Amazon Kindle Analytics

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That's one billion insights on consumer demand and shopping trends. MerchantWords gives you instant access to a worldwide audience with a database of search terms from buyers in North America, Europe, Australia, and Asia. Our proprietary algorithm uses artificial intelligence to comb through our extensive database, calculate the ranking of every single search phrase, and produce an estimated search volume, so you don't have to worry about it.

Collecting billions of keywords isn't easy, but the real work comes in understanding what keywords to use and when to use them. Need some extra help?

Business Analytics

Our fully managed Amazon SEO service, Listing Advisor saves you hours of effort, helps you attract more of the right customers to your products, and creates an Amazon store that truly represents your brand online. Our Amazon advertising management service uses the most advanced keyword identification software available to align campaign bid automation with keyword discovery, so you can surpass your Amazon business goals.

Find out more about PPCx. Selling online is hard enough, so let us be your optimization guides. Retrieved December 18, , from http: Last modified January 21, Accessed December 18, The Nieman Journalism Lab is a collaborative attempt to figure out how quality journalism can survive and thrive in the Internet age. By Joshua Benton jbenton Jan. Show tags Hide tags.

Amazon gives publishers easier control over DRM in Kindle ebooks

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The latest from Nieman Lab. Journalism becomes the escape. We should listen to the kids especially on Instagram.

What analytics tools can Kindle publishers use? - Quora

The year of the lawsuit. This wealth of available information leads publishers to rely on big data analytics for success in publishing.

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  6. As mentioned earlier, publishers now have access to an arsenal of tools that were previously unavailable. Big data analytics enables them to collect, organize, and index data from a range of sources and identify opportunities for more efficient operation. Big data analytics makes it possible for publishers to profile their readers based on "revealed preferences," thereby using data on actual measured behavior, as opposed to the past practice of relying solely on "stated preference" information garnered from surveys and third-party data.

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    Moreover, advanced customer segmentation enables publishers to use further statistical and predictive modeling and to utilize various metrics such as:. These segments can be continuously tailored and updated using real-time data about consumer behavior.

    When leveraged correctly, the ROI from this data is extremely high for both publishers and advertisers. In fact, major eBook platforms collect much more than sales statistics.

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    This is where reader analytics tools come into play, allowing publishers to go deep into details and utilize information about consumer reading habits. Today's technologies allow publishers to gather data about how long it took a consumer to read a book, the points at which they stopped reading, how many pages they read, and the specific passages that a customer highlighted or bookmarked. Imagine being able to accurately forecast what your customers will desire in the future, know about future market trends and reduce business risks in advance.

    Well, thanks to predictive analytics, this is now feasible for publishers. Used to identify meaningful patterns that exist in big data to intelligently predict the direction that consumer preferences will go, these analytics tools are game-changers in the publishing industry.