Analytics Section: Beware of the giraffes in your data

Marketers and analysts are always on the lookout for exciting new insights which can translate into action items and provide strategic advantage, but they often miss them. They can even make the wrong decisions – because they fail to account for the “giraffe effect” in their data.

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Analytics section: Choose the Right Chart Type for your Data thro’ Bhavin Dalal

Charts help you visualize numeric data in a graphical format but the problem is there are just too many types of charts to choose from. You have bar charts, bubble charts, pie charts, line histograms and so on.

If you are finding it hard to pick the right chart type for your type of data, refer to chart chooser diagram, also available as a PDF, designed by Andrew Abela.

Start from the center of the poster and take the route that best matches your data type. Is the data static or does it change over time?

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Analytics Section:Customer Migration thro’ V Bharath

Much of the database analysis of customers deals with acquisition and retention, assuming that a customer is a customer. That may be true with most products and services, but it certainly is not true for all. If you study customer purchase patterns over a period of time, you can see patterns, which can be useful in marketing. The goal is to influence behavior. 

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Analyzing the Analyzers by Ajay Kelkar

The attached book is an interesting description of  analysts. I feel the challenge for us is the blend of these different skills that we need to bring together & the fact that our clients really don’t care “what is under the hood”. This makes both jobs –the analytics one & the client manager’s role a very tough ask.

I loved the grid below that shows the wide variety of skills needed & as long as we don’t look for “god” in our analyst employees but find ways to build complementary team structures.

Analyzing_the_Analyzers

Inside the cave – How analytics, campaigns helped Obama’s election victory by Samit Malkani

I don’t think any campaign gets bigger than the one to win the White House. Here you go on the details that made it happen. Here’s an inside view of how it all happened. Great learning for marketing folks who are involved in data, analytics, digital and campaigns.

Inside the Cave

Analytics Section: Want to predict human behavior? Use these 6 lessons based on data from 10 million households

Anyone who’s ever had a temperamental friend or relative may be skeptical that human behavior is predictable. But upon evaluating a larger dataset, a different picture emerges: by using the right analytical tools and approaches, you can actually predict human behavior with exceptional accuracy.

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Show me the money. Doing business on the web means understanding visits, leads, applications, enrollments, starts and completions

For anyone interested in web analytics Avanish Kaushik’s blog Occam’s Razor is the place to go. He is a master and his posts are always detailed and highly educative. What’s more there’s very active community of readers who comment on every post, and as a bonus, Avinash actively participates in the comment stream by adding and debating with commentators.

This post is about understanding the money funnel using Google Analytics. How businesses and individuals can drill down and make sense of all that’s happening on a site by looking at the data and finding patterns.

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Tools & Technology Section: Tableau, Python and R

With 8.0, Tableau’s expanding from its exploratory, client-server success base to offer extensive web browser-authoring and server deployment capabilities. The new server’s a good start; the challenge now is to enhance supporting meta-data to make the tool competitive as a production-ready, shared visual reporting environment.

For programmers, an important new 8.0 feature is the JavaScript API, providing the capability to integrate Tableau content into custom applications. A personal favorite is the data engine API that allows coders to write Tableau data extracts in C/C++, Java and Python. The extract library for Python installs easily and functions as advertised, permitting data to be reliably pushed to Tableau users. I enjoyed duping it out with some of my data sets.

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Analytics Section: Analytics Is Like Driving

While we’re all standing around and looking at the trunk of the tree, business intelligence (BI) concerns itself with the roots. BI is all about understanding the complexity of business processes and things that go on behind the scenes (underground).

However, digital analytics, says Stéphane, is about customers – all those green leaves, waving around in the air. Digital analytics is about the complexity of the public and their reactions to breezes and diseases and…well, you get the point.

It’s wonderful when an analogy just puts everything into perspective.

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Big Data’s Effects on Direct Marketing

Nowadays, marketers have access to an increasing number of channels to send direct, personalized messages and engage prospects and customers in one-to-one conversations. With each log-in, interaction, click, or message sent, data is being distributed and received. Each day, 354 billion corporate emails, 400 million tweets, and 1 billion Facebook posts are created. This presents marketers with an opportunity to gather this mass of data and use it to their advantage when creating and executing direct marketing campaigns.

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