Queries · q161 · Head of Lifecycle Marketing · week 1, night 1

Loyalty member vs non-member customer behavior

“Do enrolled loyalty members actually order more often, spend more, stick around longer, and return items less than non-member customers, as of today?”

Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told

1. A business event lands on the Head of Lifecycle Marketing's desk

   Week 1; shared with Tobias Lindqvist, Chief Financial Officer

   Is the loyalty and referral programme worth it? Do members and referred customers behave differently from others, and what does the programme cost?

   One of the 35 events we wrote for the role play; it shaped the manager's first note. How events arise · All the events

2. Aisha Rahman writes to Ingrid Solberg

   The manager, at the start of week 1

   Subject: Loyalty & referral programme — what I need for Friday's memo

   Hi Ingrid,

   Welcome aboard — thank you for jumping on this in week one. Tobias and I need to give the leadership team a joint, evidence-based view on whether the loyalty and referral programme is paying for itself. The deliverable is your Friday memo, which Tobias and I will use to decide whether we expand the programme, leave it as is, or rework it. Given the slot budget, let's prioritize breadth of the right questions over depth on any one — we can always follow up next week.

   Here's what I'm trying to answer, in my own words:

   1. Do loyalty members actually buy differently? Compared with similar customers who aren't enrolled, do members order more often, spend more, or stick around longer? I want to know if membership correlates with genuinely better customer behavior, not just that our best customers happen to join.
   2. Do referred customers turn into good customers? When someone comes in through a referral, how do they behave in their first months compared to customers acquired other ways — do they buy, do they come back, do they stay?
   3. What is the programme actually costing us? I want a sense of the points/rewards and referral payouts we're giving out, ideally relative to the revenue those same members or referred customers generate, so we can see if the economics make sense.
   4. Is participation worth the overhead? How many of our customers are actually enrolled or have used a referral code, and is that share growing or flat? If take-up is small, the upside is capped no matter how good the behavior looks.
   5. Any red flags on how the programme is used? For example, signs that rewards are being redeemed without real repeat purchase behavior behind them, or that referral activity is concentrated in a way that looks more like gaming than genuine word-of-mouth.

   Given we only have a few report slots a night, I'd rank these in order 3, 1, 2 as the must-haves for Friday; 4 and 5 are nice-to-haves if slots allow. Please loop in whatever Tobias's analyst is already pulling on the finance side so we're not duplicating report requests.

   Thanks, Ingrid — happy to talk through any of this before you start drafting requests.

   Best, Aisha

   Show the whole note
   A note from the Chief Financial Officer, Tobias Lindqvist

   Subject: Finance angle on the loyalty/referral review

   Hi Ingrid,

   A few things I'd like us to cover as you scope this:

   1. What is the programme actually costing us net — points and referral rewards issued versus redeemed/liability outstanding — and how does that compare to the margin uplift (if any) from members? I don't want to judge this on engagement alone.
   2. Do members and referred customers carry a different refund/return profile than regular customers? If the programme is quietly skewing our refund rate, that matters for how we read "incremental" revenue.
   3. For the B2B side especially, are enrolled accounts paying on better terms — faster settlement, fewer overdue balances — or is that unrelated to the programme?

   Happy to look at a first cut together before it goes wider.

   Tobias

3. Ingrid Solberg plans the night

   The analyst · week 1, night 1, request 2 of 3

   Prioritizing Aisha's must-haves (3,1,2) which also cover most of Tobias's asks (net cost, refund profile). Night 1 establishes three independent baselines: (a) program cost/liability trend, (b) member vs non-member behavior, (c) referred vs non-referred early behavior. I'm folding Tobias's refund-rate question into (b) and (c) as extra columns rather than separate reports, since it's a natural measure at the same grain. Deliberately NOT yet controlling for tenure/cohort matching in (b) - that's the obvious follow-up once I see whether raw numbers show a gap (my risk-focused read: if members already look no better on raw numbers, that itself is a red flag worth flagging Friday). Saving participation-rate trend (#4) and gaming/concentration red flags (#5) for night 2 once I've seen these results and know where to dig. Will flag to Aisha/Tobias to confirm their analyst isn't pulling the same cost report before night 2.

   Read the whole plan
   Loyalty member vs non-member customer behavior

   Question

   Do enrolled loyalty members actually order more often, spend more, stick around longer, and return items less than non-member customers, as of today?

   One row per

   customer segment: 'loyalty member' vs 'non-member'

   Columns

   • segment
   • customer count
   • avg lifetime orders per customer
   • avg lifetime revenue per customer (EUR)
   • avg order value (EUR)
   • avg customer tenure days (first order to last order)
   • repeat purchase rate (% with 2+ orders)
   • refund rate (% of orders with any amount refunded or an associated return)

4. Omar writes the SQL

   The engineer · 1 dry run on the empty schema, then the submission

   Dry run 1 · STATUS: COMPILES (dry run on the empty schema, no data read)

   WITH orders_base AS (
     SELECT
       o.id AS order_id,
       o.tenant_id,
       o.user_id,
       o.status,
       o.currency_code,
       o.grand_total,
       COALESCE(o.placed_at, o.created_at) AS order_date
     FROM orders o
     JOIN users u ON u.id = o.user_id AND u.tenant_id = o.tenant_id
     WHERE u.is_staff = false
       AND COALESCE(o.placed_at, o.created_at) >= DATE '2023-01-01'
       AND COALESCE(o.placed_at, o.created_at) < DATE '2026-01-01'
   ),
   orders_enriched AS (
     SELECT
       ob.*,
       CASE WHEN ob.currency_code = 'EUR' THEN ob.grand_total
            ELSE ob.grand_total / er.rate END AS eur_amount,
       EXISTS (SELECT 1 FROM payments p WHERE p.order_id = ob.order_id AND p.amount_refunded > 0) AS has_refund,
       EXISTS (SELECT 1 FROM order_returns r WHERE r.order_id = ob.order_id) AS has_return
     FROM orders_base ob
     LEFT JOIN exchange_rates er
       ON er.base_code = 'EUR' AND er.quote_code = ob.currency_code AND er.as_of = CAST(ob.order_date AS DATE)
   ),
   customer_agg AS (
     SELECT
       user_id,
       COUNT(*) AS total_orders,
       COUNT(*) FILTER (WHERE status <> 'cancelled') AS noncancelled_orders,
       SUM(eur_amount) FILTER (WHERE status <> 'cancelled') AS noncancelled_revenue,
       MIN(order_date) AS first_order_date,
       MAX(order_date) AS last_order_date,
       COUNT(*) FILTER (WHERE has_refund OR has_return) AS refunded_orders
     FROM orders_enriched
     GROUP BY user_id
   ),
   segmented AS (
     SELECT
       ca.*,
       CASE WHEN EXISTS (SELECT 1 FROM loyalty_accounts la WHERE la.user_id = ca.user_id)
            THEN 'loyalty member' ELSE 'non-member' END AS segment
     FROM customer_agg ca
   )
   SELECT
     segment,
     COUNT(*) AS customer_count,
     ROUND(AVG(total_orders)::numeric, 2) AS avg_lifetime_orders,
     ROUND((SUM(noncancelled_revenue) / NULLIF(COUNT(*),0))::numeric, 2) AS avg_lifetime_revenue_eur,
     ROUND((SUM(noncancelled_revenue) / NULLIF(SUM(noncancelled_orders),0))::numeric, 2) AS avg_order_value_eur,
     ROUND((AVG(EXTRACT(EPOCH FROM (last_order_date - first_order_date)) / 86400.0))::numeric, 1) AS avg_tenure_days,
     ROUND((100.0 * COUNT(*) FILTER (WHERE total_orders >= 2) / NULLIF(COUNT(*),0))::numeric, 1) AS repeat_purchase_rate_pct,
     ROUND((100.0 * SUM(refunded_orders) / NULLIF(SUM(total_orders),0))::numeric, 1) AS refund_rate_pct
   FROM segmented
   GROUP BY segment
   ORDER BY CASE WHEN segment = 'loyalty member' THEN 0 ELSE 1 END, segment

   “Query compiles cleanly with the expected 8 columns, one row per segment. Submitting.”

   Submitted · the SQL of the last dry run, unchanged · Submitted to tonight's batch. Your work on this request is done.

   “Submitted. Summary of key choices baked into the query, also noted for the analyst: - Population/date filter: orders where COALESCE(placed_at, created_at) falls in 2023‑01‑01..2025‑12‑31, for non-staff users. - Segment: loyalty member if the user has any loyalty_accounts row, else non-member. - Avg lifetime orders / repeat rate / refund rate: computed over all orders regardless of status (matches "any status" population wording). - Avg lifetime revenue & avg order value: numerator/denominator restricted to non-cancelled orders, per the spec. - EUR conversion: grand_total / rate where rate is the EUR→order-currency rate as of the order date (1:1 when already EUR). - Tenure: days between first and last order date per customer (0 for single-order customers), computed via epoch seconds/86400 for dialect-safety.”

5. The report comes back overnight

   As Ingrid Solberg saw it, from the small dataset the agents worked on

   segment        | customer_count | avg_lifetime_orders | avg_lifetime_revenue_eur | avg_order_value_eur | avg_tenure_days | repeat_purchase_rate_pct | refund_rate_pct
   ---------------+----------------+---------------------+--------------------------+---------------------+-----------------+--------------------------+----------------
   loyalty member |           1330 |                3.72 |                   687.99 |              194.40 |           199.7 |                     67.7 |            32.4
   non-member     |           8310 |                5.55 |                  1511.84 |              281.57 |           234.1 |                     71.7 |            15.5
   (2 rows)

6. What came of it

   The memo and the queries that followed; links found by title, so inferred

   Later queries that build on this report:

   • q163Loyalty member vs non-member behavior by account tenure
   • q164Loyalty member order behavior before vs after enrollment
   • q171Loyalty programme unit economics: points cost vs revenue per customer

7. It becomes benchmark query q161

   Changed only where the benchmark needs the same result on every system

   • spellingcast (6×)
   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision; bare NUMERIC cast that only served the rounding removed; AVG over an integer computed as a decimal: AVG(CAST(x AS NUMERIC(18,6)))
   • numericsdocs/NUMERICS.md: EXTRACT(EPOCH FROM (b - a)) cast to NUMERIC(18,6)
   • numericsdocs/NUMERICS.md: x / exchange rate cast to NUMERIC(18,6)
   • made explicita selected t.col gets AS col, its standard name made explicit (5×)
   • placeholderseconds between
   • determinismthe standard leaves the place of NULLs in a sort to the implementation: NULLS LAST ascending, NULLS FIRST descending, as in the reference results (2×)
   The benchmark text, against the submitted SQL

   WITH orders_base AS (  SELECT    o.id AS order_id,    o.tenant_id,    o.user_id,    o.status,    o.currency_code,    o.grand_total,    o.tenant_id AS tenant_id,    o.user_id AS user_id,    o.status AS status,    o.currency_code AS currency_code,    o.grand_total AS grand_total,    COALESCE(o.placed_at, o.created_at) AS order_date  FROM orders o  JOIN users u ON u.id = o.user_id AND u.tenant_id = o.tenant_id  WHERE u.is_staff = false    AND COALESCE(o.placed_at, o.created_at) >= DATE '2023-01-01'    AND COALESCE(o.placed_at, o.created_at) < DATE '2026-01-01'),orders_enriched AS (  SELECT    ob.*,    CASE WHEN ob.currency_code = 'EUR' THEN ob.grand_total         ELSE ob.grand_total / er.rate END AS eur_amount,         ELSE CAST(ob.grand_total / er.rate AS NUMERIC(18,6)) END AS eur_amount,    EXISTS (SELECT 1 FROM payments p WHERE p.order_id = ob.order_id AND p.amount_refunded > 0) AS has_refund,    EXISTS (SELECT 1 FROM order_returns r WHERE r.order_id = ob.order_id) AS has_return  FROM orders_base ob  LEFT JOIN exchange_rates er    ON er.base_code = 'EUR' AND er.quote_code = ob.currency_code AND er.as_of = CAST(ob.order_date AS DATE)),customer_agg AS (  SELECT    user_id,    COUNT(*) AS total_orders,    COUNT(*) FILTER (WHERE status <> 'cancelled') AS noncancelled_orders,    SUM(eur_amount) FILTER (WHERE status <> 'cancelled') AS noncancelled_revenue,    MIN(order_date) AS first_order_date,    MAX(order_date) AS last_order_date,    COUNT(*) FILTER (WHERE has_refund OR has_return) AS refunded_orders  FROM orders_enriched  GROUP BY user_id),segmented AS (  SELECT    ca.*,    CASE WHEN EXISTS (SELECT 1 FROM loyalty_accounts la WHERE la.user_id = ca.user_id)         THEN 'loyalty member' ELSE 'non-member' END AS segment  FROM customer_agg ca)SELECT  segment,  COUNT(*) AS customer_count,  ROUND(AVG(total_orders)::numeric, 2) AS avg_lifetime_orders,  ROUND((SUM(noncancelled_revenue) / NULLIF(COUNT(*),0))::numeric, 2) AS avg_lifetime_revenue_eur,  ROUND((SUM(noncancelled_revenue) / NULLIF(SUM(noncancelled_orders),0))::numeric, 2) AS avg_order_value_eur,  ROUND((AVG(EXTRACT(EPOCH FROM (last_order_date - first_order_date)) / 86400.0))::numeric, 1) AS avg_tenure_days,  ROUND((100.0 * COUNT(*) FILTER (WHERE total_orders >= 2) / NULLIF(COUNT(*),0))::numeric, 1) AS repeat_purchase_rate_pct,  ROUND((100.0 * SUM(refunded_orders) / NULLIF(SUM(total_orders),0))::numeric, 1) AS refund_rate_pct  AVG(CAST(total_orders AS NUMERIC(18,6))) AS avg_lifetime_orders,  (SUM(noncancelled_revenue) / NULLIF(COUNT(*),0)) AS avg_lifetime_revenue_eur,  (SUM(noncancelled_revenue) / NULLIF(SUM(noncancelled_orders),0)) AS avg_order_value_eur,  (AVG({{seconds_between(first_order_date, last_order_date)}} / 86400.0)) AS avg_tenure_days,  (100.0 * COUNT(*) FILTER (WHERE total_orders >= 2) / NULLIF(COUNT(*),0)) AS repeat_purchase_rate_pct,  (100.0 * SUM(refunded_orders) / NULLIF(SUM(total_orders),0)) AS refund_rate_pctFROM segmentedGROUP BY segmentORDER BY CASE WHEN segment = 'loyalty member' THEN 0 ELSE 1 END, segmentORDER BY CASE WHEN segment = 'loyalty member' THEN 0 ELSE 1 END NULLS LAST, segment NULLS LAST

   As each system received it; highlighted, the parts spelled for that system

   DuckDB · Hyper · GizmoSQL · CedarDB · pg_duckdb · PostgreSQL · DataFusion · Firebolt · clickhouse_brewClickHouse · chDB

   WITH orders_base AS (
     SELECT
       o.id AS order_id,
       o.tenant_id AS tenant_id,
       o.user_id AS user_id,
       o.status AS status,
       o.currency_code AS currency_code,
       o.grand_total AS grand_total,
       COALESCE(o.placed_at, o.created_at) AS order_date
     FROM orders o
     JOIN users u ON u.id = o.user_id AND u.tenant_id = o.tenant_id
     WHERE u.is_staff = false
       AND COALESCE(o.placed_at, o.created_at) >= DATE '2023-01-01'
       AND COALESCE(o.placed_at, o.created_at) < DATE '2026-01-01'
   ),
   orders_enriched AS (
     SELECT
       ob.*,
       CASE WHEN ob.currency_code = 'EUR' THEN ob.grand_total
            ELSE CAST(ob.grand_total / er.rate AS NUMERIC(18,6)) END AS eur_amount,
       EXISTS (SELECT 1 FROM payments p WHERE p.order_id = ob.order_id AND p.amount_refunded > 0) AS has_refund,
       EXISTS (SELECT 1 FROM order_returns r WHERE r.order_id = ob.order_id) AS has_return
     FROM orders_base ob
     LEFT JOIN exchange_rates er
       ON er.base_code = 'EUR' AND er.quote_code = ob.currency_code AND er.as_of = CAST(ob.order_date AS DATE)
   ),
   customer_agg AS (
     SELECT
       user_id,
       COUNT(*) AS total_orders,
       COUNT(*) FILTER (WHERE status <> 'cancelled') AS noncancelled_orders,
       SUM(eur_amount) FILTER (WHERE status <> 'cancelled') AS noncancelled_revenue,
       MIN(order_date) AS first_order_date,
       MAX(order_date) AS last_order_date,
       COUNT(*) FILTER (WHERE has_refund OR has_return) AS refunded_orders
     FROM orders_enriched
     GROUP BY user_id
   ),
   segmented AS (
     SELECT
       ca.*,
       CASE WHEN EXISTS (SELECT 1 FROM loyalty_accounts la WHERE la.user_id = ca.user_id)
            THEN 'loyalty member' ELSE 'non-member' END AS segment
     FROM customer_agg ca
   )
   SELECT
     segment,
     COUNT(*) AS customer_count,
     AVG(CAST(total_orders AS NUMERIC(18,6))) AS avg_lifetime_orders,
     (SUM(noncancelled_revenue) / NULLIF(COUNT(*),0)) AS avg_lifetime_revenue_eur,
     (SUM(noncancelled_revenue) / NULLIF(SUM(noncancelled_orders),0)) AS avg_order_value_eur,
     (AVG(CAST(EXTRACT(EPOCH FROM (last_order_date - first_order_date)) AS NUMERIC(18,6)) / 86400.0)) AS avg_tenure_days,
     (100.0 * COUNT(*) FILTER (WHERE total_orders >= 2) / NULLIF(COUNT(*),0)) AS repeat_purchase_rate_pct,
     (100.0 * SUM(refunded_orders) / NULLIF(SUM(total_orders),0)) AS refund_rate_pct
   FROM segmented
   GROUP BY segment
   ORDER BY CASE WHEN segment = 'loyalty member' THEN 0 ELSE 1 END NULLS LAST, segment NULLS LAST

   • Firebolt{ "errors": [ { "description": "Line 20, Column 35: Operations between decimals with different precision and scale is not supported at the moment. Explicitly cast one of the decimals to other decimal precision and scale." } ], "query": { "query_id": "75dd1b48-0332-4bf8-afe1-4b4881d4d934", "query_lab
   • DataFusionThis feature is not implemented: Physical plan does not support logical expression Exists(Exists { subquery: <subquery>, negated: false })
   • ClickHouseCode: 69. DB::Exception: Decimal result's scale is less than argument's one: while executing function divide on arguments __table13.grand_total Decimal(14, 4) Decimal64(size = 0), __table16.rate Nullable(Decimal(12, 6)) Nullable(size = 0, Decimal64(size = 0), UInt8(size = 0)). (ARGUMENT_OUT_OF_BOUND

   Scale factor 1000, c8a.24xlarge, after the last load. q161 on the start page, with every configuration.

8. Everything, as recorded

   The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted

   Omar's whole session, 7 turns

   • The request, as Omar received it

     Report request from Ingrid Solberg (analyst for the Head of Lifecycle Marketing):
     
     Title: Loyalty member vs non-member customer behavior
     Question: Do enrolled loyalty members actually order more often, spend more, stick around longer, and return items less than non-member customers, as of today?
     Grain: one row per customer segment: 'loyalty member' vs 'non-member'
     Columns:
     - segment
     - customer count
     - avg lifetime orders per customer
     - avg lifetime revenue per customer (EUR)
     - avg order value (EUR)
     - avg customer tenure days (first order to last order)
     - repeat purchase rate (% with 2+ orders)
     - refund rate (% of orders with any amount refunded or an associated return)
     Details: Population: all users (is_staff = false) across all tenants who have placed at least one order (any status) between 2023-01-01 and 2025-12-31. Segment = 'loyalty member' if the user has a row in loyalty_accounts (any program, any tenant), else 'non-member'. Revenue and order value: use orders.grand_total converted to EUR via exchange_rates using orders.currency_code and the rate as_of orders.placed_at (fall back to created_at if placed_at is null). Avg lifetime revenue per customer = total EUR revenue from non-cancelled orders / customer count in segment. Avg order value = total EUR revenue / total order count in segment. Tenure = days between each customer's first and last placed order (0 for single-order customers). Repeat purchase rate = share of customers in the segment with 2 or more orders. Refund rate = (orders with payments.amount_refunded > 0 OR an order_returns row) / total orders in the segment. Order rows with 'loyalty member' first, then 'non-member'.
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Omar calls dry_run

     WITH orders_base AS (
       SELECT
         o.id AS order_id,
         o.tenant_id,
         o.user_id,
         o.status,
         o.currency_code,
         o.grand_total,
         COALESCE(o.placed_at, o.created_at) AS order_date
       FROM orders o
       JOIN users u ON u.id = o.user_id AND u.tenant_id = o.tenant_id
       WHERE u.is_staff = false
         AND COALESCE(o.placed_at, o.created_at) >= DATE '2023-01-01'
         AND COALESCE(o.placed_at, o.created_at) < DATE '2026-01-01'
     ),
     orders_enriched AS (
       SELECT
         ob.*,
         CASE WHEN ob.currency_code = 'EUR' THEN ob.grand_total
              ELSE ob.grand_total / er.rate END AS eur_amount,
         EXISTS (SELECT 1 FROM payments p WHERE p.order_id = ob.order_id AND p.amount_refunded > 0) AS has_refund,
         EXISTS (SELECT 1 FROM order_returns r WHERE r.order_id = ob.order_id) AS has_return
       FROM orders_base ob
       LEFT JOIN exchange_rates er
         ON er.base_code = 'EUR' AND er.quote_code = ob.currency_code AND er.as_of = CAST(ob.order_date AS DATE)
     ),
     customer_agg AS (
       SELECT
         user_id,
         COUNT(*) AS total_orders,
         COUNT(*) FILTER (WHERE status <> 'cancelled') AS noncancelled_orders,
         SUM(eur_amount) FILTER (WHERE status <> 'cancelled') AS noncancelled_revenue,
         MIN(order_date) AS first_order_date,
         MAX(order_date) AS last_order_date,
         COUNT(*) FILTER (WHERE has_refund OR has_return) AS refunded_orders
       FROM orders_enriched
       GROUP BY user_id
     ),
     segmented AS (
       SELECT
         ca.*,
         CASE WHEN EXISTS (SELECT 1 FROM loyalty_accounts la WHERE la.user_id = ca.user_id)
              THEN 'loyalty member' ELSE 'non-member' END AS segment
       FROM customer_agg ca
     )
     SELECT
       segment,
       COUNT(*) AS customer_count,
       ROUND(AVG(total_orders)::numeric, 2) AS avg_lifetime_orders,
       ROUND((SUM(noncancelled_revenue) / NULLIF(COUNT(*),0))::numeric, 2) AS avg_lifetime_revenue_eur,
       ROUND((SUM(noncancelled_revenue) / NULLIF(SUM(noncancelled_orders),0))::numeric, 2) AS avg_order_value_eur,
       ROUND((AVG(EXTRACT(EPOCH FROM (last_order_date - first_order_date)) / 86400.0))::numeric, 1) AS avg_tenure_days,
       ROUND((100.0 * COUNT(*) FILTER (WHERE total_orders >= 2) / NULLIF(COUNT(*),0))::numeric, 1) AS repeat_purchase_rate_pct,
       ROUND((100.0 * SUM(refunded_orders) / NULLIF(SUM(total_orders),0))::numeric, 1) AS refund_rate_pct
     FROM segmented
     GROUP BY segment
     ORDER BY CASE WHEN segment = 'loyalty member' THEN 0 ELSE 1 END, segment

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: segment, customer_count, avg_lifetime_orders, avg_lifetime_revenue_eur, avg_order_value_eur, avg_tenure_days, repeat_purchase_rate_pct, refund_rate_pct

   • OmarQuery compiles cleanly with the expected 8 columns, one row per segment. Submitting.
   • Omar calls submit

     WITH orders_base AS (
       SELECT
         o.id AS order_id,
         o.tenant_id,
         o.user_id,
         o.status,
         o.currency_code,
         o.grand_total,
         COALESCE(o.placed_at, o.created_at) AS order_date
       FROM orders o
       JOIN users u ON u.id = o.user_id AND u.tenant_id = o.tenant_id
       WHERE u.is_staff = false
         AND COALESCE(o.placed_at, o.created_at) >= DATE '2023-01-01'
         AND COALESCE(o.placed_at, o.created_at) < DATE '2026-01-01'
     ),
     orders_enriched AS (
       SELECT
         ob.*,
         CASE WHEN ob.currency_code = 'EUR' THEN ob.grand_total
              ELSE ob.grand_total / er.rate END AS eur_amount,
         EXISTS (SELECT 1 FROM payments p WHERE p.order_id = ob.order_id AND p.amount_refunded > 0) AS has_refund,
         EXISTS (SELECT 1 FROM order_returns r WHERE r.order_id = ob.order_id) AS has_return
       FROM orders_base ob
       LEFT JOIN exchange_rates er
         ON er.base_code = 'EUR' AND er.quote_code = ob.currency_code AND er.as_of = CAST(ob.order_date AS DATE)
     ),
     customer_agg AS (
       SELECT
         user_id,
         COUNT(*) AS total_orders,
         COUNT(*) FILTER (WHERE status <> 'cancelled') AS noncancelled_orders,
         SUM(eur_amount) FILTER (WHERE status <> 'cancelled') AS noncancelled_revenue,
         MIN(order_date) AS first_order_date,
         MAX(order_date) AS last_order_date,
         COUNT(*) FILTER (WHERE has_refund OR has_return) AS refunded_orders
       FROM orders_enriched
       GROUP BY user_id
     ),
     segmented AS (
       SELECT
         ca.*,
         CASE WHEN EXISTS (SELECT 1 FROM loyalty_accounts la WHERE la.user_id = ca.user_id)
              THEN 'loyalty member' ELSE 'non-member' END AS segment
       FROM customer_agg ca
     )
     SELECT
       segment,
       COUNT(*) AS customer_count,
       ROUND(AVG(total_orders)::numeric, 2) AS avg_lifetime_orders,
       ROUND((SUM(noncancelled_revenue) / NULLIF(COUNT(*),0))::numeric, 2) AS avg_lifetime_revenue_eur,
       ROUND((SUM(noncancelled_revenue) / NULLIF(SUM(noncancelled_orders),0))::numeric, 2) AS avg_order_value_eur,
       ROUND((AVG(EXTRACT(EPOCH FROM (last_order_date - first_order_date)) / 86400.0))::numeric, 1) AS avg_tenure_days,
       ROUND((100.0 * COUNT(*) FILTER (WHERE total_orders >= 2) / NULLIF(COUNT(*),0))::numeric, 1) AS repeat_purchase_rate_pct,
       ROUND((100.0 * SUM(refunded_orders) / NULLIF(SUM(total_orders),0))::numeric, 1) AS refund_rate_pct
     FROM segmented
     GROUP BY segment
     ORDER BY CASE WHEN segment = 'loyalty member' THEN 0 ELSE 1 END, segment

   • The warehouse answers

     Submitted to tonight's batch. Your work on this request is done.

   • OmarSubmitted. Summary of key choices baked into the query, also noted for the analyst: - **Population/date filter**: orders where `COALESCE(placed_at, created_at)` falls in 2023‑01‑01..2025‑12‑31, for non-staff users. - **Segment**: `loyalty member` if the user has any `loyalty_accounts` row, else `non-member`. - **Avg lifetime orders / repeat rate / refund rate**: computed over all orders regardless of status (matches "any status" population wording). - **Avg lifetime revenue & avg order value**: numerator/denominator restricted to non-cancelled orders, per the spec. - **EUR conversion**: `grand_total / rate` where `rate` is the EUR→order-currency rate as of the order date (1:1 when already EUR). - **Tenure**: days between first and last order date per customer (0 for single-order customers), computed via epoch seconds/86400 for dialect-safety.

   Not shown here: 240 KB of context the agent was given, its instructions and the schema documentation; it is in the raw transcript.

   Omar's transcript (277 KB) · Ingrid Solberg's transcript (906 KB) · Aisha Rahman's transcript (261 KB) · the night's plan (6 KB) · the night's reports (11 KB) · the week's memo (4 KB)