108 of 11.001 listings match.
Time = 5y,
src = 14/14,
lang = all,
dict = D1,
terms = 9(ANY). last watermark · 2026-05-06
Demand index · last 4 wk vs window mean
4454%
above baseline
Reg. pressure (D5 mentions)
3
across all listings in window
Top mover · D1
DATEV
∞ (z=99.0)
Recurring clusters
4
near-duplicate templates
01All 20 terms · Tools
11/20 have ≥1 citation · sparkline = trend · click row to filter · zero-count terms shown at bottom
02Term trend · top 5
anomalies marked · z > 2.5
DATEVPostgresPower BILexofficeExcel
03Top movers · current half vs prev half
largest |Δ%| · z-score (Poisson)
| Term | Now | Prev | Δ % | z |
|---|---|---|---|---|
| DATEV | 66 | 0 | ∞ | 99.0 |
| Confluence | 2 | 0 | ∞ | 0.0 |
| Postgres | 24 | 0 | ∞ | 99.0 |
| sevdesk | 3 | 0 | ∞ | 0.0 |
| Power BI | 9 | 0 | ∞ | 99.0 |
| Excel | 5 | 0 | ∞ | 99.0 |
| Jira | 2 | 0 | ∞ | 0.0 |
| Lexoffice | 9 | 0 | ∞ | 99.0 |
| Lexware | 3 | 0 | ∞ | 0.0 |
| HubSpot | 1 | 0 | ∞ | 0.0 |
| Talend | 2 | 0 | ∞ | 0.0 |
04Co-occurrence · top 20 pairs
Lift = P(A,B)/(P(A)·P(B)) · click to filter on both
| Term A | Term B | Count | Lift |
|---|---|---|---|
| Postgres | API | 8 | 2.77× |
| Postgres | integrieren | 6 | 3.38× |
| DATEV | exportieren | 6 | 1.40× |
| Postgres | pipeline | 4 | 4.50× |
| pipeline | API | 4 | 8.31× |
| DATEV | Buchhalter | 3 | 1.64× |
| Excel | Power BI | 3 | 7.20× |
| Power BI | analysieren | 3 | 9.00× |
| API | REST | 3 | 8.31× |
| Postgres | DSGVO | 3 | 4.50× |
| Lexoffice | API | 3 | 2.77× |
| DATEV | Pflege | 2 | 1.09× |
| Confluence | reporten | 2 | 27.00× |
| Confluence | Pflege | 2 | 36.00× |
| reporten | Pflege | 2 | 18.00× |
| DATEV | Postgres | 2 | 0.14× |
| DATEV | Steuerfachangestellte | 2 | 1.64× |
| Buchhalter | Steuerfachangestellte | 2 | 36.00× |
| Excel | analysieren | 2 | 10.80× |
| Power BI | automatisieren | 2 | 4.80× |
05Recurring clusters · near-duplicate listings
indicates productizable workflows · click row for records