The Pandas shift() function is used to shift the position of a dataframe or series by a specified number of periods. It’s commonly used for the creation of so-called lagged...
When working with ecommerce and marketing data in time series analysis projects, the dates of national holidays, or bank holidays, can make a big difference to customer behaviour so are...
When creating business reports or running queries against a database or web analytics platform in a business setting, you’ll often need to know the start and end dates of the...
In ecommerce and marketing it’s relatively common to use ISO week numbers when reporting data. The ISO week system is a leap week calendar that forms part of the ISO...
In ecommerce, it is often difficult to tell whether your search traffic is performing to expectations. What your boss perceives to be caused by an on-site or marketing-related issue may...
Calculating the time difference between two dates in Pandas can yield useful information to aid your analysis, help you understand the data, and guide a machine learning model on making...
Time series forecasting uses machine learning to predict future values of time series data. In this project we’ll be using the Neural Prophet model to predict future values of Google...
One common conundrum in e-commerce and marketing involves trying to ascertain whether a given change in marketing activity, product price, or site design or content, has had a statistically significant...
If you regularly work with time series data, one common thing you’ll need to do is add and subtract days from a date. If you tried doing this by hand,...
The Neural Prophet model is relatively new and was heavily inspired by Facebook’s earlier Prophet time series forecasting model. NeuralProphet is a neural network based model that uses a PyTorch...
Time series data have a reputation for being somewhat complicated, partly because they’re made up of a number of different components that work together. At the most basic level these...
Purchase latency or customer latency is a measure of the number of days between a customer’s orders and is one of the most powerful features in many propensity and churn...
When working with time series data, such as web analytics data or ecommerce sales, the time series format in your dataset might not be ideal for the analysis you’re performing...
If you regularly work with time series data in Pandas it’s probable that you’ll sometimes need to convert dates or datetimes and extract additional features from them.
Time series forecasting models are notoriously tricky to master, especially in ecommerce, where you have seasonality, the weather, marketing promotions, and holidays to consider. Not to mention pandemics.
In both B2C and B2B ecommerce, special trading periods such as Christmas, Mothers’ Day, and Valentines’ Day can often greatly contribute to sales. Indeed, the introduction of Black Friday sales...
Over the past decade I’ve written more Google Analytics API queries than I can remember. Initially, I favoured PHP for these (and still do for permanent web-based applications utilising GA...
When dealing with temporal or time series data, the dates themselves often yield information that can vastly improve the performance of your model. However, to get the best from these...
