on 24-Apr-2024 (Wed)

A typical bottleneck is caused by the fact that humans can’t deliver software (or hardware, if operating outside the cloud) fast enough. Even if they were capable of hacking code fast enough, they may be busy maintaining existing systems, which is another critically human activity. This observation helps us to realize that although “infrastructure” sounds very technical, we are not building infrastructure for the machines. We are building infrastructure to make humans more productive. This realization has fundamental ramifications to how we should think about and design infrastructure for data scientists— for fellow human beings, instead of for machines. For instance, if we assume that human-time is more expensive than computer-time, which is certainly true for most data scientists, it makes sense to use a highly expressive, productivity-boosting language like Python instead of a low-level language like C++, even if it makes workloads more inefficient to process. We will dig deeper into this question in chapter 5

The model utilizes an auto-encoder to represent features of input parameters (i.e. customer loyalty number, R, F, and M). The proposed model is the first of its kind in the literature and has many opportunities for further improvement. The model can be improved by using more training data. It is interesting to explore deeper structures of the model in auto- encoder and recursion levels. Clumpiness is another variable which can be studied as an additive to R, F, and M (i.e. RFMC) variables.

What would we expect from customers like the first ten individuals 1001–1010, who started out as occasional benefactors, but through an evolving relationship with the firm have developed a more regular transaction behavior? Will they continue this trend; will they eventually turn into the firm’s premium customers?

Project scoping for ML is incredibly challenging. Even for the most seasoned ML veterans, conjecturing how long a project will take, which approach is going to be most successful, and the amount of resources required is a futile and frustrating exercise

The simple behavioral story which sits at the core of BTYD models – while ”alive”, customers make purchases until they drop out

Traditional methods of synthetic data generation use techniques that do not intend to replicate important statistical properties of the original data. Properties such as the distribution, the patterns or the correlation between variables, are often omitted. Moreover, most of the existing tools and approaches require a great deal of user-defined rules and do not make use of advanced techniques like Machine Learning or Deep Learning

We propose and implement a flexible methodological framework that provides marketing managers with highly accurate forecasts of fine granularity both in the short and in the long run. Our method also captures seasonal peaks and customer-level dynamics and allows to differentiate between different customer groups

Applying RNNs directly to sequences of consumer actions yields the same or higher prediction accuracy than vector-based methods like logistic regression.