Exawin Posts
Mastering Complexity
Explore EXA's Unified Intelligence ecosystem that distills complex business environments into clear conclusions and redefine your enterprise strategy.
![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.
BA024. The Evolution of EXAWin Bayesian Engine: The Day Data Tuned Its Own Parameters
The EXA Bayesian Engine calculated win probabilities, but its precision depended on manually configured initial parameters. When 100 historical deals accumulated, the engine was ready to evolve on its own. Grid Search, MCMC Ensemble Sampling, and Cross-Validation — three mathematical pillars working in concert to find optimal parameters. Told as a story.
![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.