) might have unstructured data, like images, audio files from call centers, or PDFs of invoices.
Their work helps others see the most frequent digital paths taken by users, as well as “what’s hot” today. They can group customers by lifetime value, by location, and by time. Or they can name pages and products with simple terms that help others think comprehensively about what the data is saying.
As data visualizers help you think of customers in terms of lifetime value, data scientists help you understand how likely customers are to buy a given product and which digital paths of a customer’s buying journey are the most profitable for you. Which paths are dead ends? What is the most likely next step? Where is the most value?
Machine learning engineers take the classifications and groups, the results and predictive insights of the data scientists, and create models that will predict the next step in a digital path in a more dependable, automated way. Their work can be used by others in the business, taking the predictive power to more places.
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