One of the first things I ask people on the subject of data is �What do you want it to look like?�
I don't mean for the question to be complex, but I generally get an odd look and response in return. �What do you mean how do I want it to look? It�s data, it has to be accurate and structured.�
So why is it when we talk about data we picture streams of numbers, columns and rows of calculations filtering through servers or built out over multiple spreadsheets? Not all business users have the time or skills to interpret large enterprise or spreadsheet data sets.
Why is it we can't be asked the question, �What do you want it to look like?� and answer with a personal perspective? I tend to believe it's because we don't think of data as having a personality. In fact, data has quite a bit of personality.
While often quite shy and reclusive, data will talk your ear off with just a little nudge. It can also be kind of a gossip revealing interesting insight into customer behavior or an organization�s financial information. And it�s those individual users running the data analysis who can easily unleash that personality� for the good of the organization.
That�s why organizations are realizing the value and achievements that come about as a result of having a complete analytic solution built not only for the enterprise, but also for the individual user in mind.
The notion of personal analytics is about bringing agile analysis capabilities to people in an easy-to-use manner without having to rely on IT. Business users can now take advantage of solutions that bring exceptional capabilities to their desktop in terms of personalizing how they display local or enterprise data and how they solve individual or workgroup challenges � all on their terms.
Analytics is definitely becoming a more personal experience. Being able to explore data and format it in a presentation layer of the user�s choice, add built-in calculations, apply scenario modeling and do write-back on the fly, or creating traffic lights that represent key metrics that are important to the individual are all a means to answering my question, "How do you want it to look?"
Take the distribution industry, for example. What if you had the freedom to model out different scenarios based on the price of diesel fuel? An individual user can now identify the key drivers of the business, like fuel cost, then test different assumptions and identify best case, worst case and probable outcomes.
One can only imagine how this could affect you personally, with regard to your line of business in a distribution center. But what if it also affected the manufacturing floor in terms of energy prices for production costs? Would the same scenario be important to other areas of the enterprise?
In a way, building the right analytics competency is like building a business from the ground up. You need to create a growth path that combines personal analytics with enterprise scope and IT values. By providing the necessary foundation of analytics along with limited barriers of usage, you can turn your business users out to the world to explore, discover and grow as analytics professionals with a personalized capability that brings meaning and life to the data.
Knowing that these business users can turn data exploration into action by aligning their discoveries with the enterprise challenges the silos of information that personal analytics has typically produced.
Business users have traditionally created and held onto spreadsheets or data files that only they maintain and which are not reflected across workgroups for greater use. Today's organizations demand a bridge between what line of business users want and what IT requires to run a smooth enterprise environment.
Personal analytics creates that bridge.
So the next time someone asks you, "What do you want your data to look like?" tell them you want it to look interesting and attractive, prescriptive and distinctive, honest and meaningful, and actionable.
But above all else, tell them you want it to have personality!
� "My business is changing on a weekly and sometimes daily basis, and in order to stay competitive I need quick access to the data without IT getting in my way."
Are these comments common inside of your organization?
It�s an interesting battle of wits: Business users needing that fast agility to get at information, and IT needing to ensure governance and control.
IT is often painted as the bad guys because they create roadblocks and are unable to deliver what the business wants � quickly and consistently.
Business is viewed like spoiled brats who have no patience or vision and ultimately rebel.
It�s a dysfunctional relationship that thrives only because these �factions� are more similar than they realize. And, they need each other. It should be very symbiotic�if only they realized.
They are both working towards the same goal: driving the business forward.
But, in order to feel like they are accomplishing their goals, they need freedom from each other. Some might say they need an �open relationship.�
IT doesn�t want to be strapped to a barrage of mundane requests. Business doesn�t want to be constrained to the complicated systems and processes IT has set up for them.
Ultimately, business wants to live in a world where they can easily access the information they need (from any source), manipulate the data without having to be a spreadsheet programmer, and share it with others.
IT wants to be able to leverage the analysis the business user has been working on and still maintain the governance and control to ensure consistent information and use of that information across the organization.
Depending on which side of the aisle you sit, there is an answer and an easy way both can be successful. In fact, both sides can have their cake and eat it too.
We invite you to view the first chapter of our Business vs. IT story in the video below.
In the simplest of definitions, analytics is all about maximizing probability.
In other words, how do you use the information around you to gain a better advantage?
For marketers, business analytics has become an easy way to measure and prove success, but also to support the decisions that drive campaigns, help anticipate customer actions and even guide the selection of messaging and content.
Yes, a scientific approach has become an absolute necessity for modern marketing.
Lest not scoff at the idea of cold, clinical data driving marketing decisions. Heck, it�s been proven that spending $1 on business analytics technology will yield almost $11 in return.
Using analytics to drive better customer experience unshackles the organization from ignorance and maximizes the probabilities for increased customer loyalty, better up/cross-sell and sales conversion.
These organizations focus their analytics capability to gain insight on cost reduction and not at consumer personalization.
Most marketing efforts focus on segmentation efficiency, such as increasing the conversion of a selected group of customers by reduction and removal of messages (for instance, avoiding delivery of identical catalogs to multiple household members), thus lowering the cost of communication.
These firms can increase customer retention by up to 9 percent, capture 2 percent more wallet share and convert an extra 3 percent of inbound contacts into a cross-sell event.
Stage Two � Sharing the Goods
To keep pace with the mobile generation, organizations within this second stage must have a clear customer analytics strategy that enables information sharing across any digital device and channel.
In fact, research shows that tri-channel buyers spent an average of two and a half times more than single-channel buyers.
The most sophisticated marketing organizations in this stage apply analytics for marketing event optimization, an approach that leverages analytics as a �horizontal control tower� to optimize the organization�s various direct marketing events over a given time period over multiple channels.
This better aligns marketing with customers� needs � and varying personas � related to those devices/channels.
Stage Three � From Reaction to Action
This stage focused on information responsiveness.
These organizations are leveraging �raw� data as it streams customers� social commentary and changing moods.
To avoid a veritable data deluge, these organizations focus on identifying the questions that � if answered � will impact their business the most.
This acts as a filter on data collection and helps an organization avoid the task of collecting all available information and then deciding what to do with it after the interminable wait to standardize and analyze it.
Companies able to perform real-time external data analysis combined with rules-based actions have experienced average conversion rates of 16.9 to 38.2 percent.
Stage Four � Next Best Action
This stage focuses on executing a strategy that enables information on demand.
This approach combines all the skills developed in earlier stages with in-depth segmentation approaches and leading-edge work in multichannel customer monitoring and real-time action recommendation (read: Decision Management).
Using predictive analytics (combined with business rules), organizations are able to engage with the customer throughout the buying cycle � from the point of needs identification through the exploration and discovery phase to the purchasing cycle.
Those companies able to apply real-time predictive analytics while executing a multichannel next-best action strategy had an average converted response rate of 24.1 to 64.3 percent.
� Understand the different stages and get a better handle of your organization�s analytics maturity by downloading the full "Customer Analytics Pays Off" white paper.
� Also, read the "Five Steps to Improving Business Performance through Customer Intimacy� white paper.
�Registerfor the �Customer Analytics Pays Off� webcast (Feb. 15 at 1:00 pm ET).
It's been 25 years since the iconic 1980s movie Top Gun hit the big screen, but the message from the movie is still true today � �We have the need, the need for speed.�
If you�ve never seen the movie, it�s about Lt. Pete "Maverick" Mitchell, played by Tom Cruise, and his adventures to overcome shortcomings as a fighter pilot at the Top GunFighter Tactics Instructor program.
Maverick was dangerous, he took chances, relied on his gut, made poor decisions, and �his ego wrote checks his body couldn't cash.� (Sound like anyone in your organization?)
And most importantly, he didn't buy into the classic fighter pilot methodology, the OODA (Observe, Orient, Decide, Act) Loop, developed by U.S. Air Force Col. John Boyd, and taught at the real-life Top Gun.
Boyd's philosophy was simple: Those who could quickly process this loop and react to real-time events better and faster than their adversaries could then anticipate their adversaries� thought processes and decision-making to gain an upper hand.
It's actually the same strategy that is being applied today from commercial and government organizations with IBM SPSS Decision Management technology.
The Need for Speed
Decision Management � through the combination of predictive analytics, business rules and optimization � enables organizations to automatically deliver high-value, high-volume decisions at the point of customer impact. Watch a demo here.
Essentially, it gives organizations the ability toensure optimal outcomes by injecting predictive analytics directly into the business process, such as cross-sell or up-sell marketing campaigns, reducing customer churn or minimizing the impact of fraud.
Without the combination of analytics + rules + optimization to improve a business process, an organization can effortlessly increase the velocity of bad decisions. To paraphrase from the movie, �I�ll hit the brakes and the competition can fly right by.�
(Watch the short video below of James discussing Decision Management and his new book.)
For example, if a high-value customer dials into the call center of a retail bank to complain about a product or service, IBM SPSS Decision Management may predict, based on the customer's past behaviors and interactions, that this individual is likely to churn.
The information about the current complaint, combined with the customer's history, can then be used to create a customized retention offer on the spot to prevent churn.
The bank has easily removed any blind spots that had kept them from making the right decisions, every time, with its customers.
And in an indirect way, it has turned the call center into a profit center, empowering employees to become an extension of the sales team rather than just �complaint takers.�
Never Leave Your Wingman
By the end of the movie, Maverick had finally realized that by trusting not only himself, as well as the philosophy of the OODA Loop, he could be a successful fighter pilot.
In other words, Decision Management becomes any organization�s ultimate wingman, giving the confidence to make the right decisions, at the right time, with amazing speed and agility.
Do you have the need for speed?
James Taylor, CEO and principal analyst at Decision Management Solutions, talked with us at the IBM Information on Demand conference in Las Vegas about how Decision Management works, why it's so popular, how customers are using it and best practices to get started with this technology.
You know that feeling you get when you surprisingly find money in a pocket of your clothes?
There�s nothing better. It's free money.
And according to Nucleus Research, a provider of investigative IT research and advisory services, that's exactly what business analytics is for organizations.
In a new report from Nucleus, they found that "Analytics pays back $10.66 for every dollar spent."
Let's put that another way. Let's say you spent $1,000; the return is $10,000. Spending $10,000? That's $100,000 in extra revenue. And so on... (I rounded down for easier math.)
This number was calculated from reviewing all of the Nucleus Research case studies that have been produced and examining the implementations of analytics applications, such as business intelligence (BI), performance management, and predictive analytics.
In fact, the report states that "with such high returns to be earned on the deployment of analytics, management teams should consider these technologies to be one of the most attractive investment opportunities available to the CFO."
In fact, it would bring a smile to any C-suite executive.
In speaking to David O�Connell, the author of the report, he says that it's a matter of black and white when it comes to those who have incorporated analytics into their business.
"We have found that if we lined up 3-4 firms in the same industry and vertical side by side, those using analytics to guide their decisions would win. Analytics provides such a competitive edge and improvement to the bottom line that we could almost start handing out pink slips to those firms not adopting."
The Cincinnati Zoo, an IBM business analytics customer that participated in a ROI case study (download here), was facing tough operating factors with admissions and donations going down.
"They needed to find ways in which they could change their business model that could make them more efficient and profitable," said O'Connell.
For example, the zoo used analytics to learn moreabout when visitors were most likely to buy ice cream and made smallchanges to the operating hours of the ice cream kiosks, leading to anincrease in food revenues by 20 percent.
For organizations in any industry, O'Connell believes that it only takes a few insights into data with lots of leverage that turns into serious ROI.
That's the power analytics bring to organizations � whether it's better understanding the cost for a customer segment, realizing if a product has high or low margin or determining thatphases of the moon were a big indicator when crime would occur.
It's very much like the butterfly effect where small, unrelated happenings can have major effects on results in another area.
As Nucleus proves, deploying analytics creates those few shifts that produce revenues or lower costs.
So why aren't more organizations taking advantage of this technology?
The report talks about skepticism to technologies like analytics, but O'Connell takes it further.
"There is a complete lack of understanding about how much can be learned from analytics,� said O�Connell. �Senior managers � the CXOs � don't realize how blind their decision makers are flying right now. Organizations are relying on faulty reporting, organizational folklore and gut feel."
To be successful, organizations need to communicate and understand where visibility pain points exist.
O�Connell believes that building a business case on cost reductions and revenue increases is the way to go.
�When you use analytics, you become aware of so much granular information. Organizations suddenly realize how much they didn�t know.�
Just like that $10.66 hidden inside your jeans pocket.
For more information:
� Watcha video of Cincinnati Zoo discussing how it increased revenues by half a million dollars in less than one year.
We've all turned into spooked ostriches with our heads stuck in the ground.
As Matthew Broderick eloquently re-stated in a Super Bowl commercial reprising his famous Ferris Bueller role, "Life moves pretty fast. If you don't stop and look around once in awhile you could miss it."
In the world of social media, it seems everyone is buried in their mobile devices these days reporting on the minutiaof their lives. In fact, it was reported that tweeting records were set during Super Bowl XLVI with 13.7 million total tweets sent during the game and 12,233 tweets per second by the end of the game.
I wonder if anyone really watched the game?
Unknowingly, Twitter has turned us into play-by-play announcers, bad stand-up comedians, "Negative Nancy's,� and critics. We share everything. Is it really necessary to tell everyone what you had for breakfast, what you liked most (or for Boston fans, least) about Tom Brady's performance, the coffee shop you just checked into on Foursquare, your opinion of that Matthew Broderick commercial, or what movies and actors you predict will be Academy Award winners?
If Twitter is a never-ending barrage of babble and nonsense, does it really matter?
You�re damn right it does.
Consumers have become a force de nature in the Twitterverse. Their opinions are unfiltered and unadulterated, yet unfortunately, still quite underrated when it comes to using the data to enhance customer experience. As MTV�s �Real World� once promoted, �It�s time to stop being polite, and start getting real.�
Twitter is raw, real and in your face. Businesses have it easy these days. No longer do they have to go through the formal process of focus groups and lengthy analysis. Want to know what someone is thinking, log onto Twitter.
No dodging the mighty consumer these days.They have become increasingly influential, especially as their opinions travel faster and to a wider group of consumers.
Accountability and honesty reign supreme. If consumers don�t like an organization�s strategic business decision (e.g. Susan G. Komen), new product (Netflix/Qwikster), or advertisement (Groupon/Tibet), there�s no dodging the verbal arrows.
The organizations, however, that decide to take action and analyze the millions and millions and millions of data points created in the socialsphere will own the competitive edge and be able to respond that much quicker. It�s just a matter of separating the noise from what really matters, the consumer�s thoughts, opinions, sentiment and behaviors.
Enter social analytics, the latest in noise-cancelling devices that deliver insights into what people are thinking, why they are thinking it, and most importantly, what organizations can do about it. By eliminating the minutia, social analytics helps businesses understand positive and negative sentiment,pinpoint top influencers, measure the volume of commentary and identify the geographic origin of comments across multiple channels.
Getting back to the Super Bowl�think about the value buried inside of those 13 million tweets � for advertisers, for psychologists, for the city of Indianapolis, for the NFL, etc.
And as the Twitter feed flies off the charts with major sporting events, one can only predict the same activity for the upcoming Academy Awards, especially with the commercials, the fashion faux pas, the glitz and glamour, the acceptance speeches and most importantly, the winners and losers.
Speaking of which, IBM, The Los Angeles Times and the University of Southern California Annenberg Innovation Lab have created the Oscars Senti-Meterto establish a model for measuring the volume and tone of worldwide Twitter sentiment to better understand moviegoers' opinions and customer trends.
So yeah, I guess Twitter matters. It might be noisy, but it�s chocked full of yummy goodness.
If businesses don�t check into Twitter and look around once in awhile, there�s a lot they could miss (and a lot of customers they could lose).
�You're traveling through another dimension, a dimension not only of sight and sound but of data; a journey into a wondrous land whose boundaries are limitless with business insight. That's the signpost up ahead � your next stop, theAnalytics Zone.� (Apologies to Rod Serling.)
IBMtoday unveiled �Analytics Quotient� (AQ), a measure of an organization�s readiness, ability and capacity to apply analytics tore-orientthebusiness, make better decisionsanddeliver better outcomes.
Simply put,AQ helps organizations of all sizes better assess how well they are using analytics. And, more importantly, how organizations can begin using analytics and take their first step on an analytic journey, or progress to a higher level of analytic proficiency.
For example, is your organization applying history and context from the past with the ability to make insightful forecasts, anticipate likely outcomes, and automate decisions? If so, you might be receive a high AQ score.
Just as with IQ (Intelligence Quotient) or EQ (Emotional Quotient), AQ is the hip, new way to determine just how smart an organization is at leveraging its data assets to make better decisions.
Business in the �New Normal�
Today�s organizations are dealing with greater uncertainty and risk, increased pressure from shareholders, new legislation, exploding data volumes and a proliferation of social networks and mobile devices unlike anything we�ve seen before. This is the new reality � the �new normal.�
Individualbusiness leaders are feeling immense pressure to outperform their peers and contribute significantly to their team�s and organization�s success. However, intuition and gut feel-fueled decision making informed mostly by personal experience is no longer sufficient.
Decision makers are demanding a broad range of business analytics capabilities to gain actionable insight that can help them better understand how their business is doing, why it is on or off track, and what they should be doing about it.
A recentstudyconducted by MIT Sloan Management and IBM�s Institute for Business Value indicates organizations that use analytics in the most mature ways are three times more likely to outperform their competitors than those that are just beginning to adopt analytics.
Similarly, the recentCFO studyfrom IBM shows that objective financial data validates that decision making supported by business insight contributes to enterprise outperformance, such as two times greater EBITDA growth, 36 percent greater revenue CAGR and 15 percent greater return on invested capital.
The Analytic Journey
The integration of business analytics within and across the enterprise is really a journey that may begin in one department in one business function, but with quick and tangible results adoption continues deeper and broader across the organization. Organizations on this analytic journey gain greater competitive advantage and higher and higher returns.
Therefore, the AQ measure was designed to show how far an organization has come to fully embrace business analytics. The more analytics is infused into the business, the more a business will outperform others, and the higher the AQ will climb � let alone revenues.
Hence, AQ was created to create a clearer path to value.
Every organization, team or individual is at their own point leveraging analytics to outperform. Some are masters, while others are just beginning. The maturity model illustrates the four fundamental steps to improve AQ. They include:
�Novice: Organizations have spreadsheets, but don�t trust them. They�re more reactive than proactive. They�re disconnected and want to do better.
�Builder: Organizations see current results and a little of what�s driving them. They�re sharing results with other teams in your department and they�re ready for more.
�Leader: The VP sets the strategy. Marketing and sales share metrics and plans. They�re predicting the future as well as reviewing the past.
�Master: Top-down goals meet bottom-up tactics. Insights flow freely across divisions and departments. Resources are allocated, risk minimized and outcomes maximized with equal ease and speed.
Are you ready for analytic freedom? Future success will depend on the ability to turn an ever-increasing deluge of data into better decision making. With the amount of data doubling every year, achieving a high AQ is imperative to outpacing the competition.
So, we invite you to take a trip to the Analytics Zone. No longer will you have murky insight into the business that undoubtedly leads you down the wrong path into the Twilight Zone.
Measure your AQ � Take the Quiz
Come to www.ibm.com/analytics/aq to take the fast, easy quiz. Answer a few questions to understand your AQ and see where you are on the journey to becoming analytics driven.
You�ll also be able to share ideas and connect with others who use their high AQ to outperform.
IBM has beenpreachingthat customer intimacy is the new intellectual property. The more insight an organization has on its customers, the better opportunities it will have to sell them more stuff, retain the best ones, mitigate risk or even identify possible cases of fraudulent activity.
Essentially, it�s a better way to create an ongoing and meaningful dialogue with customers. This is especially important as the power has shifted from the organization to the customer.
And, those organizations that are serious about delivering a better overall customer experience should seriously consider hiring someone who speaks the customer�s language, is concerned about their well-being, can drive profitable interactions through all channels, and can accomplish that elusive task of bringing all customer focused functional departments together with a common goal. This new strategic asset is otherwise known as the Chief Customer Officer (CCO).
To help in your quest to deliver a better customer experience, we have created a job description for a CCO to streamline your efforts.
If you see anything that you might change or add to the description, please let us know. We would love your feedback.
The ACME Corporationis seekinga highly experienced customer experience professional to fill a newly created Chief Customer Officer (CCO) position. This executive will provide a comprehensive and authoritative view of the customer and create corporate and customer strategy at the highest levels of the company to maximize customer acquisition, retention, and profitability.
Key responsibilities include, but are not limited to:
� Provide managerial oversight while implementing strategic focus and tactical direction to the sales, marketing, strategic alliances, market development, and customer support business units
� Drive profitable customer behavior through the creation of initiatives such as profitability segmentation, customer retention, loyalty, satisfaction, and improving the overall customer experience
� Design, orchestrate and improve customer experiences by ensuring consistency across all channels of customer interaction
� Identify customer pain points, define and monitor service standards, enable easy customer navigation across the organization and create new ways to enrich the buying experience
� Foster direct and meaningful relationships with customers, acting as a mediator between the customer and the corporation, especially when service shortfalls or special needs have surfaced
� Develop an overarching customer-centric corporate strategy with the ability to iteratively execute on smaller, manageable goals
� Serve as a liaison between the IT organization and the business units to ensure that systems and business processes are aligned on the customer experience
� Institute a formal process for capturing, analyzing and acting on customer feedback, including leveraging social media channels to better respond to customer needs/requests
� Create cross-functional customer service processes, resulting in a seamless hand-off from marketing to sales to service and support
� Educate, instill, and motivate a broader cultural desire across the corporation to focus on the customer, with the objective of consistently improving customer experience metrics
� Balances the C-suite and Board of Directors with their traditional focus on cost cutting and revenue-growth
� Cultivate meaningful mutually beneficial relationships with corporate partners / associations to deliver enhanced benefits to ACME customers, thus extending ACME�s own value proposition
� Ensure the company's PR and marketing message reflects the company's service delivery capabilities
Ideal criteria for selection includes, but is not limited to:
� Possess strong operational, marketing and financial background as well as political savvy to bear on critical customer-related issues
� Prior executive or VP level experience leading highly successful marketing, sales and customer service business units
� Proven ability to break down corporate and departmental barriers in order to pave new paths, which may often be in direct conflict with existing corporate culture
� Previous boardroom / shareholder experience with the ability to demonstrate tangible value to all stakeholders
� Highly skilled in building cohesive cross-functional teams
� Highly skilled in a variety of enterprise software tools including CRM, ERP, and POS systems, as well asBusiness Analyticssoftware (business intelligence and predictive analytics)
If you�re not aware of Moneyball, it�s a behind-the-scenes story of the Oakland Athletics and how they changed the game and leveled the playing field through the innovative use of analytics that allowed the team with the lowest budget to consistently compete against the big market, deep pocket teams.
Moneyballtook the veil off a much-treasured secret and demonstrated that new ideas could produce positive results in the traditional world of Major League Baseball.
The book was also recently made into a major motion picture � in theaters now � starring Brad Pitt as Billy Beane. It is receiving rave reviews.
The Information On Demand social media team had the opportunity to speak with Michael Lewis about his book, the movie and the parallels between baseball and business.
Who Is Going To Read A Book About Analytics?
In the late 1990s, Lewis was living in Berkeley, Calif., and started to pay attention to the local baseball team, the Oakland Athletics.
He had some awareness of the payroll discrepancies in baseball and thought it was strange how many games the Oakland A�s were winning given how little money they had in relation to the competition.
�The answer was so shocking to me that this team, in response to its financial disadvantage, was rethinking the game of baseball that I launched into Moneyball,� said Lewis. �This was a weird book for me. I had never written a word about sports and if you asked me what �Sabermetrics� was, I�d have guessed it would have had to do with fencing. I didn�t have any idea this world existed and didn�t realize how rich the environment was until I got into it as a writer.�
Beane, however, wasn�t worried about his secrets getting out. He was more concerned about what his mother might think of the way he spoke, mainly his profanity.
When Lewis asked him if he was going to be upset for giving away his secret formula, Beane laughed and said, �Do you really think people in baseball are going to read your book?�
Leveling the Playing Field
Today, every team in baseball has turned its sights to the once dark art of analytics and the playing field has been leveled. Now big budget teams like the Boston Red Sox are using this strategy to draft players and identify free agents.
�When the book came out, the markets were poised to become a lot more efficient,� said Lewis. �And, when the Red Sox decided they were going to apply this new way of thinking to players and baseball strategies�that was the beginning of the end for the A�s advantage. Now it�s normal. The war is over.
�If you�re a team that isn�t trying to be on the cutting edge of using data to better value players and strategies, you�re at risk of being exploited in the marketplace and everyone understands that.�
Don�t Let Statistics Become Fetishized
If baseball can take analytics to the field, why don�t more organizations use the technology in their game plans? The benefits are endless.
Lewis believes that any organization � from sports to business to government � �needs to be looking for new ways to mine their data, and new ways to think about their data.�
But he also warns that baseball provides a great best practice for any business thinking about deploying an analytic solution.
�The funny thing about this story,� said Lewis, �is that it�s true the Oakland A�s set about trying to create new data and generate new information that wasn�t on the baseball field.
�But, a lot of the inefficiency in the game came from the misuse of the data that existed. The data was there, but people were just not thinking about it properly. So you could easily calculate a player�s on base percentage, but baseball was not appreciating the value of the statistic.
�And, to me the story is not just the importance of the data, it�s a story of being careful how you use it once you have it. Because the minute you start to measure something and have a statistic, it has a tendency to become fetishized, like a player�s batting average.
�Unfortunately, it wasn�t a key offensive statistic and it led players to be misunderstood.�
It�s like a marketing department only doing simple segmentation to identify customers for a direct mail offer. This analysis provides a somewhat superficial view of the customer and leads to one-to-some direct marketing (and lot of junk mail).
Basically, it perpetuates accepted wisdom that all customers (and baseball players for that matter) are created equal.
Change is Good; Don�t Be Scared to be an Innovator
For baseball teams (and businesses and government agencies), now comes the hard part � continually innovating to find new statistics that have hidden meaning.
Lewis says the low-hanging fruit has been plucked because it was relatively easy to assign statistical credit and blame to what happens on the baseball field.
However, if athletes weren�t so expensive nowadays no one would care about the ramifications of clean data and in-depth analysis.
Nor would the business world � except in today�s environment, where acquiring a new customer is that much more expensive than proactively keeping one.
�The business decisions become extremely important,� said Lewis. �It�s worth investing in complicated ways of evaluating them [players and customers] because if you find a slight edge it means saving millions of dollars.�
That is why those in the C-suite (and in many instances IT organizations) need to become more accepting to analytical techniques and not be afraid of what the data often reveals, or how it might change business processes.
In Beane�s case, he had to change if his ballclub was going to be competitive and survive. He challenged baseball�s traditionalists and angered the gods of conventional wisdom.
Sometimes the world isn�t flat.
Sometimes it�s white, round and has 208 stitches.
For more information:
Listento an audio interview of Michael Lewis discussing the movie and his upcoming session at the conference.
Reada recent IBM interview with the head of statistical analysis for the Chicago Cubs.
Registernow for IOD11 and BAForum; and start building your schedule.
Guest post from Anuj Marfatia, Senior Market Manager, IBM Predictive Analytics Solutions
Not to frighten anyone, but there are only five weeks before the holidays. The pressure is on.
In the U.S., the holiday shopping chaos, advertisements, music and decorations now begins on Nov. 1, right after Halloween. I actually feel bad for Thanksgiving. Somehow the poor bird has lost its mojo, though I don�t have time to think about it.
With the holiday season in full swing and Black Friday looming, I�m already worried about missing out on this year�s most popular toys for my family.
Like everyone, I promise myself that I will shop earlier, but in the end, I am usually sifting through the shelves of Toys R Us or Target on Christmas Eve that are stocked with items that no one wants or are insanely overpriced.
So, I end up scrounging the floors, hoping that someone else had mistakenly dropped a toy that I could use. (How does it go? Someone�s garbage is another�s treasure?)
I have always been late to the popular Christmas toy party. Even as a child, I remember getting the Rubik�s cube not in the early 1980s when it was hot, but a mere 15 years later. I was determined not to be last when it made its comeback.
What always surprises me is that the popular items are usually talked about and expected to be popular a month or two before the holiday shopping season begins (I could bet today that the XBOX Kinect and My Pillow pets are going to be hot this year), and yet there is never any in stock � either on the shelf or online?
So what gives?
Aligning Marketing, Inventory and the Supply Chain
Assuming that the corporate strategy was not to provide fewer products, it has become apparent that organizations have a difficult time aligning inventory with demand � and the holiday season always puts a strain on operational processes.
Granted, it�s not an easy task for retailers to determine which product and how many of them need to be on which shelf of which retail location and then streamline the manufacturing and distribution processes to meet demand for that specific product.
Or is it?
Organizations traditionally have used the approach of viewing sales from previous months or years and extrapolating how many will be sold in the coming year. Then, manufacturing follows that schedule. At times, this process is more art than science.
And, these organizations aren�t receiving ample feedback from customers, nor are they listening to what their customers are discussing in the socialsphere. In addition, they don�t take into consideration:
� The complaints that recently came up on Facebook regarding a competitive product or an earlier version of its own product
� What to do if 10% of the warehouse team just quit?
� The operational processes affected knowing that raw material prices have increased by over 30% in just a few days
� How to account for decreased consumer income due to the economy?
The Gift that Keeps on Giving
Now, more than ever before, technology exists to analyze all the consumer and organizational data so decisions can be made in real-time to account for macro or micro changes.
Wouldn�t it be great to be able to predict price elasticity, how many products are needed to meet demand, where on the shelf it should go to maximize sales, and how much product can be manufactured with the raw materials and resources that are at hand?
Take, for example, a US-based consumer electronics retailer. The past few holiday seasons, some specific tablets were purchased almost immediately when placed on the shelf.
There was a lot of lag in the supply chain process and by the time additional products arrived to the store, the season was over, so they were leaving much money on the table and were overstocked during the New Year. In order to eliminate their excess stock, they were forced to provide additional discounts to try to open shelf space.
Last year, by deploying predictive analytics software they were able to better predict customer purchasing behavior and demand, and better anticipate failures in the manufacturing and supply chain processes to ensure that they had enough inventory during the holiday season.
Predictive analytics leverages all the consumer, distribution, inventory, and manufacturing data inside the organization, as well as all the social media conversations happening outside. It then runs that data through predictive models, so organizations have a probability or likelihood of what products need to be on the shelves (and always on the shelves) for the holiday shopping spree.
I�m not just being selfish when I say this, but, I know I can speak for many that if more organizations utilized predictive analytics to align supply and demand during the holiday season, there might actually be an XBOX Kinect under the tree this year.
Otherwise, it may be another year of extra Halloween candy stuffed in stockings. Sorry family!
For more information:
� Watch the Predictive Operational Analytics video
� Read the whitepaper on Predictive Operational Analytics
� Get insight into how an auto parts retailer used predictive analytics to align inventory and customer demand
If you want to know what someone thinks, just ask. Better yet, just listen.
Unsolicited feedback is everywhere and oftentimes people will tell you what they�re thinking without any prompting.
The world is a noisy place and the advent of social media has resulted in nearly nonstop dialogue in countless locales. In fact, we'd go so far as to say that we live in the "Age of Over-Sharing."
Social media has unleashed the power of self-expression. Gone are the days of the internal monologue. Today, we live under the banner of "TMI" or Too Much Information. People share everything � maybe it's therapeutic � from medical ailments to stories about their kids and pets to recipes to life's aggravations to their favorite (and more often least favorite) restaurants, brands and products.
This over-sharing might seem useless to some, but this �babble� actually has incredible hidden meaning, relationships, patterns and trends.
Companies today would be shortsighted to ignore what their customers are saying about their products and services, in their own words. Those opinions are essential nuggets and reveal much more insight than traditional demographic or transactional data.
The challenge in analyzing customer insights used to be in gathering enough data to make informed decisions. Social media has created a new challenge: understanding the rich content from endless streams of data.
That is why IBM continues to innovate and expand itsbusiness analyticsportfolio to help organizations use social media to gain insight into consumer opinions and spot trends related to products and brands.
Today, IBM announcedIBM Cognos Consumer Insight, a new social media analytics application based on technology research assets developed byIBM Research Labsin Almaden. IBM Cognos Consumer Insight allows customers to listen, measure and analyze large volumes of publicly available social media content from billions of blog posts, thousands of online forums and discussion groups, as well as publicly available content on Facebook and Twitter.
A recentWinterbury Groupreport claims that social media marketing is expected to increase 13.2% to $1.2B, where direct mail and broadcast advertising remain unchanged.
Recognizing the amount of critical data resting within these social media sources is important for businesses to capitalize on, and offers an opportunity to extract this intelligence to better interact, react and mine customer opinion and feedback.
For example, if a retail merchandising manager from a high-end fashion line wants to gain better visibility into how a newly released woman�s print dress is being received by consumers, IBM Consumer Insight identifies, captures and reports on millions of pieces of social data to provide instant feedback on that particular item.
Managers can now use this critical feedback by analyzing key words found associated with the dress to better understand buying trends � if the red print dress is being described as too loud or too bold, brands can now make recommendations to the designer on creating the dress in blackinstead or red in order to adjust to customer preferences.
It also helps organizations analyze the success of their marketing campaigns, such as what are consumers saying and hearing about my brand? What are the most talked about product attributes in my product category? Is the feedback good or bad? How are consumers responding to our new advertising campaigns? What is my competition doing to excite the market?
For example, what if a famous pop star is doing more harm than good as the spokesperson for the brand? Brand campaign managers can now evaluate data from prime social sources to make smarter decisions around advertising campaigns moving forward and tweak current campaigns.
All of this information can be easily displayed on a dashboard with tables and crosstabs, as well as pie charts and trend charts to easily understand and share what is being said in the social media landscape.
A Truly 360 Degree View of Customers
It is relatively easy for organizations to analyze transactional data to learn which customers spend the most on goods and services. Adding demographic and attitudinal data from a satisfaction survey can help further segment customers into meaningful groups and thus drive different responses or communication strategies.
The cherry on top is social media data. It provides even richer insights into customers� true feelings because they�re speaking in their own words and by their own initiative. The ability to now incorporate social media data from IBM Cognos Consumer Insight intopredictive analytics solutionsgives businesses a comprehensive understanding of their customers. By understanding customer preferences and buying patterns, businesses can build predictive models to better determine future outcomes with a great deal of confidence. Knowing that customers who complained about customer service tend to leave for competitive offerings, businesses can preemptively act to retain those customers.
Marketing no longer has a mute button. With business analytics software, organizations are better enabled to transform their customer relationships by actively listening to what customers are saying onpublic social media channels.
Most customers expect (and demand) greater levels of intimacy from the companies with which they choose to do business. The analysis of social media data moves organizations one step closer to the goal of achieving truly scalable one-to-one marketing.
By turning up the volume, organizations are more agile, precise and responsive to market and customer demands.
If you're one of the millions filling out a bracket this year (all for fun of course), I'm sure you've been asked or have asked that question.
Yes, it's time when the NCAA men's basketball tournament distracts us from our jobs as we maniacally scan the internet and listen to so-called experts hoping to get that edge and finally master the ancient art of bracketology. Sadly,Paul the Octopuspassed away recently so that �secret weapon� is no longer viable.
Sure, accurately predicting which teams are in the Final Four is important, but what separates the masters from the novices is predicting the winners/upsets in the early rounds. You can play it safe and pick the higher seeds to win, but that's a silly strategy. Besides, all four top seeds have only advanced to the Final Four once in 30 years. (Sorry President Obama.)
Rely on the data. On Monday, Nate Silver's FiveThirtyEight ran anarticle entitled, "How We Made Our NCAA Picks," which took an analytical approach to predicting the winners.
Like IBM, he sees the value in analyzing historical data to make informed � and better � decisions.
And let's be honest, everyone is looking for that competitive edge � whether its bragging rights for the brackets, or outmaneuvering the competition in business. The answers are as simple as mining mountains of data to find Key Performance Predictors (KPPs) � those 15-20 data variables that are the most relevant.���
KPPs then help guide any organization to build an amazing level of intimacy and knowledge, allowing them to determine how a specific customer is likely to behave at a precise moment in time.�
In the NCAA tournament, Nate analyzed the results for all tournament games since 2003 (a total of 512 games) and evaluated which factors best predicted success. As Nate pointed out, "The goal is to have a system that makes good statistical sense and also makes decent basketball sense, as opposed to identifying a bunch of spurious correlations."
Not all data is created equal.In fact, sometimes the correlations you think exist, turn out to be counter-intuitive. That's where KPPs come into play. And, it's why predictive analytics makes good business sense. For instance, one of our insurance customers learned that clients who remove pets from the house prior to a fire are often convicted of claims fraud. And, phases of the moon are a predictive indicator of when crime is likely to occur.�
In the NCAA setting, Nate discovered that teams playing games within 50 miles of their campus have a 24-2 record; and, conversely, teams traveling at least 1,000 miles are 121-174.
Does this change the way you think about your bracket?
That's why IBM is "betting" big on predictive analytics.IBM is hoping businesses will realize that picking "winning" customers based on mascots, team colors or flipping a coin is also a silly strategy.
Today, it's better to rely on the data to be told how to take action than making a haphazard decision that could seemingly be based on unnecessary bias (like picking an alma mater such as Boston University over Kansas). Sorry Terriers!
What if you could determine when a part might fail in a car?� Or the right time and conditions to perform surgery?�Or when a crime will occur in a specific part of town?
Or, what if a call center agent at a communications service provider could quickly and easily determine which inbound customer calls are the best candidates for an up-sell, cross-sell or retention offer, and then deploy personalized, real-time recommendations that have the greatest likelihood of acceptance by the customer?
Thousands of these types of daily decisions can now be automated and optimized for significant � and measurable � benefit.�No longer are the same bad decisions made over and over again.�
Guest post from Erick Brethenoux, Executive Program Director, Worldwide Predictive Analytics at IBM
In 1978, an emotional and dramatic award-winning documentary film was released titled �Scared Straight!� It profiled troubled teens who were taken into maximum security prisons to stand face-to-face with inmates who �explained� the harsh realities of life in prison.
The goal, according to the documentary, was �to keep at-risk teens from becoming tomorrow�s prisoners.�
Consider this an analytic scared straight moment.
We have a serious skills gap in the analytics field�and it�s getting worse.
A recent McKinsey Global Institutereportindicates that over the next seven years the need for highly skilled business analytics workers will exceed the available workforce by as much as 60 percent.
And by 2018, an additional 190,000 "deep analytical talent" workers plus 1.5 million more "data-savvy managers and analysts" will be needed to take full advantage of big data.
In conversations with customers and prospects, it has become apparent that there is a significant widening of their overall performance � both in terms of increased revenue and lower operational costs. It�s amazingly disproportional and not linear.
Organizations that are outperforming their peers, through the use of analytics, are making quicker progress, capitalizing on their growing experience while monopolizing an increasing amount of analytical talent.
This creates a wider gap, making talents even more difficult to secure for those companies jumping on the analytical bandwagon � therefore delaying their progress.
These are the harsh realities of analytical life. It�s time to make a change�and soon. Those organizations that don�t will be forced into a life of using Excel spreadsheets. Talk about doing hard time.
Spreadsheets, while fine for certain tasks, aren�t the answer to keep up with the ever-growing amount of data being created and the more and more complex decisions that have to be made.
Nor are they capable of handling the pressures of customer demand. The ability to respond to customers at the right place and right time, and with the right offer based on a customer�s current mood and sentiment, is a job for those that know how to navigatedecision management solutions.
It�s becoming an arms and skills race segmented among �The Haves,� �The Have Nots,� and the �Never Will.�
��The Haves�� These commercial and government organizations have taken the time to understand the value of analytics, hired the right people, received the proper training, identified the business issues, and deployed business analytics technology to create opportunities to drive the business.
They have been doing it for a number of years and will only keep advancing.
��The Have Nots�� These organizations are just beginning to implement analytics, or will soon. Unfortunately, they will also have to spend more money to catch up, in terms of services, training and domain knowledge. But, at least they are getting into the game.
The pool of analytics talent, however, is rather shallow at this point�either already hired by the �Haves,� or on the verge of retirement, so analytics projects will be limited or outsourced to technology vendors.
��Never Will�� These organizations will soon be destitute and on the street struggling to find anyone with basic statistics knowledge.
By the time these organizations even think about an analytics solution, even IBM might not have the resources and services to help them. The market will be so desolate that what even looks like a mirage won�t have any water. And what few professionals are available won�t come cheap.
But, all is not lost. There is hope as many of you will learn at theIBM Information on Demand(IOD11 and BAForum) conference next week in Las Vegas (Oct. 23-27).
There are many programs already in place to help ensure that organizations will reach their full analytic potential:
�Over the past five years,IBM has initiated academic programswith leading universities around the world, includingYale,DePaul,Ottawaand others, to provide analytics technology and training resources so students can be prepared for 21st century jobs in analytics.
�IBM is continually innovating to make analytics technology easier to use, such as the aforementioned decision management. Business professionals can now build a predictive model in just three clicks.
�Even business intelligence solutions are made-to-order nowadays�for everyone (from the individual to the mid-market to the enterprise�and anywhere (with the newCognos Mobile for the iPad). It�s true analytic freedom.
�And, IBM�sGlobal Business Servicescontinues to expand its pool of talent as it will have almost 9,000 card-carrying analytics experts by the end of the year.
Become an Analytics Champion
Engage today. Don�t wait. IBM has been helping organizations deploy analytics and get the most from their data for almost half a century. We would love a chance to discuss, and then we can advise on the right training and education.
And if you need a quick start to find out how mature your organization is with analytics�take ourAQ quizthat will guide you through the steps needed to continue on the analytics journey.
Just over 15 years ago, authors of a book entitled, �The Discipline of Market Leaders,� asserted there were three (and only three) ways for a company to lead their market(s).
Their thesis is not complicated: invest in unmatched value (best product, best total solution, or best total cost) in the core marketplace, while meeting minimum standards across other measures of value. Focusing the entire enterprise on improvement in the chosen value to customers will result in growth in shareholder value over time.
We're seeing that increasingly "best total solution" is the value proposition of choice because of global changes over the past 15 years. Competing on "best product" is very difficult today as the horizon on an innovation-led technology advantage is shortening all the time. The market penetration for televisions 60 years ago to reach 50M users took 12 years. The internet took four. Tablet computers took just two years.
Competing on "best total cost" is a global undertaking where massive scale is frequently the core economic requirement. By its very nature, this strategy limits the field of competitors to a select few who drive globally integrated supply chains as a core competency.
So, in today's environment "best total solution" is where so many enterprises are focused.
But, it requires an intimate understanding of the customer, the customer's customer (sometimes) and the context in which they make their purchase decisions.
With the advent of social networks, we can define the "best total solution" by thinking of the social web as a massive focus group. Enterprises who lead in "best total solution" today are Social Businesses who mine insights and analyze them to precisely understand how customers feel.
Today's leading enterprises apply scientific methods to their social business activities � continuously harvesting the data associated with the process of establishing & maintaining relationships across the customer-set.
This data is priceless to compete effectively today because customers are expressing their needs in the context of their ongoing social network activities. All enterprises need to do is listen � scientifically � to separate the proverbial wheat from the chaff and uncover the core insights that lead to happier customers.
IBM has been working on applying scientific analysis to the petabytes of social network data. We are focused on enabling businesses to become more socially skilled as they engage the "massive focus group" that is the social web.
The "data exhaust" from social business engagement is the input to sophisticated Social Analytics which interpret customer sentiment, evolving topics of discussion and unmet needs.
We are working closely to help our customers improve their capabilities in Social Analytics. Many feel less well-prepared for these challenges than they would like.
Consider recent results from the IBM Global Study Chief Marketing Officer study of what CMOs feel unprepared for. Notice that the top four areas involve social analytics because it's about huge data-sets, social web content, new social channel options and a diversity of ages & geographies to consider.
I will be doing a webinar tomorrow, March 22, 2012, entitled, �Social Analytics is Key to being a Social Business,� and will be discussing how enterprises can get started on the topics above and it touches on all four areas of unpreparedness. You can register here.
One theme you'll see me strike is how our clients are focused on adding their social analytics insights to the larger corpus of datasets that they use to run their business today.
Consider this view of the typical "360 Degree Customer View." We have tried-and-true behavioral and descriptive data, and a lot of interaction data. But, the attitudes our customers have are more elusive.
We have to conduct costly, time-consuming surveys to capture their sentiment � the "Why" behind the "How, Who and What."
So, as our clients serve the needs of their clients better, it's largely through the predictive lens which comes from blending two key things:
�Scientific interpretation of social business interactions (The "Why")
�Modeling of traditional datasets, including the attitudinal data from social media and surveys
For more information:
�Listento the webinar I recently did with BrainYard and InformationWeek, "Social Analytics - Putting the Science into Social Business." A replay is available here and the presentation is available on SlideShare here.
�Watchthe video below for a strategic viewpoint from Deepak Advani, vice president of products and solutions at IBM, on social analytics and why it�s so important today