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

This feature set was built from column definition v1; the definition is now v3. Rebuild to pick up the changes.

Feature set v1 built 29 Oct 2026 12:19

Snapshots
2,246
End of each active day
Entities
2,014
2,246 rows used
Inputs used
49 of 49
after encoding
PCA
Off
31 components would explain 90%
Scaling
Standard
Check these for outcome leaks. Each one almost settles an outcome on its own:
  • actions[co_card]: whenever it's above zero, 100% are addedToCart (so far)
  • actions[co_place]: whenever it's above zero, 100% are addedToCart (so far)
  • actions[add_grid]: whenever it's above zero, 100% are addedToCart (so far)
  • actions[co_address]: whenever it's above zero, 100% are addedToCart (so far)
  • actions[co_shipping]: whenever it's above zero, 100% are addedToCart (so far)
  • actions[co_cardform]: whenever it's above zero, 100% are addedToCart (so far)
  • actions[co_payment]: whenever it's above zero, 100% are addedToCart (so far)
  • actions[add_featured]: whenever it's above zero, 100% are addedToCart (so far)
If an input is part of the outcome (a count of purchase events is the purchase), leave it out, or the segments will just rediscover the outcome rather than the behaviour leading to it. A genuinely strong signal can stay.

Strongly correlated inputs

|r| ≥ 0.9
8 pairs move almost in step; the closest is rows and active_days (r +1.00). Keep one of each: tick Leave out on the other in Inputs, then rebuild.
InputInputr
rowsactive_days+1.00
actions[co_cardform]actions[co_payment]+1.00
actions[co_card]actions[co_place]+1.00
actions[co_address]actions[co_shipping]+0.98
actions[co_shipping]actions[co_cardform]+0.98
actions[co_shipping]actions[co_payment]+0.98
actions[co_address]actions[co_cardform]+0.96
actions[co_address]actions[co_payment]+0.96
Pairs this close carry nearly the same information, so together they count double in the segmentation. Consider leaving one of each pair out.

How the inputs relate

The 20 inputs most tied up with others, grouped so related ones sit together. Red move together, blue move opposite.
Nothing to remove: no pair reaches 0.9. The strongest is minutes and pages (+0.70).
actions[add_quickview]tenure_daysrowsactive_daysminutespagesactions[add_grid]actions[add_featured]actions[co_cardload]actions[co_card]actions[co_place]actions[co_cardform]actions[co_payment]actions[co_address]actions[co_shipping]landingPage=/checkoutsource=Directsource=Facebooksource=CraftenewVisitor
actions[add_quickview]
tenure_days0.1
rows0.1
active_days0.1
minutes0.20.30.40.4
pages0.30.50.60.60.7
actions[add_grid]0.20.20.20.30.5
actions[add_featured]0.20.20.20.30.4
actions[co_cardload]0.20.20.20.20.4
actions[co_card]0.10.10.10.20.3
actions[co_place]0.10.10.10.20.3
actions[co_cardform]0.20.20.20.30.5
actions[co_payment]0.20.20.20.30.5
actions[co_address]0.20.20.20.30.5
actions[co_shipping]0.20.20.20.30.5
landingPage=/checkout0.0-0.0-0.0-0.00.10.10.00.00.00.00.00.00.00.00.0
source=Direct0.0-0.00.00.00.10.10.00.1-0.0-0.0-0.00.00.00.00.00.5
source=Facebook-0.0-0.1-0.1-0.1-0.2-0.2-0.1-0.10.0-0.0-0.0-0.1-0.1-0.1-0.1-0.5
source=Crafte-0.00.10.20.20.10.20.10.1-0.0-0.0-0.00.10.10.10.1-0.0
newVisitor0.0-0.2-0.3-0.3-0.2-0.3-0.2-0.20.00.00.0-0.2-0.2-0.1-0.10.0-0.20.4-0.5

Outlined cells are at or above the 0.9 threshold. Hatched cells are two values of the same column (only one can be 1, so they always pull apart) and are left out of the ranking. "+N identical" means other inputs match that one exactly and are folded into its row.

Variation explained

31 of 49 components are needed for 90%: the inputs carry mostly separate information, so PCA wouldn't shrink them much. Keep it off.
Each componentRunning total
0%25%50%75%100%PC1: 15.6% on its own, 15.6% in total1PC2: 7.0% on its own, 22.6% in totalPC3: 5.1% on its own, 27.6% in totalPC4: 4.1% on its own, 31.8% in totalPC5: 3.9% on its own, 35.7% in total5PC6: 2.8% on its own, 38.4% in totalPC7: 2.7% on its own, 41.1% in totalPC8: 2.5% on its own, 43.7% in totalPC9: 2.5% on its own, 46.1% in totalPC10: 2.3% on its own, 48.4% in total10PC11: 2.3% on its own, 50.7% in totalPC12: 2.2% on its own, 52.9% in totalPC13: 2.2% on its own, 55.1% in totalPC14: 2.1% on its own, 57.2% in totalPC15: 2.1% on its own, 59.2% in total15PC16: 2.1% on its own, 61.3% in totalPC17: 2.1% on its own, 63.4% in totalPC18: 2.1% on its own, 65.4% in totalPC19: 2.1% on its own, 67.5% in totalPC20: 2.1% on its own, 69.5% in total20PC21: 2.0% on its own, 71.6% in totalPC22: 2.0% on its own, 73.6% in totalPC23: 2.0% on its own, 75.7% in totalPC24: 2.0% on its own, 77.7% in totalPC25: 2.0% on its own, 79.8% in total25PC26: 2.0% on its own, 81.8% in totalPC27: 2.0% on its own, 83.8% in totalPC28: 1.9% on its own, 85.7% in totalPC29: 1.8% on its own, 87.4% in totalPC30: 1.7% on its own, 89.2% in total3090%First 1 components: 15.6%First 2 components: 22.6%First 3 components: 27.6%First 4 components: 31.8%First 5 components: 35.7%First 6 components: 38.4%First 7 components: 41.1%First 8 components: 43.7%First 9 components: 46.1%First 10 components: 48.4%First 11 components: 50.7%First 12 components: 52.9%First 13 components: 55.1%First 14 components: 57.2%First 15 components: 59.2%First 16 components: 61.3%First 17 components: 63.4%First 18 components: 65.4%First 19 components: 67.5%First 20 components: 69.5%First 21 components: 71.6%First 22 components: 73.6%First 23 components: 75.7%First 24 components: 77.7%First 25 components: 79.8%First 26 components: 81.8%First 27 components: 83.8%First 28 components: 85.7%First 29 components: 87.4%First 30 components: 89.2%Component

26 components explain 80% of the variation, 31 explain 90%; 35 explain 95%. PCA is off: the segments use the inputs directly, and this chart is only for comparison. Fitted on 2,246 snapshots.

What PCA would combine

PCA is off, so none of this is used: it shows how inputs would group if you turned it on.
Component 1 is mostly actions[co_payment] (+0.33), actions[co_cardform] (+0.33), actions[co_shipping] (+0.33). Component 2 is mostly rows (+0.40), active_days (+0.40).
PC1PC2PC3PC4PC5PC6PC7PC8PC9PC10
device=mobile+0.01-0.04-0.04-0.23-0.66-0.11-0.08-0.03+0.04+0.05
landingPage=/-0.05-0.10-0.23+0.55-0.15-0.19+0.08+0.06-0.17-0.02
source=Facebook-0.05-0.24-0.51-0.13+0.03+0.10+0.07+0.03+0.06-0.03
source=Direct+0.03+0.17+0.50+0.03-0.05-0.14+0.21-0.07-0.04-0.13
device=desktop+0.00+0.06+0.07+0.12+0.43-0.16-0.04+0.48-0.04-0.23
source=Crafte+0.05+0.19+0.08+0.23-0.07+0.13-0.47-0.08-0.20+0.12
device=tablet-0.01-0.00-0.01+0.18+0.44+0.30+0.14-0.42-0.02+0.17
actions[add_product]+0.10-0.03+0.01-0.29+0.05+0.23-0.06+0.07-0.41-0.09
rows+0.15+0.40-0.25-0.06+0.06-0.17+0.03-0.05+0.04-0.05
active_days+0.15+0.40-0.25-0.06+0.06-0.17+0.03-0.05+0.04-0.05
actions[add_search]+0.06-0.06+0.00+0.01-0.01-0.08+0.10+0.08-0.16+0.39
newVisitor-0.08-0.24-0.13-0.22+0.07-0.14+0.39+0.09+0.12-0.07
landingPage=/product/475-chocolate-and-orange-christmas-pudding-800g-log-in-cloth+0.03-0.01+0.01-0.14+0.04+0.16-0.06+0.15-0.39-0.27
landingPage=/checkout+0.02+0.09+0.37-0.03-0.08-0.14+0.37-0.20-0.06-0.23
tenure_days+0.14+0.37-0.26-0.07+0.07-0.18+0.06-0.06+0.05-0.06
actions[co_payment]+0.33-0.12+0.02+0.07-0.02+0.04-0.06-0.02+0.10-0.10
actions[co_cardform]+0.33-0.12+0.02+0.07-0.02+0.04-0.06-0.02+0.10-0.10
actions[co_shipping]+0.33-0.12+0.02+0.07-0.02+0.06-0.06-0.01+0.12-0.12
actions[co_address]+0.33-0.11+0.03+0.08-0.03+0.06-0.05-0.00+0.11-0.13
actions[add_recommended]+0.02+0.02+0.03+0.05-0.09+0.22+0.33+0.13+0.01+0.18
landingPage=other+0.03+0.09+0.20-0.25+0.10+0.00-0.13+0.22+0.27+0.31
actions[add_quickview]+0.12-0.03-0.01+0.01-0.03+0.15+0.31+0.26-0.04+0.16
actions[add_checkout]+0.03-0.01-0.00+0.06-0.07+0.20-0.01+0.08+0.24-0.29
actions[co_place]+0.28-0.21+0.03-0.02+0.07-0.20+0.01-0.06+0.00+0.09
actions[co_card]+0.28-0.21+0.03-0.02+0.07-0.20+0.01-0.06+0.00+0.09

Inputs

Tick "Leave out", then rebuild
49 of 49 inputs are used.
InputFrom columnEncodingMeanStd devRangeZerosFilledMost correlated withOutcome linkLeave out
(activity) number · log0.7480.180.693 – 2.3 0.0%+0.60 pages+0.20
(activity) number · log0.7480.180.693 – 2.3 0.0%+0.60 pages+0.20
(activity) number · log0.1660.5520 – 3.04 89.7%+0.52 pages+0.19
minutes number · log0.5261.370 – 7.42 81.1%+0.70 pages+0.44
pages number · log0.9390.4960.693 – 4.62 0.0%+0.70 minutes+0.62
source one-hot0.9580.20 – 1 4.2%-0.45 landingPage=/checkout-0.24
source one-hot0.02980.170 – 1 97.0%+0.54 landingPage=/checkout+0.23
source one-hot0.008010.08920 – 1 99.2%-0.47 newVisitor+0.11
source one-hot0.002230.04710 – 1 99.8%+0.12 landingPage=other+0.04
source one-hot0.000890.02980 – 1 99.9%+0.08 device=tablet-0.01
source one-hot0.0004450.02110 – 1 100.0%+0.09 landingPage=other-0.01
source one-hot0.0004450.02110 – 1 100.0%+0.09 landingPage=other-0.01
landingPage one-hot0.8250.380 – 1 17.5%-0.23 actions[add_product]-0.12
landingPage one-hot0.03920.1940 – 1 96.1%+0.07 actions[add_product]-0.03
landingPage one-hot0.01420.1190 – 1 98.6%+0.06 actions[add_product]-0.02
landingPage one-hot0.01110.1050 – 1 98.9%+0.12 actions[co_cardpay]+0.07
landingPage one-hot0.00980.09850 – 1 99.0%+0.54 source=Direct+0.31
landingPage one-hot0.00890.09390 – 1 99.1%+0.04 actions[add_product]+0.03
landingPage one-hot0.006230.07870 – 1 99.4%+0.05 rows-0.03
landingPage one-hot0.005340.07290 – 1 99.5%+0.23 actions[add_product]+0.06
landingPage one-hot0.00490.06980 – 1 99.5%+0.11 actions[add_product]+0.02
landingPage one-hot0.004450.06660 – 1 99.6%-0.03 device=tablet-0.03
landingPage one-hot0.004010.06320 – 1 99.6%+0.13 actions[add_product]+0.02
landingPage one-hot0.004010.06320 – 1 99.6%+0.07 pages+0.03
landingPage one-hot0.003560.05960 – 1 99.6%+0.05 tenure_days-0.02
landingPage one-hot0.003120.05570 – 1 99.7%-0.02 minutes-0.02
landingPage one-hot0.003120.05570 – 1 99.7%+0.07 actions[add_product]+0.04
landingPage one-hot (other)0.05250.2230 – 1 94.7%-0.21 source=Facebook+0.05
device one-hot0.750.4330 – 1 25.0%+0.06 source=Facebook+0.05
device one-hot0.1320.3380 – 1 86.8%+0.08 source=com.facebook.katana-0.04
device one-hot0.1180.3230 – 1 88.2%-0.11 source=Facebook+0.03
newVisitor number0.9930.08410 – 1 0.7%-0.47 source=Crafte-0.14
actions number · log0.0520.1880 – 1.39 92.7%+0.49 pages100% addedToCart
actions number · log0.02670.1340 – 1.1 96.2%+0.47 pages100% addedToCart
actions number · log0.02580.1320 – 1.1 96.3%+0.47 pages100% addedToCart
actions number · log0.02490.130 – 1.1 96.4%+0.46 pages100% addedToCart
actions number · log0.02490.130 – 1.1 96.4%+0.46 pages100% addedToCart
actions number · log0.02280.1260 – 1.1 96.8%+0.41 pages100% addedToCart
actions number · log0.01870.1130 – 1.1 97.3%+0.27 pages100% addedToCart
actions number · log0.01360.09610 – 0.693 98.0%+0.30 pages100% addedToCart
actions number · log0.01360.09610 – 0.693 98.0%+0.30 pages100% addedToCart
actions number · log0.01160.090 – 1.1 98.4%+0.35 pages100% addedToCart
actions number · log0.007720.07270 – 0.693 98.9%-0.23 landingPage=/100% addedToCart
actions number · log0.006790.06830 – 0.693 99.0%+0.26 pages+0.54
actions number · log0.002470.04130 – 0.693 99.6%+0.10 pages+0.19
actions number · log0.0006170.02070 – 0.693 99.9%+0.06 pages+0.09
actions number · log0.0003090.01460 – 0.693 100.0%+0.10 pages+0.11
actions number · log0.0003090.01460 – 0.693 100.0%+0.05 device=tablet+0.07
actions number · log0.0003090.01460 – 0.693 100.0%+0.07 pages+0.07

Settings

Built from column definition v3
Snapshot timing

When to take a picture of each entity. Each snapshot only uses rows up to that moment.

Taken at the start or the end?

For sessions or days. Predicting ahead needs the start: then a session can't give away its own outcome.

Known at the start: source, landingPage, device. Change this per column on the Columns tab.

Activity features

Worked out from the ID, time and session columns. Added to the columns you chose.

Scaling and correlation
Principal components (PCA)

Replaces the inputs with fewer combined ones. The chart below always shows what it would keep.

Feature set versions

VersionBuiltDefinitionSnapshotsInputsPCAScaling
v830 Oct 2026 12:27v3 End of each active day18offStandard
v729 Oct 2026 17:16v3 End of each active day18offStandard
v629 Oct 2026 14:08v3 End of each active day18offStandard
v529 Oct 2026 14:01v2 End of each active day18offStandard
v429 Oct 2026 13:09v2 End of each active day21offStandard
v329 Oct 2026 12:50v1 End of each active day21offStandard
v229 Oct 2026 12:28v1 End of each active day49offStandard
v129 Oct 2026 12:19v1 End of each active day49offStandard