Big Data Promotes a Culture of Data-Informed Decision Making and Adaptive Marketing – Antony Young-Mindshare

March 15, 2013

Big Data is quickly being catapulted to the top of Marketing’s agenda, but it remains a challenge for many companies in preparing for this shift. According to a survey conducted by IBM, less than half of CMO’s feel prepared to cope with this increasing amount of marketing data over the next 5 years, with the data explosion cited as their #1 headache. The problem isn’t obtaining data, it’s figuring out how to turn it into marketing magic. I’m seeing a growing list of exceptional cases of marketer’s shifting their organizations to adopt a higher level of data-informed decision making, often with astonishing results.

It’s not so much big data, but smart data used at scale

Last week, I had dinner with Joe Rospars, founding partner at Blue State Digital, who served as Obama’s Chief Digital Strategist for his 2008 and 2012 campaigns, and asked him about big data. He responded, their approach “wasn’t so much big data, but smart data used at scale.” To win this election, they needed to get very granular in their targeting. By extracting voter files and collecting information via the tens of thousands of polling calls made to homes every night, they were able to identify by household individual voter likelihood, and then determine the communications they needed to deliver.

The Obama campaign expertly targeted via online advertising, email, door to door and phone canvassing very personalized messaging. They cleverly extended this strategy via social media. Nearly a million supporters that ‘liked’ the Obama 2012 page also allowed access to their profile data via Facebook Connect. This enabled Obama’s people to identify their Facebook friends in battleground States, cross tabulate with their own databases, which they then asked supporters to email or even personally call their friends that fit likely Obama voter profiles, to remind them to register or vote early.

Data is the engine for Adaptive Marketing

Data is allowing brands to move quicker and more decisively to gain a market advantage by dynamically informing their messaging and media.

Samsung a big investor in data, worked with insights firm Networked Insights, to use real-time social listening to help them keep a finger on the pulse of consumer sentiment and adjust their communications to capitalize on the web discussion about brands.

Within a couple of hours of Apple’s Tim Cook revealing their iPhone 5, Samsung reading the reaction in social channels, drafted new print, digital, and TV ads. The following week as the iPhone hit the stores, they aired TV ads mocking Apple customers queuing up for the new phone and some of its less flattering features. The commercial was a hit, and received more than 70 million views online.

They also used social listening as a real time guide to evaluate how effective their ads were with consumers by measuring what people are saying about them and what effect they’ve having on competitors’ brands. Stressing the importance of data in informing their marketing, Brian Wallace, the former VP of Marketing at Samsung, (who recently moved to Motorola to a global marketing role) said, “The data guys lead these conversations. Not the creative guys. Not the sale guys. And it’s not just analytics — it’s analysis.” He added, “[data] does not crush the art of advertising. It simply informs it — and ultimately improves it.” Samsung’s shift to a strategy of employing social data at the center was one of the key factors that assisted them to move from the number 4 mobile device manufacturer to pass the mighty Apple.

Creating a more personalized customer experience

I’m seeing a focus on data enabling marketers to create smarter, more engaged customer experiences.

I recently chaired a panel which included Sandra Zoratti, co-author of the book Precision MarketingShe cited Caesar’s Entertainment as a marketer that centralized data to better formulate its approach to marketing. They identified 0.15% of their customers that contributed to 12% of their casino revenues. This led to them employing Good Luck Ambassadors to monitor these customers. If they weren’t having a good night on the tables, they offered complimentary tickets to a show or dinner based on their known preferences to ensure they left their casinos with a positive experience.

Building a fluid organization that can capitalize on the data

Shifting to a fast moving data marketing organization isn’t just about software and strategy. It requires a shift in how the agency and clients teams work.

The Obama campaign quadrupled their data team from the previous election campaign, adding data technologists, behavioral scientists and mathematicians to crunch the data and help interpret them into actionable marketing insights.

According to Rospars, to improve speed of activation, they established a persona playbook on how the brand should speak, to allow them to delegate decision making down.

Personally, I love this shift to data-informed decision making. It is creating more adaptive, more relevant and more commercial marketing programs. We are barely scratching the surface, but it’s clear that going forward, data will be an enabler of more potent marketing.

Thanks to Brand Media Strategy


Big data and the future of health care

January 21, 2013

Big Data and the Future of Healthcare


Humanizing Big Data

January 9, 2013

HUMAN FACE OF BIG DATA
Some App Results

In less than two months, more than 3 million share and compare questions have been answered, in more than 100 countries, through “The Human Face of Big Data” smartphone survey app.

By collating and analyzing these 3 million+ responses we gained some insightful conclusions related to the attitudes and approaches to life from men and women, young and old, all over the world. Here are just a few of the most interesting findings…

In asking the question “What is most important for good health – diet, exercise, environment or genes?” we discovered that Americans are more likely to believe that good health is in their hands, choosing diet and exercise, while Europeans seem to believe their health is predetermined or out of their control, predominantly selecting either genes or environment

In response to the question “What do you do to help cope with stress most?” we learned that as we get older work and prayer tend to replace friends or the arts as our primary means of stress relief, indicating that older generations prefer to bury themselves in work or deal with stress on their own, rather than by seeking entertainment or distraction
When asked “If I could alter the DNA of my unborn child I would improve their: lifespan, intelligence, immunity or appearance” the findings showed that Americans are most concerned about their children’s education and job prospects, while Europeans worry most about their children’s health, perhaps reflecting the current unemployment rates and standards of available healthcare in these two nations.

While these findings give only a brief snapshot of the world around us, the goal of this app was to encourage people to embrace the subject of big data and to consider its potential to help us shape and change our daily lives. Hundreds of striking examples of ways this is already happening are illustrated in the photographs, infographics and essays within the Human Face of Big Data book.

The anonymous data complied from the app will be made available for educators, data scientists, researchers and the general public to access as a valuable research tool, in order to conduct further in-depth sifting and sorting of the results, that may one day be considered an invaluable snapshot of human history.


The future of CX

December 18, 2012

This is a great video from RightNow Technologies.


Three V’s of Big Data with Example:

November 22, 2012

1. Volume:

TB’s and PB’s and ZB’s of data that gets created:

From the webinar “How to Walk The Path from BI to Data Science: An interview with Michael Driscoll, data scientist and CEO of Metamarkets” – A global surge in Data

2. Velocity:

The speed at which information flows.

Example: 50 Million tweets per day!

twitter 50 million tweets per day

(This is back in Nov. of 2010 – the number must have increased!)

3. Variety:

All types of data is now being captured which may be in structured format or not.

Example: Text from PDF’s, Emails, Social network updates, voice calls, web traffic logs, sensor data, click streams, etc

data variety big data

Image courtesy

And this may be followed by other V’s like V for Value.

Conclusion:

In this blog-post, we saw Three V’s of Big Data with Example

Thanks to http://parasdoshi.com/2012/11/22/three-vs-of-big-data-with-example/


Big data: a retailer’s guide to likes, tweets, reviews, customer data, and basically everything else (infographic)

November 20, 2012

When it comes to retailers, big data is perhaps a little too big.

Half of retailers can’t aggregate all their data in one place to make detailed reports and conclusions. 45 percent don’t use available data to personalize marketing communications, and another 42 can’t link data together at the individual customer level.

That is perhaps understandable, because 90 percent of the data that’s ever been created has been created in the last two years, and it’s growing fast.

Read more at http://venturebeat.com/2012/11/19/big-data-a-retailers-guide-to-likes-tweets-reviews-customer-data-and-basically-everything-else-infographic/#DoJMDx85f3svhhPH.99

Thanks to http://www.venturebeat.com


The Formula for Analytics Success: Data Knowledge

November 12, 2012

Companies spend a small fortune continually investing and reinvesting in making their business analysts self-sufficient with thelatest and greatest analytical tools. Most companies have multiple project teams focused on delivering tools to simplify and improve business decision making. There are likely several standard tools deployed to support the various data analysis functions required across the enterprise: canned/batch reports, desktop ad hoc data analysis, and advanced analytics. There’s never a shortage of new and improved tools that guarantee simplified data exploration, quick response time, and greater data visualization options, Projects inevitably include the creation of dozens of prebuilt screens along with a training workshop to ensure that the users understand all of the new whiz bang features associated with the latest analytic tool incarnation.  Unfortunately, the biggest challenge within any project isn’t getting users to master the various analytical functions; it’s ensuring the users understand the underlying data they’re analyzing.

If you take a look at the most prevalent issue with the adoption of a new business analysis tool is the users’ knowledge of the underlying data.  This issue becomes visible with a number of common problems:  the misuse of report data, the misunderstanding of business terminology, and/or the exaggeration of inaccurate data.  Once the credibility or usability of the data comes under scrutiny, the project typically goes into “red alert” and requires immediate attention. If ignored, the business tool quickly becomes shelfware because no one is willing to take a chance on making business decisions based on risky information.

All too often the focus on end user training is tool training, not data training. What typically happens is that an analyst is introduced to the company’s standard analytics tool through a “drink from a fire hose” training workshop.  All of the examples use generic sales or HR data to illustrate the tool’s strengths in folding, spindling, and manipulating the data.  And this is where the problem begins:  the vendor’s workshop data is perfect.  There’s no missing or inaccurate data and all of the data is clearly labeled and defined; classes run smoothly, but it just isn’t reality  Somehow the person with no hands-on data experience is supposed to figure out how to use their own (imperfect) data. It’s like someone taking their first ski lesson on a cleanly groomed beginner hill and then taking them up to the top of an a black diamond (advanced) run with step hills and moguls.  The person works hard but isn’t equipped to deal with the challenges of the real world.  So, they give up on the tool and tell others that the solution isn’t usable.

 

All of the advanced tools and manipulation capabilities don’t do any good if the users don’t understand the data. There are lots of approaches to educating users on data.  Some prefer to take a bottom-up approach (reviewing individual table and column names, meanings, and values) while others want to take a top-down approach (reviewing subject area details, the associated reports, and then getting into the data details).  There are certainly benefits of one approach over the other (depending on your audience); however, it’s important not to lose sight of the ultimate goal: giving the users the fundamental data knowledge they need to make decisions.  The fundamentals that most users need to understand their data include a review of

The above details may seem a bit overwhelming if you consider that most companies have mature reporting environments and multi-terabyte data warehouses.  However, we’re not talking about training someone to be an expert on 1000 data attributes contained within your data warehouse; we’re talking about ensuring someone’s ability to use an initial set of reports or a new tool without requiring 1-on-1 training.  It’s important to realize that the folks with the greatest need for support and data knowledge are the newbies, not the experienced folks.

There are lots of options for imparting data knowledge to business users:  a hands-on data workshop, a set of screen videos showing data usage examples, or a simple set of web pages containing definitions, textual descriptions, and screen shots. Don’t get wrapped up in the complexities of creating the perfect solution – keep it simple.  I worked with a client that deployed their information using a set of pages constructed with PowerPoint that folks could reference in a the company’s intranet. If your users have nothing – don’t’ worry about the perfect solution – give them something to start with that’s easy to use.

Remember that the goal is to build users’ data knowledge that is sufficient to get them to adopt and use the company’s analysis tools.  We’re not attempting to convert everyone into data scientists; we just want them to use the tools without requiring 1-on-1 training to explain every report or data element.

Thanks to http://evanjlevy.wordpress.com/2012/11/12/the-formula-for-analytics-success-data-knowledge/


Should a company’s executives drive data governance and regulation, or its IT department?

October 9, 2012

Data governance is one of those amorphous terms that businesses struggle to define, much less implement. In broad strokes, it involves the implementation of processes and methods that govern how data analysts and others within an organization can handle and process data.

That sort of control—even in the name of regulations and quality—is liable to spark political infighting within even the most sedate organization. Does the need to quickly analyze data outweigh the risks of regulatory fines? Will the implementation of data security interfere with the efficiency of analysis? But with more and more regulations in place, business executives and IT departments have little choice but to wrestle with the issue.

“The stakes are high when it comes to data-intensive projects, and having the right alignment between IT and the business is crucial,” Michele Goetz, an analyst for Forrester, wrote in an Oct. 4 corporate blog posting. “Data governance has been the gold standard to establish the right roles, responsibilities, processes, and procedures to deliver trusted secure data.”

Policies and procedures can weed out bad data and faulty implementations, she added, making governance more crucial than ever. However, most governance is focused on risk avoidance and led by a company’s IT department, with the business side of things contributing relatively little to the discussion. That massive amount of management and process, in turn, “takes time and stifles experimentation and growth.”

Yet companies need data analysis to happen in a speedy enough way to make said data actually useful to strategy; recall how Nucleus Research, in a study released over the summer, suggested that the average half-life of data for tactical decision-makers is 30 minutes or less, while strategically-oriented data tends to go stale after only a few days. As a result, days’ worth of check and balances can rapidly degrade the useful of data.

“Data governance needs to evolve to develop policies that are not just about what you can’t do, but what you can do,” Goetz wrote. “If you really want your data governance program to mature and truly be business led, the greatest pivot will be for IT to give up control of the data and the facilitation of data governance.”

In other words, give business control: “Have the business take over and define the amount of governance and control it wants over its use. Have the business create a framework that aligns trust in data with use.”

Whether or not one agrees with Goetz that business needs more control over data governance, the fact remains that the increasing amount of data handled by organizations—and the increasing pressure to analyze it for insight—can lead to slowdowns and paralysis without a plan and structure. Some organizations are wrestling with this brave new world by hiring chief data officers to handle everything from data stewardship to communicating data schemas. Others are embracing self-service B.I. solutions that help automate and wrangle data without the need for quite so much active effort on employees’ part.

http://slashdot.org/topic/bi/does-business-or-it-drive-data-governance/

 


FLASH REPORT: How Does Big Data Affect You?

September 28, 2012

On this week’s episode of the Flash Report, Jessica tells us how big data is influencing our everyday lives in ways we don’t even realize.

According to an article published by ITWorld, scientists and experts in the fishing industry are beginning to model big data sets about specific species’ breeding habits and migration patterns in order to keep them off the endangered species lists (so we can continue to consume our favorite fish, without over-consuming them).

Retailers are also beginning to utilize big data in unexpected new ways. Harvard Business Review explains how retailers are using big data to research shoppers’ buying patterns to help improve shopping experiences. For example, when a customer goes to buy an item, they’ll know about a sale on a related item.

Businessweek and IBM published some estimates on how “big” big data really is. Studies show that the world creates approximately 2.5 quintillion bytes of new data every day. That’s 1 followed by 18 zeros.

Next, KSL.com reports that NASA now believes ‘warp drive’ may be possible, and links back to a Wired story on examples of science fiction becoming science fact. We’d like to point out that if warp drive does show up any time soon, you can bet it will come with some pretty big data.


Big Data – An Infographic Perspective

September 3, 2012

CSC is one of the pioneers in the rapidly growing field of big data.As most of us already know, ”big data” is changing dramatically right before our eyes – from the amount of data being produced to the way in which it’s structured (or not) and used. One million time as much data is lost each day than is consumed. This trend of big data growth presents enormous challenges, but it also presents incredible business opportunities (Monetization of Data). This big data growth infographic helps you visualize some of the latest trends.


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