RRepoGEO

REPOGEO REPORT · LITE

pymc-labs/pymc-marketing

Default branch main · commit fa0f060a · scanned 5/11/2026, 11:12:54 AM

GitHub: 1,144 stars · 379 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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
    Reposition the README's opening sentence to clarify its identity

    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
    PyMC-Marketing is a Python library for building advanced Bayesian Marketing Mix Models (MMM), Customer Lifetime Value (CLV), and Customer Choice Analysis (CSA) using the PyMC probabilistic programming framework.
  • mediumabout#2
    Enhance the GitHub repository description

    Why:

    CURRENT
    Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.
    COPY-PASTE FIX
    PyMC-Marketing is a Python library offering a Bayesian marketing toolbox in PyMC, including Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.
  • lowreadme#3
    Add a 'Comparison to Other Libraries' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., '## Comparison to Other Libraries' or '## Why PyMC-Marketing?'. This section should explain how PyMC-Marketing differs from general probabilistic programming libraries (like PyMC itself, Stan, Pyro) by providing pre-built, domain-specific models for marketing, and how it differs from traditional ML libraries (like Scikit-learn, XGBoost) by offering a Bayesian approach with uncertainty quantification for marketing problems.

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
0 / 2
0% of queries surface pymc-labs/pymc-marketing
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pymc-devs/pymc
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. pymc-devs/pymc · recommended 1×
  2. stan-dev/stan · recommended 1×
  3. pyro-ppl/pyro · recommended 1×
  4. pyro-ppl/numpyro · recommended 1×
  5. TuringLang/Turing.jl · recommended 1×
  • CATEGORY QUERY
    How to build a marketing mix model using a probabilistic programming library?
    you: not recommended
    AI recommended (in order):
    1. PyMC (pymc-devs/pymc)
    2. Stan (stan-dev/stan)
    3. Pyro (pyro-ppl/pyro)
    4. NumPyro (pyro-ppl/numpyro)
    5. Turing.jl (TuringLang/Turing.jl)

    AI recommended 5 alternatives but never named pymc-labs/pymc-marketing. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries are available for customer lifetime value prediction and marketing analytics?
    you: not recommended
    AI recommended (in order):
    1. Lifetimes
    2. Scikit-learn
    3. Pandas
    4. XGBoost
    5. LightGBM
    6. CatBoost
    7. Statsmodels
    8. Plotly
    9. Seaborn
    10. Matplotlib

    AI recommended 10 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 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?

  • 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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  • Brand-free category queries5 vs 2 in Lite
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