pymc-labs/pymc-marketing

• highreadme#1

  Reposition the README's opening sentence to clarify its identity

  原因：

  当前

  Unlock the power of **Marketing Mix Modeling (MMM)**, **Customer Lifetime Value (CLV)** and **Customer Choice Analysis (CSA)** analytics with PyMC-Marketing.

  复制粘贴的修复

  复制

  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.

  待办完成忽略
• mediumabout#2

  Enhance the GitHub repository description

  原因：

  当前

  Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.

  复制粘贴的修复

  复制

  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.

  待办完成忽略
• lowreadme#3

  Add a 'Comparison to Other Libraries' section in the README

  原因：

  复制粘贴的修复

  复制

  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.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How to build a marketing mix model using a probabilistic programming library?

  你：未被推荐
  AI 推荐顺序：

  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 推荐了 5 个替代方案，却始终没点名 pymc-labs/pymc-marketing。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What Python libraries are available for customer lifetime value prediction and marketing analytics?

  你：未被推荐
  AI 推荐顺序：

  1. Lifetimes
  2. Scikit-learn
  3. Pandas
  4. XGBoost
  5. LightGBM
  6. CatBoost
  7. Statsmodels
  8. Plotly
  9. Seaborn
  10. Matplotlib

  AI 推荐了 10 个替代方案，却始终没点名 pymc-labs/pymc-marketing。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of pymc-labs/pymc-marketing?

  pass
  ✓AI 明确点名了 pymc-labs/pymc-marketing

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

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

  pass
  ✓AI 明确点名了 pymc-labs/pymc-marketing

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

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

  pass
  ✓AI 明确点名了 pymc-labs/pymc-marketing

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？