Sample SaaS company

Cohort Analysis

A worked example of growth-stage SaaS analytics across retention, activation, LTV, sales performance, and churn risk.

This dashboard uses a synthetic dataset for a fictional B2B workflow automation company. The dataset covers 24 months of activity across 3,000 customers in three segments: SMB, Mid-Market, and Enterprise.

Active customers

1,973

ARR booked this qtr

$11.31M

Activation rate

8.1%

Avg monthly churn

2.8%

Enterprise LTV

$1.56M

Section 01

What does retention look like?

SMB average retention crosses below 50% at month 14. Mid-Market lands at 56% at month 24. Enterprise stays above 70% through the full 24-month data window. The shape is what we would expect based on client size: SMB churns fast, Mid-Market less so, Enterprise is sticky.

Cohort	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24
2024-03	100	100	96	92	85	82	77	75	69	66	63	62	59	59	59	54	51	51	49	46	45	41	41	39	38
2024-04	100	100	97	93	86	83	79	77	75	72	71	68	67	63	63	60	53	51	49	47	47	46	44	41
2024-05	100	100	98	94	92	89	85	81	79	76	73	72	69	67	66	62	58	57	56	53	51	51	47
2024-06	100	100	95	92	84	80	78	72	67	65	63	60	58	56	55	53	49	48	47	43	42	41
2024-07	100	100	93	91	88	83	75	71	67	66	66	65	61	60	55	53	51	50	48	46	44
2024-08	100	100	91	88	86	84	78	78	73	70	69	66	63	60	57	56	54	50	49	44
2024-09	100	100	99	96	86	81	77	73	71	64	62	62	61	57	54	54	53	51	51
2024-10	100	100	98	95	92	86	82	79	76	73	71	69	68	67	60	56	51	46
2024-11	100	100	99	97	92	89	87	82	80	79	78	74	73	70	66	65	63
2024-12	100	100	98	89	87	84	78	78	74	70	64	61	57	54	51	48
2025-01	100	100	96	90	87	84	79	74	71	64	61	61	59	57	53
2025-02	100	100	95	90	85	85	82	80	74	70	69	65	64	63
2025-03	100	100	96	94	92	84	81	80	78	68	66	60	58
2025-04	100	100	96	94	91	89	83	80	76	72	71	69
2025-05	100	100	95	91	88	86	81	76	74	68	66
2025-06	100	100	95	92	87	82	78	78	75	73
2025-07	100	100	96	94	87	85	82	80	74
2025-08	100	100	95	90	85	80	74	70
2025-09	100	100	97	95	93	89	87
2025-10	100	100	97	89	86	83
2025-11	100	100	94	87	84
2025-12	100	100	94	90
2026-01	100	100	97
2026-02	100	100
2026-03	100

Retention %

0 — 100

Average retention curve by segment

Next action

Diagnose the SMB drop between months 6 and 14 with a churn-reason analysis or exit interviews of recently-churned SMB customers.

Section 02

Where does the activation funnel break?

Out of 3,000 signups, only 242 activate (8.1%). The steepest single-stage drop is workflow built → activated, where only 35% of customers who built a workflow ever used it in a way that predicted retention. Earlier drop-offs are typical for B2B SaaS; the workflow-to-activation cliff is the real friction point and where the product team should focus.

Overall funnel

Signed Up

3,000

Account Setup

2,499

83%

Admin Invited

1,607

64%

Workflow Built

698

43%

Activated

242

35%

Stage-to-stage conversion by segment

Signup → Setup

Setup → Admin

Admin → Workflow

Workflow → Activated

Next action

Run user research or session replays on the workflow-to-activation gap to identify what blocks customers from moving past their first workflow into repeated, scheduled use.

Section 03

Are the segments economically healthy?

LTV (lifetime value) is the total revenue a customer pays over their tenure. CAC (customer acquisition cost) is what it costs to acquire that customer. As a rule of thumb, LTV should be at least 3× CAC to be economically viable. All three segments clear that bar, and Enterprise economics are roughly 475× larger than SMB — the reason every CS dollar disproportionately goes there next quarter.

Segment	LTV	CAC	LTV / CAC	Monthly churn
Enterprise	$1.56M	$25,000	62.5×	1.17%
Mid-Market	$58.6K	$4,000	14.7×	2.69%
SMB	$3.3K	$400	8.2×	4.66%

LTV / CAC ratio (3× healthy threshold)

Next action

Reweight new-business spend toward Enterprise. Each Enterprise customer delivers ~475× the LTV of an SMB customer, so even small increases in Enterprise acquisition meaningfully impact revenue.

Section 04

How are reps performing this quarter?

Seven of ten reps cleared quota. Allison Hill and Noah Rhodes (both Enterprise) booked over 5× their target. Three SMB reps finished below 100%: Abigail Shaffer (95%), Connie Lawrence (94%), Gina Moore (90%). The Enterprise outperformance is partly a windfall from two large deals; the SMB underperformance is more interesting because it's spread across three reps.

Quarter: 2026-01-01 to 2026-04-01

Enterprise

Mid-Market

SMB

Next action

Audit SMB territory assignments and pipeline coverage to determine whether the underperformance is structural (territory mix) or top-of-funnel (lead volume).

Section 05

Who's most at risk of churning?

Of 1,973 active customers, 157 sit in the high or severe risk bands and 43 are in the severe band. The risk score is a 0–100 composite of four equal-weighted components: login dormancy, open tickets, MRR contraction, and NPS. It's a hand-tuned heuristic, not a machine-learning model — interpretable, defensible, and easy for a CS rep to act on.

Risk score distribution — 1,973 active customers

Top 20 at-risk customers — call list for Customer Success

Company	Segment	Days idle	Tickets	MRR Δ %	NPS	Risk
Abbott Inc	SMB	50	5	-17.9%	0	100
Sanchez PLC	SMB	25	4	-18.7%	1	95
Beck-Banks	Mid-Market	31	4	-17.0%	5	95
Swanson Group	Enterprise	44	4	-17.7%	6	95
Sanchez, Mullins and Moreno	SMB	58	4	-21.6%	6	95
Webb-Baker	SMB	25	4	-18.3%	0	95
Sanchez-Carey	Mid-Market	57	4	-15.6%	1	95
Brooks, Hughes and Miller	Mid-Market	54	3	-21.5%	3	90
Burgess-Lewis	Mid-Market	52	3	-16.5%	4	90
Gay LLC	Mid-Market	54	3	-20.9%	0	90
Taylor-Love	SMB	43	3	-22.6%	6	90
Wood PLC	SMB	23	5	-8.1%	1	90
Jimenez-Jimenez	Mid-Market	52	5	-11.1%	2	90
Sims-Kirby	SMB	38	3	-21.4%	0	90
King-Jensen	Mid-Market	43	5	-7.5%	1	90
Myers and Sons	SMB	49	5	-9.9%	4	90
Thompson-Murray	Mid-Market	46	3	-23.0%	1	90
Carson LLC	Mid-Market	47	3	-15.6%	3	90
Fisher, Zuniga and Torres	SMB	42	3	-19.9%	1	90
Moore, Kirby and Scott	SMB	36	3	-18.5%	5	90

Next action

Customer Success to call all 43 severe-risk customers within the next 14 days, starting with the top 20 in the leaderboard. Reps should log the churn-reason cited by each customer so the heuristic can be refined over time.