Last Sunday was Father�s Day. This is a paradoxical �holiday� in the U.S., as it is a day to honor fathers with gifts and food, but they are still required to work in the yard, fix stuff, yell at kids and run errands.
I received thoughtful, useful and handmade gifts from my three wonderful kids. They included a converter that lets me play my iPhone through my cassette tape deck in my car (needless to say I�m not driving a 2012 model); a homemade comic strip card about mutant aliens; and, a personalized gum wallet made of duct tape (see picture below).
The real challenge was what to get my father for Father�s Day. In fact, I face this conundrum every gift-giving occasion with my father.
As those of you with fathers can attest, the typical dad has everything he will ever need in his entire life by the age of 31, plus or minus two years. And, I mean everything � tools, gadgets, sweaters and golf paraphernalia.
This personal challenge is what prompted me to use the recently released IBM Analytical Decision Management to provide a recommended action related to my gift selection. My strategic objective was to have my father accept and enjoy my gift.
Because we have been talking a lot about Customer Analytics, Next Best Action and IBM Signature Solutions at this year�s IBM Business Analytics Analyst Summit (search #ibmbas12 on Twitter to follow the commentary), you can understand why I could easily configure my IBM Analytical Decision Management solution. (Hint: Replace �father� with �customer� and �gift� with �offer.�)
Following were the steps to my recommended decision:
�Using years of historical fatherly gift giving data (e.g., ties, golf shirts, jive coupons with the promise of a �car wash�), I restricted the analysis of my data so that the recommended action(s) would be based only on those gifts given in the summer months (e.g., nothing with long sleeves).
�I also opted to exclude �no action� from the recommended action list, which is often a viable decision for retention offers but not for gift giving to my father, especially if I hope to stay in the Peckman will. Just kidding. Sorta.
�I defined the list of the potential recommended outcomes linked to my objective: Give a product, a service; or a combination of the two. Then, I built new business rules and predictive models that were not included since the last time I used IBM Analytical Decision Management. For example, new rules:
If (golf_hndcp[current] > golf_hndcp[lastyear]) & (golf_complaints > 3) then add risk points;
If balance_giftcard > 0 then add risk points;
If (favorite_child[current_month] = me) then subtract risk points;
� and so on.
�Similarly, I created new predictive models:
Before deploying the gift giving decision management solution for use in the field by end users (like me, my wife, my children) I ran all the proper �what if� scenarios and used the new constraint-based optimization functionality in an attempt to maximize enjoyment and minimize effort to carry/use and subject to cost constraints. (To see the other new features in IBM Analytical Decision Management, read the data sheet.)
For example, a new Audi has a predictive acceptance of 100 percent (1.00) but falls outside cost limits for the gift; and, $5.00 tickets to Ballet in the Park (performed by an up-and-coming troupe of back-ups to the back-up dancers) fall within cost constraints, but have a predictive acceptance of less than 2 percent or 0.01667.
By completing all of these steps, �IBM Decision Management for Gift Giving� (the next Signature Solution?) is ready to generate a recommended action to my wife�s question, �What should we get your dad for Father�s Day?�
My recommended outcome >>> Gift certificate to the Olive Garden.
The next step is to put my updated application up into the cloud (read more about Analytical Decision Management SaaS) so my extended social network can run the SaaS version for batch gift recommendations.
And, in case you have any wild ideas, I have a patent pending on the personalized gum wallet made of duct tape.
There was anarticlein The New Yorker last week entitled, �Why Smart People are Stupid.�
Its premise stated, �When people face an uncertain situation, they don�t carefully evaluate the information or look up relevant statistics. Instead, their decisions depend on a long list of mental shortcuts, which often lead them to make foolish decisions. These shortcuts aren�t a faster way of doing the math; they�re a way of skipping the math altogether.�
Given all the work organizations do to collect and align data, there really is no reason why foolish decisions should be made any longer, especially when there�s a huge price tag associated with bad decisions.
And, when you think about how many decisions an organization makes on a daily basis (thousands, millions?), being foolish is no longer an option � especially calculating the cost between one foolish decision and a million foolish decisions.
And, most of these transactional or tactical decisions need to be made in an instant, such as a customer service agent deciding to give a customer a discount to combat churn; an insurance claims system determining whether a potentially fraudulent activity should be escalated for investigation; or, a logistics manager deciding if a truck is safe to put on the road for the next delivery.
To end this foolishness, IBM has introducedAnalytical Decision Managementto help organizations automate and optimize decision making in real time to ensure the best outcomes occur every time.
Essentially, Analytical Decision Management takes the complexity out of big data by quickly analyzing and embedding analytics directly into business systems (in a call center, on a website, on the manufacturing floor) to empower employees and systems on the front lines with the ideal action.
It also allowsbusiness users to run multiple �what if� simulations, compare the outcomes of different approachesand test the best business outcomes before the analytics are deployed into the operational system. Even analytics follow the old adage, �Measure twice, cut once.�
IBM Analytical Decision Management
According toIDC, the Decision Management software market is expected to exceed $10 billion by 2014. To meet this growing demand,IBM Analytical Decision Management is the first in a series ofIBM Smarter Analyticsinnovations that will change how organizations weave analytics into the fabric of their business, fueling all systems, decisions and actions to consistently deliver optimized outcomes, while adapting to changing conditions.
The newly released Analytical Decision Management combines and integratespredictive analytics, business rules, scoring, and now, optimization techniques, into an organization�s systems to:
�Maximize every customer interaction to grow revenues and increase loyalty
�Detect and prevent threats and fraud in real time to reduce risk
�Proactively manage resources by predicting equipment failure, staffing downtime and service disruptions to contain cost
For example, Santam Insurance is using Analytical Decision Management to transform its claims processing byenhancing fraud detection capabilities and enabling faster payouts for legitimate claims. In fact, in the first four months of use, Santam saved $2.4 million on fraudulent claims. (Readthe full case study.)
Santam can now automatically assess if there is any fraud risk associated with incoming claims and allow frontline claims representatives to distribute claims to the appropriate processing channel for immediate settlement or further investigation, which in turn, optimizes operational efficiency.
As all customers and claims are not created equally, Analytical Decision Managementadapts its recommended actions in real time to accommodate changing conditions as new data is collected and outcomes are recorded.
Analytical Decision Management is also equipped to automatically prepare, cleanse and transform data for the best possible analytics through the newEntity Analyticscapabilities.
There can be challenges when diverse enterprise-wide data is integrated � especially when this data contains natural variability (e.g., Bob versus Robert), unintentional errors (e.g., a transposed month and day in a date of birth), and at times professionally fabricated lies (e.g., a fake identity).
The Entity Analytics feature allows data scientists to overcome some of the toughest data preparation challenges and create the most complete view of an individual record. Users can generate higher quality analytic models and, as a result, organizations will enjoy better business outcomes whether the goal is detecting and preempting risk or better responding to a customer�s needs.
Guest post from Kurt Peckman, Program Director, IBM Predictive Analytics
Last Friday I took a different train into my office here in Chicago.
This particular station has a diner located right next door and within steps of where I would be catching my train. They only serve breakfast and lunch and it immediately hits me that I�ve stumbled upon a diner with an optimized location and manufacturing schedule.
Speaking of which, I had optimized my wait time for my train. No gross surplus of minutes to waste on the platform; no deficit of time causing a heart attack-inducing sprint from my car to the train. I immediately headed to the diner.
The waitress, who I�ve never met before today, immediately greeted me with, �Hi, honey� $1 egg sandwich today?�
I didn�t fall for the �honey� play. I�m old enough to know that any good waitress worth her salt will refer to me as: honey, sugar, handsome, and the like in an attempt to up-sell me from coffee to coffee plus. And given my experience in up-selling myself (discussed in my last blog) I was naturally on guard.
However, I was very, very intrigued by the price of the $1 dollar egg sandwich.
I said, �No, thanks,� which was tough to do. I love egg sandwiches and one dollar is a heck of a deal for a diner-based product. (Notice the use of the word �deal� and not �price,� which implies �value� to me.) I am trying really hard not to eat so many egg sandwiches so I declined. But, the critical fact in this story is that I paid $1.75 for a cup of coffee.
Secretly, what I really wanted to do was take the entire day off of work to interview �Flo� the waitress (my customer service rep), the chef, and other patrons about the implications of the $1 egg sandwich. I especially wanted to interview the owner (who I think was sitting in the corner reading a paper) as to how the execution of the egg sandwich is tied to his overall business strategy.
How was that price determined? Is it an optimized price? Can a diner really make a profit on a $1 egg sandwich? If so, does it include the cost of all goods: materials, labor, overhead (e.g., utilities, wear and tear on the grill, depreciation on the spatula, etc.)?
Or was the pricing objective pull marketing for the diner? The deal didn�t prompt me to go into the diner, and I�m not even sure there was a sign out front stating the terms of the deal. But, there was signage inside that I realized only after she pitched the deal. Now my mind was spinning.
Is Friday the best day for the egg sandwich promotion? Is this an optimized campaign � right offer, price, channel, day and time? I didn�t even get a chance to ask if every Friday is a $1 egg sandwich day. If so, I might be inclined to invite my colleague Bob (who regularly commutes to/from this station) about the end-of-week-deal at this diner.
Given my love of egg sandwiches, I might even be tempted to take to social media to sing the praises of this diner.
Other questions scrambled my mind: do they pre-make the $1 egg sandwiches? They must. There is no way the diner can meet the short-term, burst demands dictated by the average time one waits for a train.
And what is the optimized inventory of egg sandwiches that minimizes spoilage and maximizes freshness, demand, labor�? The $1 egg sandwich production quickly becomes an n-dimensional optimization problem.
And by �optimization� I mean the mathematical definition: maximizing (or minimizing) some outcome or value within a set of predetermined constraints. A classic example is an investment portfolio: we are all trying to maximize the value of our portfolio subject to the constraints of contributions, time, risk, market direction, etc. But I digress� back to the eggs.
Maybe the $1 egg sandwich starts at $2 earlier in the day and, by the time I arrived, the decision was made to drop price due to surplus inventory. Wouldn�t it be something to find out that a mom & pop diner was using sophisticated optimization algorithms to price egg sandwiches that maximize profit and minimize spoilage?
At this point three things become apparent:
1. Tying strategy to execution is as critical to the mom & pop diner as it is to Global 100 companies;
2. The best decision management solutions must include an optimization component; and,
3. I have an unhealthy obsession with egg sandwiches.
Guest post from Kurt Peckman, Program Director, IBM Predictive Analytics
About a month ago I moved.
I closed after lunch on a Friday afternoon. The only reason that is relevant to this story is the timing: my cable provider called me the next day � Saturday morning around 9 a.m.
I knew it was my provider, thanks to caller ID. Granted I�m not that old, but not too long ago you had to actually answer the phone to know who it was. In fact, I now have a phone that will announce out loud who is calling me. Ah, technology.
Being a Wisenheimer, I answered the phone not with a �hello,� but with, �I bet you are calling about the sale of this house.�
Without missing a beat, the customer representative answered, �Yes I am, and I�d like to get you the best possible package for your new house.� Note the use of the word �best.�
Thus began my willingness to be retained.And, at the time I wondered to what extent predictive analytics were being used to �retain� me during the conversation.
Because �best� was enough to get my attention, I let him ask me the location of my new house. He was quick to pull it up and confirm the deal he had in mind could actually be pitched.
�Yep, looking at your location, I can get you set up with the following package at [about half of what I was paying before!].�
Here is the critical fact in this story: the �package� he pitched included internet connectivity speeds at 2X-3X what I had before the move AND a television package that was two upgrades above what I was leaving. All for half the price I was paying before the move. Too good to be true?
Efficient retention. Impressive.
As someone who has held sales positions, works in predictive analytics, and has a technical background, I could really appreciate the efficiency of this win-win transaction. My provider retained me as a customer on a Saturday morning with a single 10-minute phone call AND my new house will have quadruple the package of the previous house for half the price.
Hold on. It gets more impressive from the telco�s standpoint.
Then I had a revelation. After only two weeks in the new house enjoying my new services (key word �my,� read �personalized�), I figured out that if I paid more than I am paying now � but not much more than I used to pay in the old house � then I could have the top-of-the-line package: super-duper connectivity, high definition, DVR, and on and on.
That is to say, I just up-sold myself as a result of a 10-minute phone call on Saturday morning four weeks prior!
Needless to say, my telco provider must be leveraging elements of a robust Decision Management solution. In particular, I�m sure they used my high predictive score for up-sell, coupled with the business rules that governed the initial offer, such as�
�IF (provider_jump = false) and,
�IF (previous_package = XYZ ) and,
�IF (number_complaints < 2) and�
�to produce an outcome that demonstrates the importance of predictive analytics and rules to guide optimized and automated decisions.
Said another way, my telco provider not only retained me, but got more monthly subscription revenue out of me in a very efficient manner.
And this is just one personal example from telco. Think of how predictive analytics and rules can (and are!) being used in tandem to optimize and automate recommendations in retail (e.g., customer analytics), manufacturing (e.g., preventative maintenance), insurance (e.g., claims processing), and beyond.
Speaking of optimization, stay tuned for Part III of my Decision Management series.
And, if you missed my �Ode to Rules� in Part I, you can read it here.
Guest post from Kurt Peckman, Program Director, IBM Predictive Analytics
Spoiler alert: If you have never seen �2001: A Space Odyssey� forgive me for spoiling the plot, but you�ve had 44 years to see the movie.
In 2001, a space crew was voyaging to Jupiter along with HAL 9000, the spaceship�s computer that wasfoolproof and incapable making poor decisions.During the journey, HAL began to malfunction, slowly go mad, and refusing to cooperate, turned on his crewmen and methodically �eliminated� them one-by-one.
Ultimately, HAL had to be powered down � against his own will � to keep him from making any further decisions on his own.
IBM has learned a lot from HAL in the last 44 years. For instance, one can�t leave all the decisions to the HAL 9000 (or any other operational system, business process, platform or person), but can leverage the combined strengths to enable sound decision making.
And what are those strengths?
Taking information from everywhere (and I do mean everywhere � transactional, social media, call center notes, video, sensors, etc.) with an end goal of providing recommendations for action, such as identifying claims fraud, reducing churn or reducing costs via preventative maintenance.
More specifically, organizations can now employ these systems in the Cloud using all of its proprietary �local� data along with cloud-resident data to tie strategy to execution by means of decision management systems.
By combining predictive models, rules, scoring and optimization techniques to generate recommended actions, decision management systems allow users to automatically deliver high-volume, optimized decisions at the point of impact, such as in a call center, on a website, in a store, etc.
As an additional option for customers, IBM recently launched IBM SPSS Decision Management Software as a Service � one such system that helps organizations make these decisions in the cloud without the administrative overhead and expense of on-site software.
Decision management is an ideal solution for organizations in a range of industries, especially those with high volumes of interactions � such as in retail, banking and financial services, and insurance, as well as government agencies and academic organizations.
For example, some decisions and recommendations will be heavily dependent on rules (e.g., �do not make offer A to customer B��), while others will be based on predictive analytics (e.g., �� unless the propensity to churn is greater than 90 percent...�). Some decisions and recommendations will be based on internal data (e.g., past purchase patterns and RFM analysis), and others on external sources (e.g., credit score and tweeter feed).
The main point, however, is that all are tied to generating a specific outcome, whether a tactical or strategic decision. And, even before deploying these recommendations into an operational system, multiple simulations and �what if� scenarios can be run to compare the best outcomes.
Let�s get the recommendation right first so the same bad decisions aren�t made over and over again.
If HAL taught us anything it�s that the outcome is king. It�s time to start deploying analytics into operational systems before customers start being methodically eliminated�one-by-one.
When it comes to fitness and exercise, a little motivation goes a long way.
That motivation can come in many different forms � looking in the mirror, ridicule from friends, preparing for a big race, or a personal fitness coach.
Getting off your butt is only the first step though�you also need to manage important decisions about nutrition, when to exercise, what types fitness you both enjoy and are best suited based on your physical condition, and determining realistic goals and objectives.
Fitness and weight loss is now powered by analytics and business rules to provide personalized feedback to userswhether they are missing, meeting or beating their targets, activity options for making up shortfalls in daily goals for burning calories, nutritional analysis of each day�s eating patterns, and advice to stay on course toward achieving the overall goals.
InA Smarter Planet blogpost, BodyMedia�s CEO, Christine Robins, commented, �Using a predictive calculation unique for each person, we can help users understand if they will fall short of daily goals and will offer precise suggestions that help make the goal a reality � like hopping on the treadmill for twenty minutes or taking a ten minute walk outside or even providing meal suggestions.�
Recently, IBM, BodyMedia and Summa, an IBM Business Partner, held a webinar to discuss how this system works and how IBM Decision Management is providing personalized, automated recommendations to individuals for fitness, the same way organizations use this technology to enhance operational systems to identify fraud in insurance or healthcare, reduce churn in telecommunications, or enhance marketing offers in retail.
You can watch the free half hour webinar on demand here.
Following are some highlights from the webinar:
�The capture and analysis of personal data can have a direct impact on fitness goals and weight loss.
�The business rules defined in the system determine all of the possible recommendations, while analyzing, selecting and filtering the most relevant feedback and suggestions to display in the BodyMedia FIT Activity Manager (see image).
�More than 300 rules are used to make these recommendations.
�The rules system uses natural language that makes it easy understand and develop.
�BodyMedia is a great example of where business rules and business events come together. For instance, if someone is more consistently behind in meeting their goals, the solution recommends a more achievable goal.
�The rules engine can also be customized, in this case for BodyMedia�s partner, Jillian Michaels 360o Weight Loss Navigator, that is incorporated into the entire BodyMedia system.
So, what does this all mean? It�s only April. New Year�s resolutions are still feasible�both for fitness goals and injecting better decisions across an organization's operational systems with Decision Management technology.
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).
Some people might argue, but former rapper and musician Vanilla Ice was a visionary.
Truth be told, he probably wasn�t talking about business analytics when he eloquently penned those famous lyrics in �Ice, Ice Baby.� But, he could have been.
We live in a collaborative world today�whether we like it or not. The realm of �social� is slowly morphing personal and professional, ultimately making life more efficient and transparent.
And some people and organizations are still rejecting this notion altogether.
Which is why at a company of approximately 400,000, with team members spread across the world, collaboration is a way of life, and a necessity in the IBM survival kit.
It bridges the gap of the world of social with the world of business. It allows us to now connect people and insights to gain alignment inside of the organization, as well as hold people accountable.
Decision making is no longer a game of telephone where important elements of that decision are lossed as it is passed on�one person at a time. When the decision is finally executed, does anyone even know if it was right, if the right people were involved, who made the decision, or why?
That�s where the power of business analytics and collaboration come together.
Organizations can lose tremendous productivity as they search for invaluable information hidden in various meeting notes, manual processes, emails and people�s notebooks.
Collaborative business intelligence(BI) streamlines and improves decision-making by providing capabilities for forming communities, capturing annotations and opinions, and sharing insights with others around the information itself.
It also allows organizations to communicate and coordinate tasks to engage the right people at the right time.
In fact, industry analyst Dave Menninger from Ventana Research commented that �innovative organizations recognize the processes involved in BI are as important as the technology and take steps to provide collaborative support to their BI activities.�
With built-in collaboration and social networking, collaborative BI harnesses the collective intelligence of the organization to connect people and insights and gain alignment.
What was once a dysfunctional buffet style decision making process is now a formal dining experience, with collaborative BI as the lazy susan passing reports and dashboards around the table for feedback and discussion.
Everyone now has input into the process, can easily connect with and understand context with others who are relevant to the decisions being made,and can now learn from history with a centralized corporate memory.
But realistically, before we can all sit down and enjoy this collaborative feast, it must be an accepted practice in the organization.
Culture is at the heart of this. It has to want to happen. Collaboration cannot be forced.
And, once you have embraced it�well, there�s no turning back.
Before too long, you have access to the people and expertise you need to discuss and refine ideas, data and information for the best results.
Had Vanilla Ice lived in today�s world of social networks and business analytics, he might have been able to lengthen his career, better market himself, sell more records, write better songs, connect with fans and shave less eyebrows.
Ok, maybe not.
But, he would have lived true to his mantra of collaborating with his producers and writers and listening to the general collective before making any decisions.
(I apologize if you now have Vanilla Ice stuck in your head for the rest of the day, but at least you�ll be thinking about how you can establish collaborative BI processes across the organization.)
Learn more about IBM Cognos Collaboration by:
� Registering for the January 17 IBM TechTalk: �Enabling Better Decision Making Through Highly Collaborative BI� (Begins at 12:00 pm ET)
� Watching the demo to see how to use built-in collaboration and social networking tools to connect people and insights
Twas the night ofbusiness analytics, when all through the org
No one in IT was stirring, the business felt like a morgue. Cognos Mobiledashboards were delivered to the iPad with care,
In hopes that the CEO would soon review them there.
The business line managers were nestled all snug in their beds,
While visions ofDecision Managementdanced in their heads.
With business rules and predictive models working in sync,
Automated, optimized decisions happen in a blink.
While over in finance there wasn�t any stress,
WithFinancial Performance Managementit�s no longer a guess.
Away to the budgets everyone flies like a flash,
To create flexible, rolling forecasts to always know how much cash.
And as the year ends, it�s time to look back
To close, consolidate and report to keep everyone on track.
When, what to the CFO�s wondering eyes should appear,
But an easy way to complete thelast mile of regulatory reportingto stay in the clear.
To anticipate customer behaviors, it�s hotter than a flame,
The industry is shouting, and calling for business analytics by name!
"NowCognos! Now,SPSS! Now,AlgorithmicsandOpenPages! IBM is taking business analytics out of the Dark Ages!
Lose the excel spreadsheets and head to the top of the charts
Measure yourAQ, that�s where the journey starts!"
With all these pieces any organization should be so proud,
Confronting the obstacle of big data? Let�s take it to the cloud.
And to not forget about all the social media noise
Taking things a step further, and to make all business users merry
2012 is when analytics gets personal, like a sundae topped with a cherry.
Interact and explore, build models and share insight
All without the help of IT, oh yeah, that�s right!
So spring to the laptop or any mobile device,
Away the business will fly, decisions no longer made by a throw of the dice.
And hear all employees exclaim, analyzing with all their might,
"Business Analyticsto all, and to all a good-night!"
There's a series of AT&Ttelevision commercialsrunning in the U.S. that portray how quickly things move in today's digital age.
Twitter, Facebook and YouTube (among others) make it easy � and difficult � to keep up with the latest news, trends and funny baby or animal videos.
By the time you see these items on your desktop or mobile device you quickly realize you are behind the times. That was so :27 seconds ago. Or worse.
In the world of analytics this has never been more true.
27 seconds (or less) is all a retailer, telecommunications provider or insurance company has today to effectively interact with a customer and take the appropriate action � making an offer, fixing a problem, or identifying possible fraudulent activity.
Time is the essence�especially in the world of social media.
Reigning in social media chatter has become a necessity. It�s not just listening to what people are saying, but understanding what they are doing, what they�re thinking and how to better engage with them.
Henkel, a leading producer of laundry and home care, cosmetics and toiletries and adhesive technologies based in Germany, recently deployedIBM social analyticsto better understand what its customers were saying about its brands in the social sphere, and more importantly where, so it could refine its message and take better action.
One of the interesting discoveries for its cosmetics business was that customers that were talking about hair were doing it on a cooking social network. They figured that once at a site, people were likely to remain on that site and continue talking about various topics. Knowing this, Henkel was able to better optimize keywords and better market appropriately on this same site.
While Henkel is finding success, many organizations are still unable to tap this precious resource due to lack of understanding of analytics or lack of in-house analytics skills.
This is why more and more universities are creating programs specifically focused on analytics, includingNorthwestern University, who recently announced two new programs, a full-time Masters of Science in Analytics in the McCormick School of Engineering and Applied Science and a part-time Masters of Science in Predictive Analytics program in the School of Continuing Studies.
Students coming out of college today are byproducts of the digital age and intuitively understand social platforms. They are not only the largest consumers of digital information, but also the purveyors of the content, and are the ones that will parlay their social media prowess into a lucrative career that will turn this social data into business value.
Scott Kellert, a student at the McCormick School of Engineering at Northwestern, commented that organizations will soon realize they need his skills to turn vast quantities of data, especially social media data, into something meaningful that can be quickly applied to improve the business.
�What I love is that analytics can be applied to everything � from insurance fraud to marketing to student retention,� said Kellert. �The new program at Northwestern will take my skills to the next level. Future employers will have confidence that I will know exactly what to do when I encounter large data sets and how to get value from them.�
Value is the operative word�and quickly.
If 27 seconds is all organizations have, they better be precise � and be adaptive to data that changes every minute to catch trends as they are happening, such as in the entertainment (X Factor) or fashion (high-heeled shoes) industries.
Think about if organizations are actually still using a spreadsheet to analyze their data, let alone social media data.
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.
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
Guest post from David Pugh, Program Director, Product Management, IBM Business Analytics
I really love speaking to customers who are pushing our software to its limits. Those customers who are at the bleeding edge � the innovators and early adopters � regularly have great input to the creation of the products of the future.
Why? Because they are using the products in anger � pushing the software up to and beyond its intended use in order to drive competitive advantage and increased revenue.
The story starts five years ago when I went on a �world tour� to spend time with customers with some of my product management colleagues.
In particular, we were interested in customers who were actively deploying and using the results of predictive analytics in their day to day business operations, such as:
�Auto insurancecompanies using predictive modeling to determine on the fly � while the customer is on the phone describing the accident � whether the claim was possibly fraudulent
�Retail banksproviding personalized marketing offers to their online banking customers, trying to sell them additional products
�Mobile telecommunicationsproviders scoring millions upon millions of customers every night looking for any signs that they were going to defect to a competitor
As I said, the customers we visited were using our products in anger, like a coach demanding the most from his/her players.
For instance, the customers were using Decision Management in conjunction with other applications (e.g. CRM, call center, websites and campaign management); had stringent performance requirements; and, all had inventedtheir own methodology for managing / updating the predictive models that were being deployed into these front-end, operational environments.
It became apparent that the processes used to create, deploy and manage predictive models were eerily similar.
In fact, thanks to our customers, we were able to develop a list of best practices to easily create predictive models and inject the results of the analysis directly into business processes to improve outcomes.
The Seven Steps to Analytics Deployment
1)Acquire the Data.Customers use a mix of data including transactional, demographic, call center notes, social media, and attitudinal data from customer surveys as input to the modeling process.
2)Identify the AudienceDetermine the population for whom the outcome of the decision is valid. For example, with regards to insurance fraud, the customer may want to exclude any insurance claims that are caused by natural disasters (and process them a different way).
3)Define the Desired OutcomesThis is the heart of �The Seven Steps� where the customer determines the range of �Decisions� that could be delivered into their operational environment.
For insurance fraud, the desired outcomes that would be ideally delivered as a �Decision� to the call center agent processing the claim could be:
�Fast track the claim � low risk of fraud and low cost
�Push through standard processing � low / medium risk of fraud
�Refer the claim to the special investigations unit � possible fraud.
4)Enlist Business Rules and Predictive Analytics to Determine the Ideal Outcome.If Step 3 is the heart, Steps 4 and 5 are the brains. Staying with the insurance fraud example, there may be a number of policy (business) rules that need to be applied to the decision, such as �All claims made within two weeks of setting up the policy must be investigated for fraud.�
Customers were also building predictive models that determined � based on historical examples of fraud � the liklihood that this particular claimant was behaving fraudulently.
5)Optimize the Outcome.What if the business rules output says �Refer the claim to the Special Investigation Unit� and the predictive model says �Push through standard processing�? The user may decide whether rules override models or vice-versa.
For marketing applications it is here that a user could optimize which of the five valid marketing offers would be made based on factors such as liklihood to respond, revenue and cost.
6)Deploy, Deploy, Deploy!Take the intelligence defined in steps 1-5 and deliver it to the appropriate business process. Once the IT configuration has been completed this is typically a one-click process.
7)Report and Monitor.Watch the performance of the deployed application and ensure that it continues to perform well, as well as share the results across the organization in easy to understand reports and dashboards.
Business users typically need to update rules, predictive models or the way in which the result is optimized on an ongoing basis. Automated techniques such as �Champion-Challenger� modeling are used to ensure the best models are always deployed.
If you�re a customer using our products in anger, please get in touch. Your input will help us build the next generation ofIBM Business Analytics software.
For more information:
�Readthe whitepaper on how Decision Management creates a closed-loop system that continually incorporates valuable feedback into your decision-making processes.
�Watchthe video of industry analyst James Taylor discussing the importance of Decision Management.
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.
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.�