Crafte Sessions analysis 2026 09 29 2026 10 29

sessions_analysis-2026-09-29-2026-10-29.csv · 2,246 rows · 15 columns · definition v3 saved 29 Oct 2026 14:08

Segments v24 built 29 Oct 2026 16:27 on feature set v6

A newer feature set (v8) exists. These segments were built on v6.
Segments found
15
fixed
In a segment
99%
2,223 of 2,246 snapshots
Unknown
1.0%
0 history · 0 between · 23 novel
Rules match the model
98%
for named segments
device was segmented separately: each device group has its own segments.

What to do

From these segments' numbers. Setting buttons fill in the Build segments tab (then Build segments); merge buttons change the table below (then Save labels, no refit).
  • Worth doingS8, S9, S10, S11, S12, S13, S14, S15 are very small (1 to 26 snapshots)

    Under 30 snapshots, a segment's rates swing with a couple of people, and here the fit is mostly splitting off a few odd snapshots. Merge each into the segment it's most like (S8 → S4, S9 → S2, S10 → S4, S11 → S2, S12 → S2, S13 → S7, S14 → S3, S15 → S2). Keep one only if it's a group you specifically care about.

  • Worth doingordered: use S6, S5, S7 as the targets on Outcomes

    S6, S5, S7 hold 31% of ordered in 10% of the snapshots (3.1× the overall rate).

  • Fine as isUnknowns: 1.0% of snapshots

    A small unknown share means nearly everyone fits a segment, while the odd ones aren't forced in.

Segments

Name them, merge near-duplicates, or send one to unknown. Nothing is refitted.
SegmentLabelSnapshotsShare What sets it apartMerge intoUnknown
S1 1,24655.5% mobile Slightly more often landingPage is /
S2 26711.9% tablet Typical: nothing stands out
S3 22510.0% mobile Much less often landingPage is /
S4 1928.5% desktop Slightly more often landingPage is /
S5 1426.3% mobile Much longer-standing (returning); much more visits so far
S6 542.4% mobile Much more often source is Direct; lower newVisitor; slightly more often landingPage is other
S7 331.5% desktop Much more visits so far; much longer-standing (returning); slightly more often landingPage is /product/75-christmas-cake-boxed
S8 261.2% desktop Much less often source is Facebook; much more often landingPage is other; lower newVisitor
S9 150.7% tablet Much more often landingPage is /product/79-chocolate-fruit-cake-500g-boxed
S10 70.3% desktop Much more often landingPage is /product/79-chocolate-fruit-cake-500g-boxed
S11 60.3% tablet Much more often landingPage is /product/75-christmas-cake-boxed
S12 40.2% tablet Much lower newVisitor; much less often source is Facebook; much more visits so far
S13 40.2% desktop Much more often landingPage is /product/474-port-and-prune-pudding-800g-log-in-cloth; longer-standing (returning); more visits so far
S14 10.0% mobile Much more add_search in actions; much higher pages; much higher minutes
S15 10.0% tablet Much more sub_start in actions; much higher pages; much higher minutes
? Unknown: fits no segment 231.0% high rows (+2.3 sd), high tenure_days (+2.2 sd), high pages (+2.1 sd)

Outcome concentration

Share of each segment's snapshots whose entity eventually had the outcome. Lift = rate ÷ overall rate.
S6 has the highest rate: 19% for ordered, 4.8× the overall 3.8%.
SegmentSnapshots Shareordered
RateLiftCaptured
S11,24655.5%2.1%0.5×30%
S226711.9%3.0%0.8×9%
S322510.0%2.7%0.7×7%
S41928.5%4.2%1.1×9%
S51426.3%9.9%2.6×16%
S6542.4%18.5%4.8×12%
S7331.5%9.1%2.4×3%
S8261.2%3.8%1.0×1%
S9150.7%13.3%3.5×2%
S1070.3%0.0%0.0×0%
S1160.3%0.0%0.0×0%
S1240.2%0.0%0.0×0%
S1340.2%25.0%6.5×1%
S1410.0%100.0%26.1×1%
S1510.0%0.0%0.0×0%
Unknown: fits no segment231.0%26.1%6.8×7%
Everyone2,246100%3.8%

How each segment differs

Average of each input, in standard deviations from everyone. Red higher, blue lower.
source=Facebook separates the segments most: from -4.8 sd in S6 to +0.2 sd in S1.
S1S2S3S4S5S6S7S8S9S10S11S12S13S14S15Novel
actions[add_search]-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0+47.4-0.0-0.0
actions[sub_start]-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0+47.4-0.0
newVisitor+0.1+0.1+0.1+0.1+0.1-1.2+0.1-0.8+0.1+0.1+0.1-11.8+0.1+0.1+0.1-2.0
landingPage=/product/75-christmas-cake-boxed-0.1-0.1+0.4-0.1-0.0-0.1+0.5-0.1-0.1-0.1+9.4-0.1-0.1-0.1-0.1+1.6
landingPage=/product/474-port-and-prune-pudding-800g-log-in-cloth-0.1-0.1+0.7-0.1-0.1-0.1-0.1-0.1-0.1-0.1-0.1-0.1+8.3-0.1-0.1+1.0
pages-0.1-0.2+0.2-0.1+0.3+1.0-0.1+0.2+0.3+0.6-0.4+1.8-0.4+6.3+4.3+2.1
landingPage=/product/79-chocolate-fruit-cake-500g-boxed-0.2-0.2+1.2-0.2-0.2-0.2-0.2-0.2+5.0+5.0-0.2-0.2-0.2-0.2-0.2+0.9
source=Facebook+0.2+0.2+0.2+0.2+0.2-4.8+0.2-4.8+0.2+0.2+0.2-4.8+0.2+0.2+0.2-1.3
source=Direct-0.2-0.2-0.2-0.2-0.2+4.6-0.2+3.7-0.2-0.2-0.2+1.3-0.2-0.2-0.2+0.8
rows-0.3-0.1-0.3-0.3+2.4+0.2+3.0-0.3+0.3+0.0+0.1+2.5+0.8-0.3-0.3+2.3
device=desktop-0.4-0.4-0.4+2.7-0.4-0.4+2.7+2.7-0.4+2.7-0.4-0.4+2.7-0.4-0.4+0.0
tenure_days-0.3-0.0-0.3-0.3+2.5+0.1+2.6-0.3+0.4-0.1-0.1+2.4+1.1-0.3-0.3+2.2
device=tablet-0.4+2.6-0.4-0.4-0.4-0.4-0.4-0.4+2.6-0.4+2.6+2.6-0.4-0.4+2.6-0.0
minutes-0.1-0.1+0.0-0.1+0.3+1.0-0.0+0.3-0.2-0.2-0.4-0.2-0.4+2.4+2.3+1.7
landingPage=/+0.5+0.3-2.2+0.3-0.2-0.9-0.5-1.0-2.2-2.2-2.2-0.2-2.2+0.5+0.5-1.6
actions[add_recommended]-0.1-0.1-0.1-0.1+0.5-0.1-0.1-0.1-0.1-0.1-0.1-0.1-0.1-0.1-0.1+2.1
landingPage=other-0.2-0.1+1.0-0.2+0.3+0.6+0.3+2.0-0.2-0.2-0.2+0.9-0.2-0.2-0.2+0.5
device=mobile+0.6-1.7+0.6-1.7+0.6+0.6-1.7-1.7-1.7-1.7-1.7-1.7-1.7+0.6-1.7-0.0

Movement between segments

Where each entity's next snapshot lands, as a share of the row
There are 232 steps from one snapshot to the next. 39% stayed in the same segment, so people often change segment between visits.
From ↓   to →S1S2S3S4S5S6S7S8S9S10S11S12S13S14S15Novelmoves
S11%92%7%100
S288%8%4%26
S36%24%53%18%17
S486%14%14
S594%6%36
S611%78%11%9
S7100%15
S850%50%2
S967%33%3
S10100%1
S12100%3
S13100%1
Unknown: fits no segment40%20%40%5

Segments over time

Share of snapshots per week
From the first week (05 Oct 2026) to the last (26 Oct 2026), S3 grew the most (+19% points) and S1 shrank the most (-23% points).
S1S2S3S4S5S6S7S8Other (7 segments)Unknown
0%25%50%75%100%05 Oct 2026 · S1: 287 snapshots (70%)05 Oct 2026 · S2: 40 snapshots (10%)05 Oct 2026 · S4: 44 snapshots (11%)05 Oct 2026 · S5: 4 snapshots (1%)05 Oct 2026 · S6: 23 snapshots (6%)05 Oct 2026 · S7: 2 snapshots (0%)05 Oct 2026 · S8: 8 snapshots (2%)05 Oct 2026 · Other (7 segments): 1 snapshots (0%)05 Oct 2026 · Unknown: 1 snapshots (0%)05 Oct12 Oct 2026 · S1: 469 snapshots (65%)12 Oct 2026 · S2: 105 snapshots (14%)12 Oct 2026 · S4: 76 snapshots (10%)12 Oct 2026 · S5: 35 snapshots (5%)12 Oct 2026 · S6: 18 snapshots (2%)12 Oct 2026 · S7: 10 snapshots (1%)12 Oct 2026 · S8: 4 snapshots (1%)12 Oct 2026 · Other (7 segments): 3 snapshots (0%)12 Oct 2026 · Unknown: 6 snapshots (1%)12 Oct19 Oct 2026 · S1: 277 snapshots (42%)19 Oct 2026 · S2: 74 snapshots (11%)19 Oct 2026 · S3: 140 snapshots (21%)19 Oct 2026 · S4: 54 snapshots (8%)19 Oct 2026 · S5: 60 snapshots (9%)19 Oct 2026 · S6: 10 snapshots (2%)19 Oct 2026 · S7: 12 snapshots (2%)19 Oct 2026 · S8: 12 snapshots (2%)19 Oct 2026 · Other (7 segments): 14 snapshots (2%)19 Oct 2026 · Unknown: 8 snapshots (1%)19 Oct26 Oct 2026 · S1: 213 snapshots (47%)26 Oct 2026 · S2: 48 snapshots (11%)26 Oct 2026 · S3: 85 snapshots (19%)26 Oct 2026 · S4: 18 snapshots (4%)26 Oct 2026 · S5: 43 snapshots (10%)26 Oct 2026 · S6: 3 snapshots (1%)26 Oct 2026 · S7: 9 snapshots (2%)26 Oct 2026 · S8: 2 snapshots (0%)26 Oct 2026 · Other (7 segments): 20 snapshots (4%)26 Oct 2026 · Unknown: 8 snapshots (2%)26 Oct

ordered rate by week (grey: under 20 snapshots, unreliable)

05 Oct12 Oct19 Oct26 Oct
S11%3%2%2%
S20%4%1%6%
S32%4%
S45%5%2%6%
S550%11%5%12%
S622%22%10%0%
S70%0%17%11%
S812%0%0%0%
Other (7 segments)0%0%14%10%
Unknown100%33%25%12%

How good are these segments?

Separation 0.66 (clear) and stability 0.54 (fairly stable). Least stable: S3, S5, S10, S15.
Separation
0.66
clear
Stability
0.54
fairly stable
Confidence
100%
average placement
SegmentSeparationStabilityConfidence
S10.85 clear0.82 very stable100%
S20.72 clear0.92 very stable100%
S3-0.07 overlapping0.17 unstable100%
S40.85 clear0.92 very stable100%
S50.07 overlapping0.37 shaky100%
S60.22 weak0.73 fairly stable100%
S70.07 overlapping0.75 fairly stable100%
S80.20 weak0.84 very stable100%
S90.63 clear0.70 fairly stable100%
S100.69 clear0.20 unstable100%
S110.91 clear1.00 very stable100%
S120.53 clear1.00 very stable100%
S130.70 clear0.57 fairly stable100%
S140.00 overlapping1.00 very stable100%
S150.00 overlapping0.34 shaky100%

Choosing the number of segments

Groups with fewer than 50 snapshots can't be segmented on their own and are counted as too little history.
deviceSnapshotsSegments
mobile1,6855fixed
tablet2965fixed
desktop2655fixed

The unknowns

23 snapshots (1.0%) are unknown: 0 too little history, 0 between segments, 23 fit no segment.
KindSnapshotsNearest segment
Unknown: fits no segment
Doesn't resemble any segment. Worth a look: new patterns and fraud often start here.
23S5 (8), S3 (5), S6 (4)

Boundary rules

How to recognise each segment
Together the rules agree with the model 98% of the time: good enough to use as a description.
Each segment gets its own short rules for picking it out from everyone else, in each feature's own units. Taken together, simple rules reproduce 98% of the assignments. Rules show where the lines fall, not why: use the profile above for what defines a segment. Unknowns have no rules; they come from how well a snapshot fits, not from where it sits.
S1 1 rule covering 100% of the segment
  • device = mobile and landingPage = / and tenure_days ≤ 0.00638 · covers 100% of the segment, 98% correct
S2 1 rule covering 100% of the segment
  • device = tablet and landingPage is not /product/79-chocolate-fruit-cake-500g-boxed and landingPage is not /product/75-christmas-cake-boxed · covers 100% of the segment, 98% correct
S3 1 rule covering 100% of the segment
  • landingPage is not / and device = mobile and source = Facebook · covers 100% of the segment, 87% correct
S4 1 rule covering 100% of the segment
  • device = desktop and tenure_days ≤ 0.389 and source = Facebook · covers 100% of the segment, 94% correct
S5 1 rule covering 97% of the segment
  • rows > 1.5 and device = mobile and source = Facebook · covers 97% of the segment, 96% correct
S6 1 rule covering 100% of the segment
  • source is not Facebook and device = mobile · covers 100% of the segment, 100% correct
S7 2 rules covering 85% of the segment
  • device = desktop and tenure_days > 0.389 and landingPage = / · covers 64% of the segment, 100% correct
  • device = desktop and tenure_days > 0.389 and landingPage is not / · covers 21% of the segment, 70% correct
S8 1 rule covering 100% of the segment
  • source is not Facebook and device = desktop · covers 100% of the segment, 100% correct
S9 2 rules covering 100% of the segment
  • landingPage = /product/79-chocolate-fruit-cake-500g-boxed and device = tablet and pages ≤ 1.5 · covers 53% of the segment, 100% correct
  • landingPage = /product/79-chocolate-fruit-cake-500g-boxed and device = tablet and pages > 1.5 · covers 47% of the segment, 100% correct
S10 1 rule covering 100% of the segment
  • landingPage = /product/79-chocolate-fruit-cake-500g-boxed and device = desktop · covers 100% of the segment, 100% correct
S11 1 rule covering 100% of the segment
  • landingPage = /product/75-christmas-cake-boxed and device = tablet · covers 100% of the segment, 100% correct
S12 1 rule covering 100% of the segment
  • newVisitor ≤ 0.5 and device is not mobile · covers 100% of the segment, 67% correct
S13 1 rule covering 100% of the segment
  • landingPage = /product/474-port-and-prune-pudding-800g-log-in-cloth and device is not mobile · covers 100% of the segment, 67% correct
S14 no clean rule: this segment overlaps others on these features
S15 no clean rule: this segment overlaps others on these features

Compare runs

Find the best segmentation
The pick is v20: its targets hold 83% of ordered in 25% of snapshots (3.3× the rate), with target stability 0.52.
RunFeaturesGroupsShapeSegmentsUnknownSeparationStabilityTargetsCapturedLiftTarget stability
v29
30 Oct 2026 12:50
v8device evened outFull81.1% 0.640.88 S2, S5, S633% in 12%2.8× 0.96 ★
v28
30 Oct 2026 09:20
v7device evened outDiagonal81.3% 0.540.92 ★ S2, S3, S5, S681% in 30%2.7× 0.66
v27
29 Oct 2026 17:17
v6device evened outDiagonal81.3% 0.540.92 ★ S2, S3, S5, S681% in 30%2.7× 0.66
v26
29 Oct 2026 16:29
v6-Diagonal51.0% 0.450.59 S3, S419% in 5%3.4× ★ 0.21
v25
29 Oct 2026 16:28
v6device evened outDiagonal51.0% 0.490.41 S2, S383% in 31% ★2.7× 0.39
v24
29 Oct 2026 16:27
v6device separatelyDiagonal151.0% 0.66 ★0.54 S5, S6, S731% in 10%3.1× 0.62
v23
29 Oct 2026 16:26
v6device ignoredFull50.7% ★ 0.630.37 S2, S341% in 18%2.2× 0.42
v22
29 Oct 2026 16:26
v6-Full51.0% 0.440.60 S3, S419% in 6%3.4× 0.21
v21
29 Oct 2026 16:26
v6device separatelyFull151.0% 0.650.51 S5, S627% in 9%3.0× 0.57
v20 pick
29 Oct 2026 16:24
v6device evened outFull51.0% 0.500.27 S2, S483% in 25% ★3.3× 0.52
v19
29 Oct 2026 16:22
v6-Full51.0% -- --- -
v18
29 Oct 2026 16:20
v6-Full51.0% -- --- -
v17
29 Oct 2026 16:20
v6-Full51.0% -- --- -
v16
29 Oct 2026 16:19
v6-Full51.0% -- --- -
v15
29 Oct 2026 14:10
v6-Full51.0% -- --- -
v14
29 Oct 2026 14:09
v6-Diagonal41.0% -- --- -
v13
29 Oct 2026 14:08
v6-Diagonal91.1% -- --- -
v12
29 Oct 2026 14:08
v6-Diagonal71.0% -- --- -
v11
29 Oct 2026 14:01
v5-Diagonal71.0% -- --- -
v10
29 Oct 2026 13:10
v4-Diagonal71.0% -- --- -
v9
29 Oct 2026 13:10
v4-Diagonal789.8% -- --- -
v8
29 Oct 2026 13:10
v4-Diagonal101.0% -- --- -
v7
29 Oct 2026 13:09
v4-Diagonal71.0% -- --- -
v6
29 Oct 2026 12:51
v3-Diagonal71.0% -- --- -
v5
29 Oct 2026 12:51
v3-Diagonal101.0% -- --- -

Search results

49 setups · 30 Oct 2026 12:43 · judged on ordered

Best: K-means · device evened out (8 segments). Its target segments (S2, S3, S4) hold 77% of ordered in 20% of the training snapshots, 3.8× the overall rate, and come back at 0.96 stability when refitted.

Every setup was fitted on the same 1,828 training snapshots (1,611 people); the 20% of people held out for testing weren't used, so Outcomes can still test the winner honestly.
#SetupSegments UnknownSeparationStabilityCaptured LiftTarget stabilityScore
1K-means · device evened out81.3% 0.640.9677% in 20%3.8× 0.962.93
2Bayesian Gaussian mixture · Diagonal · device evened out81.0% 0.540.6384% in 31%2.7× 0.752.25
3Bayesian Gaussian mixture · Full · source ignored81.0% 0.600.6978% in 27%2.9× 0.542.23
4K-means · device separately181.3% 0.730.9057% in 14%3.9× 0.942.23
5K-means · source evened out81.1% 0.630.7659% in 16%3.7× 0.722.19
6Gaussian mixture · Diagonal · device evened out81.0% 0.550.6381% in 31%2.6× 0.722.14
7Gaussian mixture · Full · device evened out81.0% 0.550.6181% in 31%2.6× 0.782.13
8Bayesian Gaussian mixture · Diagonal · device ignored81.0% 0.670.8181% in 31%2.6× 0.772.11
9Gaussian mixture · Full · device ignored81.0% 0.660.7781% in 33%2.5× 0.722.00
10Bayesian Gaussian mixture · Full · device evened out81.0% 0.550.5978% in 31%2.5× 0.742.00
11Gaussian mixture · Diagonal · device ignored81.0% 0.670.8478% in 31%2.5× 0.751.99
12Gaussian mixture · Full · device separately191.1% 0.670.8262% in 20%3.1× 0.561.96

Settings

One segmentation with exactly these settings. To try many automatically, use Find the best.
Feature set

The inputs to segment on.

Number of segments

Search a range and let the fit decide, or fix it.

When to say "unknown"

Rather than forcing everyone into a segment.

Groups

A column such as device or caseworker that should be treated differently in this run.

Each segment count is fitted 2 times on up to 60,000 snapshots. A range of 8 counts on 20,000 snapshots takes about half a minute; "Diagonal" is several times faster.