Datasets:

shaypal5
/

leadforge-lead-scoring-v1

account_id stringlengths 11 11	industry stringclasses 4 values	region stringclasses 2 values	employee_band stringclasses 4 values	estimated_revenue_band stringclasses 4 values	process_maturity_band stringclasses 3 values	contact_id stringlengths 10 10	role_function stringclasses 4 values	seniority stringclasses 5 values	buyer_role stringclasses 4 values	lead_id stringlengths 11 11	lead_created_at stringdate 2024-01-01 00:00:00 2024-01-30 00:00:00	lead_source stringclasses 3 values	touch_count float64 0 22 ⌀	inbound_touch_count float64 0 20 ⌀	outbound_touch_count float64 0 22 ⌀	session_count float64 0 8 ⌀	pricing_page_views float64 0 9 ⌀	demo_page_views float64 0 6 ⌀	total_session_duration_seconds float64 0 2.93k ⌀	touches_days_0_7 float64 0 11 ⌀	touches_last_7_days float64 0 7 ⌀	days_since_first_touch float64 0.87 43.4 ⌀	activity_count float64 0 14 ⌀	days_since_last_touch float64 -27.97 36.2 ⌀	opportunity_created bool 2 classes	has_open_opportunity bool 2 classes	opportunity_estimated_acv float64 -135,242.67 237k ⌀	expected_acv float64 -131,017.68 236k ⌀	total_touches_all int64 0 25	converted_within_90_days bool 2 classes
acct_000773	logistics	UK	200-499	$50M-$200M	low	cnt_001124	procurement_manager	vp	end_user	lead_004250	2024-01-08	inbound_marketing	9	9	0	3	0	0	796	4	2	28.015722	6	4.035906	true	true	27,780.912782	18,677.346374	12	true
acct_000043	logistics	UK	500-999	$10M-$50M	high	cnt_003354	it_director	c_suite	technical_evaluator	lead_001565	2024-01-01	inbound_marketing	7	7	0	1	0	0	536	3	1	30.114696	4	6.327766	true	true	60,684.994618	65,794.820847	10	true
acct_000319	logistics	US	200-499	$1M-$10M	medium	cnt_000537	ap_manager	director	champion	lead_002296	2024-01-05	partner_referral	13	0	13	5	0	0	1,286	5	1	28.645028	4	2.558992	true	true	45,139.938972	44,352.635947	13	true
acct_000476	healthcare_non_clinical	US	200-499	$10M-$50M	medium	cnt_001478	ap_manager	director	champion	lead_003320	2024-01-29	inbound_marketing	6	6	0	0	null	0	0	2	1	27.74771	4	0	true	true	14,915.368801	12,893.722694	13	false
acct_000243	manufacturing	US	1000-1999	$10M-$50M	low	cnt_000276	vp_finance	individual_contributor	economic_buyer	lead_001192	2024-01-01	sdr_outbound	8	0	8	0	null	0	0	4	1	30.418252	2	null	true	true	91,636.393457	89,072.290006	10	true
acct_000353	manufacturing	UK	2000+	$1M-$10M	medium	cnt_002665	procurement_manager	vp	end_user	lead_000123	2024-01-27	sdr_outbound	6	0	6	2	2	1	586	2	2	24.856868	4	1.195655	true	true	124,595.562561	118,802.812016	7	false
acct_000029	healthcare_non_clinical	US	500-999	$10M-$50M	low	cnt_001377	procurement_manager	individual_contributor	end_user	lead_001076	2024-01-18	sdr_outbound	9	0	9	4	2	0	1,160	4	1	30.036208	3	5.959612	true	true	71,376.558191	79,837.857352	14	false
acct_001411	professional_services	US	200-499	$10M-$50M	high	cnt_002913	vp_finance	c_suite	economic_buyer	lead_001584	2024-01-09	sdr_outbound	3	0	3	1	0	0	234	3	0	29.669506	0	27.561285	false	false	null	46,471.803555	3	false
acct_001003	manufacturing	US	500-999	$10M-$50M	medium	cnt_001340	vp_finance	director	economic_buyer	lead_000953	2024-01-05	partner_referral	7	0	7	1	0	0	457	3	2	27.019685	1	5.930869	true	true	76,317.464545	78,412.041349	12	true
acct_001102	logistics	US	2000+	$10M-$50M	medium	cnt_001944	it_director	manager	technical_evaluator	lead_001798	2024-01-22	inbound_marketing	7	7	0	2	0	0	615	4	0	29.802155	6	14.352813	true	true	111,007.145551	99,474.721638	16	true
acct_001054	manufacturing	UK	500-999	$50M-$200M	high	cnt_002889	ap_manager	individual_contributor	champion	lead_002040	2024-01-19	sdr_outbound	8	0	8	3	0	0	1,092	8	0	29.904927	1	23.874579	false	false	null	84,614.157378	8	false
acct_000733	logistics	US	200-499	$1M-$10M	low	cnt_003704	ap_manager	manager	champion	lead_002271	2024-01-29	inbound_marketing	9	9	0	1	1	0	336	1	2	25.928511	6	0.530511	true	true	27,445.625599	28,420.10136	9	false
acct_000982	healthcare_non_clinical	US	200-499	$10M-$50M	high	cnt_001964	it_director	individual_contributor	technical_evaluator	lead_002238	2024-01-21	inbound_marketing	4	4	0	0	0	0	0	1	1	24.860675	null	4.314926	true	true	46,329.507688	48,319.130458	5	true
acct_001445	logistics	UK	200-499	$1M-$10M	high	cnt_002973	it_director	individual_contributor	technical_evaluator	lead_000860	2024-01-27	inbound_marketing	5	5	0	1	0	0	543	3	0	25.907564	4	17.50917	true	true	25,978.326943	24,692.453928	11	false
acct_000252	professional_services	US	500-999	$200M+	high	cnt_003153	vp_finance	director	economic_buyer	lead_000588	2024-01-28	inbound_marketing	9	9	0	4	2	0	1,362	2	2	29.960822	6	4.82216	true	true	53,301.644897	54,313.198754	11	true
acct_000393	professional_services	US	500-999	$10M-$50M	high	cnt_001419	procurement_manager	director	end_user	lead_004542	2024-01-20	sdr_outbound	16	0	16	5	7	0	1,744	5	4	28.850807	6	0.101192	true	true	29,143.07089	34,880.573763	16	true
acct_000671	logistics	US	1000-1999	$50M-$200M	high	cnt_001970	procurement_manager	individual_contributor	end_user	lead_002802	2024-01-12	inbound_marketing	10	10	0	2	0	3	598	6	1	29.737263	4	4.493106	true	true	75,408.898297	84,367.807686	15	true
acct_001489	healthcare_non_clinical	UK	200-499	$1M-$10M	medium	cnt_000684	it_director	individual_contributor	technical_evaluator	lead_001062	2024-01-06	sdr_outbound	10	0	10	3	0	0	717	6	1	30.654889	5	4.973508	true	true	16,287.616818	27,475.910897	12	false
acct_001021	manufacturing	UK	200-499	$10M-$50M	medium	cnt_002033	ap_manager	c_suite	champion	lead_002967	2024-01-04	sdr_outbound	7	0	7	1	0	0	238	3	0	28.935082	3	11.397946	true	false	null	54,183.466385	7	false
acct_000525	logistics	US	500-999	$10M-$50M	low	cnt_002167	ap_manager	director	champion	lead_003255	2024-01-21	inbound_marketing	14	14	0	3	3	1	1,246	8	1	30.088642	6	3.611394	true	true	58,071.162247	56,619.209022	14	true
acct_000243	manufacturing	US	1000-1999	$10M-$50M	low	cnt_002650	ap_manager	director	champion	lead_002753	2024-01-05	sdr_outbound	7	0	7	2	0	0	505	1	2	24.758278	5	0.006138	true	true	89,485.115254	89,358.101448	7	true
acct_001395	manufacturing	US	2000+	$50M-$200M	high	cnt_004093	ap_manager	director	champion	lead_002390	2024-01-28	partner_referral	3	0	3	0	0	0	0	2	0	27.923988	5	19.545742	true	false	null	91,320.048769	3	false
acct_000309	manufacturing	UK	2000+	$10M-$50M	medium	cnt_004171	ap_manager	vp	champion	lead_003663	2024-01-20	sdr_outbound	6	0	6	1	1	0	413	1	1	28.349776	6	1.75618	true	true	173,422.399931	176,135.505092	12	false
acct_000458	manufacturing	UK	200-499	$10M-$50M	medium	cnt_003163	ap_manager	vp	champion	lead_001738	2024-01-24	inbound_marketing	9	9	0	2	2	2	null	2	0	26.840081	4	10.805701	true	true	21,488.186722	29,521.065912	11	true
acct_000473	healthcare_non_clinical	UK	2000+	$50M-$200M	high	cnt_001858	vp_finance	manager	economic_buyer	lead_001845	2024-01-18	sdr_outbound	15	0	15	1	2	0	584	5	2	30.209836	7	0	true	false	null	86,024.266565	15	false
acct_000915	healthcare_non_clinical	UK	1000-1999	$50M-$200M	low	cnt_003621	it_director	manager	technical_evaluator	lead_000227	2024-01-19	inbound_marketing	7	7	0	3	2	0	1,020	4	0	29.288681	5	13.332027	true	true	100,805.898464	97,561.5442	7	true
acct_001286	professional_services	UK	1000-1999	$1M-$10M	medium	cnt_004106	it_director	individual_contributor	technical_evaluator	lead_001773	2024-01-28	inbound_marketing	7	7	0	3	0	0	1,152	3	1	26.720157	4	3.459087	false	false	null	27,224.398567	7	false
acct_000743	logistics	US	500-999	$10M-$50M	medium	cnt_000694	it_director	manager	technical_evaluator	lead_002705	2024-01-02	partner_referral	4	0	4	2	2	2	608	1	0	30.010783	5	9.847651	true	true	78,915.038909	72,740.098974	10	true
acct_000683	professional_services	US	500-999	$10M-$50M	high	cnt_003722	it_director	director	technical_evaluator	lead_004022	2024-01-03	inbound_marketing	6	6	0	1	0	0	176	2	0	29.151524	5	6.49533	true	true	54,889.224746	48,588.811205	9	true
acct_001245	logistics	UK	1000-1999	$10M-$50M	medium	cnt_002580	procurement_manager	manager	end_user	lead_004398	2024-01-28	inbound_marketing	7	7	0	1	2	0	587	3	1	29.872621	4	4.100671	true	true	55,639.836644	59,523.487968	7	true
acct_000246	healthcare_non_clinical	UK	500-999	$50M-$200M	high	cnt_003871	ap_manager	manager	champion	lead_004224	2024-01-01	sdr_outbound	6	0	6	1	0	0	278	2	1	22.586598	2	2.515309	true	true	51,694.68163	45,891.18989	13	false
acct_000047	manufacturing	UK	500-999	$10M-$50M	high	cnt_001400	procurement_manager	director	end_user	lead_003601	2024-01-07	inbound_marketing	2	2	0	1	0	0	325	0	0	9.283366	3	8.013507	true	true	68,627.886709	70,703.653543	2	true
acct_000794	healthcare_non_clinical	UK	500-999	$10M-$50M	medium	cnt_001642	vp_finance	manager	economic_buyer	lead_000831	2024-01-03	sdr_outbound	5	0	5	1	0	0	237	1	1	22.977493	5	6.479554	true	true	38,208.156798	33,980.835415	12	true
acct_000255	manufacturing	US	2000+	$50M-$200M	medium	cnt_000299	ap_manager	individual_contributor	champion	lead_001919	2024-01-08	partner_referral	7	0	7	4	0	0	1,393	2	0	30.222703	3	15.793179	true	false	null	88,207.377738	7	false
acct_001192	manufacturing	US	1000-1999	$10M-$50M	high	cnt_003238	it_director	manager	technical_evaluator	lead_000966	2024-01-29	sdr_outbound	8	null	8	1	0	null	414	3	1	28.5823	5	4.180156	true	true	71,702.73057	75,210.130138	12	true
acct_001008	professional_services	US	200-499	$200M+	low	cnt_003118	vp_finance	vp	economic_buyer	lead_001807	2024-01-23	sdr_outbound	11	0	11	3	4	0	893	3	1	29.388739	5	4.160089	true	true	25,660.443456	22,130.374541	14	true
acct_001118	professional_services	UK	1000-1999	$200M+	low	cnt_001514	procurement_manager	director	end_user	lead_000221	2024-01-26	partner_referral	6	0	6	1	0	0	121	3	3	30.068799	5	0.488628	true	true	101,647.58848	104,825.620903	12	true
acct_000005	manufacturing	US	200-499	$1M-$10M	low	cnt_001252	procurement_manager	individual_contributor	end_user	lead_000139	2024-01-25	inbound_marketing	3	3	0	0	0	0	null	2	1	28.758591	3	1.163582	true	true	18,626.239503	22,925.386583	7	false
acct_000853	manufacturing	UK	200-499	$10M-$50M	high	cnt_004178	it_director	manager	technical_evaluator	lead_000425	2024-01-16	sdr_outbound	12	0	12	3	3	0	1,112	5	2	27.403457	6	1.589277	true	true	53,213.635001	48,625.173997	17	true
acct_000794	healthcare_non_clinical	UK	500-999	$10M-$50M	medium	cnt_001642	vp_finance	manager	economic_buyer	lead_003017	2024-01-02	partner_referral	9	0	9	3	2	2	1,013	2	2	29.43709	3	4.134154	true	true	34,154.55232	29,966.957761	13	false
acct_001007	manufacturing	US	1000-1999	$1M-$10M	low	cnt_000761	vp_finance	manager	economic_buyer	lead_002299	2024-01-29	sdr_outbound	8	0	8	2	4	0	789	3	1	29.918056	5	1.252038	true	true	100,289.388444	95,214.791604	12	true
acct_001206	manufacturing	US	500-999	$1M-$10M	low	cnt_004200	procurement_manager	manager	end_user	lead_000858	2024-01-21	inbound_marketing	8	8	0	1	0	2	275	5	0	29.982832	9	12.451921	true	true	51,227.361636	53,228.454495	11	false
acct_000422	healthcare_non_clinical	US	500-999	$50M-$200M	low	cnt_001051	vp_finance	vp	economic_buyer	lead_002275	2024-01-25	sdr_outbound	3	0	3	1	2	1	443	1	0	29.111584	7	11.917249	true	true	48,459.269901	39,466.156967	4	false
acct_000593	manufacturing	UK	1000-1999	$10M-$50M	high	cnt_003102	procurement_manager	vp	end_user	lead_003685	2024-01-21	sdr_outbound	null	0	10	3	0	0	1,165	4	3	29.771346	4	0.828085	true	true	105,808.922653	104,184.84173	13	false
acct_000240	healthcare_non_clinical	US	500-999	$1M-$10M	medium	cnt_001289	ap_manager	vp	champion	lead_003876	2024-01-29	partner_referral	14	0	14	5	3	2	2,236	5	null	29.493891	8	3.213911	true	true	79,901.515375	67,037.563632	15	true
acct_000370	manufacturing	UK	200-499	$10M-$50M	high	cnt_000523	ap_manager	c_suite	champion	lead_004242	2024-01-08	inbound_marketing	4	4	0	1	0	0	186	1	2	23.312472	11	0.74856	true	true	35,799.871938	34,022.001957	5	true
acct_000462	logistics	UK	1000-1999	$1M-$10M	high	cnt_001245	ap_manager	director	champion	lead_003353	2024-01-23	sdr_outbound	7	0	7	3	4	0	1,006	2	1	24.67032	11	0.969133	true	true	57,710.924966	58,002.128721	12	true
acct_000686	professional_services	US	500-999	$10M-$50M	low	cnt_001726	it_director	manager	technical_evaluator	lead_003585	2024-01-04	inbound_marketing	7	7	0	3	3	0	1,243	1	3	23.116573	3	0	true	true	45,737.534893	36,850.730544	9	true
acct_000713	professional_services	US	500-999	$1M-$10M	medium	cnt_000608	it_director	individual_contributor	technical_evaluator	lead_003154	2024-01-11	sdr_outbound	10	0	10	3	3	0	1,375	2	0	26.450514	4	6.986316	true	true	79,182.386898	76,331.752114	10	false
acct_001469	professional_services	UK	200-499	$10M-$50M	low	cnt_002330	vp_finance	manager	economic_buyer	lead_004389	2024-01-24	sdr_outbound	8	0	8	4	3	1	1,165	1	0	26.550865	1	9.982536	true	false	null	55,990.197154	8	false
acct_000825	healthcare_non_clinical	UK	500-999	$10M-$50M	medium	cnt_001301	it_director	director	technical_evaluator	lead_004494	2024-01-20	partner_referral	1	0	1	1	0	0	134	1	0	28.876326	0	29.560211	true	false	null	null	1	false
acct_000653	manufacturing	US	200-499	$10M-$50M	low	cnt_003600	ap_manager	individual_contributor	champion	lead_000814	2024-01-01	sdr_outbound	7	0	7	2	0	0	517	2	1	23.58097	5	6.434454	true	true	46,195.879667	58,247.064479	11	false
acct_001124	manufacturing	US	1000-1999	$200M+	high	cnt_000063	ap_manager	manager	champion	lead_004241	2024-01-27	inbound_marketing	7	7	0	3	1	1	1,102	2	0	28.676165	6	12.649471	true	true	119,693.006232	123,857.916168	9	false
acct_000398	healthcare_non_clinical	US	500-999	$10M-$50M	low	cnt_002428	procurement_manager	director	end_user	lead_000465	2024-01-29	inbound_marketing	6	6	0	1	2	0	312	1	1	25.226458	4	null	true	true	42,099.477539	48,064.74172	8	true
acct_001256	professional_services	US	1000-1999	$50M-$200M	medium	cnt_002705	ap_manager	vp	champion	lead_000869	2024-01-12	inbound_marketing	4	4	0	0	0	0	0	4	0	28.995429	5	23.804581	true	true	92,896.688072	94,440.133064	10	false
acct_000421	manufacturing	US	500-999	$1M-$10M	medium	cnt_000973	it_director	director	technical_evaluator	lead_002383	2024-01-26	inbound_marketing	7	null	0	4	2	0	1,468	null	2	29.088705	1	0.476478	true	true	50,711.73359	55,267.162591	8	false
acct_000210	professional_services	US	500-999	$50M-$200M	low	cnt_003895	it_director	manager	technical_evaluator	lead_004072	2024-01-01	inbound_marketing	0	0	0	0	0	0	0	0	0	null	0	null	false	false	null	87,013.049798	0	false
acct_001317	logistics	US	500-999	$10M-$50M	high	cnt_000333	procurement_manager	individual_contributor	end_user	lead_000143	2024-01-25	sdr_outbound	9	0	9	3	1	0	956	4	2	29.450696	6	0.040576	true	true	56,436.792148	60,364.085006	11	true
acct_000465	healthcare_non_clinical	US	200-499	$1M-$10M	medium	cnt_001793	ap_manager	vp	champion	lead_002337	2024-01-19	sdr_outbound	6	0	6	1	0	0	421	0	3	22.017378	4	0.087521	true	true	30,457.493678	24,157.299877	11	false
acct_000558	healthcare_non_clinical	UK	500-999	$50M-$200M	low	cnt_002361	procurement_manager	manager	end_user	lead_001533	2024-01-08	partner_referral	5	0	5	1	1	0	245	1	1	23.062062	4	4.453699	true	true	43,769.861742	48,689.075008	11	false
acct_000690	logistics	US	200-499	$1M-$10M	low	cnt_002769	ap_manager	manager	champion	lead_002756	2024-01-12	inbound_marketing	7	7	0	2	1	1	627	2	3	30.447807	8	5.353816	true	true	15,664.699617	13,306.842614	12	false
acct_000713	professional_services	US	500-999	$1M-$10M	medium	cnt_002414	vp_finance	manager	economic_buyer	lead_001978	2024-01-17	inbound_marketing	6	6	0	0	0	0	0	0	2	18.587579	5	2.129612	true	true	79,809.483858	84,769.449382	11	false
acct_000215	manufacturing	UK	500-999	$1M-$10M	high	cnt_001129	ap_manager	individual_contributor	champion	lead_001396	2024-01-02	inbound_marketing	9	9	null	4	1	0	1,521	4	1	26.725122	6	4.578159	true	true	22,744.494108	25,870.345185	17	false
acct_000168	healthcare_non_clinical	US	1000-1999	$10M-$50M	medium	cnt_001441	ap_manager	manager	champion	lead_002718	2024-01-29	inbound_marketing	9	9	0	3	1	0	588	5	0	30.577583	8	9.901386	true	true	90,522.416779	84,469.587535	11	false
acct_000437	professional_services	US	1000-1999	$200M+	medium	cnt_002189	it_director	director	technical_evaluator	lead_000640	2024-01-18	inbound_marketing	13	13	0	4	3	2	1,517	5	2	29.363789	7	4.367042	true	true	102,506.104802	100,512.687159	15	false
acct_000788	manufacturing	UK	500-999	$50M-$200M	medium	cnt_000900	ap_manager	manager	champion	lead_002543	2024-01-10	inbound_marketing	11	11	0	5	4	2	927	2	2	27.803001	8	1.335505	true	true	84,122.833852	77,583.539283	13	false
acct_000927	manufacturing	US	200-499	$50M-$200M	medium	cnt_001349	ap_manager	manager	champion	lead_003664	2024-01-09	sdr_outbound	9	0	9	3	2	1	848	3	1	29.818159	5	2.278553	true	true	39,674.543948	33,436.794603	18	false
acct_001193	manufacturing	UK	500-999	$10M-$50M	medium	cnt_001758	ap_manager	individual_contributor	champion	lead_002241	2024-01-21	inbound_marketing	4	4	0	0	0	0	0	1	1	27.746596	6	1.576475	true	true	40,974.70302	47,552.530096	6	false
acct_000903	healthcare_non_clinical	US	500-999	$50M-$200M	high	cnt_001924	vp_finance	manager	economic_buyer	lead_000022	2024-01-21	sdr_outbound	8	0	8	4	3	0	1,422	4	1	27.540766	6	1.979774	true	true	34,084.838171	34,362.948111	11	true
acct_001078	professional_services	US	500-999	$10M-$50M	medium	cnt_002511	ap_manager	director	champion	lead_000700	2024-01-30	inbound_marketing	7	7	0	1	2	1	334	4	2	29.884915	2	0.428767	true	true	46,316.6652	37,201.597736	13	false
acct_000332	healthcare_non_clinical	UK	200-499	$200M+	high	cnt_001574	procurement_manager	manager	end_user	lead_002598	2024-01-11	sdr_outbound	7	0	7	1	0	0	487	3	1	27.863202	4	0.398085	true	true	23,034.162679	22,899.355272	8	false
acct_000069	manufacturing	UK	500-999	$50M-$200M	low	cnt_003809	it_director	individual_contributor	technical_evaluator	lead_001715	2024-01-28	inbound_marketing	8	8	0	0	0	0	0	4	1	28.923587	7	6.365041	true	true	66,632.198611	61,344.082096	10	true
acct_000590	professional_services	US	500-999	$50M-$200M	high	cnt_001274	ap_manager	manager	champion	lead_003500	2024-01-26	sdr_outbound	7	0	7	3	3	2	659	2	1	28.353552	4	2.976388	true	true	42,197.329958	51,226.536935	11	false
acct_000749	manufacturing	UK	200-499	$1M-$10M	high	cnt_001840	it_director	individual_contributor	technical_evaluator	lead_004298	2024-01-08	inbound_marketing	8	8	0	3	1	2	867	3	1	27.745144	3	3.906795	true	true	50,254.213751	50,157.971314	9	true
acct_000519	healthcare_non_clinical	US	500-999	$1M-$10M	medium	cnt_000601	procurement_manager	individual_contributor	end_user	lead_002671	2024-01-17	partner_referral	8	0	8	3	0	0	1,449	3	1	27.934317	4	1.735113	true	true	63,953.857493	52,429.540167	13	true
acct_001028	logistics	UK	500-999	$10M-$50M	medium	cnt_000612	ap_manager	director	champion	lead_004455	2024-01-07	sdr_outbound	11	0	11	3	2	0	1,035	5	1	29.398899	7	3.686779	true	true	61,570.471342	58,326.857369	13	true
acct_000909	professional_services	UK	2000+	$1M-$10M	low	cnt_003362	it_director	vp	technical_evaluator	lead_001427	2024-01-25	sdr_outbound	3	0	3	1	0	0	503	1	0	24.875416	0	21.134749	false	false	null	20,140.671135	3	false
acct_000628	logistics	UK	200-499	$1M-$10M	high	cnt_002873	vp_finance	individual_contributor	economic_buyer	lead_003556	2024-01-26	partner_referral	7	0	7	2	0	0	484	3	0	29.312902	3	14.43532	true	true	37,578.102664	35,928.789953	10	true
acct_000195	healthcare_non_clinical	US	200-499	$10M-$50M	medium	cnt_002097	it_director	director	technical_evaluator	lead_002734	2024-01-16	partner_referral	8	0	8	0	0	0	0	4	2	29.893698	3	1.128911	true	true	34,804.676019	37,822.184405	10	false
acct_000343	logistics	UK	200-499	$10M-$50M	medium	cnt_002345	procurement_manager	director	end_user	lead_003165	2024-01-12	partner_referral	6	0	6	3	3	1	937	3	1	23.957986	8	1.560243	true	true	-135,242.668985	38,622.150031	11	true
acct_000354	manufacturing	US	200-499	$1M-$10M	medium	cnt_000162	it_director	director	technical_evaluator	lead_003753	2024-01-05	partner_referral	8	0	8	2	0	0	863	3	1	29.820828	4	0	false	false	null	24,054.20284	14	false
acct_001140	professional_services	US	200-499	$10M-$50M	medium	cnt_001031	procurement_manager	manager	end_user	lead_000060	2024-01-20	inbound_marketing	5	5	0	1	0	0	405	1	0	24.123282	2	7.711622	true	true	32,766.48942	26,875.579452	6	false
acct_001456	logistics	UK	500-999	$10M-$50M	high	cnt_000164	ap_manager	manager	champion	lead_001165	2024-01-07	inbound_marketing	0	null	0	0	0	0	0	0	0	null	0	null	false	false	null	54,449.664052	0	false
acct_000788	manufacturing	UK	500-999	$50M-$200M	medium	cnt_001401	procurement_manager	individual_contributor	end_user	lead_002058	2024-01-22	sdr_outbound	6	0	6	2	2	0	767	4	2	28.677654	2	2.171849	true	true	71,978.216781	71,535.804125	6	false
acct_000249	professional_services	US	500-999	$10M-$50M	medium	cnt_000300	vp_finance	director	economic_buyer	lead_000927	2024-01-18	inbound_marketing	10	10	0	1	0	0	167	3	1	28.986113	null	1.686969	true	true	37,688.969124	39,065.154313	14	true
acct_001173	manufacturing	US	2000+	$1M-$10M	medium	cnt_003500	procurement_manager	individual_contributor	end_user	lead_001257	2024-01-02	inbound_marketing	4	4	0	1	0	0	405	0	null	20.350906	2	0	false	false	null	24,331.921781	7	false
acct_000424	healthcare_non_clinical	UK	200-499	$50M-$200M	high	cnt_002209	ap_manager	manager	champion	lead_003893	2024-01-18	sdr_outbound	10	0	10	4	2	0	1,037	4	2	29.753337	7	5.406143	true	true	20,424.272567	15,622.427267	13	true
acct_000357	logistics	US	500-999	$1M-$10M	medium	cnt_000700	it_director	manager	technical_evaluator	lead_002069	2024-01-15	inbound_marketing	null	11	0	1	1	2	350	1	1	24.280214	4	5.210727	true	true	59,038.384034	61,109.505783	15	false
acct_000194	healthcare_non_clinical	UK	200-499	$10M-$50M	high	cnt_003075	vp_finance	vp	economic_buyer	lead_001281	2024-01-30	inbound_marketing	7	7	0	1	0	0	141	3	1	29.147338	2	5.697676	true	true	43,605.708055	48,812.823454	13	false
acct_001097	professional_services	US	200-499	$200M+	medium	cnt_003942	vp_finance	individual_contributor	economic_buyer	lead_004843	2024-01-19	inbound_marketing	12	12	0	3	0	0	1,313	5	2	30.432745	4	0.361834	true	true	24,512.049271	26,146.326579	17	false
acct_001356	logistics	UK	500-999	$200M+	low	cnt_001504	procurement_manager	individual_contributor	end_user	lead_003093	2024-01-26	sdr_outbound	13	0	13	3	null	0	1,040	5	0	29.953698	7	10.299857	true	false	null	132,321.153772	13	false
acct_001261	logistics	US	2000+	$10M-$50M	medium	cnt_000584	ap_manager	director	champion	lead_000348	2024-01-13	inbound_marketing	8	8	0	1	0	0	184	1	4	30.26078	3	1.743917	true	true	100,688.119642	105,663.106011	12	false
acct_000364	healthcare_non_clinical	US	500-999	$50M-$200M	low	cnt_003082	it_director	manager	technical_evaluator	lead_002739	2024-01-30	inbound_marketing	13	13	0	4	0	0	1,134	4	1	29.631287	6	4.744958	true	true	60,642.464931	63,503.150707	17	false
acct_000480	healthcare_non_clinical	US	1000-1999	$10M-$50M	medium	cnt_002076	it_director	individual_contributor	technical_evaluator	lead_004253	2024-01-30	sdr_outbound	14	0	14	6	0	0	1,674	6	1	30.52084	6	4.112276	true	true	81,456.057437	75,379.40009	17	true
acct_000017	logistics	UK	500-999	$10M-$50M	high	cnt_002782	vp_finance	manager	economic_buyer	lead_000836	2024-01-14	sdr_outbound	5	0	5	2	0	0	388	2	1	29.107859	null	2.887609	true	true	63,991.330981	63,633.58428	7	false
acct_000240	healthcare_non_clinical	US	500-999	$1M-$10M	medium	cnt_001289	ap_manager	vp	champion	lead_001384	2024-01-13	inbound_marketing	6	6	0	1	0	0	185	1	2	27.015962	5	0.450495	true	true	35,743.857481	38,860.298906	11	false
acct_000211	healthcare_non_clinical	US	2000+	$10M-$50M	medium	cnt_000752	it_director	individual_contributor	technical_evaluator	lead_004409	2024-01-07	inbound_marketing	7	7	0	2	2	3	742	0	1	21.901406	5	0	true	true	140,206.880096	145,698.544457	12	false
acct_000468	healthcare_non_clinical	US	200-499	$10M-$50M	medium	cnt_003456	vp_finance	director	economic_buyer	lead_000088	2024-01-27	inbound_marketing	7	7	0	2	1	0	579	5	2	28.660891	6	2.271648	true	true	30,293.805799	21,802.461669	7	true
acct_001268	healthcare_non_clinical	US	200-499	$10M-$50M	high	cnt_000198	vp_finance	individual_contributor	economic_buyer	lead_002992	2024-01-12	inbound_marketing	7	7	0	0	0	0	0	4	3	30.264681	1	0	true	true	48,832.305775	42,942.573775	13	false
acct_000395	logistics	US	2000+	$200M+	low	cnt_002753	procurement_manager	manager	end_user	lead_004174	2024-01-25	inbound_marketing	7	7	0	3	0	0	1,198	4	0	29.916075	4	15.297031	false	false	null	139,575.599506	7	false

End of preview. Expand in Data Studio

LeadForge: Synthetic B2B Lead Scoring Dataset (`leadforge-lead-scoring-v1`)

A relational, reproducible, three-tier synthetic CRM dataset family for teaching lead scoring at scale. Created by Shay Palachy Affek and generated by leadforge, an open-source Python framework for synthetic CRM/funnel data. The framework version is decoupled from the dataset version: the package stays at 1.x; the dataset is published under the explicit …-v1 tag.

Why lead scoring matters in 2024–2026

Mid-market SaaS vendors entered 2024–2026 with growth slowing and customer-acquisition costs rising (median public-SaaS growth 30%→25% from 2023 to 2025; New CAC Ratio rose materially in 2024), so predicting which leads convert within a fixed window has moved from a marketing nicety to a survival skill. This dataset teaches that skill on a relational substrate, with the realistic confusions (snapshot-window discipline, leakage traps, channel signal weaker than vendor blogs imply) that students will hit when they finally get hands on real CRM data.

What's inside

.
├── intro/ intermediate/ advanced/    # student_public bundles, one per difficulty tier
│   ├── manifest.json                 # provenance + file hashes
│   ├── metrics.json                  # per-tier headline metrics (medians + spreads)
│   ├── dataset_card.md               # auto-rendered per-bundle card
│   ├── feature_dictionary.csv        # authoritative column spec
│   ├── lead_scoring.csv              # flat convenience CSV (all splits)
│   ├── tables/*.parquet              # 7 snapshot-safe relational tables
│   └── tasks/converted_within_90_days/{train,valid,test}.parquet
├── docs/                             # vendored DGP / leakage / break-me docs (agent-readable)
├── metrics.json                      # top-level cross-tier metrics summary
├── claims_register.{md,json}         # claims → backing-artifact map (agent-readable)
├── README.md                         # this file (HF dataset card)
├── dataset-cover-image.png           # dataset thumbnail
└── LICENSE

student_public bundles ship the snapshot-safe relational view; research_instructor companions ship the full-horizon view plus the hidden causal structure (DAG, latent registry, mechanism summary) under metadata/. The full layout is documented in each bundle's manifest.json.

Agent-reviewable artifacts

The published bundle is self-contained for AI review and offline auditing — every numeric / structural claim on this page can be verified without following an external link:

metrics.json (root) + <tier>/metrics.json — deterministic JSON view of the headline LR AUC / AP / P@100 / Brier / conversion rate / cohort-shift / cross-tier-ordering medians, with JSON-path back-references to validation/validation_report.json (the source of truth).
claims_register.{md,json} — every numerical or structural claim on this page paired with the artifact and path that backs it. Rendered from claims_register_source.yaml by scripts/build_claims_register.py.
docs/ — vendored copies of generation_method.md, channel_signal_audit.md, break_me_guide.md, feature_dictionary.md, v1_acceptance_gates_bands.yaml, v2_decision_log.md, plus a hand-authored relational_table_schemas.csv documenting every column of every relational table. These match the GitHub-blob links cited below but ship inside the bundle so a reviewer never needs network access.
<tier>/manifest.json — SHA-256 hash for every file plus the full redaction contract (structural_redactions.columns, omitted_tables, relational_snapshot_safe, snapshot_day).
Kaggle / HuggingFace preview pages additionally inject a schema.org/Dataset JSON-LD block in their <head> for agent ingestion without HTML parsing.

Quick start

# Flat CSV
df = pd.read_csv("intermediate/lead_scoring.csv")

# Parquet task splits (recommended)
train = pd.read_parquet("intermediate/tasks/converted_within_90_days/train.parquet")
test  = pd.read_parquet("intermediate/tasks/converted_within_90_days/test.parquet")

# Relational tables (feature engineering — example)
leads   = pd.read_parquet("intermediate/tables/leads.parquet")
touches = pd.read_parquet("intermediate/tables/touches.parquet")
my_touch_count = (
    touches.groupby("lead_id").size().rename("my_touch_count").reset_index()
)
features = leads.merge(my_touch_count, on="lead_id", how="left")

# Reproduce from source
# pip install leadforge
# leadforge generate --recipe b2b_saas_procurement_v1 --seed 42 \
#                    --mode student_public --difficulty intermediate --out my_bundle

The label converted_within_90_days resolves over a 90-day window; engagement features (touch_count, session_count, etc.) are computed strictly over events on days [0, 30]. The deliberate exception is total_touches_all, the leakage trap — flagged leakage_risk=True in feature_dictionary.csv. Drop it from your feature set unless you're demonstrating leakage detection.

Evaluation note — account and contact overlap

518 of 557 test accounts (≈93 %) appear in train on the intermediate bundle; the other tiers are similar. Contact-level overlap is comparable in magnitude: most test contacts also have activity in the training set. The random-split headline metrics therefore ride both account-level and contact-level signal across the split boundary and over-estimate generalisation to unseen accounts and contacts. For a faithful out-of-sample number, retrain with GroupKFold(account_id) and report both metrics. Notebook 02 demonstrates the detection recipe; break_me_guide.md §5 gives the worked example.

Dataset summary

Tiers are prevalence and noise axes, not modelling-complexity axes. LR AUC is ~0.88 in every tier by design. The tiers differ in conversion rate, missingness, and noise — not rank discrimination. Choose a tier based on the teaching exercise, not on expected AUC:

	Intro	Intermediate	Advanced
Tier purpose	High-prevalence warm-up	Default benchmark	Low-prevalence · calibration · noise exercise
Leads	5,000	5,000	5,000
Accounts	1,500	1,500	1,500
Contacts	4,200	4,200	4,200
Snapshot columns	31 / 34*	31 / 34*	31 / 34*
Target	`converted_within_90_days`	`converted_within_90_days`	`converted_within_90_days`
Conversion rate (acceptance band, gate G7.*)	24–61%	12–31%	4–12%
Conversion rate (observed median, seeds 42–46)	42.67%	21.60%	8.40%
Signal strength	0.90	0.70	0.50
Noise scale	0.10	0.30	0.55
Missing rate	2%	8%	18%

* student_public / research_instructor. Difficulty is modulated by the simulation engine — signal strength on latent-trait weights, Gaussian noise on float features, MCAR missingness, outlier rate — not post-hoc label flipping. The acceptance band is the recipe gate's tolerance window (v1_acceptance_gates_bands.yaml G7.*), not the achievable range — observed five-seed spreads sit comfortably inside the band.

The scenario

Veridian Technologies is a fictional Series B startup (Austin, US) selling Veridian Procure, a procurement / AP automation SaaS, to mid-market firms (200–2,000 employees) in the US and UK. The funnel runs through inbound marketing (45%), SDR outbound (35%), and partner referrals (20%); four personas drive deals (VP Finance, AP Manager, IT Director, Procurement Manager). Task: predict whether a lead converts (closed_won) within 90 days. ACV bands are $18k–$120k. See docs/release/generation_method.md for the full DGP, and the deeper "what's modelled / approximate / not modelled" breakdown that this README only summarises.

Public vs instructor: what's redacted

Filtering happens during rendering, not during simulation. The redaction contract is single-sourced in leadforge/validation/leakage_probes.py; the snapshot-safe writer and the validator import the same constants, so they cannot drift apart.

Source-of-truth constant	Public bundle treatment
`BANNED_LEAD_COLUMNS = ("converted_within_90_days", "conversion_timestamp")`	Dropped from `tables/leads.parquet`
`BANNED_OPP_COLUMNS = ("close_outcome", "closed_at")`	Dropped from `tables/opportunities.parquet`
`BANNED_TABLES = ("customers", "subscriptions")`	Omitted from public bundles
`SNAPSHOT_FILTERED_TABLES` (touches, sessions, sales_activities, opportunities)	Filtered per-lead by `lead_created_at + snapshot_day`
Snapshot redaction (`current_stage`, `is_sql`)	Stripped from `tasks/` splits and `tables/leads.parquet`
`total_touches_all` (deliberate trap)	Retained in both modes; flagged `leakage_risk=True`

Each bundle's manifest.json records relational_snapshot_safe, redacted_columns, and snapshot_day, so the bundle is self-describing.

Calibration

Every realism / calibration / difficulty claim in this README is backed by validation/validation_report.md, regenerated by scripts/validate_release_candidate.py with bands declared in docs/release/v1_acceptance_gates_bands.yaml. Headline cross-seed medians (seeds 42–46):

Tier	LR AUC	AP	P@100	Brier	`calibration_max_bin_error`
intro	0.879	0.761	0.80	0.130	0.25
intermediate	0.886	0.575	0.59	0.110	0.25
advanced	0.886	0.351	0.34	0.061	0.52

Reading this table: LR AUC is flat across tiers by design — the tiers are a prevalence / noise axis, not a rank-discrimination axis. Brier score improves as prevalence falls (a prevalence effect, not better calibration); use calibration_max_bin_error to assess calibration quality. Advanced's 0.52 max-bin error means the model's predicted probabilities are materially mis-scaled against actual conversion rates — a realistic miscalibration exercise.

AP, P@100, conversion-rate, and lift orderings hold across the intended prevalence axis (intro > intermediate > advanced).

Intended uses

Teaching baseline lead-scoring on a flat snapshot.
Teaching relational feature engineering against snapshot-safe tables.
Teaching leakage detection (the total_touches_all trap is designed to be discoverable).
Teaching calibration, lift, P@K, value-aware ranking (expected_acv × P(convert)), and cohort-shift evaluation.
Comparing model families under a controlled DGP.

Out-of-scope uses

Production lead scoring. The company, product, and customers are fictional.
Vendor benchmarking / paper baselines. Difficulty tiers are calibrated for pedagogy, not cross-paper comparability.
Causal-inference research that requires recovery of the true DGP. The instructor companion exposes the hidden graph for teaching, not designed counterfactuals.
Demographic / fairness research. v1 does not model protected attributes.

Known limitations

Tiers are a prevalence / noise axis, not a modelling-complexity axis. LR AUC is ~0.88 in every tier; the three tiers differ in conversion rate (43% / 22% / 8%), noise scale, and missingness — not in rank discrimination. Use AP, P@K, and calibration metrics to see the difficulty gradient; AUC alone will not show it.
93% account and contact overlap across train / test splits. Random splits are keyed on lead ID; most test accounts and contacts also appear in train. Headline metrics over-state generalisation to unseen accounts and contacts. Use GroupKFold(account_id) for a faithful estimate.
GBM does not consistently beat LR (gate G7.4.4). GBM−LR AUC delta is slightly negative in every tier (intro −0.0045, intermediate −0.0072, advanced −0.0133); v1's snapshot is dominated by linear features. v2 will inject non-linear interactions in the simulator.
Channel signal is weak. Per docs/release/channel_signal_audit.md, out-of-sample univariate AUC of lead_source is ≈0.50–0.52 across all tiers and the per-channel rate spread is ≤0.05. The simulator does not encode channel-conditional probabilities; channel-conditional encoding is post-v1 work.
Cohort-shift degradation is small. v1 has no time-of-year drift baked in; the cohort-shift gate (G6.4) is informational and will bite in v2.
Advanced-tier noise can produce artifact zeros in count and duration columns. Gaussian noise is applied before MCAR missingness; the snapshot builder clamps results below zero to zero. What users observe is therefore not negative values but zeros that may be noise artifacts rather than true zero values — e.g. days_since_last_touch = 0 might mean "noised below zero, clamped" rather than "touched today". Treat suspicious zero clusters in the Advanced tier as intentional data-cleaning exercise material.

Composition

Entities. Accounts, contacts, leads, touches, sessions, sales_activities, opportunities (public); plus customers and subscriptions (instructor only). Per-row counts per bundle live in manifest.json.
Features. 31 public columns grouped by analytical role in docs/release/feature_dictionary.md; the per-bundle feature_dictionary.csv is the authoritative machine-readable spec.
Label. converted_within_90_days (boolean), event-derived from the simulator. Never sampled directly.
Splits. 70/15/15 train/valid/test, deterministic given seed; recorded in tasks/converted_within_90_days/task_manifest.json. Splits are keyed on lead_id; see the Evaluation note above for the account-overlap caveat.
Provenance. Recipe b2b_saas_procurement_v1, seed 42, package version stamped in manifest.json.

Maintenance, adversarial framing, license

We want the dataset to be broken. The break-me guide catalogues nine adversarial patterns to look for (leakage, split contamination, ranking inversions, calibration drift) with worked-example pointers back into the notebooks. Issue templates ship under .github/ISSUE_TEMPLATE/: a breakage report form for findings on the bundle itself, and a realism feedback form for distributional critiques. Accepted findings are logged in docs/release/v2_decision_log.md. File issues at leadforge-dev/leadforge; PRs welcome.

Field	Value
Generator	leadforge `1.0.0+`
Recipe	`b2b_saas_procurement_v1`
Canonical seed	42 (cross-seed sweep: 42–46)
Bundle schema version	5
Format	Parquet (canonical) + CSV (convenience)
License	MIT — see LICENSE