No send history, no signal
Outbound-trained timing needs a long send history. For a brand-new contact there is nothing to learn from, so the model falls back to a global default and guesses.
Wave · Cadence Affinity
Send-time optimization is a rearview mirror. Wave sees ahead.
Every ESP learns send-time from the emails you already sent, so it is blind on new contacts and conflates when someone opened your last message with when they actually want to hear from you. Wave learns from how each person behaves with your inbound content and predicts their receptivity window, even from the first few interactions.
Quick answer
Cadence Affinity is a Wave feature that predicts, per person, when and how often to reach them. It learns each person's timing from how they actually engage: the window they show up in, the rhythm they keep, and the point where more touches stop helping, trained on inbound behavior rather than your outbound send history.
Key capabilities
- Per-person send window prediction
- A personal outreach rhythm for every contact
- Timing your automation can route on
- Knows when more touches stop helping
- A confidence grade on every prediction
- Trained on inbound behavior, not send history
- HubSpot writeback today, Marketo next, with a per-tenant kill switch
Last updated July 2026
The problem
Send-time trained on past sends breaks where it matters most.
Send-time optimization is a crowded space, but every major ESP trains on outbound engagement, which creates two problems that hit exactly the contacts you care about.
Opened is not the same as wanted
When someone opened your last email is not the same as when they actually want to hear from you. Training on opens optimizes the wrong moment.
No sense of saturation
Most tools optimize the hour but say nothing about frequency, so spray-and-pray cadences quietly burn warm contacts until they go cold.
How Wave does it
Learn from inbound behavior, predict a personal timing profile per person.
Wave reads when each person engages with your inbound content and turns it into a timing profile any marketer can act on without a spreadsheet.
- 01
Read inbound timing behavior
Wave learns which days a person visits your content, what time of day they download gated assets, and how frequently they re-engage between touches, all in their own local time.
- 02
Predict window and day
Wave scores each person's preferred window and the days they actually engage, so outreach lands when they are most likely to act rather than just open.
- 03
Predict rhythm and fatigue point
Wave learns each person's natural rhythm between touches and the point where more outreach stops helping, so cadences stop short of fatigue.
- 04
Write the timing to your stack
The predicted send window and target cadence interval write back to your CRM, governed by a per-tenant kill switch, so your existing automation rules route on them with no data engineering.
Where it fits
The timing axis of Wave's person-level intelligence layer.
Cadence Affinity answers when and how often, the dimension that content, channel, and committee products do not cover, so the rest of Wave's recommendations land at the right moment.
Your sources
Wave
HubSpot
Wave reads from your existing systems through native connectors, trains one model per tenant so your data never trains on anyone else's, and writes the predicted send window and target cadence interval back to HubSpot today, with Marketo next. The rest of the timing profile is visible in the Wave console, and every writeback is logged with previous value, new value, and the model version.
Works alongside Channel Affinity, Send Schedule Calendar, The full Wave platform.
Why Wave is different
The only cadence intelligence trained on inbound behavior.
Every send-time optimizer learns from how people responded to your past outreach. Wave learns from how buyers actually behave with your content in the wild, which is the signal that exists for new and early-funnel contacts.
Most tools
Wave
ESPs train send-time on outbound open and click history.
Wave trains on inbound behavior, so it has signal even for a contact you have never emailed.
Timing optimizes the hour and ignores frequency entirely.
Wave predicts each person's rhythm and fatigue point, so cadence design has a real signal.
A new contact gets a global default send-time.
Wave can produce a timing prediction from the first few inbound interactions, and when there is not yet enough signal it says so instead of guessing.
Send-time is a black box you cannot inspect.
Wave surfaces a full timing profile per person with a confidence grade, all visible in the console.
FAQ
Questions buyers ask about Cadence Affinity.
What is Cadence Affinity in Wave?
Cadence Affinity is a Wave feature that predicts when and how often to reach each person. It learns each person's timing from how they actually engage: the window they show up in, the rhythm they keep, and the point where more touches stop helping, all trained on inbound behavior rather than your send history.
How is this different from ESP send-time optimization?
Every major ESP trains send-time on outbound engagement, what you sent and when it was opened. Wave trains on how each person behaves with your inbound content, so it has signal for new and early-funnel contacts where outbound-trained models fall back to a default.
What timing does Wave predict?
Each person's preferred window, their personal outreach rhythm, and the point where more touches stop helping. Wave writes the timing your campaigns can act on to your CRM and keeps the rest visible in the console, so cadence stops being one global rule for everyone.
How does Wave prevent fatigue?
It predicts how many touches in a given window a person will tolerate before disengaging. That lets you design cadences that stop short of fatigue instead of spraying a list until warm contacts go cold.
Does it work for brand-new contacts?
It can produce a timing prediction from the first few inbound interactions, and when there is not yet enough signal it says so instead of guessing, so you get a real signal where it exists.
Where do the timing predictions end up?
Wave writes the predicted send window and target cadence interval back to HubSpot today, with Marketo next, refreshed on a daily cycle. Your existing automation rules can route on them with no data pipeline or engineering work.
Can I control or pause the writeback?
Yes. Cadence Affinity writeback is governed by a per-tenant kill switch and defaults to off until you opt in, so you can score timing predictions without writing them, then enable writeback when you are ready.
How do I see Cadence Affinity on my own contacts?
Book a 20-minute walkthrough. We will configure a tenant against your stack and show the timing profile Wave would produce on your real engagement data.
See it on your data
See when your contacts actually want to hear from you.
Book a 20-minute walkthrough. We will run Cadence Affinity against your engagement data and show the timing profile Wave would produce for your contacts.
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