Want to learn what to consider when choosing an error metric for your machine learning model? Read this post I wrote on Cardinal Path’s blog! The main considerations are: Do we want to punish overestimates or underestimates more heavily? Are there segments which have greater costs associated with incorrect predictions? Should we punish larger errors […]
Category Archives: Uncategorized
Reproducibility in R
Want to make sure your R code will run on anyone’s computer, at any time? Read my post on Cardinal Path’s blog to get some tips! Always include your data with your R Code Comment and Document Your code Using knitr/Rmarkdown to Document Your Process Learn more here!
My Favorite R Packages
Me and some colleagues talk about our favourite R packages over on the Cardinal Path blog. Here is one of mine: googlesheets The R package googlesheets is an easy to use way to access and manage Google Sheets through R. I like being able to connect directly to data stored in a Google Sheet because […]
How to Best Use Customer Lifetime Value Analysis Results
Check out my post on how to action the results from a customer lifetime value model from the Cardinal Path blog! No matter what analysis or model you are doing for a business, it is all useless if the model doesn’t get used. Learning what decisions can be influenced by what approach will help drive […]
Sharing Your R Code
I love R and I love sharing! Read my post on Cardinal Path’s blog for tips on how to share your R code with others. I’ve written a couple of posts in the past about the programming language ‘R’, which is used to help predict outcomes and measure the impact of certain actions on your […]
Are You Spending Too Much?
Are you spending too much on online advertising? Find out how to answer this here. In this post I wrote on the Cardinal Path blog, I explain how diminishing returns can be used to optimize your spending budget. As you are probably aware, every dollar spent on advertising does not generate equal return. When looking […]
Data Driven Attribution or Media Mix Modelling?
Read this post I wrote on Cardinal Path’s blog to learn the difference between Attribution and Media Mix Modeling. It can be a bit confusing since they both seem to answer the same or similar questions, but depending on your business, one is probably better suited to your needs. This table from the post summarises […]
Assessing a Data-Driven Attribution Solution
Is your organization thinking about attribution? Who should you choose to help you solve the attribution problem? I wrote this post on Cardinal Path’s blog to help you ask the right questions to get the best attribution vendor for you. Data-driven attribution is used to determine which marketing channels have the largest impact on conversion. […]
Data Leakage: How Data Collection Impacts the Decisions We Make and Vice Versa
I wrote this post on Cardinal Path’s blog. There is a lot to consider when building a model: Data leakage. Data leakage occurs when the data you are using to train a machine learning algorithm happens to include unexpected information related to what you are trying to predict, allowing the model or algorithm to make unrealistically […]
When all you have is a hammer, everything looks like a nail: choosing the right tool for the job
Check out this post that I wrote over on Cardinal Path’s blog that discusses finding the right tool for the job: A few weeks ago, a coworker asked me for some help with a data cleaning task. I consider him to be one of the best Tableau users in our office, and someone I frequent […]