# Amazon High Value Customer Segmentation in AMC

Canonical page: https://www.kuudo.com/guides/amc-high-value-audience-segmentation/

Use Amazon high value customer segmentation in AMC to build audience seeds, compare three value rules, and check lookalike activation limits.

Author: Thomas Spicer
Updated: Oct 19, 2026

## Summary

High-value audience work should test three seeds separately: Subscribe & Save buyers, multi-ASIN purchasers, and total-spend cohorts. Use `conversions_all_for_audiences` for the final `user_id` seed and `conversions_all` for the companion sizing query. Keep each seed inside the 1,000 to 450,000 practical buffer before pushing to AMC Audiences.

Amazon high value customer segmentation in Amazon Marketing Cloud (AMC) starts with a defensible value rule, not an arbitrary revenue cutoff. This workflow builds three seed strategies from conversion data, checks each seed against lookalike activation limits, and preserves enough buffer for privacy suppression and normal audience decay before the segment reaches DSP.

The Slack ping landed on a Thursday from our CRM lead: *"Who are our actual best customers, and can we build a demand-side platform (DSP) audience that targets people who look like them?"*

It sounds like a five-minute question. Ask ChatGPT and you get a single "high value" SQL block, almost always a total-spend filter, usually against the wrong table variant, almost never inside the seed-size band [Amazon Marketing Cloud](/features/amc/) actually requires. Search the AMC console's Instructional Query library and you find the answer, except it lives in two separate playbooks you have to stitch together yourself. By the time you've reconciled them, the campaign launch has slipped a day.

### Ground each seed strategy in AMC guidance

So I asked our agent. It runs on [Amazon Agent Atlas](/features/agent-atlas/), a curated corpus of AMC playbooks, DSP activation guides, and event-subtype references indexed for semantic retrieval. The answer came back in the shape the IQ library actually recommends: three seed audiences, tested separately, each sized into the 1,000 to 450,000 buffer before activation.

## Why a generic high-value audience definition produces a weak seed

I ran the same prompt through a frontier model with no retrieval first. The SQL it produced looked competent. It would have failed at audience creation in at least four distinct ways.

> **Five failure modes I saw in a single un-grounded response**
> - It selected `user_id` from `conversions_all`. Audience-build queries that return user IDs must run against the `_for_audiences` variant, in this case `conversions_all_for_audiences`. The sizing wrapper does the opposite: it counts user IDs against `conversions_all`. The Atlas chunk is explicit, "change `user_id` to `count(user_id)`, remove the suffix of `_for_audiences` in the table name."
> - It returned one blended seed that combined SnS, multi-purchase, and a spend threshold joined with `AND`. The result was an overly specific cohort of around 300 users. The IQ guidance is the opposite, "we recommend testing these audiences separately to avoid overly specific seeds."
> - It hallucinated the event filter as `event_subtype = 'SnS'`. The real values are `event_subtype IN ('firstSnSOrder', 'repeatSnSOrder')`. The `repeatSnSOrder` value was added to Flexible Shopping Insights on 02/05/2024, so any model with a training cutoff before that date does not know it exists and will quietly miss every recurring SnS shipment.
> - It said nothing about the 500 to 500,000 hard sizing bound. Audience refresh fails outside that range, silently for the operator until the DSP line item runs empty.
> - It left a comment as the final line of the SQL. AMC's audience pusher rejects any query whose last line is a comment, with an error message that does not tell you which line.

Any one of these would have cost the launch day. The combination would have looked like a working audience right up until the DSP line item failed to spend.

## What the AMC high-value audience workflow must retrieve

When the agent gets the question, it does a semantic search across the `amazon_ads` collection and pulls six chunks before writing a single line of SQL:

- The **Introduction to AMC Lookalike Audiences** playbook, section 3.2, which enumerates the three high-value seed strategies (Subscribe and Save, Multiple Purchases, Total Purchase Value) and gives the verbatim recommendation to test them separately to avoid overly specific seeds.
- The same **Introduction to AMC Lookalike Audiences** chunk on the companion measurement query template, the 500 to 500,000 hard bound, and the recommended 1,000 to 450,000 seed-size buffer.
- The **Understanding Amazon's Subscribe & Save Repeat Purchases** chunk, which documents the `firstSnSOrder` and `repeatSnSOrder` event subtypes, the 02/05/2024 enhancement date when repeat signals were added, and the Sandbox restriction on SnS data.
- The **Flexible Shopping Insights Trial Guide** chunk, which carries the working `conversions_all_for_audiences` template for the SnS seed and the audience-vs-sizing table-name swap pattern.
- The **Identifying High Value Customer Segments** chunk, which frames Total Spend seeds through percentile rank (top 71-100, top 96-100) rather than a flat dollar threshold, and endorses these segments specifically as lookalike seeds.
- The **AMC Lookalike Audiences for Promotional Events** chunk, which explains the gap lookalikes fill (the high-value new-to-brand and impulse buyers that DSP rule-based audiences miss) and warns against using lookalikes in the lead-out phase of a promotion.

Atlas does not generate the SQL. It surfaces the playbooks with the right caveats attached, and the agent adapts them.

## Build three AMC high-value audience seed strategies

The agent returned four SQL blocks: three seed audience queries and one companion sizing wrapper. The seed queries go into the AMC Audiences query editor, the sizing wrapper runs in the main query editor against the non-`_for_audiences` table.

```sql
-- =========================================================================
-- Companion measurement query (sizing): runs in the MAIN AMC query editor
-- against conversions_all (not _for_audiences). Use this BEFORE pushing each
-- seed to audience creation. Hard fail: <500 or >500,000.
-- Recommended buffer: 1,000 to 450,000.
-- =========================================================================
SELECT
    COUNT(user_id) AS user_count
FROM (
    {UPDATE: paste one of the three seed queries below here, but swap
     conversions_all_for_audiences -> conversions_all and
     SELECT DISTINCT user_id -> SELECT user_id }
)
GROUP BY 1


-- =========================================================================
-- SEED 1: Subscribe & Save subscribers
-- Run in the AMC AUDIENCES query editor (NOT the main editor) because it
-- selects individual user_id values.
-- STRIP ALL COMMENT LINES before pushing to audience creation. The trailing
-- GROUP BY 1 at the very end is what makes the push succeed.
-- =========================================================================
WITH sns_users AS (
    SELECT
        user_id,
        COUNT(DISTINCT conversion_id) AS sns_purchase_times
    FROM conversions_all_for_audiences
    WHERE event_subtype IN ('firstSnSOrder', 'repeatSnSOrder')
      -- Optional: scope to your ASIN(s). Remove this AND clause to capture
      -- all SnS purchasers across the brand.
      AND tracked_asin IN ('{ASIN_1}', '{ASIN_2}')   -- {UPDATE} or remove
    GROUP BY user_id
)
SELECT DISTINCT user_id
FROM sns_users
WHERE sns_purchase_times >= 1
GROUP BY 1


-- =========================================================================
-- SEED 2: Multi-Purchase (users with multiple distinct ASIN orders)
-- =========================================================================
WITH multi_asin_users AS (
    SELECT
        user_id,
        COUNT(DISTINCT tracked_asin) AS distinct_asin_count
    FROM conversions_all_for_audiences
    WHERE event_subtype = 'order'
      AND tracked_asin IN ('{ASIN_1}','{ASIN_2}','{ASIN_3}')  -- {UPDATE}
    GROUP BY user_id
)
SELECT DISTINCT user_id
FROM multi_asin_users
WHERE distinct_asin_count >= 2   -- {UPDATE: raise to 3+ if seed > 450k}
GROUP BY 1


-- =========================================================================
-- SEED 3: Total Spend threshold
-- Threshold is configurable. Atlas recommends a percentile-rank approach
-- (e.g., top 71-100 percentile) rather than a hardcoded dollar value.
-- A flat dollar threshold is shown here for simplicity.
-- =========================================================================
WITH spend_by_user AS (
    SELECT
        user_id,
        SUM(purchase_amount) AS total_spend
    FROM conversions_all_for_audiences
    WHERE event_subtype = 'order'
      AND tracked_asin IN ('{ASIN_1}','{ASIN_2}','{ASIN_3}')  -- {UPDATE}
    GROUP BY user_id
)
SELECT DISTINCT user_id
FROM spend_by_user
WHERE total_spend >= {SPEND_THRESHOLD}   -- {UPDATE: configurable, e.g., 200}
GROUP BY 1
```

Three queries, not one. The IQ library explicitly recommends testing these audiences separately to avoid overly specific seeds, and the math agrees: combining filters with `AND` collapses the overlap into a cohort that often falls under the 500-user hard bound. The operator runs all three through the companion sizing query first, confirms each lands in the 1,000 to 450,000 buffer, and only then optionally unions them.

- The agent does not start by unioning. The table name swaps between the seed and the sizing wrapper: `conversions_all_for_audiences` is required for the seed queries because AMC's audience builder only permits `SELECT user_id` against the `_for_audiences` table variants, while the sizing wrapper runs in the main query editor against the regular `conversions_all` table.

- The agent stages the swap inside the wrapper so the operator cannot paste the wrong table name into the wrong editor.

### Include first and repeat subscription orders

- The SnS filter includes both `firstSnSOrder` and `repeatSnSOrder`. Limiting to `repeatSnSOrder` alone would exclude users on their first scheduled subscription order, which is exactly the cohort you want to lookalike against. Both values together capture the full SnS-engaged audience that became available on 02/05/2024 when Flexible Shopping Insights was enhanced to include repeat purchase signals. The Multi-Purchase and Total Spend queries are not verbatim Atlas snippets.

- The agent adapted the SnS template pattern, swapping the WHERE filter to `event_subtype = 'order'` and the aggregation to either `COUNT(DISTINCT tracked_asin)` or `SUM(purchase_amount)` per user.

- The Total Spend threshold is a placeholder. The right number is corpus-specific. Atlas points at a percentile-rank approach (top 71-100 percentile, or the tighter top 96-100 for premium-spend seeds) rather than a hardcoded dollar value. The `{SPEND_THRESHOLD}` token in the SQL is operator-configurable, and the parenthetical "e.g., 200" is illustrative only, not a recommendation.

## Which activation checks protect AMC lookalike seeds

This is the part that separates retrieval-grounded responses from fluent guesses. Without being asked, the agent attached a short list of caveats to the artifact.

> **What Atlas surfaced that the operator didn't ask for**
> - **Hard sizing bounds: 500 and 500,000.** Audience refresh fails if the seed size falls below 500 or above 500,000. Aim for the 1,000 to 450,000 buffer to keep refreshes alive as the cohort drifts.
> - **Sandbox gap.** The `sns_subscription_id` field and the repeat SnS purchase signals are not available in AMC Sandbox. You cannot dry-run the SnS seed there. Run against production, or you will see zero rows and assume the seed is broken.
> - **Version cliff on 02/05/2024.** Repeat SnS purchase signals were added to Flexible Shopping Insights on that date. Any model whose training data predates it will produce a SnS seed that returns zero rows. This is one of the cleanest examples of what Atlas catches that an ungrounded model cannot.
> - **Comment-line trap.** Per the IQ playbook, we recommend removing all comment lines from the query before pushing to audience creation. The trailing `GROUP BY 1` works as a safe terminator because the audience pusher will not accept a query whose last line is a comment, but only if the comments above are also stripped.
> - **Category eligibility.** Only certain product categories (Beauty, Grocery, and a handful of others) are eligible for Subscribe & Save. If your catalog sits outside those categories, the SnS seed will undersize regardless of how broad you make the ASIN filter.
> - **Not for the lead-out phase.** Lookalike audiences are not appropriate for the lead-out phase of a promotional campaign, where the NTB mix shifts. For lead-out, switch to AMC rule-based audiences instead.

Each of these would have cost the operator at least an hour to discover by hitting the failure first.

## What happens next

Once each seed lands inside the 1,000 to 450,000 buffer, the operator pushes it to AMC Audiences from the Audiences query editor. AMC compiles the audience and activates it to Amazon DSP. The standard DSP activation lag is around 48 hours before the audience materializes and becomes targetable in line items, so the push has to land at least two days before the campaign launch, not on the day of.

The agent also recommends setting each seed query as a recurring AMC workflow so the lookalike model rebuilds on a cadence. Weekly is typical for high-value cohorts, because the SnS seed in particular grows as repeat purchase signals accumulate week over week. Static lookalikes age out fast.

For the DSP build, the agent targets each lookalike in a separate line item rather than stacking them in one. Three line items, one per seed, gives a clean read on NTB rate and return on ad spend (ROAS) by seed strategy after the first 14 days. The Total Spend lookalike usually wins on order value, the Multi-Purchase lookalike on order frequency, and the SnS lookalike on retention. Knowing which is which informs the next round of seeds.

### Connect seed analysis to account actions

The full workflow runs through the [Amazon Agent Data layer](/features/amazon-agent-flow/): [Amazon Ads MCP](/features/amazon-ads-mcp/) brings AMC and DSP signals, the [Selling Partner MCP](/features/amazon-selling-partner-mcp/) can add catalog and Amazon Standard Identification Number (ASIN) context, Atlas grounds the seed rules, and [Skills](/features/skills/) keep each seed test on a reviewable cadence.

## Why a value rule must survive the next rerun

Three seeds, three sizes, one DSP activation: the difference between a good guess and a working audience. The seed strategies are not novel, but the constraints around them are, and most of those constraints are not in any single playbook. The agent's value is not that it wrote better SQL than a model could write from memory.

The agent's value is that it pulled the right two playbooks, applied the right table swap, surfaced a six-month-old `event_subtype` that most models still do not know about, and attached the activation timing the operator needed before they hit it.

If your agents are guessing at AMC high-value seeds, they don't have to be.

<hr>
*Part of an ongoing series on how agents grounded in Amazon Agent Atlas approach real AMC workflows. Next: [lookalike audiences for Prime Day](/guides/amc-lookalike-audience-prime-day/), taking these three seeds through the promotional-event activation playbook, including the lead-in versus lead-out split that determines whether a lookalike belongs in the line item at all.*


## Frequently asked questions

### What are the three high-value AMC seed strategies?

Start with Subscribe & Save subscribers, multi-ASIN purchasers, and total-spend cohorts. Test them separately before combining filters.

### Which table should high-value audience seeds use?

Use `conversions_all_for_audiences` in the AMC Audiences editor when returning `user_id`. Use `conversions_all` for sizing in the main query editor.

### Why did my AMC seed query fail or return too few users?

The most common causes are combining all seed filters with `AND`, using the wrong table variant, or setting the spend threshold above the usable audience size.

### What seed size should I target?

The hard window is 500 to 500,000 users, but the practical operating buffer is 1,000 to 450,000 so refreshes survive normal cohort drift.

### Should I combine Subscribe & Save and spend thresholds?

Not before testing each seed. Combining them too early creates an over-specific audience that can pass SQL validation but fail activation.

### How should the DSP test be structured?

Use separate line items per seed so you can read new-to-brand rate, ROAS, and order value by seed strategy.


## Sources

- [Amazon Marketing Cloud product overview](https://advertising.amazon.com/solutions/products/amazon-marketing-cloud/)

- [Extend reach while maintaining relevancy with Amazon Marketing Cloud lookalike audiences](https://advertising.amazon.com/en-ca/resources/whats-new/extend-reach-while-maintaining-relevancy)

- [Amazon Marketing Cloud custom audience creation and activation](https://advertising.amazon.com/en-gb/resources/whats-new/amc-custom-audience-creation-and-activation)