Analyzing Wish E-Commerce Data [Hands-on]

Analyzing Wish E-Commerce Data [Hands-on]

A step-by-step guide. I have many friends in e-commerce — some are seeing greatly reduced sales, while others are seeing more traffic than ever. I wanted to find insights in the data to help anyone with an e-commerce store, so I analyzed this Kaggle dataset on over 1,500 Wish products.

I have many friends in e-commerce — some are seeing greatly reduced sales, while others are seeing more traffic than ever.

I wanted to find insights in the data to help anyone with an e-commerce store, so I analyzed this Kaggle dataset on over 1,500 Wish products.

The Data

During the lockdown, many e-commerce stores saw a boost in traffic, but with the “new normal,” this spike isn’t lasting. Google searches for the e-commerce company _Wish _are way down since May.

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I uploaded the data — which contains product listings, ratings, and sales performance — to Apteo in order to pinpoint what drives sales.

Total Ratings Drive Sales

We can see that the biggest driver of sales (a column called units_sold), by far, is the number of ratings on a product. As LinkedIn reports, having online reviews can boost sales up to 4X. What’s telling is that the number of ratings in total is far more important than the number of good _or _bad ratings.

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data-analysis machine-learning ecommerce data-science artificial-intelligence

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