From Practical Data Science with R, Second Edition by Nina Zumel and John Mount
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Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever-expanding field of data science. You’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
Ultimately it is undeniable that customer data is an incredibly important component for running a successful business, whether that be through lead tracking, market research, purchase trends, or any other number of everyday datasets that businesses utilize. Understanding how best to use these things and make them work for you and your company, however, is just as vital. Moreover, analyzing customer data gives companies the opportunity to improve their marketing along with their products and services, especially if they are branching out and using SMS messaging from companies such as Tatango, to connect with their customers more. For instance, sending out a survey to the most frequent buyers to ask about marketing preferences gives a company valuable insight on how to improve, and customer data analysis can shine a light on which channels work best. It is therefore exciting to think about what the future might hold for customer data and market research.
Correspondingly, if you would like help and support with using customer data as part of your marketing efforts, you can get in touch with Magid: Market Research Minneapolis. Their website is filled with useful tools and resources that can be used to better your business through the use of market research. Above all, using customer data allows companies to build a strategy that speaks directly to customers in a way that they want to be spoken to.
Numerous updates in this brand new edition include: an introduction to the vtreat data preparation tool, a section on model explanation, and additional modeling techniques such as boosting and regularized regression! Learn more in the slide deck below.