Queries · q260 · Head of Growth Marketing · week 2, night 2

Loyalty worth by vertical: repeat-purchase rate

“Controlling for category, do loyalty members actually come back and buy again more than non-members, or does that pattern not hold either once you look within a vertical?”

Manager · asked the questionChloé MarchettiHead of Growth Marketing. Joined from a direct-to-consumer brand. Wants to sell merchants more of Tidewick's marketing tools.

Analyst · planned the reportSofie NilsenStoryteller: looks for the two or three findings that matter and digs until each is explained.

Engineer · wrote the SQLOmarPragmatic: the shortest correct query, aggregates first and joins dimensions afterwards, FILTER clauses for conditional counts.

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

Chloé Marchetti writes to Sofie Nilsen
The manager, at the start of week 2
Subject: Good progress — four things I want nailed down this week

Hi Sofie,

Thanks for this — genuinely useful, and the vertical breakdown on discounting is exactly the kind of "don't trust the pooled number" catch I was worried about. A few things surprised me and I want to go deeper before I take anything to leadership.

What surprised me / needs explaining:
- Aisha's team flagged that loyalty members "cost" nearly a fifth of their own revenue in redeemed points, plus a large pile of points issued but not yet redeemed sitting as a liability. That's a very different lens from AOV — a program can look flat on AOV and still be expensive. I want these two views reconciled, not treated as separate facts.
- Aisha also walked back the "loyalty members return more" claim once category mix was accounted for. That's a good catch, but it makes me nervous about the apparel-vs-electronics AOV comparison in your memo — is that difference also just a category mix artifact, or is it real?
- Referrals look like our best-performing lever by far, yet reach is under 1% of customers. That gap between "strong where it exists" and "barely exists" feels like the single biggest opportunity I've seen all week.
My questions for this week, roughly in priority order:
1. Does discounting actually cost us margin, and in which verticals is it worst? This was already your next step — please keep it top of the list. I need a real cost picture (not just return rates) before I tell merchants to rein in sitewide sales.
2. Is the loyalty programme actually worth running as-is? I want one clear picture that weighs what members cost us (redeemed and owed points) against what they're worth (spend, frequency, retention), controlling for the category mix issue Aisha found. I need to decide whether we fix it, shrink it, or let it be.
3. What would it take to grow referrals, and who's a good referrer? Given how strong but tiny this program is, I want to understand what referring customers look like (so we can find more of them) and whether growth here looks more promising than fixing loyalty.
4. Of the orders we can't currently link to a channel, is there any pattern at all (timing, merchant type, repeat vs new customer) that hints at what's driving them? I don't expect a clean answer — I just want to know if this blind spot is worth investing in closing, or if it's simply untrackable and I should stop asking.
What I need this for: I'm trying to turn this into a short list of 2-3 concrete recommendations for merchants and for our own roadmap — where to tell them to pull back (discounting), where to double down (referrals), and where we might be quietly burning money (loyalty payouts). I'd rather have fewer, firmer answers than a wider, shakier set.

Thanks again — this is coming together well.

Chloé
Sofie Nilsen plans the night
The analyst · week 2, night 2, request 2 of 3
Night 1 settled Q1: discounting's real cost is heavily vertical-dependent. Apparel, Beauty, Home & Living, and Sports & Outdoor show discounted orders costing 17-42 percentage points more of GMV (combined discount+refund) than full-price orders in the same vertical — genuine margin leakage worth telling merchants to rein in. Food & Coffee and Pet Supplies show even bigger leakage % (83-89%), but that's near-free subscription trial offers, a different (acquisition-cost) story, not wasteful promo. The coupon-code report found only 5 distinct code templates drive almost all 2025 redemptions platform-wide, with WELCOME10 alone responsible for €51k net cost (discount+refund) — confirms and quantifies Oliver's finding, and the monthly trend confirms leakage worsened into Q3/Q4, tracking the refund-rate creep CEO/CFO flagged. Q1 is in good shape for the memo. Tonight I'm moving to Q2 (loyalty): Ingrid's team already gave the company-wide cost number (19.4% of member revenue redeemed, €669k outstanding liability) and the category-mix correction on returns, so I'm pushing that down to the vertical level (cost) and adding the one 'worth' metric Chloé asked for that we're missing — retention/repeat-purchase rate, controlled by category. Third slot starts Q3 (referrals) at the same vertical grain for comparability, to see where reach is least tapped.
Loyalty worth by vertical: repeat-purchase rate
Question
Controlling for category, do loyalty members actually come back and buy again more than non-members, or does that pattern not hold either once you look within a vertical?
One row per
industry and segment (loyalty member vs non-member), industries with an active loyalty program only
Columns
- industry - tenants.industry
- segment - 'loyalty member' or 'non-member'
- users_count - distinct users in that segment who placed >=1 order with placed_at in 2025 in this industry
- repeat_rate_pct - share of those users with >=2 distinct orders (by placed_at date) in 2025, *100, rounded to 1 decimal
- avg_orders_per_user - average count of 2025 orders per user in that segment, rounded to 2 decimals
- avg_order_value_eur - average grand_total (EUR) of that segment's 2025 orders in this industry, rounded to 0 decimals

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 loyalty_industries AS (
  SELECT DISTINCT t.industry
  FROM loyalty_programs lp
  JOIN tenants t ON t.id = lp.tenant_id
  WHERE lp.is_active = true AND t.industry IS NOT NULL
),
orders_2025 AS (
  SELECT
    o.id AS order_id,
    o.user_id,
    o.tenant_id,
    t.industry,
    o.placed_at,
    o.grand_total,
    o.currency_code,
    CASE WHEN EXISTS (
      SELECT 1 FROM loyalty_accounts la
      WHERE la.user_id = o.user_id AND la.tenant_id = o.tenant_id
    ) THEN 'loyalty member' ELSE 'non-member' END AS segment
  FROM orders o
  JOIN tenants t ON t.id = o.tenant_id
  WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01'
    AND t.industry IN (SELECT industry FROM loyalty_industries)
),
orders_eur AS (
  SELECT oe.*,
    CASE WHEN oe.currency_code = 'EUR' THEN oe.grand_total
         ELSE oe.grand_total / er.rate
    END AS grand_total_eur
  FROM orders_2025 oe
  LEFT JOIN exchange_rates er
    ON er.base_code = 'EUR' AND er.quote_code = oe.currency_code AND er.as_of = CAST(oe.placed_at AS DATE)
),
user_stats AS (
  SELECT industry, segment, user_id,
    COUNT(*) AS order_count,
    COUNT(DISTINCT CAST(placed_at AS DATE)) AS distinct_order_days
  FROM orders_eur
  GROUP BY industry, segment, user_id
),
agg_users AS (
  SELECT industry, segment,
    COUNT(*) AS users_count,
    COUNT(*) FILTER (WHERE distinct_order_days >= 2) AS repeat_users,
    AVG(order_count) AS avg_orders_per_user
  FROM user_stats
  GROUP BY industry, segment
),
agg_orders AS (
  SELECT industry, segment, AVG(grand_total_eur) AS avg_order_value_eur
  FROM orders_eur
  GROUP BY industry, segment
)
SELECT
  au.industry,
  au.segment,
  au.users_count,
  ROUND(100.0 * au.repeat_users / NULLIF(au.users_count,0), 1) AS repeat_rate_pct,
  ROUND(au.avg_orders_per_user::numeric, 2) AS avg_orders_per_user,
  ROUND(ao.avg_order_value_eur::numeric, 0) AS avg_order_value_eur
FROM agg_users au
JOIN agg_orders ao ON ao.industry = au.industry AND ao.segment = au.segment
ORDER BY au.industry ASC, au.segment ASC

“The query compiles and returns the expected columns. 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 modeling choices (also noted for the analyst): - Segment: a user is a "loyalty member" if any loyalty_accounts row exists for their (user_id, tenant_id), independent of program status. - Repeat rate: based on ≥2 distinct placed_at calendar days in 2025 (not just ≥2 order rows), matching "distinct orders (by placed_at date)" in the request. - avg_orders_per_user: counts all 2025 orders per user (not deduped by day). - Industry scope: an industry qualifies if any tenant in it has an active loyalty program, but once qualified, all orders/users from all tenants in that industry are included in the comparison — this is the natural reading of "industries with an active loyalty program only" at the grain requested (one row per industry), but flagged in case the intent was to restrict to only the tenant(s) with the active program. - FX: grand_total converted to EUR via exchange_rates (EUR base) as of the order's placed_at date, with EUR orders passed through at 1.0.”

The report comes back overnight

As Sofie Nilsen saw it, from the small dataset the agents worked on

industry              | segment        | users_count | repeat_rate_pct | avg_orders_per_user | avg_order_value_eur
----------------------+----------------+-------------+-----------------+---------------------+--------------------
Apparel & Footwear    | loyalty member |         619 |            58.5 |                2.53 |                 165
Apparel & Footwear    | non-member     |        2140 |            59.6 |                2.62 |                 189
Beauty                | loyalty member |          45 |            62.2 |                2.84 |                 102
Beauty                | non-member     |         447 |            65.1 |                3.22 |                  96
Books & Stationery    | loyalty member |           8 |            62.5 |                3.13 |                  69
Books & Stationery    | non-member     |          47 |            63.8 |                2.85 |                  48
Consumer Electronics  | loyalty member |          25 |            72.0 |                2.28 |                 446
Consumer Electronics  | non-member     |         250 |            45.2 |                2.08 |                1114
Food & Coffee         | loyalty member |           7 |           100.0 |                7.71 |                  61
Food & Coffee         | non-member     |         831 |            85.1 |                8.64 |                  30
Home & Living         | loyalty member |          26 |            65.4 |                2.23 |                 144
Home & Living         | non-member     |         710 |            52.0 |                2.26 |                 149
Jewelry & Accessories | non-member     |          29 |            41.4 |                1.72 |                 217
Pet Supplies          | loyalty member |           4 |            75.0 |                4.00 |                  49
Pet Supplies          | non-member     |         229 |            80.3 |                6.28 |                  37
Sports & Outdoor      | loyalty member |         128 |            53.9 |                2.56 |                 355
Sports & Outdoor      | non-member     |         330 |            49.7 |                2.09 |                 372
(17 rows)

What came of it
The memo and the queries that followed; links found by title, so inferred
It builds on:
- q058Is loyalty members' lower spend a within-industry effect or a merchant-mix effect?

It becomes benchmark query q260

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

spellingcast (2×)
numericsdocs/NUMERICS.md: AVG over an integer computed as a decimal: AVG(CAST(x AS NUMERIC(18,6)))
numericsdocs/NUMERICS.md: rounding removed; results keep full precision; bare NUMERIC cast that only served the rounding removed
numericsdocs/NUMERICS.md: x / exchange rate cast to NUMERIC(18,6)
made explicita selected t.col gets AS col, its standard name made explicit (10×)
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 loyalty_industries AS (  SELECT DISTINCT t.industry  SELECT DISTINCT t.industry AS industry  FROM loyalty_programs lp  JOIN tenants t ON t.id = lp.tenant_id  WHERE lp.is_active = true AND t.industry IS NOT NULL),orders_2025 AS (  SELECT    o.id AS order_id,    o.user_id,    o.tenant_id,    t.industry,    o.placed_at,    o.grand_total,    o.currency_code,    o.user_id AS user_id,    o.tenant_id AS tenant_id,    t.industry AS industry,    o.placed_at AS placed_at,    o.grand_total AS grand_total,    o.currency_code AS currency_code,    CASE WHEN EXISTS (      SELECT 1 FROM loyalty_accounts la      WHERE la.user_id = o.user_id AND la.tenant_id = o.tenant_id    ) THEN 'loyalty member' ELSE 'non-member' END AS segment  FROM orders o  JOIN tenants t ON t.id = o.tenant_id  WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01'    AND t.industry IN (SELECT industry FROM loyalty_industries)),orders_eur AS (  SELECT oe.*,    CASE WHEN oe.currency_code = 'EUR' THEN oe.grand_total         ELSE oe.grand_total / er.rate         ELSE CAST(oe.grand_total / er.rate AS NUMERIC(18,6))    END AS grand_total_eur  FROM orders_2025 oe  LEFT JOIN exchange_rates er    ON er.base_code = 'EUR' AND er.quote_code = oe.currency_code AND er.as_of = CAST(oe.placed_at AS DATE)),user_stats AS (  SELECT industry, segment, user_id,    COUNT(*) AS order_count,    COUNT(DISTINCT CAST(placed_at AS DATE)) AS distinct_order_days  FROM orders_eur  GROUP BY industry, segment, user_id),agg_users AS (  SELECT industry, segment,    COUNT(*) AS users_count,    COUNT(*) FILTER (WHERE distinct_order_days >= 2) AS repeat_users,    AVG(order_count) AS avg_orders_per_user    AVG(CAST(order_count AS NUMERIC(18,6))) AS avg_orders_per_user  FROM user_stats  GROUP BY industry, segment),agg_orders AS (  SELECT industry, segment, AVG(grand_total_eur) AS avg_order_value_eur  FROM orders_eur  GROUP BY industry, segment)SELECT  au.industry,  au.segment,  au.users_count,  ROUND(100.0 * au.repeat_users / NULLIF(au.users_count,0), 1) AS repeat_rate_pct,  ROUND(au.avg_orders_per_user::numeric, 2) AS avg_orders_per_user,  ROUND(ao.avg_order_value_eur::numeric, 0) AS avg_order_value_eur  au.industry AS industry,  au.segment AS segment,  au.users_count AS users_count,  100.0 * au.repeat_users / NULLIF(au.users_count,0) AS repeat_rate_pct,  au.avg_orders_per_user AS avg_orders_per_user,  ao.avg_order_value_eur AS avg_order_value_eurFROM agg_users auJOIN agg_orders ao ON ao.industry = au.industry AND ao.segment = au.segmentORDER BY au.industry ASC, au.segment ASCORDER BY au.industry ASC NULLS LAST, au.segment ASC NULLS LAST

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

WITH loyalty_industries AS (
  SELECT DISTINCT t.industry AS industry
  FROM loyalty_programs lp
  JOIN tenants t ON t.id = lp.tenant_id
  WHERE lp.is_active = true AND t.industry IS NOT NULL
),
orders_2025 AS (
  SELECT
    o.id AS order_id,
    o.user_id AS user_id,
    o.tenant_id AS tenant_id,
    t.industry AS industry,
    o.placed_at AS placed_at,
    o.grand_total AS grand_total,
    o.currency_code AS currency_code,
    CASE WHEN EXISTS (
      SELECT 1 FROM loyalty_accounts la
      WHERE la.user_id = o.user_id AND la.tenant_id = o.tenant_id
    ) THEN 'loyalty member' ELSE 'non-member' END AS segment
  FROM orders o
  JOIN tenants t ON t.id = o.tenant_id
  WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01'
    AND t.industry IN (SELECT industry FROM loyalty_industries)
),
orders_eur AS (
  SELECT oe.*,
    CASE WHEN oe.currency_code = 'EUR' THEN oe.grand_total
         ELSE CAST(oe.grand_total / er.rate AS NUMERIC(18,6))
    END AS grand_total_eur
  FROM orders_2025 oe
  LEFT JOIN exchange_rates er
    ON er.base_code = 'EUR' AND er.quote_code = oe.currency_code AND er.as_of = CAST(oe.placed_at AS DATE)
),
user_stats AS (
  SELECT industry, segment, user_id,
    COUNT(*) AS order_count,
    COUNT(DISTINCT CAST(placed_at AS DATE)) AS distinct_order_days
  FROM orders_eur
  GROUP BY industry, segment, user_id
),
agg_users AS (
  SELECT industry, segment,
    COUNT(*) AS users_count,
    COUNT(*) FILTER (WHERE distinct_order_days >= 2) AS repeat_users,
    AVG(CAST(order_count AS NUMERIC(18,6))) AS avg_orders_per_user
  FROM user_stats
  GROUP BY industry, segment
),
agg_orders AS (
  SELECT industry, segment, AVG(grand_total_eur) AS avg_order_value_eur
  FROM orders_eur
  GROUP BY industry, segment
)
SELECT
  au.industry AS industry,
  au.segment AS segment,
  au.users_count AS users_count,
  100.0 * au.repeat_users / NULLIF(au.users_count,0) AS repeat_rate_pct,
  au.avg_orders_per_user AS avg_orders_per_user,
  ao.avg_order_value_eur AS avg_order_value_eur
FROM agg_users au
JOIN agg_orders ao ON ao.industry = au.industry AND ao.segment = au.segment
ORDER BY au.industry ASC NULLS LAST, au.segment ASC NULLS LAST

Firebolt{ "errors": [ { "description": "Line 28, 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": "b657c4ce-c394-4cd9-afa8-6ab7da24b050", "query_lab
DataFusionThis feature is not implemented: Physical plan does not support logical expression Exists(Exists { subquery: <subquery>, negated: false })
ClickHouseCode: 48. DB::Exception: Current query is not supported yet, because can't find correlated column '__table8.tenant_id' in current header: __table8.user_id, __table8.placed_at, __table8.currency_code, __table9.industry. (NOT_IMPLEMENTED)

Scale factor 1000, c8a.24xlarge, after the last load. q260 on the start page, with every configuration.

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 Sofie Nilsen (analyst for the Head of Growth Marketing):

Title: Loyalty worth by vertical: repeat-purchase rate
Question: Controlling for category, do loyalty members actually come back and buy again more than non-members, or does that pattern not hold either once you look within a vertical?
Grain: one row per industry and segment (loyalty member vs non-member), industries with an active loyalty program only
Columns:
- industry - tenants.industry
- segment - 'loyalty member' or 'non-member'
- users_count - distinct users in that segment who placed >=1 order with placed_at in 2025 in this industry
- repeat_rate_pct - share of those users with >=2 distinct orders (by placed_at date) in 2025, *100, rounded to 1 decimal
- avg_orders_per_user - average count of 2025 orders per user in that segment, rounded to 2 decimals
- avg_order_value_eur - average grand_total (EUR) of that segment's 2025 orders in this industry, rounded to 0 decimals
Details: Source: orders joined to tenants (industry) and loyalty_accounts (member match on user_id+tenant_id), same member/non-member classification as last week. Restrict to industries with an active loyalty program (loyalty_programs.is_active=true for some tenant in that industry). Filter placed_at in 2025. Convert grand_total to EUR using exchange_rates as of placed_at date (EUR=rate 1). Order by industry ascending, then segment ascending ('loyalty member' before 'non-member'). Expect ~9 industries x 2 rows.

Check your query with the warehouse's dry_run tool, then hand it in with submit.

Omar calls dry_run

WITH loyalty_industries AS (
  SELECT DISTINCT t.industry
  FROM loyalty_programs lp
  JOIN tenants t ON t.id = lp.tenant_id
  WHERE lp.is_active = true AND t.industry IS NOT NULL
),
orders_2025 AS (
  SELECT
    o.id AS order_id,
    o.user_id,
    o.tenant_id,
    t.industry,
    o.placed_at,
    o.grand_total,
    o.currency_code,
    CASE WHEN EXISTS (
      SELECT 1 FROM loyalty_accounts la
      WHERE la.user_id = o.user_id AND la.tenant_id = o.tenant_id
    ) THEN 'loyalty member' ELSE 'non-member' END AS segment
  FROM orders o
  JOIN tenants t ON t.id = o.tenant_id
  WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01'
    AND t.industry IN (SELECT industry FROM loyalty_industries)
),
orders_eur AS (
  SELECT oe.*,
    CASE WHEN oe.currency_code = 'EUR' THEN oe.grand_total
         ELSE oe.grand_total / er.rate
    END AS grand_total_eur
  FROM orders_2025 oe
  LEFT JOIN exchange_rates er
    ON er.base_code = 'EUR' AND er.quote_code = oe.currency_code AND er.as_of = CAST(oe.placed_at AS DATE)
),
user_stats AS (
  SELECT industry, segment, user_id,
    COUNT(*) AS order_count,
    COUNT(DISTINCT CAST(placed_at AS DATE)) AS distinct_order_days
  FROM orders_eur
  GROUP BY industry, segment, user_id
),
agg_users AS (
  SELECT industry, segment,
    COUNT(*) AS users_count,
    COUNT(*) FILTER (WHERE distinct_order_days >= 2) AS repeat_users,
    AVG(order_count) AS avg_orders_per_user
  FROM user_stats
  GROUP BY industry, segment
),
agg_orders AS (
  SELECT industry, segment, AVG(grand_total_eur) AS avg_order_value_eur
  FROM orders_eur
  GROUP BY industry, segment
)
SELECT
  au.industry,
  au.segment,
  au.users_count,
  ROUND(100.0 * au.repeat_users / NULLIF(au.users_count,0), 1) AS repeat_rate_pct,
  ROUND(au.avg_orders_per_user::numeric, 2) AS avg_orders_per_user,
  ROUND(ao.avg_order_value_eur::numeric, 0) AS avg_order_value_eur
FROM agg_users au
JOIN agg_orders ao ON ao.industry = au.industry AND ao.segment = au.segment
ORDER BY au.industry ASC, au.segment ASC

The warehouse answers

STATUS: COMPILES (dry run on the empty schema, no data read)
COLUMNS: industry, segment, users_count, repeat_rate_pct, avg_orders_per_user, avg_order_value_eur

OmarThe query compiles and returns the expected columns. Submitting.

Omar calls submit

WITH loyalty_industries AS (
  SELECT DISTINCT t.industry
  FROM loyalty_programs lp
  JOIN tenants t ON t.id = lp.tenant_id
  WHERE lp.is_active = true AND t.industry IS NOT NULL
),
orders_2025 AS (
  SELECT
    o.id AS order_id,
    o.user_id,
    o.tenant_id,
    t.industry,
    o.placed_at,
    o.grand_total,
    o.currency_code,
    CASE WHEN EXISTS (
      SELECT 1 FROM loyalty_accounts la
      WHERE la.user_id = o.user_id AND la.tenant_id = o.tenant_id
    ) THEN 'loyalty member' ELSE 'non-member' END AS segment
  FROM orders o
  JOIN tenants t ON t.id = o.tenant_id
  WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01'
    AND t.industry IN (SELECT industry FROM loyalty_industries)
),
orders_eur AS (
  SELECT oe.*,
    CASE WHEN oe.currency_code = 'EUR' THEN oe.grand_total
         ELSE oe.grand_total / er.rate
    END AS grand_total_eur
  FROM orders_2025 oe
  LEFT JOIN exchange_rates er
    ON er.base_code = 'EUR' AND er.quote_code = oe.currency_code AND er.as_of = CAST(oe.placed_at AS DATE)
),
user_stats AS (
  SELECT industry, segment, user_id,
    COUNT(*) AS order_count,
    COUNT(DISTINCT CAST(placed_at AS DATE)) AS distinct_order_days
  FROM orders_eur
  GROUP BY industry, segment, user_id
),
agg_users AS (
  SELECT industry, segment,
    COUNT(*) AS users_count,
    COUNT(*) FILTER (WHERE distinct_order_days >= 2) AS repeat_users,
    AVG(order_count) AS avg_orders_per_user
  FROM user_stats
  GROUP BY industry, segment
),
agg_orders AS (
  SELECT industry, segment, AVG(grand_total_eur) AS avg_order_value_eur
  FROM orders_eur
  GROUP BY industry, segment
)
SELECT
  au.industry,
  au.segment,
  au.users_count,
  ROUND(100.0 * au.repeat_users / NULLIF(au.users_count,0), 1) AS repeat_rate_pct,
  ROUND(au.avg_orders_per_user::numeric, 2) AS avg_orders_per_user,
  ROUND(ao.avg_order_value_eur::numeric, 0) AS avg_order_value_eur
FROM agg_users au
JOIN agg_orders ao ON ao.industry = au.industry AND ao.segment = au.segment
ORDER BY au.industry ASC, au.segment ASC

The warehouse answers

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

OmarSubmitted. Summary of key modeling choices (also noted for the analyst): - **Segment**: a user is a "loyalty member" if any `loyalty_accounts` row exists for their (user_id, tenant_id), independent of program status. - **Repeat rate**: based on ≥2 distinct `placed_at` calendar days in 2025 (not just ≥2 order rows), matching "distinct orders (by placed_at date)" in the request. - **avg_orders_per_user**: counts all 2025 orders per user (not deduped by day). - **Industry scope**: an industry qualifies if *any* tenant in it has an active loyalty program, but once qualified, all orders/users from all tenants in that industry are included in the comparison — this is the natural reading of "industries with an active loyalty program only" at the grain requested (one row per industry), but flagged in case the intent was to restrict to only the tenant(s) with the active program. - **FX**: `grand_total` converted to EUR via `exchange_rates` (EUR base) as of the order's `placed_at` date, with EUR orders passed through at 1.0.

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 (274 KB) · Sofie Nilsen's transcript (793 KB) · Chloé Marchetti's transcript (263 KB) · the night's plan (6 KB) · the night's reports (8 KB) · the week's memo (3 KB)

Loyalty worth by vertical: repeat-purchase rate

Chloé Marchetti writes to Sofie Nilsen

Sofie Nilsen plans the night

Omar writes the SQL

The report comes back overnight

What came of it

It becomes benchmark query q260

Everything, as recorded