HiON
Stop-lossGoogle CPL crossed $500 for the 4th consecutive week· Google CPL this week: $651 (4 leads at $2,603 spend). Recommendation: pause current creative, test a new angle.
Investigate →
Stop-lossMeta CPL crossed $400 for the 13th consecutive week· Meta CPL this week: $958 (4 leads at $3,832 spend). Recommendation: pause current creative, test a new angle.
Investigate →
HiON · FranDev · Cohort lab

Are the cohorts behaving the way we hoped?

Monthly + quarterly — the analyst’s playground. Reveals whether things are getting better, or whether recent cohorts just haven’t had time to convert yet.

Synthesized · Claude narrative
Confidence · High

Three insights from the trailing 13 weeks. (1) Recent acquisition cohorts (W18–W20) reach SQL faster than older cohorts by ~3 days — the script update on May 8 is doing its job. (2) MUMBO cohort has 2.4x higher retention through Financial Validation than single-unit. (3) LinkedIn-sourced cohorts converge on a tighter signed-curve shape — lower volume, higher quality, faster cycle. Win/loss clustering below shows partner alignment is the dominant loss reason for MUMBOs — same pattern as Q1; structural, not random.

01Cohort heatmap
Retention through funnel stages by acquisition cohort. Pick the dimension to slice by.
Cohort by
Lead
Contacted
SQL
Education
Fin val.
Discovery
Under agreement
Signed
W15
n = 16
80%
73%
24%
21%
14%
8%
4%
2%
W16
n = 18
77%
70%
23%
20%
13%
7%
4%
2%
W17
n = 19
74%
67%
22%
19%
13%
7%
4%
2%
W18
n = 21
71%
65%
22%
18%
12%
7%
4%
2%
W19
n = 22
68%
62%
21%
17%
12%
6%
3%
2%
W20
n = 24
65%
59%
20%
17%
11%
6%
3%
2%
Retention by week 10 of acquisition:
0%
50%
100%
02Time-to-signed survival curves
Kaplan-Meier style — % of cohort still in pipeline over time. Reveals stall points.
MUMBO via LinkedIn
Single-unit via Google
Single-unit via Meta
03Win / loss reason clusters
Lost candidates grouped by reason. Claude-synthesized insight at top of each card.
Chose competitor2 · 33% of losses

Watch the named alternatives — common patterns suggest where HiON's pitch is weakest. Phase 3+: add a competitor field.

Example records
  • Theo CaldwellLinkedIn Outbound · MUMBO
  • Naomi HendersonFranchise Direct · MUMBO
Other1 · 17% of losses

Investigate manually — Other should stay under 10% of losses.

Example records
  • Theo RomeroMeta Ads
Timing off / partner alignment1 · 17% of losses

8 of 9 in this cluster had MUMBO flag = TRUE and reached at least Financial Validation — suggests partner alignment is a bottleneck specific to multi-unit deals. Recommended: add an explicit 'is your partner aligned?' qualifier at SQL stage for any MUMBO.

Example records
  • Priya BradfordLinkedIn Outbound
Funding fell through1 · 17% of losses

Most lost during or after Financial Validation. Tightening pre-qualification at SQL may reduce wasted Closer hours.

Example records
  • Mateo PenaGoogle Ads · MUMBO
Cold feet1 · 17% of losses

Often correlates with longer-than-average cycle. Consider a 'commit-or-close' check at week 8 to flush these out earlier.

Example records
  • Vincent ChenGoogle Ads
04ICP refinement
Attribute comparison between signed and lost candidates. Lift = signed % minus lost %. Significant flagged when |lift| ≥ 15pp.
AttributeSignedLostLiftSignificant
Source = Meta50%17%+33pp
MUMBO flag = TRUE25%50%-25pp
Capital verified = documented or pre-approved25%50%-25pp
Units committed > 125%50%-25pp
Source = LinkedIn (outbound or organic)0%33%-33pp
Source = Google Ads0%33%-33pp

Output of this section feeds the next quarter’s targeting and qualifying script. Attributes with strong positive lift become acquisition focus; strong negative lift become disqualifying signals at SQL stage.