One ID & Tags · Segments
One audience engine — four ways to build, every one grounded in real members.
Describe an audience in plain language, build it with rules over your behavioral and value data, find more members like your best ones, or let AI surface the plays you're missing. Whichever way you start, the audience comes sized and validated against your actual members.
Describe
Say it in plain language
Build
Build it with rules
Look-alike
Find more of your best members
Discover
Let AI surface what you're missing
One grounded audience
sized, validated, ready to activate
The problem
A blank condition builder only helps if you already know what to build.
Blank-page segmentation
Most tools wait for you to describe an audience. The opportunities you forget to describe stay invisible.
Data locked away from marketers
The recency, spend and category signals that make a good segment sit in analytics — not in the hands of the person building the campaign.
Guesswork on reach
Without a real count you can't tell whether an audience is worth a campaign, too small to bother, or accidentally your whole base.
Four ways to build
Start however you think — the engine does the rest.
The same audience engine meets you where you are: type it, click it, clone your best members, or let AI propose it. Every path ends at a validated, sized, ready-to-activate audience.
Describe
Say it in plain language
Type “lapsed VIPs in Shanghai who bought skincare” and the AI drafts real, ready-to-check rule candidates — validated against your fields, sized against your members, explained in plain English. You review and save; nothing is guessed.
Build
Build it with rules
A visual builder over your governed data — lifecycle, points and tier, purchase recency and frequency, spend and average order value, category and brand affinity, store, channel and consent. Every rule reads certified data, so the audience means the same thing everywhere.
Look-alike
Find more of your best members
Pick a winning segment, a tag, or a handful of members as a seed and Flash finds the members who most resemble them — inside your own base, weighing shared traits and buying rhythm alongside spend tier, favorite product category and churn outlook. Every match comes with a “Why they're similar” summary. Never uploaded to an ad platform.
Discover
Let AI surface what you're missing
The advisor audits which standard retention plays you already run and proposes the ones you've left on the table; auto-discovery finds natural groupings in your member base you'd never think to describe. Each one arrives sized and ready to adopt.
What you can segment on
Behavioral, value and category data — in the marketer's hands.
The signals other stacks bury in an analytics module are first-class rules here. Segment on how recently, how often and how much a member buys, what categories they favor, where they shop, how they can be reached — even attributes unique to your business.
Lifecycle & identity
New, active, lapsing or dormant · enrolment date · sign-up channel · birthday month
Loyalty
Points balance · tier · lifetime points earned · redemptions · referrals
Purchase behavior
Recency (days since last purchase) · order count · net spend · average order value · return rate
Category & brand affinity
Bought a category or brand · primary category · breadth of categories · replenishment timing
Store & channel
Home store · store visit recency & count · preferred channel · orders by channel
Consent & reachability
Email marketing consent · push-reachable · do-not-sell · suppression status
Value & engagement tiers
Historical value tier · engagement score · recency/frequency/value bucket · intent signal
Predicted next actions
Days until expected next purchase (negative = overdue) · click propensity (0–100 likelihood of clicking a campaign)
Your own custom fields
Any attribute you added to members — membership number, preferred store, a child's birthday — segmentable the moment it exists
These read the same certified recency, frequency, value and category data the rest of the platform uses — so a “high-value, at-risk skincare buyer” means exactly the same thing in a segment, a dashboard and an agent's decision.
The two predicted conditions stay honest. An expected next purchase date is each member's own buying rhythm projected forward, with a ± window from how regular they actually are — it only exists once a member has four or more purchases and a reasonably regular rhythm, and overdue shows as overdue, never a made-up date. Click propensity is learned nightly from your own recent sends — clicks only, never opens, which mail privacy inflates — and only members actually emailed recently get a score. “Days until expected next purchase between −14 and 3” is a ready-made replenishment-reminder audience, in one filter.
Grounded by default
No audience leaves the builder unchecked.
Real reach, not a guess
Every audience returns a live count against your actual members — how many, and what share of the base — so you know whether it's worth a send before you build the campaign.
Caught before you send
A built-in check flags audiences that match no one, barely filter anyone, contain an impossible rule, or duplicate one you already have.
Audiences that subtract
Target one audience except another — say “win-back candidates, but not anyone already in the VIP flow” — in a single definition.
Measured value, never invented
When Flash shows a cohort's value, it's real historical spend — not a predicted lifetime value or an uplift model. The numbers on your screen are the numbers an agent acts on.
The advisor
It also tells you which audiences you forgot to build.
Beyond building on demand, Flash audits your program: it compares the standard retention plays — new, lapsing, dormant, VIP, high-frequency, birthday — against what you already run, and proposes only the gaps. Each suggestion is checked against your members and is one click to adopt.
AI proposes the plays; if it's unavailable, built-in preset plays take over. The advisor always returns a real, usable answer.
Standard retention plays
have vs. missing- covered
New Members
Enrolled in the last 30 days
- covered
VIP / High-Value
Lifetime earned over threshold
- suggested
Lapsing
No activity in 90+ days
- suggested
Dormant win-back
Long-inactive, re-engageable
- suggested
High-Frequency
Repeat buyers
- suggested
Birthday this month
Time-based outreach
Start with a running program
Ready-made plays, installed in a click.
A library of proven retention audiences ships with the platform — install them in one click and they size themselves against your base. Adjust, or use as-is.
Built to be used
An audience isn't the finish line.
Straight into campaigns
Pick a segment as a campaign audience and it resolves live at send — decrypting contact info, honoring consent, and subtracting any audience you chose to exclude.
Visible on every profile
Open a member and see exactly which segments they belong to right now — no stale, snapshotted lists.
One engine with Tags
The same rule builder powers Tags, and tags are a rule you can segment on — so audiences and labels stay perfectly composable.
AI & innovation
AI as a retention strategist, not a query box.
AI drafts audiences, finds lookalikes of your best members, discovers cohorts you'd never describe, and flags the plays you're missing — but it never decides sizing on its own. Every number is measured, and the rule builder, the advisor, lookalikes and discovery keep working with deterministic logic even when the AI is unavailable.
Proactive, not reactive
Instead of waiting for a request, it analyzes coverage and recommends the next audience to add.
Grounded in your data
Cohort sizes and value are real historical measurements of your own members — no invented audience sizes, no predicted lifetime value.
Degradation-safe
The rule builder, advisor, lookalike and discovery paths fall back to deterministic rules and presets, so the core stays useful even when the AI is unavailable.
Lookalikes, without the leak
Find more of your best members — in your own base, not an ad network's.
Ad-platform lookalikes require handing your customer list to a third party. Flash finds resemblance inside the members you already own — weighing the traits and buying rhythm your best members share, plus their spend tier, their favorite product category and their churn outlook. You get a sized, deliverable audience, and nothing is uploaded anywhere.
And the result explains itself: a “Why they're similar”summary shows exactly what each match is built on — for example “81% in the same VIP spend tier · 55% shop the same top category” (illustrative). Anything Flash doesn't have data for is skipped honestly, never faked.
What changes for the business
Segmentation stops being a blank page — audiences get described, built, discovered and sized, then flow straight into the campaigns that use them.
Four ways
describe, build, look-alike, or let AI propose
Real reach
every audience sized against your actual members
Ready to run
from audience to a live campaign, consent honored
Each audience checked against your real members for estimated reach.
Related reading
Keep exploring the pages most related to this one.
Behavior Capture
One live, unified profile — served through a governed semantic layer, the single interface the AI reads the business through.
Read more CapabilityMembers
One verified, field-encrypted member record fused across every channel into a single behavioral timeline. Each profile also shows an expected next purchase date and a click likelihood — computed only when the member's own history supports them, never invented.
Read more CapabilityTags
The state layer of the AI-facing semantic layer: governed, explainable labels for who a member is — lifecycle, value, preference, consent and risk — the certified 'who' every agent, dashboard and API reads and acts on.
Read more CapabilityEvents
Raw behavior abstracted into an event model — a per-member timeline of what happened and when, the signal every agent reads to understand why, now.
Read more CapabilityRelationships
Orders and referrals abstracted into relationship models — a member-to-member referral graph and product co-purchase / category affinity — so reach and recommendations read from real connections.
Read more CapabilityCustom Fields
Add your own attributes to the members, stores and products Flash already tracks — a membership number, a preferred store, a child's birthday — and use them everywhere: segments, personalization and import.
Read more