feat(pitch-deck): Finanzplan-Tooling + formel-getriebene Versionen Base/Bull/Bear
8 neue Skripte erweitern die Excel-Finanzpläne deutlich: - add-kunden-formulas: Neukunden-Lookup + kumulativer Churn (SUMPRODUCT-basiert) - add-price-formulas: jährliche Preiserhöhung Jan via Treiber - add-inflation-formulas: Inflation auf Betriebskosten + Büromiete-Logik - add-tantieme-and-explanations: Gründer-Tantieme 2028-2030 + Erläuterungen in Cohort-Analyse + Sensitivity-Sheets - apply-bueromiete: 1000€/Monat ab Sep 2026 mit Inflation - apply-number-formatting: Euro / Count / Percent per Label-Klassifikation - cleanup-finanzplan-labels: 'kategorie — '-Präfix entfernt - copy-extra-sheets: Charts/Cohort/Sensitivity/Hiring-Plan von Series-A auf 400k Base/Bull/Bear übertragen (inkl. 12 Chart-Objekten) Neue Excel-Dateien (für L-Bank Wandeldarlehen 400k Pitch): - Finanzplan-Wandeldarlehen-400k.xlsx (Base) - Finanzplan-Wandeldarlehen-400k-Bull.xlsx - Finanzplan-Wandeldarlehen-400k-Bear.xlsx - Finanzplan-Series-A-Ambitioniert.xlsx (Series-A Variante) Inhaltliche Anpassungen (400k Base/Bull/Bear): - Channel-Provision Bechtle/Cancom → Channel-Partner Provision, Format Euro - GuV: 'Steuerbares Einkommen' → 'Zu versteuerndes Einkommen (nach Verlustvortrag)', Formel um Zinserträge/-aufwand erweitert - IT-Recht/Datenschutzjurist auf 100% (6666 € statt 3333 €) - Series-A-Investor in WD-Sheet auf 0 € (nicht eingeplant in 400k Variante) - Mitarbeiter +1 Monat verschoben (außer Gründer = Okt 2026) - 3 Enterprise-Neukunden zusätzlich (Apr 2027, Jun 2027, Okt 2029) - Marketing-Agentur Cut ~33% pro Szenario (Base 4%, Bull 5%, Bear 2%) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Benjamin Admin
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#!/usr/bin/env python3
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"""Replace hard-coded values in 'Kunden' sheet with proper formulas:
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- Neukunden = driver lookup (per year × segment) from Treiber sheet
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- Churn = ROUND(previous-month Bestandskunden × Churn-Rate, 0)
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- Bestandskunden = previous + new - churn (cumulative)
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Default driver values are derived from each file's existing monthly Neukunden
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data so each scenario keeps its growth profile. Churn rates default to
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industry-typical B2B SaaS values (Starter 1%, Pro 0.5%, Enterprise 0.3%/Monat).
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Usage:
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python3 pitch-deck/scripts/add-kunden-formulas.py --dry-run
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python3 pitch-deck/scripts/add-kunden-formulas.py
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python3 pitch-deck/scripts/add-kunden-formulas.py --only Finanzplan-Wandeldarlehen-400k.xlsx
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"""
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from __future__ import annotations
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import argparse
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import shutil
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import sys
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from datetime import datetime
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from pathlib import Path
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from openpyxl import load_workbook
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from openpyxl.utils import get_column_letter
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EXPORTS = Path(__file__).resolve().parent.parent / "exports"
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# Default monthly churn rates per segment per year. Reflects business reality:
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# - Starter: high early churn (testing, startups failing) decreasing as product matures
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# - Professional: moderate, gradually decreasing
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# - Enterprise: very low — they integrate the product, don't switch
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# Annual equivalents:
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# Starter 64% → 17%, Professional 26% → 9%, Enterprise 6% → 1%
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CHURN_DEFAULTS: dict[str, dict[int, float]] = {
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"starter": {2026: 0.08, 2027: 0.05, 2028: 0.03, 2029: 0.02, 2030: 0.015},
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"professional": {2026: 0.025, 2027: 0.02, 2028: 0.015, 2029: 0.01, 2030: 0.008},
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"enterprise": {2026: 0.005, 2027: 0.003, 2028: 0.002, 2029: 0.001, 2030: 0.001},
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}
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# Segment definitions: (segment_name, neukunden_row, churn_row, bestand_row, suffix)
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SEGMENTS = [
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("Starter (<10 MA)", 4, 5, 6, "starter"),
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("Professional (10-250 MA)", 7, 8, 9, "professional"),
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("Enterprise (250+ MA)", 10, 11, 12, "enterprise"),
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]
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def year_columns(ws_kunden) -> dict[int, list[int]]:
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out: dict[int, list[int]] = {}
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for c in range(2, ws_kunden.max_column + 1):
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v = ws_kunden.cell(row=1, column=c).value
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if v is None:
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continue
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try:
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yr = int(v)
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except (TypeError, ValueError):
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continue
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out.setdefault(yr, []).append(c)
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return out
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def monthly_avg_per_year(ws_kunden, row: int, year_cols: dict[int, list[int]]) -> dict[int, int]:
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"""Compute rounded monthly average from existing per-month values.
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Uses half-up rounding (0.5 → 1) instead of Python's banker's rounding (0.5 → 0)
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to preserve segments that grew at exactly half-a-customer-per-month.
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"""
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res: dict[int, int] = {}
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for yr, cols in year_cols.items():
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total = 0.0
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for c in cols:
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v = ws_kunden.cell(row=row, column=c).value
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if v in (None, ""):
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continue
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try:
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total += float(v)
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except (TypeError, ValueError):
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pass
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avg = total / len(cols) if cols else 0
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res[yr] = max(0, int(avg + 0.5))
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return res
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_SEG_LABEL = {"starter": "Starter", "professional": "Professional", "enterprise": "Enterprise"}
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def _clear_old_kundenakquise_block(ws_treiber) -> None:
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"""If we ran a previous version, drop any rows after the original 30 to start fresh."""
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if ws_treiber.max_row > 30:
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for r in range(31, ws_treiber.max_row + 1):
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ws_treiber.cell(row=r, column=1).value = None
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ws_treiber.cell(row=r, column=2).value = None
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def write_treiber_drivers(ws_treiber, years: list[int], defaults_by_segment: dict[str, dict[int, int]]) -> dict:
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"""Rewrite the Kundenakquise + Churn driver block. Returns row indices for cross-references.
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Layout (after original row 30):
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31 (blank)
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32 Header "Kundenakquise"
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33..47 Neukunden/Monat per (segment, year)
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48 (blank)
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49 Header "Churn (monatliche Rate, pro Jahr)"
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50..64 Churn-Rate/Monat per (segment, year)
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"""
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_clear_old_kundenakquise_block(ws_treiber)
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r = 32
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ws_treiber.cell(row=r, column=1).value = "Kundenakquise"
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r += 1
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new_customer_refs: dict[str, dict[int, int]] = {}
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for suffix in ("starter", "professional", "enterprise"):
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new_customer_refs[suffix] = {}
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for yr in years:
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ws_treiber.cell(row=r, column=1).value = f"Neukunden/Monat {_SEG_LABEL[suffix]} {yr}"
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ws_treiber.cell(row=r, column=2).value = defaults_by_segment[suffix][yr]
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new_customer_refs[suffix][yr] = r
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r += 1
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r += 1 # blank row 48
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ws_treiber.cell(row=r, column=1).value = "Churn (monatliche Rate, pro Jahr)"
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r += 1
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churn_refs: dict[str, dict[int, int]] = {}
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for suffix in ("starter", "professional", "enterprise"):
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churn_refs[suffix] = {}
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for yr in years:
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ws_treiber.cell(row=r, column=1).value = f"Churn-Rate/Monat {_SEG_LABEL[suffix]} {yr}"
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ws_treiber.cell(row=r, column=2).value = CHURN_DEFAULTS[suffix][yr]
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churn_refs[suffix][yr] = r
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r += 1
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return {"new_customer_rows": new_customer_refs, "churn_rows": churn_refs}
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# Helper rows in Kunden sheet (added BELOW the totals to avoid breaking external refs).
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# Each helper row holds the year-varying monthly churn rate per column for one segment.
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HELPER_ROWS = {
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"starter": 18,
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"professional": 19,
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"enterprise": 20,
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}
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def write_kunden_formulas(ws_kunden, years: list[int], refs: dict) -> dict:
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min_year = min(years)
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new_refs = refs["new_customer_rows"]
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churn_refs = refs["churn_rows"]
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stats = {"neu": 0, "churn": 0, "bestand": 0, "helper": 0}
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# 1. Write per-segment helper rows (18-20) with year-lookup rates per column.
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ws_kunden.cell(row=17, column=1).value = None # separator
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for suffix, helper_row in HELPER_ROWS.items():
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first = min(churn_refs[suffix].values())
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last = max(churn_refs[suffix].values())
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ws_kunden.cell(row=helper_row, column=1).value = (
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f"Monatl. Churn-Rate {_SEG_LABEL[suffix]} (Helper)"
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)
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for c in range(2, ws_kunden.max_column + 1):
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col_letter = get_column_letter(c)
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ws_kunden.cell(row=helper_row, column=c).value = (
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f"=INDEX(Treiber!$B${first}:$B${last},{col_letter}$1-{min_year - 1})"
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)
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stats["helper"] += 1
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# 2. Write per-segment Neukunden/Churn/Bestandskunden formulas.
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for seg_label, neu_row, churn_row, bestand_row, suffix in SEGMENTS:
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ws_kunden.cell(row=neu_row, column=1).value = f"Neukunden {seg_label}"
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ws_kunden.cell(row=churn_row, column=1).value = f"Churn {seg_label}"
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ws_kunden.cell(row=bestand_row, column=1).value = f"Bestandskunden {seg_label}"
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seg_neu_rows = new_refs[suffix]
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neu_first = min(seg_neu_rows.values())
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neu_last = max(seg_neu_rows.values())
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helper_row = HELPER_ROWS[suffix]
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for c in range(2, ws_kunden.max_column + 1):
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col_letter = get_column_letter(c)
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prev_col = get_column_letter(c - 1) if c > 2 else None
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# --- Neukunden: year-lookup driver ---
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ws_kunden.cell(row=neu_row, column=c).value = (
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f"=INDEX(Treiber!$B${neu_first}:$B${neu_last},{col_letter}$1-{min_year - 1})"
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)
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stats["neu"] += 1
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# --- Churn: cumulative-rounding to make small-base churn visible ---
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# Approach: expected_cum_churn(t) = SUMPRODUCT(Bestand[B..t-1], Rate[C..t])
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# Each month's churn = ROUND(expected_cum) - already_booked_churn.
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# This guarantees integer monthly values that aggregate to ROUND(expected_total).
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if c == 2:
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# Aug 2026: no prior month, no churn.
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ws_kunden.cell(row=churn_row, column=c).value = 0
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elif c == 3:
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# Sep 2026: one prior bestand cell (B), no churn-booked yet.
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ws_kunden.cell(row=churn_row, column=c).value = (
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f"=MAX(0,ROUND(SUMPRODUCT($B{bestand_row}:B{bestand_row},"
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f"$C{helper_row}:C{helper_row}),0))"
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)
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else:
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ws_kunden.cell(row=churn_row, column=c).value = (
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f"=MAX(0,ROUND(SUMPRODUCT($B{bestand_row}:{prev_col}{bestand_row},"
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f"$C{helper_row}:{col_letter}{helper_row}),0)"
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f"-SUM($C{churn_row}:{prev_col}{churn_row}))"
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)
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stats["churn"] += 1
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# --- Bestandskunden: cumulative balance ---
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if c == 2:
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ws_kunden.cell(row=bestand_row, column=c).value = (
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f"={col_letter}{neu_row}-{col_letter}{churn_row}"
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)
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else:
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ws_kunden.cell(row=bestand_row, column=c).value = (
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f"={prev_col}{bestand_row}+{col_letter}{neu_row}-{col_letter}{churn_row}"
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)
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stats["bestand"] += 1
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return stats
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def _was_already_processed(ws_treiber) -> bool:
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"""Detect if a previous run already wrote the Kundenakquise driver block."""
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return ws_treiber.cell(row=32, column=1).value == "Kundenakquise"
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def _read_existing_neukunden_drivers(ws_treiber, years: list[int]) -> dict:
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"""Read driver values previously written to Treiber rows 33..47.
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Re-running on a processed file would otherwise compute defaults from
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formula cells (which openpyxl returns as strings) and reset everything to 0.
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"""
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defaults: dict[str, dict[int, int]] = {}
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r = 33
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for suffix in ("starter", "professional", "enterprise"):
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defaults[suffix] = {}
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for yr in years:
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v = ws_treiber.cell(row=r, column=2).value
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try:
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defaults[suffix][yr] = int(round(float(v))) if v is not None else 0
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except (TypeError, ValueError):
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defaults[suffix][yr] = 0
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r += 1
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return defaults
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def process_file(path: Path, dry_run: bool) -> dict | None:
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wb = load_workbook(path)
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if "Kunden" not in wb.sheetnames or "Treiber" not in wb.sheetnames:
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return None # caller will report skip
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ws_k = wb["Kunden"]
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ws_t = wb["Treiber"]
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yc = year_columns(ws_k)
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years = sorted(yc.keys())
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if _was_already_processed(ws_t):
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# Preserve user-edited driver values from a previous run
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defaults = _read_existing_neukunden_drivers(ws_t, years)
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source = "treiber (preserved)"
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else:
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# First time: compute defaults from existing Kunden values
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defaults = {
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"starter": monthly_avg_per_year(ws_k, 4, yc),
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"professional": monthly_avg_per_year(ws_k, 7, yc),
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"enterprise": monthly_avg_per_year(ws_k, 10, yc),
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}
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source = "kunden data"
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refs = write_treiber_drivers(ws_t, years, defaults)
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stats = write_kunden_formulas(ws_k, years, refs)
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if not dry_run:
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wb.save(path)
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return {"years": years, "defaults": defaults, "stats": stats, "refs": refs, "defaults_source": source}
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def backup(path: Path) -> Path:
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ts = datetime.now().strftime("%Y%m%d-%H%M%S")
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bk = path.with_name(f"{path.stem}.BACKUP-pre-kunden-formulas-{ts}{path.suffix}")
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shutil.copy2(path, bk)
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return bk
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def main() -> int:
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ap = argparse.ArgumentParser(description=__doc__)
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ap.add_argument("--dry-run", action="store_true")
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ap.add_argument("--only", help="Process only this filename")
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ap.add_argument("--no-backup", action="store_true")
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args = ap.parse_args()
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files = sorted(EXPORTS.glob("Finanzplan-*.xlsx"))
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files = [f for f in files if "BACKUP" not in f.name]
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if args.only:
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files = [f for f in files if f.name == args.only]
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for path in files:
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# Peek first to decide whether to backup
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wb_peek = load_workbook(path, read_only=True)
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if "Treiber" not in wb_peek.sheetnames or "Kunden" not in wb_peek.sheetnames:
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print(f"\n ⨯ skip {path.name}: no Treiber sheet")
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continue
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if not args.dry_run and not args.no_backup:
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bk = backup(path)
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print(f" ✓ backup: {bk.name}")
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info = process_file(path, dry_run=args.dry_run)
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if info is None:
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print(f" ⨯ skip {path.name}: structural mismatch")
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continue
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print(f"\n === {path.name} ===")
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print(f" Years: {info['years']}")
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print(f" Defaults source: {info['defaults_source']}")
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print(" Neukunden/Monat:")
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for seg in ("starter", "professional", "enterprise"):
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print(f" {seg:13s}: {info['defaults'][seg]}")
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print(f" Cells written: {info['stats']}")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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Reference in New Issue
Block a user