Jeffrey D. Oldham
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Brand Attitudes and Search Engine Queries
Jeffrey P. Dotson
Elea McDonnell Feit
Yi-Hsin Yeh
Journal of Interactive Marketing, 37 (2017), pp. 105-116 (to appear)
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Search engines record the queries that users submit, including a large number of queries that include brand names. This data holds promise for assessing brand health. However, before adopting brand search volume as a brand metric, marketers should understand how brand search relates to traditional survey-based measures of brand attitudes, which have been shown to be predictive of sales. We investigate the relationship between brand attitudes and search engine queries using a unique micro-level data set collected from a panel of Google users who agreed to allow us to track their individual brand search behavior over eight weeks and link this search history to their responses to a brand attitude survey. Focusing on the smartphone and automotive markets, we find that users who are actively shopping in a category are more likely to search for any brand. Further, as users move from being aware of a brand to intending to purchase a brand, they are increasingly more likely to search for that brand, with the greatest gains as customers go from recognition to familiarity and from familiarity to consideration. Additionally, users that own and use a particular automotive or smartphone brand are much more likely to search for that brand, even when they are not in market suggesting that a substantial volume of brand search in these categories is not related to shopping or product search. We discuss the implications of these findings for assessing brand health from search data.
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RLint: Reformatting R Code to Follow the Google Style Guide
Alex Blocker
Andy Chen
Andy Chu
Tim Hesterberg
Caitlin Sadowski
Tom Zhang
R User Conference (2014)
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RLint (https://code.google.com/p/google-rlint/) both checks and reformats R code to the Google R Style Guide. It warns of violations and optionally produces compliant code. It considers proper spacing, line alignment inside brackets, and other style violations, but like all lint programs does not try to handle all syntax issues.
Code that follows a uniform style eases maintenance, modification, and ensuring correctness, especially when multiple programmers are involved. Thus, RLint is automatically used within Google as part of the peer review process for R code. We encourage CRAN package authors and other R programmers to use this tool. A user can run the open-source Python-based program in a Linux, Unix, Mac or Windows machine via a command line.
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Experiences Scaling Use of Google's Sawzall
DIMACS Workshop on Parallelism: A 2020 Vision, http://dimacs.rutgers.edu/Workshops/Parallel/ (2011)
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Sawzall is a procedural language developed at Google for parallel analysis of very large data sets. Given a log sharded into many separate files, its companion tool named saw runs Sawzall interpreters to perform an analysis.
Hundreds of Googlers have written thousands of saw+Sawzall programs, which form a significant minority of Google's daily data processing. Short programs grew to become longer programs, which were not easily shared nor tested. In other words, scaling naively written Sawzall led to unmaintainable programs.
The simple idea of writing programs functionally, not iteratively, yielded shareable, testable programs. The functions reflect fundamental map reduction concepts: mapping, reducing, and iterating. Each can be easily tested.
This case study demonstrates that developers of parallel processing systems should also simultaneously develop ways for users to decompose code into sharable pieces that reflect fundamental underlying concepts. As importantly, they must develop ways for users to easily write tests of their code.
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