Tag Archives: Analytics

IBM Launches Business Email That Integrates Social Media

IBM Verse also includes a built-in personal assistant that can learn from a user’s behavior.

http://recode.net/2014/11/18/ibm-launches-business-email-that-integrates-social-media/

Analytics data shows Apple is delivering far more iPhone 6 units than 6 Plus

New data released by mobile analytics firm MixPanel (via TechCrunch) has revealed a trend in adoption rates of the 4.7-inch iPhone versus the 5.5-inch model. According to the report, the smaller model appears to be outperforming the larger one is deliveries today, and by no small margin. While the larger model has claimed about .

http://9to5mac.com/2014/09/19/iphone-6-versus-6-plus-analytics/

Why Your Analytics are Failing You

Many organizations investing millions in big data, analytics, and hiring quants appear frustrated. They undeniably have more and even
better data. Their analysts and analytics are first-rate, too. But managers still seem to be having the same kinds of business arguments and debates — except with much better data and analytics.

More here

20140409-082102.jpg

Big data troll

“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it” – Duke University professor & TED speaker Dan Ariely

 

Awesome quote …

Image

Story-driven Data Analysis

 

Great analysts tell great stories based on the results of their analyses.  Stories, after all, make results user-friendly, more conducive to decision-making, and more persuasive.

But that is not the only reason to use stories.  Time and time again in our experience, stories have been more than an afterthought; they have actually enabled a more rigorous analysis of data in the first place.  Stories allow the analysts to construct a set of hypotheses and provide a map for investigating the data.

We recently worked with a department store retailer and a team of analysts looking for creative insights into customer loyalty.  Based on our work with a department store expert, we started out with a storyline, a narrative hypothesis, according to which a customer experiences different journeys through the department store over time and rewards the retailer with a certain level of loyalty.

How will these journeys unfold? Does the customer start in cosmetics and then move into clothing?  Does she go from the second floor to the first floor to buy a handbag to match a new outfit?  Does she have shopping days where she takes a lunch break in the restaurant before continuing her shopping?  Do less loyal customers make different journeys from more loyal ones?

In other words, we were interested not just in what customers were buying, but in the mechanics of how they make their purchases and how this may make them loyal.  After the analysis, the true story of a customer’s path to loyalty is in fact revealed.

Where do these stories come from?  In our experience, they can come either from the experience of an expert in the sector or brand, as was the case in the previous example, or from qualitative research using observation or in-depth interviews.

We recently advised a telco client in developing the “jobs to be done” for a range of new products and services.  We interviewed consumers and heard their own stories of how they go about using their mobile devices throughout the day.  The general narrative hypothesis we drew from listening to these stories is that consumers cobble together mobile solutions to suit their lifestyles.

One consumer revealed that he actually owns two SIM cards for the same smartphone and told us in what context he changes from one to the other.   Another customer told us about the parental control and other relevant apps and browsing that she has discovered and collected and which facilitate her lifestyle as a mother.

What we are seeing here is a multi-usage context (characterized by two SIM cards) and a “Mobile Mommy” context, each of which calls for a distinct analysis and possibly different products/services to be developed subsequently.  In other words, we found that the customers’ homemade solutions could be used by brand managers to identify what kind of data to gather and what kind of analysis to perform.

The analyses will in their turn enrich the initial stories and lead to deeper insights.  What is important here is that the storyline, told before the analyses, enables an authentic human element to surface that would be more difficult to extract from the data alone

In order for a story to truly enable analytics, the story development process needs to be rigorous.   We use the framework of Grounded Theory to ensure that the data and overarching storyline inform each other and are coherent with each other.  The idea is for the analyst to navigate back and forth between the data and the developing story to ensure a good balance between the creative narrative and the analytics that reveal the facts and details of the story.

The enabling storyline should not be too restrictive: it needs to support the development of the plot and characters as they emerge from the analysis, but without bias.  Conversely, the storyline can suggest specific questions to be asked of the data for a more in-depth analysis.

In a world that’s flooded with data it becomes harder to use the data; there’s too much of it to make sense of unless you come to the data with an insight or hypothesis to test. Building stories provides a good framework in which to do that.

 

Re-blogged from HBR

 

Judy Bayer is Director Strategic Analytics for Teradata International. Marie Taillard is a professor of marketing and Director of the Creativity Marketing Centre at ESCP Europe Business School in London, UK.

THE RISE OF THE DIY DATA SCIENTIST

A COMPANY CALLED KAGGLE RUNS COMPETITIONS TO BUILD THE BEST PREDICTIVE MODEL FOR EVERYTHING FROM THE PROPERTIES OF A MOLECULE TO THE PRICE OF BULLDOZERS. SURPRISINGLY, THE TYPICAL KAGGLE WINNER IS SELF-TAUGHT, NOT AMERICAN, AND BUILDS THE WINNING ALGORITHM ON A LAPTOP–NOT A UNIVERSITY SUPERCOMPUTER. KAGGLE’S CHIEF SCIENTIST JEREMY HOWARD GIVES US HIS HYPOTHESIS ON WHY THE BEST DATA SCIENTISTS TODAY ARE COMING OUT OF LIVING ROOMS, NOT LABS.

More here

Is Little Data The Next Big Data?

Just because a metric is easy to capture doesn’t mean it’s the right metric to use.

It’s like the old adage about the drunk searching for his keys. One night a policeman sees a drunk scouring the ground around a streetlight so he asks the drunk what he is looking for. The drunk says “I lost my keys,” and the policeman, wanting to be helpful, joins in the search. After a few fruitless minutes combing the area, the policeman asks the drunk “are you sure you dropped them here?” “Not sure,” the drunk says, “I have no idea where I dropped them.” “Then why are we searching under the street light?” asks the policeman. “Because that is where the light is,” the drunk replies.

More here

20130909-080711.jpg