Earlier I wrote another post about predicting the spend of a single known customer. There is a related problem which is predicting the total spend of all your customers, or a sizeable segment of them.
If you don’t need to predict the spend of an individual customer, but you’re happy to predict it for groups of customers, you can bundle customers up into groups. For example rather than needing to predict the future spend of Customer No. 23745993, you may want to predict the average spend of all customers in Socioeconomic Class A at Store 6342.
Fast Data Science - London
In this case the great advantage is that you would not have so many empty values in your past time series. So your time series may look like this:
This means you can use a time series library such as Prophet, developed by Facebook.
Here’s what Prophet produces when I give it the data points I showed above, and ask it to produce a prediction for the next few days. You can see that it’s picked up the weekly cycle correctly.
This approach would be very useful if you only needed the data for budgeting or stock planning purposes for an individual store and not for individual customers.
However if you had small enough customer segments, you may find that the prediction for a customer’s segment is adequate as a prediction for that customer.
The next step up in complexity is multilevel models, where you use a different level of model for each region or economic group of customers, and combine them into a single group model.
To get the maximum predictive power you can try ways of combining time series methods with a predictive modelling approach, such as taking the results of a time series prediction for a customer’s segment and using it as input to a predictive model.
If you have a prediction problem in retail, or would like to some help with another business problem in data science or AI, I’d love to hear from you. Please contact me via the contact form.
Looking for experts in Natural Language Processing? Post your job openings with us and find your ideal candidate today!
Post a Job
When can lawyers, litigants in person, and expert witnesses use AI in court documents? In the last few years in the UK, the USA, Canada, Ireland and other jurisdictions, cases have been reported where submissions were made to a court where the author of a document used generative AI tools such as ChatGPT to create those documents. This has wasted court time, resulted in submissions being rejected or even resulted in changes to cost awards.
A person has recently returned from a camping trip and has a fever. Should a doctor diagnose flu or Lyme disease? Would this be any different if they had not mentioned their camping trip? Here’s how LLMs differ from human experts.
How can you predict customer churn using machine learning and AI? In an earlier blog post, I introduced the concept of customer churn. Here, I’d like to dive into customer churn prediction in more detail and show how we can easily and simply use AI to predict customer churn.
What we can do for you
