> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sourcemedium.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Demographic Data: What You Can (and Can’t) Do

> Where demographic attributes come from in SourceMedium, and how to use them for LTV analysis.

Brands often ask a version of:

> “Meta Ads Manager can break down performance by age/gender/region. Can we ingest that into the warehouse—and use it for LTV?”

This page explains what’s available in SourceMedium today, what typically isn’t, and the most practical paths to **LTV by demographic**.

## The key constraint: LTV needs a customer-level identifier

Ad platforms (including Meta) can report **campaign performance by demographic buckets** (e.g., impressions/conversions by age range), but those buckets generally **don’t include customer PII** and **can’t be joined to orders/customers** in your warehouse.

That’s why platform demographic reporting is usually useful for:

* **Creative and targeting diagnostics** (within the ad platform)
* **Top-of-funnel performance by demographic**

But not directly useful for:

* **Customer LTV by demographic**

## What SourceMedium already provides (by default)

These are common “building blocks” you can use for demographic-style segmentation.

### Geographic attributes

* Customer address geography in `your_project.sm_transformed_v2.dim_customer_addresses` (e.g., `customer_address_city`, `customer_address_province`, `customer_address_country`) and a flag for the primary address (`is_default_address_for_customer`).
* Order-level shipping geography in `your_project.sm_transformed_v2.obt_orders` (e.g., `order_shipping_city`, `order_shipping_state`, `order_shipping_country`).

### Customer identity fields (PII)

* `your_project.sm_transformed_v2.dim_customers` includes `customer_first_name`, `customer_last_name`, `customer_email`, `customer_phone_number`, and `customer_email_hashed`.

<Warning>
  PII and inferred demographic attributes can be sensitive. Make sure your collection, storage, and usage complies with your privacy policy and applicable laws.
</Warning>

### Tags you can use for enrichment

* Customer tags: `your_project.sm_transformed_v2.dim_customers.customer_tags_csv`
* Order tags: `your_project.sm_transformed_v2.dim_orders.order_tags_csv`

These are the most common way customers attach **self-reported** or **internally-derived** attributes (e.g., `gender:female`, `survey_age:25_34`, `persona:fitness`).

## What SourceMedium typically does NOT provide by default

* **Age** and **gender** (unless you collect/attach them yourself)
* **Household** or **income** data
* **Ad-platform demographic breakdown tables** (e.g., Meta “age/gender” breakdowns) in the standard transformed dataset

If you want ad-platform demographic breakdowns as raw tables for analysis, contact SourceMedium support to scope feasibility and coverage.

## Practical ways to get “LTV by demographic”

### Option 1: Collect zero-party demographics (recommended when possible)

Ask customers directly (pre-purchase quiz, account creation, post-purchase survey), then store the response in a way that lands in the warehouse (commonly via customer tags or order tags).

<Tip>
  Keep tag values normalized and stable (avoid free-text) so they’re usable in SQL, e.g., `gender:female`, `age_range:25_34`.
</Tip>

### Option 2: First-party enrichment you already control

Some brands collect attributes (gender, life stage, preferences) in their own systems and push them into e-commerce/CRM fields or tags. If your platform writes tags into your commerce system, those tags can flow into `customer_tags_csv` / `order_tags_csv`.

### Option 3: Join third-party demographic enrichment

If you purchase demographic enrichment from a third party, you can usually join it to SourceMedium customers using one of these join keys (depends on what your vendor provides):

* `customer_email_hashed` (privacy-safe matching)
* `customer_phone_number`
* Address fields from `dim_customer_addresses` (street/zip/city)

We recommend materializing a customer-level table keyed by `sm_customer_key` (for example, `dim_customer_demographics`) and keeping vendor fields in one place.

### Option 4: Infer demographics (use cautiously)

You can infer some attributes from PII (most commonly **gender from first name**). This can work as an 80/20 directional view at scale, but it will be imperfect and biased.

<Note>
  Age inference is usually much less reliable than gender inference. If age is a must-have, prioritize collection (Option 1) or a vetted enrichment vendor (Option 3).
</Note>

### Option 5: Encode “audience segments” in campaign/creative naming (good for LTV by strategy)

If the real question is “Which *targeting strategy* drives the best long-term customers?”, you can treat the “audience” as a **label** you control (not a true demographic attribute):

1. Create dedicated campaigns / ad sets / creatives for each targeting strategy (keep overlap low).
2. Put the segment label into a **joinable field that makes it to the warehouse**:
   * Prefer UTMs on the landing page URL (e.g., `utm_campaign`, `utm_content`, `utm_term`).
   * Campaign/ad set/ad names are useful only if you also pass that label through UTMs or another first-party capture method.
3. Analyze LTV by that label using order/customer-level tables.

In SourceMedium, last-click UTM fields like `sm_utm_campaign` / `sm_utm_content` / `sm_utm_term` are available on orders (see `your_project.sm_transformed_v2.obt_orders`), and event-level UTMs are available on funnel events (see `your_project.sm_transformed_v2.obt_funnel_event_history`).

<Note>
  This yields **LTV by targeting strategy**, not “LTV by customer age/gender”. It’s often the most actionable answer for paid social, because the label is joinable to orders.
</Note>

#### Implementation checklist

* Use a consistent UTM taxonomy across paid social (see [UTM Setup](/help-center/core-concepts/attribution/utm-setup)).
* Keep the segment label coarse (e.g., `segment:female_25_34`, not an ad ID per creative).
* QA by spot-checking recent orders in the warehouse and confirming the expected `sm_utm_*` values are populated (and not falling back to `(direct) / (none)`).

## Example: Average customer LTV by primary country

This example uses `dim_customer_addresses` for a customer's primary country and `obt_orders` for net revenue.

<Warning>
  Always filter `obt_orders` with `WHERE is_order_sm_valid = true` to exclude cancelled, test, and invalid orders from your analysis.
</Warning>

```sql theme={null}
with customer_geo as (
  select
    sm_customer_key,
    customer_address_country as country
  from your_project.sm_transformed_v2.dim_customer_addresses
  where is_default_address_for_customer = true
),
customer_ltv as (
  select
    sm_customer_key,
    sum(order_net_revenue) as ltv
  from your_project.sm_transformed_v2.obt_orders
  where is_order_sm_valid = true
  group by 1
)
select
  coalesce(g.country, 'Unknown') as country,
  count(*) as customers,
  avg(l.ltv) as avg_ltv
from customer_ltv l
left join customer_geo g using (sm_customer_key)
group by 1
order by avg_ltv desc;
```

<Note>
  This query uses a `LEFT JOIN` so customers without a default address are grouped under "Unknown" rather than excluded. If you prefer to exclude them, use an `INNER JOIN` instead.
</Note>

## Example: Cohort LTV by "first purchase attribute" (discover what dimensions you have)

If you use UTMs to encode audience strategy, a convenient way to view LTV over time is the cohort LTV report table:
`your_project.sm_transformed_v2.rpt_cohort_ltv_by_first_valid_purchase_attribute_no_product_filters`.

<Warning>
  This table contains **three rows per cohort**—one for each `sm_order_line_type` value (`all_orders`, `one_time_orders_only`, `subscription_orders_only`). Always filter to `sm_order_line_type = 'all_orders'` unless you specifically need subscription-only or one-time-only analysis.
</Warning>

To see which cohort dimensions exist in your warehouse:

```sql theme={null}
select
  acquisition_order_filter_dimension,
  count(*) as row_count
from your_project.sm_transformed_v2.rpt_cohort_ltv_by_first_valid_purchase_attribute_no_product_filters
where sm_order_line_type = 'all_orders'
group by 1
order by row_count desc;
```

To see example values for each dimension:

```sql theme={null}
select
  acquisition_order_filter_dimension,
  count(distinct acquisition_order_filter_dimension_value) as distinct_values,
  array_agg(distinct acquisition_order_filter_dimension_value limit 5) as example_values
from your_project.sm_transformed_v2.rpt_cohort_ltv_by_first_valid_purchase_attribute_no_product_filters
where sm_order_line_type = 'all_orders'
group by 1
order by distinct_values desc;
```

### Available dimensions

| Dimension                       | Description                   | Example Values                                                          |
| ------------------------------- | ----------------------------- | ----------------------------------------------------------------------- |
| `source/medium`                 | UTM source and medium         | `google / organic`, `facebook / paid`, `bing / cpc`                     |
| `campaign`                      | UTM campaign name             | `fps-3-in-1-zp-amz`, `email #1 - bundle offer`                          |
| `discount_code`                 | First-order discount code     | `SAVE10`, `SAVE100`                                                     |
| `sub_channel`                   | Marketing sub-channel         | `Paid Social`, `Paid Search`, `Online DTC`                              |
| `zero_party_attribution`        | HDYHAU / post-purchase survey | `user_input: web search`, `user_input: my dermatologist recommended it` |
| `order_type_(sub_vs._one_time)` | Subscription vs one-time      | `Subscription`, `One-time`, `Subscription & One-time`                   |
| `no_filters`                    | Unfiltered cohort totals      | `No Filters`                                                            |

### Example: 12-month LTV by acquisition source/medium

<Note>
  Cohorts need 12+ months of history to have 12-month LTV values. Filter to cohorts at least 12 months old, or `ltv_12m` will be NULL.
</Note>

```sql theme={null}
select
  acquisition_order_filter_dimension_value as source_medium,
  cohort_month,
  cohort_size,
  max(case when months_since_first_order = 12
      then cumulative_order_net_revenue / nullif(cohort_size, 0) end) as ltv_12m
from your_project.sm_transformed_v2.rpt_cohort_ltv_by_first_valid_purchase_attribute_no_product_filters
where acquisition_order_filter_dimension = 'source/medium'
  and sm_order_line_type = 'all_orders'
  and cohort_month >= date_sub(current_date(), interval 24 month)
  and cohort_month < date_sub(current_date(), interval 12 month)
group by 1, 2, 3
having ltv_12m is not null
order by cohort_month desc, ltv_12m desc;
```

## Related resources

<CardGroup cols={2}>
  <Card title="Customer Record Enrichment" icon="users" href="/help-center/core-concepts/customer-record-enrichment/index">
    Central source of truth for customers, enrichment, and audiences
  </Card>

  <Card title="UTM Setup" icon="bullseye" href="/help-center/core-concepts/attribution/utm-setup">
    Make campaign and audience labels joinable to orders
  </Card>

  <Card title="Data Enrichment" icon="gear" href="/data-transformations/data-enrichment">
    How enrichment fits into SourceMedium’s transformation layer
  </Card>

  <Card title="Customer & Order Tagging" icon="tags" href="/help-center/faq/account-management-faqs/are-you-utilizing-customer-and-order-tagging-for-deeper-enrichment-of-your-data">
    Practical tagging patterns for segmentation and enrichment
  </Card>

  <Card title="dim_customers" icon="table" href="/data-activation/data-tables/sm_transformed_v2/dim_customers">
    Customer identity fields and join keys (including hashed email)
  </Card>

  <Card title="dim_customer_addresses" icon="table" href="/data-activation/data-tables/sm_transformed_v2/dim_customer_addresses">
    Customer geo attributes for segmentation
  </Card>
</CardGroup>