I assume that you like SQL and want to refine your querying skills to up your querying game. And you have probably heard that indexing is great for query optimization, but you are not sure about what exactly it is, why is it used, and how to use it.
Welcome! You are at the exact place where you should be. I’ll explain it in a very easy-to-understand manner, and I promise you’ll enjoy learning about it.
I recently helped Colgate-Palmolive optimize the product strategy for its online catalog on Amazon. I applied data science methods to forecast the e-commerce sales, predict the profitability of the products at a granular level, and optimize the profit of the firm. And finally, the project achieved buy-in for operationalization from the firm’s eCommerce Insights & Analytics team.
This article is an extension of the experiences and learnings gained through that project. I’ll explore the different avenues where data analytics can be employed in the e-commerce retail industry, particularly by multinational consumer goods companies like Unilever, Nestle, P&G, etc. …
Welcome to another interesting article on SQL. This article aims to refine your querying skills by pointing out some common, yet ignored mistakes. I’ll elaborate on them using hypothetical tables, and also provide fixes for each. So, sit tight and get ready to polish your querying skills.
Steve Jobs, while answering a tough question in 1997 had said,
“You’ve got to start with the customer experience and work backward to the technology. You can’t start with the technology and try to figure out where to sell it.”
I believe A/B testing is based precisely on this idea. Most of the innovative companies have moved on from HiPPO (highest paid person’s opinion) to data-driven decision-making. They are spending a lot on digital experiments to ensure the best customer experience and organizational decision-making.
Talking about Facebook’s investment in the huge testing framework, in an interview, Mark Zuckerberg said,