Social Analytics – New uses of Social Intelligence

Article posted SmartData  -  B2C -  Analytic Bridge

We all live in a digital world or rather ‘social digital’ world. This is because so many of us have taken to Facebook, Twitter and other social networking sites/tools that this has become an obsession to many.  

Obviously, the huge social media traffic has provided companies an unprecedented opportunity to showcase and message themselves. Marketers and bankers have happily followed the traffic.  Now every company has a social media strategy,   mostly directed at messaging and feeling the pulse of the customers.  I often think many companies have a strategy more as a ‘place holder’  than to deduce any ‘monetizable’  intelligence ( if there is no such word, I have coined one!). 

How are retailers using social intelligence? Retailers arguably have the largest presence on social media and have successfully used it to build brand recognition by driving traffic to their sites. Yet, many big retailers had negative return on their investments on social media sites since ‘likes’ often did not result in purchase.  Definitely this is not the end of retailers’ social media adventure.  I do expect sharper social analytics will help craft a renewed and financially sustainable strategy in the near future. Why? The traffic is simply too finger licking good to ignore.  So do expect more here. 

How are banks using social intelligence? Definitely banks have made some headway in extracting actionable risk intelligence from social data. For example, to detect fraudulent loan applicants, some banks now seek to cross-match applicants’ personal details like date of birth, location, photos etc. with social data that is readily available from Facebook, Twitter etc. Mismatches are subject to greater /manual scrutiny. 

Risk managers and collection agencies can identify social network behavior of customers whose payment behavior is already known. We can use the same analytical paradigm by taking a set of existing customers (known behavior) and matching it with social networking data – travels (location), payment transactions etc  to glean insights. For example we can track delinquent customers’ travels and see if their delayed payments arise from travel and travel related splurges.  Frequent holiday travel / vacation data emanating from social network intelligence can alert lenders to proactively minimize / reduce / freeze credit lines. The legal and regulatory ramifications of such actions are unclear and will be tested in courts or when regulators take a stand.  But it is well known that Banks have used, are using and will not hesitate to use customer intelligence in creative ways to manage their profitability.

While social analytics is gaining ground, my personal view is that overall impact of social intelligence today has been a mixed bag of success. One key reason is that the extant social analytics tool kit is evolving. Often we find we are not able to garner cool, actionable, disruptive intelligence because we don’t know what to do with this ocean of social data. But that is changing and better tools are coming into the market.

A new paper published by McKinsey points out that social intelligence is now playing a powerful role in helping companies gain strategic insights and develop competitive strategy. New tools are helping social analytics teams to look into user groups, user forums etc. and catch up on what customers are complaining about. 

A talented social analytics team can piece together information on competitor’s new product strategies!  How?  If you listen closely to Twitter chatters, technical help forums etc. , there is wealth of ‘confidential’ information.  For example, techies while seeking help on complex problems they are trying to solve, inadvertently let the cat out of the bag.  That gives you the ‘what’ in the puzzle. Cross matching this user forum posts information / twitter handles with face book or Linkedin can lead to ‘who’ and ‘where’ puzzle pieces. Now thinking social analysts can collate the ‘what’, ‘who’ and ‘where’ and come up with missing pieces. The paper says it is often surprisingly easy to get at confidential information on competitors by deploying appropriate social analytics tools.  

Well, this is scary for companies and do expect more restrictions on employees after the CEOs have taken the time to read this paper.  Behind this emerging scenario is the hidden hand of intelligent social analytics.

Succeeding in Banking Analytics – Choosing the right business model

Also available here

What is an appropriate business engagement model for succeeding in the analytics business?  This is a great question, but has no simple answer. This question seems to be on the minds of leadership in analytics service providers. What engagement model do you choose to build long term trusted relationships with your clients? What is unique in banking analytics space?  In fact, this question also came up recently, rather unexpectedly, when I was having dinner with two old friends in beautiful Los Angeles.

Professional services provided by the vendors range from staff augmentation on one end of the spectrum to high-end solutions consulting that seeks to solve complex business problems.  All this emanates from the thought construct insiders refer to as the engagement maturity model.  This model tracks the morphing of provider-consumer relationship as the two embark on their journey over time. High-end consulting offers higher and obviously desirable ROI. Hence vendors strive to attain this utopia in each of their relationships. In an ideal world, if you can design solutions / offerings that will move clients up on the value chain - from staff augmentation to high end consulting, you have a winning recipe that will make you the top vendor with an enviable revenue stream.  All this is an ideal world. But, how do we work this magic in banking and financial services space in the real world?

Businesses that serve banking and financial services clients face hidden challenges.  It is well known that analytics is what differentiates successful banks. Large, well-run institutions have star-studded analytical teams that have the depth and skills to crunch through well-organized data and come up with insights to make the right decisions.  That is the upside. The down side is that, these teams may sometimes engage in dueling analytics in an effort to be one up on the other, an avoidable waste of resources and talent horsepower. Smaller institutions on the other hand, have smaller talent pool, less organized data and limited capability to undertake complex analytical projects on their own.  

Another key dynamic is that the outcomes of analytical projects impact the banks’ core decisions.  Hence banks often prefer to work with the crème-de-la crème in the business that they can trust. This provides the vendors a great window of opportunity to showcase their excellence in domain expertise, execution and delivery to win the trust of banks. Winning the trust of banks is a prerequisite for deeper, long-term engagements.  

In other words, the analytical ecosystems in these institutions are very different – ranging from the highly competitive and sometimes counterproductive to those with less sophisticated analytical infrastructure. Understanding the extant analytical ecosystem is critical in choosing the right business model for banking analytics providers.

But in the real world what engagement mix should we choose?  Seriously it depends on the prevailing analytical ecosystem of the banking customer. My personal view is that emerging and growing businesses tend to generate a significant proportion of their revenue via an on/off site staff augmentation model. Smaller boutique vendors have successfully demonstrated this as a key entry strategy in a very competitive business.  On the other hand, there seem to be fewer examples of providers choosing high end solutions as the dominant component of their mix. But I think an understanding of the nuances of this industry and the interplay of analytical ecosystem is fundamental to succeeding in banking analytics. This understanding helps discover the right mix.

The coming shortage of Analytic Skills

(Article also available here)

A   study by Avendus Capital published recently points out skill shortage in global analytics business.  According to this study, by 2018 the US will face a huge shortage of analytics professionals – a shortfall in the range of 140,000 to 190000 skilled professionals. The coming skill gap has serious consequences for

a)       companies where analytics plays a central role

b)      analytics service providers and

c)       analytics professionals  

Banking and financial services are the biggest users of analytics followed by retail, healthcare and pharmaceutical sectors in that order. Banking and financial services companies lead probably because they started using analytics long before analytics became a buzz word in the IT world. In fact many of the innovations – in data storage, business intelligence and deploying tools and business intelligence software (BI) for analytics - have been powered by demand and investments from this business sector.

While analytics is old game, the industry never attracted the kind of positive attention seen now. The analytics professional, sometimes referred to derisively as quant jock or what have you, never had it so good. Whatever the nomenclature, they essentially formed the bulwark of back office decision support, drawing useful insights from extant data. However, companies that integrated sophisticated analytics into their business decision process knew their importance all along. But in the recent recession, even as late as 2008 – 2009 or even later, they were laid off in droves from banks, retailers and pharmaceutical and other companies. They were on the chopping block whenever and wherever restructuring occurred. But today’s forecast shortage makes that look like ages ago. Clearly the analytics industry has turned around big time. 

It is common knowledge that the massive amounts of data now being generated from all-round in our digital world has engendered the big data genie. More data means more analytics which yields more insights. Suddenly companies have realized that they need a thought structure to handle this genie, else they risk losing their pecking order in their industry. It is this genie that has magically and swiftly created this colossal asymmetry in demand and supply of skillsets that we are currently witness to. 

In the short term – at least next 12 months or so, the demand for experienced analytic skills will outstrip supply. I know from my past experience in building and leading analytical teams that it takes approximately eighteen months to put together highly skilled analytical teams, albeit from ground up.

For companies that rely on analytics, the huge talent gap may force them to turn to third party analytics service providers. This may actually benefit some since if they move to offshore resources, their costs may actually come down. Consequently, these companies may discover new opportunities to expand their analytics infrastructure. On the other hand, companies that shy away from outsourcing, will have to freeze expansion plans or make do with the talent they have.

Major analytics vendors who already have built their teams will continue to stay ahead and will reap a bonanza for being at the right place at the right time. However, their time in El Dorado will be tantalizingly short since the demand for analytics has already spawned more players and most of them are investing heavily in nurturing good talent. I would definitely expect to see this intense competition put pressure on profitability for all the players. Of course, in the interim, I think there will poaching of talent galore!  I would not be surprised to see several mergers and acquisitions in this space. The big boys will swallow up the smaller players. 

There has never been a better time to be an analytics professional. The skill gap and poaching holds promises of greenbacks and an upward career graph.  This space is already attracting the younger and top talent every day which bodes well for the analytics industry.  As I have written earlier, I think the analytics industry is here to stay and will continue to play a pivotal role in constantly leap frogging the quality of decision making in the world of business in the foreseeable future.  

Think about it. The fortunes of the analytics business have been greatly impacted by happenings elsewhere – the genie sired by big data flow and data management technology. But I think in today’s business ecosystem where interconnectedness of data and insight is the key, it is hard to tell where one ends and where another begins.  But for all those who thought these guys were just quant jocks, wait till you see who laughs all the way to the bank. In the truest sense, it is the revenge of the nerds.

Succeeding in the Business Analytics Business

(Article also available here)

Business Analytics has now attracted attention as never before.  Together with Mobility, Cloud, and Social, Analytics is now trending the IT world.

Business Analytics in itself is not new. But the unprecedented flow of data is. This has engendered a new breed of technology that has produced immense scalability and processing power. This has arguably been responsible for the new surge in quest for analytics to provide new business insights. This marriage between technology and the quest for insights promises to radically change the way business is conducted. This is already being witnessed with companies making large investments to keep up with these new changes.

Business Analytics is a big and fast growing market segment and global IT majors have fallen over each other get their fair share. IT majors may have dominated execution and delivery, but are often surprised by the intense competition they face from boutique firms which offer highly specialized services in this space. Round one appears to have gone to the smaller firms since they seem to have a better game plan, the correct skill mix, albeit limited and clever targeting. But it is only a matter of time before the biggies catch up.

But what does it take to succeed in this business?   This piece identifies five areas which are critical for success in the analytics business.

Have a focus area approach: Analytics pervades every sphere of the business. It is important to have a focus area approach to analytics. Deep analytical talent in focus area gives the customer greater confidence in the relationship. For example building expertise and depth in banking loan portfolio analytics – from generating periodic KPIs to using predictive analytics to optimize customer engagement strategies is a great way to deepen relationships with banks. This not only helps get the foot in the door but also build a sustainable relationship with customers. What areas to offer specialized services and how to build the skills is where domain and subject matter experts can contribute.  It is important not to spread the resources thin by offering solution/ services where there is no depth of talent.

Target business leadership: The business leadership in the company is charged with making the right decisions to steer the company forward.  They have always relied on number-crunchers to help support their decisions. Hence they not only consume the end product, but also are the key drivers of analytics in the organization. A successful strategy for analytics companies must be woven around winning the business leadership.

Solicit emerging market segments: According to IBM’s Rob Ashe, mid-sized companies comprise the fastest growing segment in analytics business.  This is because these companies are realizing that they need an analytics strategy of their own, outside of the capabilities in the tools they have invested in. These companies face intense competition and are seeking help to make the right and informed decisions. This is where business analytics helps. However, many global IT companies are pushing business analytics solutions only to their captive relationship base. They must enlarge their strategy to include the middle tier businesses to stay relevant in business analytics space.

Does the client have top Information Infrastructure: A top rated data infrastructure is a must-have since it helps client companies to quickly deploy analytics. In a broad sense, analytics sits on top of the Information food chain, above the reporting / business intelligence layer, which in turn sits on top of the underlying data layer. Good data begets good insights. It is obvious that customers can derive powerful benefits of business analytics when sophisticated data layers are successfully in place. Customers typically tend to seek repeat engagements in their attempts to solve several related or unrelated business problems once they start getting insights from their investments in data infrastructure. Many companies are investing to shore up their data warehousing and BI capabilities so that they continue to maintain their competitive edge.

Build top analytical skill base: The sudden focus and demand for analytics has logically led to a shortage of trained skillsets. The problem only compounds because skilled analytical talent has limited or no exposure to the technical side. Even more limited are crossover skills where technical and analytical expertise coexists. Given the growth potential for this sector, IT majors must focus on developing and nurturing business analytics skill sets – more specifically the cross-over skill sets.

As companies compete and build top class analytical skills, competition will take on a new meaning as the battle focuses on capturing customer’s insightspace; hopefully will prove insightful for both clients and global IT majors. It won’t be a long wait to find out.

Advancing Analytics to Predict Specific Needs

Great piece on Advanced Analytics

http://www.huffingtonpost.com/christer-johnson/ibm-advancing-analytics_b_1760680.html

Christer Johnson  :IBM Global Business Services Leader, Business Intelligence and Advanced Analytics

One of the many things I've learned from more than 19 years of using analytics to solve challenging business problems is that the word analytics means different things to different people. So before diving into numbers, I define analytics by the objectives they intend to achieve, and the decisions they intend to improve or accelerate. In that context, analytics falls into three categories: descriptive, predictive, and prescriptive.

Descriptive analytics, also referred to as business intelligence, provide a clear understanding of what has happened in the past, through visualization of key performance metrics or other data in a report or dashboard. Today, the past can be as recent as just a millisecond ago.

The sports world has long been a leader in the use of descriptive analytics to provide fans, coaches, and players with a wide range of statistical reports that help them understand what's happening on the field -- whether a coach wants to improve play, or fans want to win their fantasy league.

However, with descriptive analytics, fans and coaches alike must rely on their intuition and ability to interpret the data in order to gain any insight on the relationship or correlation between data inputs and data outputs.

That's where predictive analytics, the second category of analytics, comes into play.

In predictive analytics, the objective is to use advanced mathematical techniques on that past data to understand the underlying relationship between data inputs, outputs and outcomes. Effective predictive models let us quickly understand and estimate outcomes across a wide array of scenarios and conditions. Commonly used for forecasting, simulation, root cause analysis, and data mining, predictive modeling techniques provide insight into complex data that we can't manually interpret from a report or interactive dashboard.

Billy Beane of the Oakland A's famously used predictive modeling techniques to uncover new data inputs that were highly correlated with the outcome of winning baseball games. In tennis, IBM recently began using predictive analytics to automatically sift through a multitude of factors from seven years of data about every point played in the Grand Slam tournaments -- all to estimate the top three keys to each player's match.

Predictive analytics still requires manual evaluation of the various scenarios and the predictive results of each scenario, in order to make a decision. This works well when a decision involves just a few options and the decision maker has time to interpret the predictive results from the various scenarios (for example, a coach using past game statistics to plan for the next game).

It does not work well, however, when a decision maker is faced with thousands or millions of options. Nor does it work well when a decision is needed just seconds after key data inputs are received. This is where prescriptive analytics comes into play.

This third category of analytics, prescriptive analytics, uses mathematical optimization to take into account a multitude of data inputs and constraints related to an objective. The formulas sift through potentially millions of possible decisions to prescribe the actions that will maximize the user's objectives.

Major League Baseball now uses a complex collection of optimization models to create its schedule each year. And some of the most-common uses of optimization outside of sports include pricing optimization for airlines, hotels, and retail chains; transportation planning and scheduling for distribution companies; and the decisions around how to allocate marketing dollars across channels and product categories.

Analyzing Big Data
Today, even small companies are armed with the software and hardware platforms that can efficiently and effectively perform these three types of analytics on enormous volumes of data -- Big Data.

Big Data is defined by the four Vs: Volume (terabytes, petabytes, or more), Velocity (streaming data), Variety (structured variables in a database, versus unstructured text, voice, or video), and Veracity (the degree to which data is accurate and can be trusted).

With the explosion of unstructured data on social media, companies are rushing to analyze this type of Big Data to better understand customers' views, preferences, and behaviors. As exemplified during the Olympics, there are few industries that generate more excitement, discussion, and ultimately data than sports.

The key for sports franchises, as with any company needing to make the most of big data, is to start with the question to be answered, and the decision to be made. Once the question and decision are clear, you have a much higher chance of collecting the right data; using the most-appropriate analytical techniques; and producing insight that you can turn into value for your customers, your company, and yourself.

Social Media Intelligence – A Disaster?

Just read this piece on Bloomberg about how retailers are shutting down their Facebook shops.  I have written in the past about how many companies are using social media to glean actionable intelligence about customers. Very specifically I had discussed how social media has the potential to provide risk intelligence to credit risk managers in banks and financial institutions. While there was flicker of interest, it has not actually picked up for several reasons.

For starters, social media sites like Face book are more an online venting of personal idiosyncrasies and little nonsense we all like to indulge ourselves when our guards are down. Personally I think it is more akin to street corner gathering of teenagers who hangout more for social companionship rather than any serious discussion. Of course, there are serious discussions at these hangouts, but they are heavily outnumbered by the day-to-day sharing of simple trivialities. That is the nature of Facebook transactions.

The most obvious reason seems to be the inability of many leading companies / brands to monetize their huge followings or “likes” on Face book. Many companies opened Facebook stores because they did not want to lose out. However, these initiatives have not provided the expected return on investments and many leading brands -  JC Penny, Gamestop, Gap, Nordstrom, just to name a few – have shut down their Facebook stores. See article for more details.

Banks and financial services, on the other hand, have been more cautious in their social media strategy. Credit Risk Managers were not blown off their feet by Facebook’s ability to provide risk intelligence. While collection agencies are using Facebook to locate delinquent customers, it is a far cry from replacing Credit bureaus as a primary source of customer data. I think we have to wait and see how this evolves in the coming months.

Current social media strategy has hurt companies in other ways too. Many companies that bet on Facebook and have invested heavily are already seeing negative returns. Poor revenue streams from these strategic decisions will show up in the balance sheets of many software companies as early as 1Q of 2012. It will be interesting to see how the markets respond to the poor results.

I am not prognosticating a complete failure of social media strategy. Rather, it is a time for introspection and realignment for future course of action, given what we are learning. As always, I believe failure is a great educator and leads to innovation; innovation is the key distinguisher in a competitive business environment. I believe when the dust settles, it will lead companies to engender a more compatible and sustainable social media strategy.  Definitely, the current social media strategy a.k.a “Facebook Strategy” of many companies does not seem to be working.  Stay tuned folks.