Channel cost is a first-class input. The model ranks channels by expected value, not raw response probability — so a channel that lifts response 2× but costs 100× more will not win unless the customer's expected value justifies it. You configure the cost per send per channel; the model does the math.
Opt-outs immediately remove that channel from the ranking. The model does not attempt to route around opt-outs. When a customer resubscribes, they re-enter the ranking as a cold-start on that channel, with cohort-based defaults until personal signal accumulates.
Yes, as long as you're capturing response signal from those channels in your warehouse. Direct mail is scored based on redemption codes or QR scans; paid retargeting is scored on the conversion attribution windows you already track. Any channel with a measurable response can be ranked.
Platform-native adaptive channel features (Braze, Iterable, Customer.io) rank channels using platform-collected data only. NBC ranks using your warehouse — which includes offline events, POS transactions, product usage, support tickets, and every other signal your platform doesn't see. The result is meaningfully better ranking for anyone whose customer relationship goes beyond email opens.
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