RRepoGEO

REPOGEO REPORT · LITE

pymc-labs/pymc-marketing

Default branch main · commit 56d6d34b · scanned 6/21/2026, 3:46:45 PM

GitHub: 1,172 stars · 382 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
67 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface pymc-labs/pymc-marketing, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify CLV capabilities in README to explicitly include "prediction" and "churn probability"

    Why:

    CURRENT
    Unlock the power of **Marketing Mix Modeling (MMM)**, **Customer Lifetime Value (CLV)** and **Customer Choice Analysis (CSA)** analytics with PyMC-Marketing.
    COPY-PASTE FIX
    Unlock the power of **Marketing Mix Modeling (MMM)**, **Customer Lifetime Value (CLV)** (including **prediction** and **churn probability**), and **Customer Choice Analysis (CSA)** analytics with PyMC-Marketing.
  • mediumreadme#2
    Add a clear differentiator statement to the README's introduction

    Why:

    CURRENT
    This open-source marketing analytics tool empowers businesses to make smarter, data-driven decisions for maximizing ROI in marketing campaigns.
    COPY-PASTE FIX
    PyMC-Marketing provides **pre-built, opinionated, and robust Bayesian models** specifically for common marketing problems, making advanced probabilistic marketing analytics accessible within the PyMC framework.
  • lowtopics#3
    Add more specific topics related to CLV prediction and churn

    Why:

    CURRENT
    btyd, buy-till-you-die, clv, customer-lifetime-value, data-science, marketing, marketing-mix-modeling, media-mix-modeling, mmm, python
    COPY-PASTE FIX
    btyd, buy-till-you-die, clv, customer-lifetime-value, clv-prediction, customer-churn-prediction, data-science, marketing, marketing-mix-modeling, media-mix-modeling, mmm, python

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
1 / 2
50% of queries surface pymc-labs/pymc-marketing
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
microsoft/LightGBM
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/LightGBM · recommended 2×
  2. stan-dev/stan · recommended 2×
  3. pymc-devs/pymc · recommended 2×
  4. paul-buerkner/brms · recommended 1×
  5. facebookexperimental/Robyn · recommended 1×
  • CATEGORY QUERY
    How can I build a Bayesian marketing mix model to optimize ad spend?
    you: #1
    AI recommended (in order):
    1. pymc-marketing (pymc-labs/pymc-marketing) ← you
    2. LightGBM (microsoft/LightGBM)
    3. Stan (stan-dev/stan)
    4. PyMC (pymc-devs/pymc)
    5. brms (paul-buerkner/brms)
    6. Robyn (facebookexperimental/Robyn)
    7. Lightweight MMM (google/lightweight_mmm)
    Show full AI answer
  • CATEGORY QUERY
    What Python library helps predict customer lifetime value and churn probability?
    you: not recommended
    AI recommended (in order):
    1. lifetimes (CamDavidsonPilon/lifetimes)
    2. scikit-learn (scikit-learn/scikit-learn)
    3. XGBoost (dmlc/xgboost)
    4. LightGBM (microsoft/LightGBM)
    5. CatBoost (catboost/catboost)
    6. PyMC (pymc-devs/pymc)
    7. Stan (stan-dev/stan)

    AI recommended 7 alternatives but never named pymc-labs/pymc-marketing. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of pymc-labs/pymc-marketing?
    pass
    AI did not name pymc-labs/pymc-marketing — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts pymc-labs/pymc-marketing in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pymc-labs/pymc-marketing explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo pymc-labs/pymc-marketing solve, and who is the primary audience?
    pass
    AI named pymc-labs/pymc-marketing explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)
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pymc-labs/pymc-marketing — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite