Bayesian Posts
Bayesian EXAWin-Rate Forecaster
Precisely predict sales success by real-time Bayesian updates of subtle signals from every negotiation. With EXAWin, sales evolves from intuition into the ultimate data science.
![BA04-1. [Novel] Probability in Saigon — The Day Data Beat Intuition (Part 1)](/_next/image?url=%2Fstatic%2Fimages%2FBA041-saigon-probability-1.png&w=3840&q=75)
BA04-1. [Novel] Probability in Saigon — The Day Data Beat Intuition (Part 1)
The ultra-high-rise condo pre-sales market in Ho Chi Minh City. A showdown between an intuition-driven ace salesman and a data-driven rookie. This novel format explains how the EXAWin Bayesian engine becomes a tool for victory in the Southeast Asian real estate sales competition. Part 1: The calm before the storm — two salesmen in Saigon.
![BA04-2. [Novel] Probability in Saigon — The Day Data Beat Intuition (Part 2)](/_next/image?url=%2Fstatic%2Fimages%2FBA042-saigon-probability-2.png&w=3840&q=75)
BA04-2. [Novel] Probability in Saigon — The Day Data Beat Intuition (Part 2)
The conclusion of the 480-unit condo pre-sales war in Ho Chi Minh City. President Phan's contract, Tuấn's awakening, and the turnaround led by Park Jun-hyuk's EXAWin. The showdown between intuition and data finally reaches its conclusion.
![[BA03. On-Time Risk: Appendix 1] Anatomy of the EXA Bayesian Engine: Mixture Distributions and Observational Deviation](/_next/image?url=%2Fstatic%2Fimages%2FBA03_1.png&w=3840&q=75)
[BA03. On-Time Risk: Appendix 1] Anatomy of the EXA Bayesian Engine: Mixture Distributions and Observational Deviation
This is the first article in a technical explanation series identifying the operating principles of the EXA engine, which played a major role in the novel-style series [BA03 On-Time Material Inbound: Bayesian MCMC]. Since this series covers Mixture Distributions and MCMC (Markov Chain Monte Carlo) Gibbs Sampling—which are advanced techniques in Bayesian inference—the content may be deep and the calculation process somewhat complex. Therefore, we intend to approach this in a detailed, step-by-step manner to make it as digestible as possible, and it is expected to be a fairly long journey. We recommend reading the original novel first to understand the overall context. Furthermore, as Bayesian theory expands its concepts incrementally, reviewing the episodes and mathematical explanations of BA01 and BA02 beforehand will be much more helpful in grasping this content. The preceding mathematical concepts and logic are being carried forward.
![BA03. [On-Time Material Inbound: Bayesian MCMC] The Real Game in Business is the Fight Against Uncertainty](/_next/image?url=%2Fstatic%2Fimages%2FBA030.png&w=3840&q=75)
BA03. [On-Time Material Inbound: Bayesian MCMC] The Real Game in Business is the Fight Against Uncertainty
BA03. [On-Time Material Inbound: Bayesian MCMC] The Real Game in Business is the Fight Against Uncertainty
![BA02.[Appendix 3] Sales Success Probability Decision System](/_next/image?url=%2Fstatic%2Fimages%2FBA02_imp.png&w=3840&q=75)
BA02.[Appendix 3] Sales Success Probability Decision System
In the previous Parts 1 and 2 of the [BA02. Exa Bayesian Inference: The Invisible Hand of Sales—A 60-Day Gamble] episode, we explored how the Bayesian engine establishes 'prior beliefs' and tracks the trajectory of probabilities through 'signals' and 'silence.' Now, we hold in our hands the pure posterior probability $ P_{raw} $, precisely calculated by the Bayesian parameters α and β. However, it is not over yet. The final decision-making process remains. Even with a 60% probability, the weight of the decision can vary completely depending on whether it was derived from a single meeting or dozens of negotiations.
![BA02.[CRM Bayesian Engine] The Invisible Hand: A 60-Day Gamble](/_next/image?url=%2Fstatic%2Fimages%2FBA022.png&w=3840&q=75)
BA02.[CRM Bayesian Engine] The Invisible Hand: A 60-Day Gamble
BA02.[CRM Bayesian Engine] The Invisible Hand: A 60-Day Gamble
![BA04-5. [Series Part 4/Final] Bayesian A/B Testing — Which Sales Strategy is More Effective?](/_next/image?url=%2Fstatic%2Fimages%2FBA045-ab-testing.png&w=3840&q=75)
BA04-5. [Series Part 4/Final] Bayesian A/B Testing — Which Sales Strategy is More Effective?
Email vs. Phone call, Technical demo vs. Business meeting, Discount offer vs. Value proposition — Comparing the effectiveness of sales strategies in real-time using Bayesian probabilities instead of frequentist p-values. Even automating strategy optimization utilizing Thompson Sampling.
![BA04-4. [Series Part 3] Competitive Analysis through Conditional Probability — Knowing the Enemy Reveals the Win Rate](/_next/image?url=%2Fstatic%2Fimages%2FBA044-competitive-analysis.png&w=3840&q=75)
BA04-4. [Series Part 3] Competitive Analysis through Conditional Probability — Knowing the Enemy Reveals the Win Rate
How does the presence of competitors affect our P(Win)? A methodology for mathematically analyzing competitive landscapes and designing strategic response scenarios using conditional probability, Bayesian networks, and the Competition Impact Factor.
![BA04-3. [Series Part 2] Portfolio Probability Management — Reading the Entire Pipeline with Bayes](/_next/image?url=%2Fstatic%2Fimages%2FBA043-portfolio-management.png&w=3840&q=75)
BA04-3. [Series Part 2] Portfolio Probability Management — Reading the Entire Pipeline with Bayes
Beyond the P(Win) of individual deals, we calculate the expected revenue of the entire sales pipeline using Bayes. Conservative/optimistic forecasts, deal priority matrices, and optimal resource allocation — the mathematics and strategy of Bayesian portfolio management.