Guest post from Burke Powers, Managing Predictive Analytics Consultant, IBM Business Analytics
Today, every company of appreciable size has some social media presence. Most companies I speak with are either just monitoring social media or are engaged in �spray-and-pray� tactics that are only loosely tied to corporate goals.
To realize the value in social media it is important to integrate social media into broader customer analytics programs and business decision making.
Too often, companies ask, �What are customers saying about us?�
An objective like this is too vague to direct an analysis and identify actions. What we really need to be able to ask is, �Product XYZ will launch in two weeks. We have done A, B, and C campaigns to create awareness and to position the product.
�What kind of buzz (as measured by D, E, and F KPI�s) has this created around each of our message points?
�Are there other topics that we did not anticipate?
�Can we setup real-time reporting of the topics so that we can monitor the customer reaction to the product once they begin using it?
�Can we monitor any emerging, unanticipated topics after the launch?�
The objective should focus on an area of the business where you are confident additional insight can lead to quick improvements. The best opportunity might be related to a product, the service level of a critical customer touch point, competitor actions, a specific brand attribute, or a customer behavior.
The sheer volume of social data requires some planning. There are a limited number of data aggregators (major aggregators include BoardReader, Gnip, & DataSift) and each comes with its own benefits and trade-offs.
To choose an aggregator that best fits your needs, decide how important data history is, the cost of hosting the data, and the importance of access to all social media data (full fire-hose) versus sampling.
Secondly, decide whether to integrate additional data sources. Using the same filtering and reporting for social media and survey verbatims makes them more comparable for analysis and reporting. Also, determining whether to include internal social network data from Yammer or Lotus Connections may be a factor.
3) Plan and Execute the Analytics
By its nature, social media data is going to be different from what most business analysts are used to analyzing. It is unsolicited and unstructured and tends to be rich in attitudinal and usage information. It is frequently strongly positive or strongly negative.
But, it provides tremendous value because it has rich customer narratives of every product feature and customer touch-point that no other data source can offer. It brings traditionally dry analysis to life for business decision makers.
Most existing social media analytics tools offer only a limited ability to search and trend terms as well as view some sort of sentiment. Some allow filtering by the source metadata as well. These are necessary elements of any serious analysis, but stop short of offering the tools needed to take the data to an actionable level.
To be truly useful across many parts of the business, the free-text data needs to be understood in context and translated into an accessible format for reporting and analysis. This capability is one of the strongest differentiators for IBM Cognos Consumer Insight.
4) Motivate Actions
Once the analysis is ready, it is time to deliver the information to the decision maker at the right time, in the appropriate context to make a decision, and in a persuasive manner.
Finally, be sure to include a rich narrative quote that illustrate the argument and provides an additional persuasive hook that augments the analysis and builds buy-in from the �gut� of business leaders.
For example, let�s say your company recently launched the �Wonder Widget.� You are preparing the first report on how the product has been received by customers. Include a positive customer quote to support the data and drive the point home.
Ideally, the quote says exactly what your analysis leads to, �I love your new �Wonder Widget,� it is already making a difference. Except for one thing, the XYZ dial has got to be moved closer to the display so that I don�t have to look away. Fix this and I can easily justify ordering more units.�
There are many social metrics that could be used, from numbers of followers or tweets generated, to the ratio of issues resolved, and to issues raised via social channels.
Additionally, you could track the results via click-throughs usingIBM Coremetricsor email campaign response using IBM Unica.
You also might choose to experiment through customer support channels and monitor perceptions via both social media and surveys.
Finally, the metrics and actions need to be tied back to financial metrics either as revenue-generating or cost-reducing. This may require knowing the cost of resolving an issue via a social channel versus contact center or perhaps the cost of a response via one promotional channel versus another.
Identify a New Objective and Repeat
Now that we�ve gone through the process from beginning to end, it can now be repeated again with a new objective. A disciplined approach using these best practices will generate rapid returns on virtually any social media analytics endeavor.
For more information:
Read the whitepaperon techniques for gaining valuable customer insight with social media analytics
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
Rules are meant to be broken.
No one likes restrictions, to be controlled or be told what to do. In reality, however, rules are broken so better, stronger, and more appropriate rules can be created.
In other words, an established rule is often a starting point (or some critical point) of a rule�s �evolution.� Good rules evolve so better actions and decisions can be made.
For instance, some rules are about governance. Traffic rules govern some of the largest, most complex systems in the world. Motorists are surprisingly (mostly) cooperative thanks to these �rules of the road.�
And, what�s even more interesting is the apparent global rules of the road (that apply everywhere) in contrast to parts of the world have �local� rules � due to geography, culture and necessity. For example, making a right-hand turn in the United States is very different then Australia�s �hook turn.�
Rules are also about policy. For example, never go in your mom�s purse, never call someone after 9:00 p.m. or before 9:00 a.m. (Yes, I�m showing my age. I realize that nowadays we text each other 24/7).
Speaking of texting, ALL CAPS � as a rule � means you are screaming at someone. Oh, and never, ever text an image that will come back to haunt you later. Don�t be a Weiner. When you are on the golf course, there�s a rule that you shouldn�t talk about business before the 3rd or 4th hole � and try to finish up by the 15th or 16th.
I once had a psychology major tell me the vast majority of interpersonal behavior can be explained by two rules: birds of a feather flock together and opposites attract.
Think about the rules that apply to reviewing and selecting candidates for a job opening from hundreds of applicants, quickly building a large world-wide team for a last-minute project, or even during a round of speed dating.
These show the fine line between governance and policy and demonstrate how �rules� become important in guiding decisions. Specifically, they become a necessary component of a Decision Management solution �especially when the volume of decisions increases and the time to make decisions dramatically decreases.
Decision Management allows 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.
Overall, rules help link day-to-day execution to organizational objectives. Consider sports. Every rule book for every sport has a catch-all rule that enables an official to make a �judgment call.�
In basketball a referee has discretion when determining if someone is being malicious on a foul. There are criteria (e.g., a set of rules) to determine if a foul is flagrant � was the player really going for the ball, did the foul seem to have the right balance of aggression and sportsmanship, was the foul committed during a breakaway.
Finally, let�s discuss gaming. In casinos, each game has its own set of rules. I like this as an example because there are global rules about gambling (the house always has the edge) and local rules (in the US you have to be 21 years old to gamble). The local rule in my house is that I always win.
Consider all the systems I mentioned � traffic, sports, gaming � and consider the complexity of these systems, then think about how a good set of evolving rules helps establish structure, policy and governance.
But, rules can be inflexible and limiting to good decision making. Decision Management solutions must have rules, but they also can�t rely entirely on these rules. After all, a good process might be bad if it speeds up bad decisions or outcomes.
Rules must be balanced with business analytics for optimal decisions. I�ll cover that in part 2 of this discussion.
By the way, what is your favorite rule that you like to break?
Data is the oil of the 21st century, and analytics is the combustion engine.
Les Rechan, General Manager, IBM Business Analytics said it first in his opening address, �Data is the oil of the 21st century.� In no field is this truer than in risk and financial services. Rechan certainly wasn�t the last to make this powerful statement. Throughout the morning, speaker after speaker explained this new fundamental truth.
The volume of digitaldata in 2011 totaled 1.8 zettabytes, and that number is growing exponentially, explained Sarah Diamond, General Manager of Global Consulting Services for IBM, during her plenary address.
Dr. Michael Zerbs, President of Algorithmics, an IBM Company, and Vice President of IBM Risk Analytics, explained how, according to the IBM IBV / MIT Sloan Management Review Study 2011, 58 percent of organizations said they are using analytics as a competitive advantage. These organizations are 220 percent more likely to outperform their peers. There is no doubt: Analytics-driven organizations outperform. This is the thesis of Smarter Analytics.
In risk management, becoming a true analytics-driven organization requires a focus on the whole enterprise in order to fully optimize outcomes. See how Algorithmics, an IBM Company is helping its Banking, Financial Markets, Insurance and Asset Management clients achieve this in enterprise stress testing here, and in enterprise credit management here.
Regulatory Compliance is insufficient to be successful in Financial Services.
Dr. Laura Kodres, Division Chief for the Global Financial Stability Division in the Monetary and Capital Markets Department of the International Monetary Fund, argued in her plenary address that simply meeting the Liquidity requirements of Basel III is not enough. Systemic liquidity risk was at the heart of the financial crisis, and nothing in Basel III addresses the role played by 'non-banks' in systemic liquidity risk, not to mention the relationships between them. (Dr. Kodres was careful to note that these are her own opinions and not necessarily those of the IMF).
Earlier in the day Dr. Zerbs alluded to the same argument, stating that higher capital requirements alone do not make the financial system more stable. Firms must be cognizant of the �unintended consequences� of regulatory reform. And, in order to not only significantly mitigate a firm�s risk exposure, but further enhance its performance, organizations must transform business models.
Embracing analytics for risk-aware decision making transforms business models.
Gone are the days of risk management in �silos.� It is absolutely essential that financial services organizations embed risk analytics in decision making processes. This requires vertical integration � bringing risk-aware decision making from the back-office to front-office � and a holistic view across risk types, such as market risk, credit risk, liquidity risk and operational risk.
Firms must capture the intrinsic linkages across risks and asset classes, and ensure consistency across business lines in order to succeed. And, risk intelligence must be weaved into the fabric of the business � this is what Dr. Zerbs meant when he discussed brining risk out of the back-office.
If you can optimize outcomes at the point of impact, for example with real-time decision support in the trading desk, you can enable action based on risk insights. That is the essence of Smarter Analytics, and the mission of IBM Risk Analytics for the Financial Services Sector. It is also how IBM Risk Analytics intends to become essential in Financial Services.
Watch all the action.
To access the full suite of coverage from Day One at ARC 2012: Risk 360, register here to watch the replay of the Livestream coverage from our May 8, 2012 plenary sessions.
Join us again tomorrow via Livestream using the same registration link to hear further insights from Algorithmics and IBM at 9:10 am UK. The May 9, 2012 plenary sessions have a particular focus on how Algorithmics and IBM are working together to leverage each other�s strengths in both technology and services.
And, don�t forget to follow @IBMRisk on Twitter and the event at #ARCRisk360 on May 9, 2012 for more coverage by-the-minute, including links to additional resources on our various topics.
Visionis IBM�s global conference for finance and risk professionals to help improve planning, budgeting and forecasting, identify and mitigate risk, and meet the demanding requirements of XBRL, IFRS, Basel II and Solvency II with greater confidence.
I talked to Mauboussin about his book, making data-driven decisions, some common pitfalls as decision makers, and his upcoming talk atVision.
�What's very exciting is that in the last half dozen years, we've had a real influx of data, and we're now just learning how to tap that data for the benefit of better decision making,� said Mauboussin. �Now we can create a better intersection between value creation and making decisions.�
The problem however, according to Mauboussin, is that we still have the same cognitive makeup and the propensity to make common mistakes.
�We often think about our own decision making as being objective and fact based and rationale. And we tend to underestimate systematically how important the social context is for our decision making,� said Mauboussin.
To illustrate this point he told an interesting story from his book.
Researchers went into the wine section of a supermarket and set up French and German wines next to each other that were roughly matched in price and quality. Over a two week period they alternated playing distinctively French and distinctively German music to see if it would have any influence on purchase decisions.
Surprisingly, they found when French music played people bought French wine 77 percent of the time, and German wine 73 percent of the time when German music played. When asked if music affected their selections, the consumers unanimously said no.
�This basic experiment can be extrapolated to a lot of organizational settings where we think of ourselves as trying to be conscious and mindful as we make decisions. But indeed what is going on around us can be deeply influential to our decisions,� said Mauboussin.
So what do we do?
According to Mauboussin, integrate more data into quality decisions. However, there is still a tension between the intuitive, go by the seat of the pants experience group versus the analytically-minded group.
�Either extreme is not going to work but a blend between the two is right way,� said Mauboussin.
He then explained about the �expert squeeze� that affects decision makers. On one side are computers and algorithms that are doing jobs quicker, more accurately and cost effectively. On the other side are problems that are complex with high degrees of freedom with lots of possible outcomes.
For instance, there are certain types of tasks that people can learn, internalize and then intuition will work really well, such as chess or hitting a baseball. But, when someone steps into the domains of complexity with numerous outcomes, all bets are off.
This is whyDecision Managementsolutions are reaching a tipping point. By combining predictive models, business rules, scoring and optimization techniques, organizations can generate recommended �next best actions� for each individual customer, citizen, constituent or employee.
�The idea of running every statistic through a persistent and predictive framework can be very helpful in tightening up what organizations do in measuring their own performance,� said Mauboussin. �This doesn't mean experts are going away all together. Humans still need to think about the strategic issues and use the data to inform their decisions.�
And, we haven�t even discussed the role luck and skill play in decision making. You�ll just have to go toVisionto hear more, or read Mauboussin�s upcoming book due out in November 2012,The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing.
However, Mauboussin does offer a few more additional pieces of advice for organizations:
�Focus on the process. While outcomes are what matter, the key is that the proper approach to process is executed faithfully.
�Be very careful of the lessons you learn from history; examine past successes, but also the failures.
�Establish a culture of analytics. Organizations that don't are going to be at a market disadvantage because it's an important source of value creation.
�Ensure that non-financial performance measures are linked to company strategy and ultimate value creation.
�Commit to continual learning. Being able to understand big ideas from various disciplines and cultures can be extraordinarily helpful in problem solving.
Guest post from Erick Brethenoux, Director, IBM Business Analytics & Decision Management Strategy
As the saying goes, �Rugby is a rough sport for gentlemen; football (or soccer) is a gentle sport for ruffians.�
When I played rugby in my younger days in France, I suffered a number of injuries � from a dislocated shoulder to being knocked out to various gashes requiring stitches on my chin and head.
It�s no surprise that a study shows 1 in 4 rugby players will be injuredduring a season since the objective of the game is to take a hit for your teammates and keep the ball moving down the field.
In order to find new ways to keep top players healthy, the Leicester Tigers, nine-time champion of the English rugby union�s Premiership and two-time European champion, are using IBM predictive analytics to help the team better understand and reduce players' injury rates and minimize risk.
After all, losing a key player for an extended period of time can not only hurt the team on the field, it can also result in reduced ticket sales and spectator attendance if the team does not perform up to expectations.
Leicester is looking at important indicators such as fatigue, and threshold and game intensity levels in order to detect hidden patterns or anomalies. Better understanding this information will allow coaches and trainers to prevent injuries for each player by investing in adequate training programs, tailored to players� physical and psychological states.
For example, if a player has a statistically significant change in one or more of his fatigue parameters and the current intensity of training is likely to be high, the data may show that the likelihood of this player becoming injured is 80 percent greater. This type of real-time information will make it possible for the team to alter the player�s training to reduce the injury risks.
Predictive analytics also allows Leicester to analyze psychological player data to reveal other key factors which may affect performance, such as stress around away games and social or environmental elements that could significantly change the way players perform during a match.
In the manufacturing industry, plant managers, maintenance engineers and quality control champions all want to know how to sustain quality standards while avoiding expensive unscheduled downtime or equipment failure, and how to control the costs of labor and inventory for maintenance, repair and overhaul operations.
Through the use of IBM predictive analytics, they can now gather information in real time from a variety of sources, including maintenance logs, performance logs, monitoring data, inspection reports, environmental data and even financial data to determine the areas of greatest risk.
For example, an IBM customer who manufactures helicopters is able to identify and predict equipment maintenance, ultimately increasing customer satisfaction by keeping the helicopters in the air instead of grounded for repairs.
It�s the same way that Leicester is investing in business analytics to uncover the key predictors in the data �scrum� to deliver personalized training programs for players at risk and improve performance.
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.
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
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)
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).
It has almost become a daily occurrence when I arrive home from work and go through the mail to find yet another credit card offer from my bank, even though I already have a credit card with that very same bank.
Shouldn't they already know I have one of their credit cards? Do they even know me? Or, care to?
That�s the problem.
And according to Mark Smith, CEO and Chief Research Officer, atVentana Research, it�s imperative that banks and insurance providers anticipate and align their business around the customer.
�This is whybusiness analyticshas become a required must-have,� said Smith. �Business analytics is a significant set of technologies that is going through a huge transformation. It now plays a key role in everyday business processes, drives improvement and helps meet customer expectations and satisfaction.�
This is especially true for today�s customers who are savvier and price sensitive, and far less loyal. Customer experience now goes a long way.
Whether its banks or insurance providers (or any industry), business analytics is forcing disparate parts of those businesses to speak to each other, share data and create a personal experience for the customer.
However, according to research from Ventana, the problem is that only 1 in 5 banks and insurance companies are using business analytics at an innovative level of performance.
Smith said that these firms either have great people with skills, but struggle with technology, or have the technology but aren�t using it to drive improvements.
�Firms need a balanced approached across these elements in order to improve their analytics maturity,� said Smith.
What is holding these organizations back from taking the next step?
According to Ventana�s banking and insurance benchmarks, only 42 percent of banks and 37 percent of insurance providers are satisfied with their current analytic process.
Smith points to three main barriers that halt analytic success:
� Wasted time due to data related activities (preparation, fixing errors)
� Lack of intersection between the business and IT (read a recent blog post on this rivalry)
� Continued use of spreadsheets that increase risk factors and contribute to poor decisions
What can banks and insurance providers do to generate better value from their analytics?
According to Smith, 68 percent of companies surveyed said the most popular use of business analytics today is in customer service. What is most interesting, however, are the least popular uses which include customer profitability, voice of the customer, marketing campaigns, communications channel usage and social media (at a lowly 4 percent).
These are the areas where the use of customer analytics can have exponential effects, and according to Smith, become a true measure of innovative maturity.
This is when the analytic process gets deeply integrated into every business process and customer touch point to make every interaction personalized.