The Dependability of a Ratio
In the realm of data analysis, a statistical method called Empirical Bayes is making waves, particularly in the e-commerce sector. Last Tuesday, Julia Silge, a renowned figure in the data science field, discussed Empirical Bayes as her topic of the day.
Empirical Bayes is a unique statistical approach that bridges the gap between Bayesian and frequentist ideas. It treats the parameters of individual units as random draws from an unknown distribution, known as the "prior", and learns this prior distribution directly from the data rather than specifying it subjectively. This learning is done by pooling information from many related units, allowing for more stable and accurate estimates than analyzing units separately.
In the context of e-commerce transactions, Empirical Bayes can be a game-changer. For instance, it can be used to estimate true conversion rates for various products or marketing campaigns, providing more reliable estimates, especially for campaigns with low volume. It can also aid in personalized recommendations or targeting, updating the estimated effectiveness or preferences for different customer segments using their transaction data combined with overall customer behaviour patterns.
Moreover, Empirical Bayes can play a crucial role in fraud detection or risk scoring, helping to identify unusual or risky transactions by comparing them to the learned prior. It can also be beneficial in inventory and demand forecasting, allowing for dynamic adjustment of demand forecasts at the product level by borrowing strength from historical sales patterns across related products.
The analysis of Empirical Bayes can be found in 'Studying/Python/statistics/Empirical_Bayes.ipynb'. The dataset used in the analysis is a log of customers and their e-commerce and brick and mortar orders, excluding customers who never bought online.
The method calculates the confidence interval by first calculating alpha 1 and beta 1 for each observation, then using a module method to get a 95% confidence interval. This extra step gives a range of trust, making the analysis more complete.
When deciding whether to invest more time in growing e-commerce for a specific customer, a smaller confidence interval indicates a more reliable estimate. The graphic of the confidence interval is ordered by decreasing quantity of e-comm orders, with the size of the confidence interval getting bigger as the number of e-comm transactions decreases. The more the dots get close to the red line in the graphic, the more reliable the ratio is.
In conclusion, Empirical Bayes methods provide effective ways to improve decision-making in e-commerce by balancing individual transaction data with global patterns learned from the full dataset, reducing uncertainty and improving estimates of parameters such as conversion rates, customer preferences, or risk scores. This combination of Bayesian inference with data-driven priors tailored to the e-commerce data context is a powerful tool for advanced analytics and improving business decisions.
- Empirical Bayes' application beyond e-commerce could extend to medical-conditions and chronic-diseases data analysis, providing more reliable estimates for patient responses to treatments and personalizing treatment plans based on their unique symptoms and history, as it learns from a large pool of similar patients' data.
- In the field of medical-conditions and chronic-diseases research, data-and-cloud-computing technology could be leveraged to store, manage, and analyze vast amounts of patient data using Empirical Bayes, facilitating the discovery of patterns and trends that might otherwise go unnoticed, ultimately leading to better treatment outcomes.
