Queries · q176 · Head of B2B Commerce · week 1, night 2

New Business Only: Won vs Lost Opportunity Characteristics

“Restricted to genuine new-customer acquisition deals (excluding renewals), what reliably differs between won and lost opportunities — deal size, activities before close, days open, or concentration among accounts and reps?”

Manager · asked the questionPatrick O'NeillHead of B2B Commerce. Built B2B sales teams at a software company. Owns the B2B features wholesale merchants run their sales on.

Analyst · planned the reportDaniel KimFunnel thinker: measures each step of a process and where things drop out.

Engineer · wrote the SQLGraceWrites tidy, layered CTEs with descriptive names, one step per CTE, then a final select.

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

A business event lands on the Head of B2B Commerce's desk
Week 1; shared with Samuel Whitcombe, Head of Merchant Success
B2B merchants say their sales teams struggle. How do their pipelines convert from lead to won deal, how long does it take, and what predicts winning or losing?
One of the 35 events we wrote for the role play; it shaped the manager's first note. How events arise · All the events
Patrick O'Neill writes to Daniel Kim
The manager, at the start of week 1
Note to Daniel Kim — B2B pipeline health

Daniel,

Samuel and I need to give the B2B wholesale merchants (and our own leadership) a clear-eyed view of why their sales teams are struggling to close business. This feeds a joint recommendation Samuel and I are making next week on where to invest — better pipeline coaching/tooling for merchants vs. changes to how we structure stages, quotes and terms in the product. So I want the picture to be accurate more than exhaustive — pick your battles across the nights you have.

Here's what I actually want to know, in plain terms:
1. Where do deals die? Across the wholesale merchants, at what point in the journey from raw lead to signed deal do we lose the most opportunities? I want to know which stage is the graveyard, not just an overall conversion number.
2. How long does it take to close, and is that getting better or worse? From a lead first showing up to a deal actually closing (won or lost), what's the typical cycle time, and has it been stretching out or shrinking over recent months?
3. What separates a won deal from a lost one? Things like deal size, how many people/touches were involved before close, which rep or team owned it, or how old the opportunity got before it closed — anything that reliably shows up more on the winning side versus the losing side.
4. Are some merchants or reps clearly struggling more than others? I'd like a sense of whether this is a widespread pattern across our B2B merchants or concentrated in a handful of accounts/teams, since that changes whether this is a product fix or a coaching fix.
5. (If there's room) Does deal size or payment terms correlate with win rate or speed? Wholesalers care a lot about credit terms — I'm curious if longer terms or larger contract values are dragging out or killing deals.
Priority order is 1, 2, 3 first — those directly shape the recommendation. 4 and 5 are valuable but can slip if you run out of slots this week.

Keep each report to one clean question — I'd rather have four sharp answers than one crowded one. Flag anything that looks like a data quality issue rather than a real trend.

Thanks — looking forward to the Friday memo.

Patrick
A note from the Head of Merchant Success, Samuel Whitcombe
Subject: A few questions from the Merchant Success side

Hi Daniel,

Good to see this getting attention this week. A few things I'd love your analysis to help answer, from the merchant-health angle:
1. For our B2B wholesaler merchants specifically, are deals actually stalling at a particular stage, or is it more that fewer leads come in to begin with? I want to know where the real friction sits before our QBRs.
2. Among the accounts that do close, is there anything about them (size, how engaged they were beforehand, how long the courtship took) that separates the wins from the losses? I'm trying to understand whether our biggest merchants' sales teams are losing winnable deals or just working less promising ones.
3. Is there a relationship between how long a deal has been open and its odds of closing at all — i.e. do stale pipelines quietly become risk, or do they usually still land eventually?
Happy to compare notes before you finalize anything for Patrick.

Best, Samuel
Daniel Kim plans the night
The analyst · week 1, night 2, request 2 of 3
Night 1's big finding is a data-model gotcha, not yet a business trend: every wholesale tenant's 'New business' pipeline shows zero opportunities (open or closed) at Prospecting/Qualification/Proposal/Negotiation — closed deals only ever show as the terminal Closed Won/Closed Lost stage. That means current stage_id can't answer 'where do deals die' for lost opportunities; it only tells us the final outcome. It also means Report 3 (won/lost) and Report 2 (cycle time) from night 1 silently pooled two very different populations together: 'New business' (true lead-to-signed-deal acquisition, which is what Patrick and Samuel are actually asking about) and 'Renewals' (existing-customer renew/churn, where 'Renewed' carries a 1.0 default win probability and is a fundamentally easier motion). Tonight I'm spending all three slots fixing this at the root: (1) reconstruct the real graveyard stage from change_history for New-business-only lost opportunities, (2) redo the won/lost characteristics comparison scoped to New business only, (3) redo the cycle-time trend scoped to New business only. This keeps priorities 1-2-3 intact but makes them actually answer the question Patrick asked, rather than a renewals-diluted version of it. I'm holding Samuel's stale-pipeline-risk question and Patrick's #4/#5 for night 3, once I see whether change_history actually has usable coverage for the graveyard question (flagged as a risk in request 1) and whether the split reports change the picture.
New Business Only: Won vs Lost Opportunity Characteristics
Question
Restricted to genuine new-customer acquisition deals (excluding renewals), what reliably differs between won and lost opportunities — deal size, activities before close, days open, or concentration among accounts and reps?
One row per
closed outcome (won or lost), New business pipeline only
Columns
- outcome (won / lost)
- opportunities (count)
- average amount in EUR
- median amount in EUR
- average activities per opportunity before close
- average days open (closed_at - created_at)
- distinct accounts involved
- distinct owning reps (owner_user_id) involved

Grace 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 target_tenants AS (
  SELECT id
  FROM tenants
  WHERE industry ILIKE '%industrial%'
     OR industry ILIKE '%office equipment%'
     OR industry ILIKE '%wholesal%'
),
new_business_pipelines AS (
  SELECT id, tenant_id
  FROM pipelines
  WHERE name = 'New business'
),
base_opportunities AS (
  SELECT o.*
  FROM opportunities o
  JOIN new_business_pipelines p
    ON o.pipeline_id = p.id
   AND o.tenant_id = p.tenant_id
  WHERE o.tenant_id IN (SELECT id FROM target_tenants)
    AND o.is_closed = true
),
opp_with_rate AS (
  SELECT
    bo.*,
    (
      SELECT er.rate
      FROM exchange_rates er
      WHERE er.base_code = 'EUR'
        AND er.quote_code = bo.currency_code
        AND er.as_of <= date(bo.closed_at)
      ORDER BY er.as_of DESC
      LIMIT 1
    ) AS eur_rate
  FROM base_opportunities bo
),
activities_before_close AS (
  SELECT
    a.opportunity_id,
    count(*) AS activity_count
  FROM activities a
  JOIN base_opportunities bo
    ON a.opportunity_id = bo.id
   AND a.tenant_id = bo.tenant_id
  WHERE a.occurred_at <= bo.closed_at
  GROUP BY a.opportunity_id
),
opp_final AS (
  SELECT
    owr.id,
    owr.account_id,
    owr.owner_user_id,
    owr.is_won,
    owr.amount / owr.eur_rate AS amount_eur,
    EXTRACT(EPOCH FROM (owr.closed_at - owr.created_at)) / 86400.0 AS days_open,
    COALESCE(ac.activity_count, 0) AS activity_count
  FROM opp_with_rate owr
  LEFT JOIN activities_before_close ac
    ON ac.opportunity_id = owr.id
)
SELECT
  CASE WHEN is_won THEN 'won' ELSE 'lost' END AS outcome,
  count(*) AS opportunities,
  round(avg(amount_eur)::numeric, 2) AS avg_amount_eur,
  round(percentile_cont(0.5) WITHIN GROUP (ORDER BY amount_eur)::numeric, 2) AS median_amount_eur,
  round(avg(activity_count)::numeric, 2) AS avg_activities_before_close,
  round(avg(days_open)::numeric, 1) AS avg_days_open,
  count(DISTINCT account_id) AS distinct_accounts,
  count(DISTINCT owner_user_id) AS distinct_reps
FROM opp_final
GROUP BY is_won
ORDER BY CASE WHEN is_won THEN 0 ELSE 1 END

“Query compiles and returns exactly the requested columns. Submitting.”

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

“Submitted. The report returns one row per outcome (won/lost) for New-business-pipeline opportunities only, with opportunity count, average/median EUR amount, average pre-close activities, average days open, and distinct account/rep counts — mirroring the prior comparison but now isolating genuine new-customer acquisition deals from renewals.”

The report comes back overnight

As Daniel Kim saw it, from the small dataset the agents worked on

outcome | opportunities | avg_amount_eur | median_amount_eur | avg_activities_before_close | avg_days_open | distinct_accounts | distinct_reps
--------+---------------+----------------+-------------------+-----------------------------+---------------+-------------------+--------------
won     |           155 |        7097.90 |           5880.52 |                        2.30 |           9.1 |               155 |            10
lost    |            30 |        5628.59 |           6520.14 |                        3.37 |          35.5 |                30 |             9
(2 rows)

What came of it
The memo and the queries that followed; links found by title, so inferred
Later queries that build on this report:
It builds on:
- q174B2B Wholesale Opportunities: Won vs Lost Characteristics

It becomes benchmark query q176

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

spellingcast (4×)
spellingfetch first
spellingdate cast
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×)
placeholderpercentile cont
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×)
placeholderfetch first

The benchmark text, against the submitted SQL

WITH target_tenants AS (  SELECT id  FROM tenants  WHERE industry ILIKE '%industrial%'     OR industry ILIKE '%office equipment%'     OR industry ILIKE '%wholesal%'),new_business_pipelines AS (  SELECT id, tenant_id  FROM pipelines  WHERE name = 'New business'),base_opportunities AS (  SELECT o.*  FROM opportunities o  JOIN new_business_pipelines p    ON o.pipeline_id = p.id   AND o.tenant_id = p.tenant_id  WHERE o.tenant_id IN (SELECT id FROM target_tenants)    AND o.is_closed = true),opp_with_rate AS (  SELECT    bo.*,    (      SELECT er.rate      FROM exchange_rates er      WHERE er.base_code = 'EUR'        AND er.quote_code = bo.currency_code        AND er.as_of <= date(bo.closed_at)      ORDER BY er.as_of DESC      LIMIT 1        AND er.as_of <= CAST(bo.closed_at AS DATE)      ORDER BY er.as_of DESC NULLS FIRST      {{fetch_first(1)}}    ) AS eur_rate  FROM base_opportunities bo),activities_before_close AS (  SELECT    a.opportunity_id,    a.opportunity_id AS opportunity_id,    count(*) AS activity_count  FROM activities a  JOIN base_opportunities bo    ON a.opportunity_id = bo.id   AND a.tenant_id = bo.tenant_id  WHERE a.occurred_at <= bo.closed_at  GROUP BY a.opportunity_id),opp_final AS (  SELECT    owr.id,    owr.account_id,    owr.owner_user_id,    owr.is_won,    owr.amount / owr.eur_rate AS amount_eur,    EXTRACT(EPOCH FROM (owr.closed_at - owr.created_at)) / 86400.0 AS days_open,    owr.id AS id,    owr.account_id AS account_id,    owr.owner_user_id AS owner_user_id,    owr.is_won AS is_won,    CAST(owr.amount / owr.eur_rate AS NUMERIC(18,6)) AS amount_eur,    {{seconds_between(owr.created_at, owr.closed_at)}} / 86400.0 AS days_open,    COALESCE(ac.activity_count, 0) AS activity_count  FROM opp_with_rate owr  LEFT JOIN activities_before_close ac    ON ac.opportunity_id = owr.id)SELECT  CASE WHEN is_won THEN 'won' ELSE 'lost' END AS outcome,  count(*) AS opportunities,  round(avg(amount_eur)::numeric, 2) AS avg_amount_eur,  round(percentile_cont(0.5) WITHIN GROUP (ORDER BY amount_eur)::numeric, 2) AS median_amount_eur,  round(avg(activity_count)::numeric, 2) AS avg_activities_before_close,  round(avg(days_open)::numeric, 1) AS avg_days_open,  avg(amount_eur) AS avg_amount_eur,  {{percentile_cont(0.5, amount_eur)}} AS median_amount_eur,  avg(cast(activity_count as numeric(18,6))) AS avg_activities_before_close,  avg(days_open) AS avg_days_open,  count(DISTINCT account_id) AS distinct_accounts,  count(DISTINCT owner_user_id) AS distinct_repsFROM opp_finalGROUP BY is_wonORDER BY CASE WHEN is_won THEN 0 ELSE 1 ENDORDER BY CASE WHEN is_won THEN 0 ELSE 1 END NULLS LAST

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

WITH target_tenants AS (
  SELECT id
  FROM tenants
  WHERE industry ILIKE '%industrial%'
     OR industry ILIKE '%office equipment%'
     OR industry ILIKE '%wholesal%'
),
new_business_pipelines AS (
  SELECT id, tenant_id
  FROM pipelines
  WHERE name = 'New business'
),
base_opportunities AS (
  SELECT o.*
  FROM opportunities o
  JOIN new_business_pipelines p
    ON o.pipeline_id = p.id
   AND o.tenant_id = p.tenant_id
  WHERE o.tenant_id IN (SELECT id FROM target_tenants)
    AND o.is_closed = true
),
opp_with_rate AS (
  SELECT
    bo.*,
    (
      SELECT er.rate
      FROM exchange_rates er
      WHERE er.base_code = 'EUR'
        AND er.quote_code = bo.currency_code
        AND er.as_of <= CAST(bo.closed_at AS DATE)
      ORDER BY er.as_of DESC NULLS FIRST
      FETCH FIRST 1 ROWS ONLY
    ) AS eur_rate
  FROM base_opportunities bo
),
activities_before_close AS (
  SELECT
    a.opportunity_id AS opportunity_id,
    count(*) AS activity_count
  FROM activities a
  JOIN base_opportunities bo
    ON a.opportunity_id = bo.id
   AND a.tenant_id = bo.tenant_id
  WHERE a.occurred_at <= bo.closed_at
  GROUP BY a.opportunity_id
),
opp_final AS (
  SELECT
    owr.id AS id,
    owr.account_id AS account_id,
    owr.owner_user_id AS owner_user_id,
    owr.is_won AS is_won,
    CAST(owr.amount / owr.eur_rate AS NUMERIC(18,6)) AS amount_eur,
    CAST(EXTRACT(EPOCH FROM (owr.closed_at - owr.created_at)) AS NUMERIC(18,6)) / 86400.0 AS days_open,
    COALESCE(ac.activity_count, 0) AS activity_count
  FROM opp_with_rate owr
  LEFT JOIN activities_before_close ac
    ON ac.opportunity_id = owr.id
)
SELECT
  CASE WHEN is_won THEN 'won' ELSE 'lost' END AS outcome,
  count(*) AS opportunities,
  avg(amount_eur) AS avg_amount_eur,
  PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY amount_eur) AS median_amount_eur,
  avg(cast(activity_count as numeric(18,6))) AS avg_activities_before_close,
  avg(days_open) AS avg_days_open,
  count(DISTINCT account_id) AS distinct_accounts,
  count(DISTINCT owner_user_id) AS distinct_reps
FROM opp_final
GROUP BY is_won
ORDER BY CASE WHEN is_won THEN 0 ELSE 1 END NULLS LAST

WITH target_tenants AS (
  SELECT id
  FROM tenants
  WHERE industry ILIKE '%industrial%'
     OR industry ILIKE '%office equipment%'
     OR industry ILIKE '%wholesal%'
),
new_business_pipelines AS (
  SELECT id, tenant_id
  FROM pipelines
  WHERE name = 'New business'
),
base_opportunities AS (
  SELECT o.*
  FROM opportunities o
  JOIN new_business_pipelines p
    ON o.pipeline_id = p.id
   AND o.tenant_id = p.tenant_id
  WHERE o.tenant_id IN (SELECT id FROM target_tenants)
    AND o.is_closed = true
),
opp_with_rate AS (
  SELECT
    bo.*,
    (
      SELECT er.rate
      FROM exchange_rates er
      WHERE er.base_code = 'EUR'
        AND er.quote_code = bo.currency_code
        AND er.as_of <= CAST(bo.closed_at AS DATE)
      ORDER BY er.as_of DESC NULLS FIRST
      LIMIT 1
    ) AS eur_rate
  FROM base_opportunities bo
),
activities_before_close AS (
  SELECT
    a.opportunity_id AS opportunity_id,
    count(*) AS activity_count
  FROM activities a
  JOIN base_opportunities bo
    ON a.opportunity_id = bo.id
   AND a.tenant_id = bo.tenant_id
  WHERE a.occurred_at <= bo.closed_at
  GROUP BY a.opportunity_id
),
opp_final AS (
  SELECT
    owr.id AS id,
    owr.account_id AS account_id,
    owr.owner_user_id AS owner_user_id,
    owr.is_won AS is_won,
    CAST(owr.amount / owr.eur_rate AS NUMERIC(18,6)) AS amount_eur,
    CAST(EXTRACT(EPOCH FROM (owr.closed_at - owr.created_at)) AS NUMERIC(18,6)) / 86400.0 AS days_open,
    COALESCE(ac.activity_count, 0) AS activity_count
  FROM opp_with_rate owr
  LEFT JOIN activities_before_close ac
    ON ac.opportunity_id = owr.id
)
SELECT
  CASE WHEN is_won THEN 'won' ELSE 'lost' END AS outcome,
  count(*) AS opportunities,
  avg(amount_eur) AS avg_amount_eur,
  PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY amount_eur) AS median_amount_eur,
  avg(cast(activity_count as numeric(18,6))) AS avg_activities_before_close,
  avg(days_open) AS avg_days_open,
  count(DISTINCT account_id) AS distinct_accounts,
  count(DISTINCT owner_user_id) AS distinct_reps
FROM opp_final
GROUP BY is_won
ORDER BY CASE WHEN is_won THEN 0 ELSE 1 END NULLS LAST

WITH target_tenants AS (
  SELECT id
  FROM tenants
  WHERE industry ILIKE '%industrial%'
     OR industry ILIKE '%office equipment%'
     OR industry ILIKE '%wholesal%'
),
new_business_pipelines AS (
  SELECT id, tenant_id
  FROM pipelines
  WHERE name = 'New business'
),
base_opportunities AS (
  SELECT o.*
  FROM opportunities o
  JOIN new_business_pipelines p
    ON o.pipeline_id = p.id
   AND o.tenant_id = p.tenant_id
  WHERE o.tenant_id IN (SELECT id FROM target_tenants)
    AND o.is_closed = true
),
opp_with_rate AS (
  SELECT
    bo.*,
    (
      SELECT er.rate
      FROM exchange_rates er
      WHERE er.base_code = 'EUR'
        AND er.quote_code = bo.currency_code
        AND er.as_of <= CAST(bo.closed_at AS DATE)
      ORDER BY er.as_of DESC NULLS FIRST
      FETCH FIRST 1 ROWS ONLY
    ) AS eur_rate
  FROM base_opportunities bo
),
activities_before_close AS (
  SELECT
    a.opportunity_id AS opportunity_id,
    count(*) AS activity_count
  FROM activities a
  JOIN base_opportunities bo
    ON a.opportunity_id = bo.id
   AND a.tenant_id = bo.tenant_id
  WHERE a.occurred_at <= bo.closed_at
  GROUP BY a.opportunity_id
),
opp_final AS (
  SELECT
    owr.id AS id,
    owr.account_id AS account_id,
    owr.owner_user_id AS owner_user_id,
    owr.is_won AS is_won,
    CAST(owr.amount / owr.eur_rate AS NUMERIC(18,6)) AS amount_eur,
    CAST(EXTRACT(EPOCH FROM (owr.closed_at - owr.created_at)) AS NUMERIC(18,6)) / 86400.0 AS days_open,
    COALESCE(ac.activity_count, 0) AS activity_count
  FROM opp_with_rate owr
  LEFT JOIN activities_before_close ac
    ON ac.opportunity_id = owr.id
)
SELECT
  CASE WHEN is_won THEN 'won' ELSE 'lost' END AS outcome,
  count(*) AS opportunities,
  avg(amount_eur) AS avg_amount_eur,
  PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(amount_eur AS DOUBLE PRECISION)) AS median_amount_eur,
  avg(cast(activity_count as numeric(18,6))) AS avg_activities_before_close,
  avg(days_open) AS avg_days_open,
  count(DISTINCT account_id) AS distinct_accounts,
  count(DISTINCT owner_user_id) AS distinct_reps
FROM opp_final
GROUP BY is_won
ORDER BY CASE WHEN is_won THEN 0 ELSE 1 END NULLS LAST

WITH target_tenants AS (
  SELECT id
  FROM tenants
  WHERE industry ILIKE '%industrial%'
     OR industry ILIKE '%office equipment%'
     OR industry ILIKE '%wholesal%'
),
new_business_pipelines AS (
  SELECT id, tenant_id
  FROM pipelines
  WHERE name = 'New business'
),
base_opportunities AS (
  SELECT o.*
  FROM opportunities o
  JOIN new_business_pipelines p
    ON o.pipeline_id = p.id
   AND o.tenant_id = p.tenant_id
  WHERE o.tenant_id IN (SELECT id FROM target_tenants)
    AND o.is_closed = true
),
opp_with_rate AS (
  SELECT
    bo.*,
    (
      SELECT er.rate
      FROM exchange_rates er
      WHERE er.base_code = 'EUR'
        AND er.quote_code = bo.currency_code
        AND er.as_of <= CAST(bo.closed_at AS DATE)
      ORDER BY er.as_of DESC NULLS FIRST
      FETCH FIRST 1 ROWS ONLY
    ) AS eur_rate
  FROM base_opportunities bo
),
activities_before_close AS (
  SELECT
    a.opportunity_id AS opportunity_id,
    count(*) AS activity_count
  FROM activities a
  JOIN base_opportunities bo
    ON a.opportunity_id = bo.id
   AND a.tenant_id = bo.tenant_id
  WHERE a.occurred_at <= bo.closed_at
  GROUP BY a.opportunity_id
),
opp_final AS (
  SELECT
    owr.id AS id,
    owr.account_id AS account_id,
    owr.owner_user_id AS owner_user_id,
    owr.is_won AS is_won,
    CAST(owr.amount / owr.eur_rate AS NUMERIC(18,6)) AS amount_eur,
    CAST(divideDecimal(toDecimal128(dateDiff('microsecond', owr.created_at, owr.closed_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6))) / 86400.0 AS days_open,
    COALESCE(ac.activity_count, 0) AS activity_count
  FROM opp_with_rate owr
  LEFT JOIN activities_before_close ac
    ON ac.opportunity_id = owr.id
)
SELECT
  CASE WHEN is_won THEN 'won' ELSE 'lost' END AS outcome,
  count(*) AS opportunities,
  avg(amount_eur) AS avg_amount_eur,
  quantileExactInclusive(0.5)(toFloat64(amount_eur)) AS median_amount_eur,
  avg(cast(activity_count as numeric(18,6))) AS avg_activities_before_close,
  avg(days_open) AS avg_days_open,
  count(DISTINCT account_id) AS distinct_accounts,
  count(DISTINCT owner_user_id) AS distinct_reps
FROM opp_final
GROUP BY is_won
ORDER BY CASE WHEN is_won THEN 0 ELSE 1 END NULLS LAST

Firebolt{ "errors": [ { "description": "Line 53, Column 21: 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": "bfaab035-0148-4a95-8c8d-eae583e9b7da", "query_lab
DataFusionThis feature is not implemented: Physical plan does not support logical expression ScalarSubquery(<subquery>)
ClickHouseCode: 48. DB::Exception: Cannot decorrelate query, because 'Limit' step is not supported. (NOT_IMPLEMENTED)

Scale factor 1000, c8a.24xlarge, after the last load. q176 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

Grace's whole session, 7 turns

The request, as Grace received it

Report request from Daniel Kim (analyst for the Head of B2B Commerce):

Title: New Business Only: Won vs Lost Opportunity Characteristics
Question: Restricted to genuine new-customer acquisition deals (excluding renewals), what reliably differs between won and lost opportunities — deal size, activities before close, days open, or concentration among accounts and reps?
Grain: one row per closed outcome (won or lost), New business pipeline only
Columns:
- outcome (won / lost)
- opportunities (count)
- average amount in EUR
- median amount in EUR
- average activities per opportunity before close
- average days open (closed_at - created_at)
- distinct accounts involved
- distinct owning reps (owner_user_id) involved
Details: This re-runs last night's won-vs-lost comparison but adds pipeline_id = the tenant's 'New business' pipeline (join pipelines on name = 'New business') to the filter, because the prior report pooled 'New business' (acquisition) and 'Renewals' (existing-customer renew/churn) opportunities together under the same won/lost labels — two very different sales motions with likely different base win rates (the Renewals pipeline's 'Renewed' stage carries a 1.0 default win_probability vs 0.1-0.75 for New business stages), which could distort the comparison. Same tenant scope as before (industry ILIKE '%industrial%' OR '%office equipment%' OR '%wholesal%'), opportunities where is_closed = true, split by is_won. Convert amount to EUR via exchange_rates (base_code='EUR', quote_code = opportunity currency_code, rate as_of closed_at date or nearest prior day). Activities before close = count of activities rows with activities.opportunity_id = opportunities.id and occurred_at <= closed_at, averaged per opportunity within each outcome group. Order 'won' row first, then 'lost'.

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

Grace calls dry_run

WITH target_tenants AS (
  SELECT id
  FROM tenants
  WHERE industry ILIKE '%industrial%'
     OR industry ILIKE '%office equipment%'
     OR industry ILIKE '%wholesal%'
),
new_business_pipelines AS (
  SELECT id, tenant_id
  FROM pipelines
  WHERE name = 'New business'
),
base_opportunities AS (
  SELECT o.*
  FROM opportunities o
  JOIN new_business_pipelines p
    ON o.pipeline_id = p.id
   AND o.tenant_id = p.tenant_id
  WHERE o.tenant_id IN (SELECT id FROM target_tenants)
    AND o.is_closed = true
),
opp_with_rate AS (
  SELECT
    bo.*,
    (
      SELECT er.rate
      FROM exchange_rates er
      WHERE er.base_code = 'EUR'
        AND er.quote_code = bo.currency_code
        AND er.as_of <= date(bo.closed_at)
      ORDER BY er.as_of DESC
      LIMIT 1
    ) AS eur_rate
  FROM base_opportunities bo
),
activities_before_close AS (
  SELECT
    a.opportunity_id,
    count(*) AS activity_count
  FROM activities a
  JOIN base_opportunities bo
    ON a.opportunity_id = bo.id
   AND a.tenant_id = bo.tenant_id
  WHERE a.occurred_at <= bo.closed_at
  GROUP BY a.opportunity_id
),
opp_final AS (
  SELECT
    owr.id,
    owr.account_id,
    owr.owner_user_id,
    owr.is_won,
    owr.amount / owr.eur_rate AS amount_eur,
    EXTRACT(EPOCH FROM (owr.closed_at - owr.created_at)) / 86400.0 AS days_open,
    COALESCE(ac.activity_count, 0) AS activity_count
  FROM opp_with_rate owr
  LEFT JOIN activities_before_close ac
    ON ac.opportunity_id = owr.id
)
SELECT
  CASE WHEN is_won THEN 'won' ELSE 'lost' END AS outcome,
  count(*) AS opportunities,
  round(avg(amount_eur)::numeric, 2) AS avg_amount_eur,
  round(percentile_cont(0.5) WITHIN GROUP (ORDER BY amount_eur)::numeric, 2) AS median_amount_eur,
  round(avg(activity_count)::numeric, 2) AS avg_activities_before_close,
  round(avg(days_open)::numeric, 1) AS avg_days_open,
  count(DISTINCT account_id) AS distinct_accounts,
  count(DISTINCT owner_user_id) AS distinct_reps
FROM opp_final
GROUP BY is_won
ORDER BY CASE WHEN is_won THEN 0 ELSE 1 END

The warehouse answers

STATUS: COMPILES (dry run on the empty schema, no data read)
COLUMNS: outcome, opportunities, avg_amount_eur, median_amount_eur, avg_activities_before_close, avg_days_open, distinct_accounts, distinct_reps

GraceQuery compiles and returns exactly the requested columns. Submitting.

Grace calls submit

WITH target_tenants AS (
  SELECT id
  FROM tenants
  WHERE industry ILIKE '%industrial%'
     OR industry ILIKE '%office equipment%'
     OR industry ILIKE '%wholesal%'
),
new_business_pipelines AS (
  SELECT id, tenant_id
  FROM pipelines
  WHERE name = 'New business'
),
base_opportunities AS (
  SELECT o.*
  FROM opportunities o
  JOIN new_business_pipelines p
    ON o.pipeline_id = p.id
   AND o.tenant_id = p.tenant_id
  WHERE o.tenant_id IN (SELECT id FROM target_tenants)
    AND o.is_closed = true
),
opp_with_rate AS (
  SELECT
    bo.*,
    (
      SELECT er.rate
      FROM exchange_rates er
      WHERE er.base_code = 'EUR'
        AND er.quote_code = bo.currency_code
        AND er.as_of <= date(bo.closed_at)
      ORDER BY er.as_of DESC
      LIMIT 1
    ) AS eur_rate
  FROM base_opportunities bo
),
activities_before_close AS (
  SELECT
    a.opportunity_id,
    count(*) AS activity_count
  FROM activities a
  JOIN base_opportunities bo
    ON a.opportunity_id = bo.id
   AND a.tenant_id = bo.tenant_id
  WHERE a.occurred_at <= bo.closed_at
  GROUP BY a.opportunity_id
),
opp_final AS (
  SELECT
    owr.id,
    owr.account_id,
    owr.owner_user_id,
    owr.is_won,
    owr.amount / owr.eur_rate AS amount_eur,
    EXTRACT(EPOCH FROM (owr.closed_at - owr.created_at)) / 86400.0 AS days_open,
    COALESCE(ac.activity_count, 0) AS activity_count
  FROM opp_with_rate owr
  LEFT JOIN activities_before_close ac
    ON ac.opportunity_id = owr.id
)
SELECT
  CASE WHEN is_won THEN 'won' ELSE 'lost' END AS outcome,
  count(*) AS opportunities,
  round(avg(amount_eur)::numeric, 2) AS avg_amount_eur,
  round(percentile_cont(0.5) WITHIN GROUP (ORDER BY amount_eur)::numeric, 2) AS median_amount_eur,
  round(avg(activity_count)::numeric, 2) AS avg_activities_before_close,
  round(avg(days_open)::numeric, 1) AS avg_days_open,
  count(DISTINCT account_id) AS distinct_accounts,
  count(DISTINCT owner_user_id) AS distinct_reps
FROM opp_final
GROUP BY is_won
ORDER BY CASE WHEN is_won THEN 0 ELSE 1 END

The warehouse answers

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

GraceSubmitted. The report returns one row per outcome (won/lost) for New-business-pipeline opportunities only, with opportunity count, average/median EUR amount, average pre-close activities, average days open, and distinct account/rep counts — mirroring the prior comparison but now isolating genuine new-customer acquisition deals from renewals.

Not shown here: 239 KB of context the agent was given, its instructions and the schema documentation; it is in the raw transcript.

Grace's transcript (269 KB) · Daniel Kim's transcript (789 KB) · Patrick O'Neill's transcript (263 KB) · the night's plan (7 KB) · the night's reports (7 KB) · the week's memo (3 KB)

New Business Only: Won vs Lost Opportunity Characteristics

A business event lands on the Head of B2B Commerce's desk

Patrick O'Neill writes to Daniel Kim

Daniel Kim plans the night

Grace writes the SQL

The report comes back overnight

What came of it

It becomes benchmark query q176

Everything, as recorded