A large dataset with multiple dimensions and metrics stretched across millions of rows can look tiresome to the untrained human eye but can have significant signals when looked through the right techniques. A careful inspection can reveal patterns in the dataset of creating intra-interacting cohesive groups characterized by various parameters like user behavior, conversion patterns, historical traits, etc. Segmentation has become a prerequisite for any data deep dives and is widely used in every vertical, be it retail, e-commerce, banking, aviation, or astronomy, for invaluable insights about data structure.
Segmentation, as mentioned, involves identifying interesting latent traits in the data. There are multiple techniques of doing segmentation, below is a brief about the three most popular methods:
Using Segmentation In Web Analytics
Here is a quick overview of how the analytics team at Nabler successfully deployed a segmentation methodology (clustering) to achieve a significant success in outcome.
The client was among North America‘s largest retail chain. The occasion was ‘Black Friday’, one of the peak seasons for sales, discounts, rush, and shopping frenzy, be it online or offline. The big retailers plan for this day way in advance (as the financial year begins) as most make around 60% of their annual revenue in these few days itself. Our client was no exception. They gave us a task; quite a succinct one: “What should we promote on our website this ‘Black Friday’ to get the most out of the day?”
Because of the segmented view approach the client had a greater clarity. They were able to target campaigns in much customized fashion, setting personalized goals and revenue targets, based on the various characteristics that defined each segment. With our predicted traffic on website on the upcoming ‘Black Friday’ and revenue targets forecasted with the customized approach the client experienced a phenomenal lift in revenue of 17% compared to last year (marginally overshooting our expectations at 14%). A battle well won, another feather on the cap earned.