Mobile Analytics

-


Most of the things done on mobile analytics are geared toward user behavior. Companies invest heavily on this aspect of analytics. Mobile analytics is the practice of collecting user behavior data, determining intent from those metrics and taking action to drive retention, engagement, and conversion. The field includes the mobile web, but tends to focus on analytics for native iOS and Android applications.

Analysis that used to happen in Excel and SQL has largely been replaced by a handful of tools that make adhering to analytics best practices significantly easier. Consumer and business applications tend to face the same set of challenges in their mobile marketing and retention, so this guide is designed to address both.

To actually start collecting data about your users, gathering insights, and executing on them, you need the right tools. Here are five that we recommend for any high-performance mobile analytics toolchain.

Segment - for tracking events and moving data

Redshift - for data warehousing

Google Tag Manager - for tag management & Google Analytics Firebase

Apptimize - for A/B testing and feature flags

Amplitude - product analytics for web and mobile products, including cross-platform tracking

Or use your native data analytics library or tool if you know what you are doing.

Comments

Post a Comment

Popular posts from this blog

When To Use Hadoop vs In-Memory vs MPP

-
When presented with more options, choosing among big data tools can seem confusing.  This blog explains the benefits and uses of the various option paths available to help guide choices for your next big data project. Spark , Storm , or  Kafka :  It is an in-memory data grid that provides real-time data access to applications that are critical to the revenue stream of the business. MangoDB :  It’s massively parallel processing style of data management makes it an excellent choice for analytics. Hadoop :    is your research and development arm. As the landing spot for all data, and powered by a powerful SQL query engine, you can explore all data to identify new insights and opportunities you can later operationalize with MPP or in-memory. Questions you need to consider when choosing: When do I need it? Now? Later? What do I want to do with it? Singular event processing? (includes some analytics), Transactions? Exploratory analytics? ...
Read more

Book Review: The Catalyst: How to Change Anyone's Mind

-
This week, I was reading a  book  by Jonah Burger (The Catalyst: How to Change Anyone's Mind) which is one of his great collections of social influence topics. In the book, he mentions that everyone has something they want to change. Employees want to change their bosses' minds, and leaders want to transform organizations. Salespeople want to win new clients, and startups want to revolutionize industries. Parents want to change their children's behavior, and political canvassers want to sway voters. But change is hard. We pressure and coax and cajole, and often nothing moves. Could there be a better way? Whether you are trying to convince a client, change an organization, disrupt a whole industry or just get someone to adopt a puppy, the same rules apply: Reduce reactance: Allow an agency, make them feel they made the decision. Ease endowment:  Bring the cost of inaction to the service, if you don't upgrade, we won't support Shrink distance:  Uber to rid...
Read more

Math for Data Science

-
Here's an exhaustive list of learning resources (in no particular order): ==================================== 1) Linear Algebra: (used in machine learning (& deep learning) to understand how algorithms work under the hood. Basically, it's all about vector/matrix/tensor operations, no black magic is involved!) - Khan Academy Linear Algebra series (beginner friendly) (https://www.khanacademy.org/math/linear-algebra) - Coding the Matrix course (and book) (http://codingthematrix.com/, http://academictorrents.com/details/54cd86f3038dfd446b037891406ba4e0b1200d5a) - 3Blue1Brown Linear Algebra series (https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) - fastai Linear Algebra for coders course, highly related to modern ML workflow! (https://github.com/fastai/numerical-linear-algebra/blob/master/README.md) - First course in Coursera Mathematics for Machine Learning specialization (https://www.coursera.org/specializations/mathematics-machine-lear...
Read more