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Analysing your e-commerce funnel with R

Optimizing on-site or in-app sales is one of, if not the most, common problems in online retail.

My latest post explores various methods for analyzing e-commerce data with a very cool R package called ‘rga’ and addresses the issue of determining statistical significance across changes in customer’s behavior.

Click here to see the full post over at blog.yhat.com

checkout_conversion_rate_plot

 

Analysing your e-commerce funnel with R

Google Analytics, R & Plot.ly: Conversion Rate Heat Map

I always keep an eye out for new and better ways to present data for my analyses. A few months ago, I heard about a really interesting platform called plot.ly. It’s a platform (currently on the Freemium pricing model) that produces beautiful, interactive data visuzalizations. I got to experimenting with plot.ly and it’s R API and I came up with something cool: a conversion rate heatmap with a beautiful, easily accessible, interactive output.

This heatmap is useful because it shows when your most motivated customers were buying products from your online store. We assume they are the most interested because the sales in this graph are attributed to the Organic Search medium.  Stereotypically, these are the customers who were deliberately searching for a particular product(s) to buy online and decided to buy them from your store.

This data was imported from Google Analytics’ API using R and the R package: RGoogleAnalytics. I then followed the plot.ly R API documentation found here to build out this graph: