Predictive Analytics improves M&A Activity

There have always been two major ways to expand your business:  Grow it, or Buy it.  This brings up some interesting questions about which is more beneficial.  The correct answer is usually based on cost of customer acquisition and customer lifetime value.  Right now, with the cost of client acquisition being so high, companies are turning to buying distressed businesses.  One, it eliminates competition, and two, the customers can be acquired “on sale”.  While mergers and acquisitions are common across all industries, there seems to be a significant propensity for growth by buying in the banking industry.
The unique problem that is causing an increase in the ” buy them” thought process is that in banking their revenue generating power has dwindled with the decline of interest rates.   Not only that but as clients leave for competitors by natural attrition, there is a dire need for new customers.  Buying seems to solve both of these.
While it may solve the issue of new customers at a reduced cost, how to transfer the old customer base to the new bank has always been a major problem.  First, you have a bevy of new customers who have not gone through your buying process.  You have no idea who they are and why they are in the product they are in.  Secondly, you can fix problem number one by keeping the staff from the bought bank, but they’re not sure if the customers are in the correct products anymore either because they don’t know what products they have to sell. (more…)

The Predictive Analytics Revolution – Are you sitting on the sidelines?

Predictive analytics (or Big Data) is here to stay. You may not understand it. You may not believe that it really works. But the reality is this: your competitors (and it may be just one or two of them) are using predictive analytics to chew up market space as you remain on the sidelines.
Don’t believe me? (more…)

Predictive Analytics in Retail

Data is no good if you can’t get it quickly enough and act quickly enough on it. Its all about getting the data fast and acting on it fast
– Andrew Pole
Predictive analytics solves many differnt problems in a many facets of business. The New York Times published an article in February about the retail chain, Target. Target knew that if they could predict buying patterns in a certain group of consumers, they could influence those consumers purchases. Target was smart, they knew exactly who they wanted to go after.
Andrew Pole, a statistician working for Target, created a pregnancy-prediction model that was able to track spending habits and predict when a woman is pregnant. Pole said that, “We knew that if we could identify them in their second trimester, there’s a good chance we could capture them for years. As soon as we get them buying diapers from us, they’re going to start buying everything else too. If you’re rushing through the store, looking for bottles, and you pass orange juice, you’ll grab a carton. Oh, and there’s that new DVD I want. Soon, you’ll be buying cereal and paper towels from us, and keep coming back.” (more…)

New Pew Survey on Big Data | Big Data is the new Oil | 'Minority Report' software hits the real world

Why data trumps experience in trial conversion: “Using predictive analytics to qualify trial users and focus on those that are most likely to convert can double conversion rates. In a 2012 study, the Aberdeen Group published a finding that companies using predictive analytics have a 73% sales lift versus companies that did not. … Publishers should use predictive analytics to develop trial scoring rules. These scoring rules can constantly prioritize trials in their likeliness to convert which increases close rates and sales productivity. These same predictive analytics are useful in design of trial parameters such as length and access limits.” (Business Insider)
Connect Big Data With Customer Behavior to Improve Social, Email, and Web ROI: “Since we have lots of data, we have lots of integration challenges. … Mastering that flow of data between the places that generate it (click-stream, communities, sentiment analysis, email and SMS messaging, and portals) and the systems that utilize it (marketing automation, messaging delivery, and social publishing) is creating complexity, as well as opportunity.” (more…)

How Big Data Can Bring Big Sales / Insurance Industry See Vanlue in Analytics

How Big Data Can Bring Big Sales: The holy grail of retail has been to anticipate what consumers need even before they realize they need it. … Take printer cartridges, for example. There’s nothing worse than having to print a boarding pass with the taxi waiting outside and realizing you’re out of ink. Today, office supply retailers are able to track purchases of customers’ in-store credit cards and rewards cards and, based on purchase history, anticipate when a consumer might need to reorder a product. Marketing can send an email offer for printer cartridges as well as an accompanying promotion for paper, with a guaranteed delivery time of 24 hours. (ZDNet)
Massachusetts Big Data Initiative: The initiative will lead to a grants-matching program for research and development into big data, create internships, and launch the Massachusetts Big Data Consortium. That group will bring together academia, industry, and government to foster new big data tools and technologies. And [Governor Deval] Patrick has tasked the Commonwealth to work with the consortium ‘to see how data analytics and applications can help improve the efficiency and effectiveness of government programs and services.’” (IT World)
— “Chief among the initiatives is MIT’s new big data research center, known as bigdata(at)CSAIL, which will be run out of the school’s Computer Science and Artificial Intelligence Laboratory. The center will focus on data collections that are too big for current information technology systems and will call on industry, government and academic leaders to develop techniques to process, share, store and manage the large amount of data.” (AP)
Data Driven Knowledge vs. Expert Knowledge: “…data-driven knowledge, as its name suggests, is based upon data—usually, lots of it. A few decades ago, a series of statistical techniques emerged with the intent of uncovering data patterns typically hidden to the human eye. Given that we capture data in an ever-increasing volume today, these techniques are proving indispensable to extracting value from data, making processes repeatable and accurate.”
–“The movie Moneyball exemplifies that really well. In the movie, a group of experienced recruiting agents offer their first-hand knowledge and hunches on which players should be pursued to be part of the team. That is contrasted with a data-driven approach in which knowledge is extracted from the data already available for each player, and a team assembled from that. Although Moneyball chooses one type of knowledge over another, in most cases, we should and do use expert knowledge and data-driven knowledge together.” (Smart Data Collective)
Salesforce Intros Radian6 Insights for Social Big Data: “Acquired roughly 18 months ago, Radian6 is now expanding with this new platform to tackle sentiments, intents, demographics and more key metrics found within the most commonly used social media channels. The idea is to then convey this information in a way that will better enable business customers to optimize their marketing, customer service, and lead techniques.” (ZDNet)
Top 10 Categories For Big Data Sources and Mining Technologies: “Since every answer will be different, this means there’s no one-size-fits-all solution. Success lies in recognizing the different types of Big Data sources, using the proper mining technologies to find the treasure within each type, and then integrating and presenting those new insights appropriately according to your unique goals, to enable your organization to make more effective steering decisions.” See the top 10 list here (ZDNet)
Insurance Industry See Vanlue in Analytics: “‘Insurers are investing in analytics, with the survey showing that North American insurers spend about nine percent of their IT budgets on data and analytics,’ says Mark Breading, partner with SMA. ‘What is even more interesting is that business units outside of IT are spending approximately the same amount. All together this represents almost $10 billion in spending each year.'” (Property Casualty 360)

Big Data In The Travel Industry and More

How To Make The Most Of ‘Big Data’ In The Travel Industry: “There is a ‘big data’ revolution underway in the travel and hospitality industry but travel companies need to be clear about the challenges. … Data analytics is an interesting prospect for the travel sector as so many data streams can be combined. … Business analytics pulls insights from vast databases commonly referred to as ‘big data’. To be successful and maximise the value of this, firms need to be very focused and disciplined.”
— As firms plan to take the plunge, here are some expert recommendations: “Focus on areas where an impact can be made … Understand how to engage with consumers more efficiently … Identify patterns that can lead to insights around consumer acquisition, retention and marketing.” (Hospitality Net)
“Business analytics should itself be adaptive and regularly refined by new data that users feed back into the system as that is the whole purpose of predictive modeling…” [Brenda] Dietrich explained that data analysis allows companies to extrapolate outcomes linearly and decide what appropriate action to take next. Those actions also generate new data, which should be fed back into the analytics model so it is continuously refined, improved, and accurate, she said. …This notion of constantly ‘learning from the data’ is a new and exciting development in the analytics space, because it means a company can see, as time progresses in reality, whether it is moving toward X or Y, and decide the next step it should take, she said.” (ZDNet)
“Why is data science relevant?” “Benjamin Franklin is alleged to have said in response to the questioning of the value of the first hot air balloons, ‘What is the value of a newborn baby?’ Actually, data science is probably a long way from the newborn baby stage, although it still has a long way to go before it achieves full maturity.” (Network Computing)
Dell Provides Schools and Universities with Predictive Analytics: Schools and universities are turning to Dell’s Education Data Management (EDM) solution, a decision support system, to help personalize student learning, increase retention and graduation rates while improving planning, management and reporting. The solution integrates student performance and operational data with predictive analytics to help educators monitor student progress and intervene when needed to improve success. It tracks each student’s data between schools and over multiple years to help parents and educators monitor student progress and respond to developmental needs or hone in on specific interests and aptitudes. (EON)
The Hadoop bone’s connected to the SQL bone: “Microsoft has been working with Hortonworks to build a distribution of Hadoop for Windows Azure, its cloud platform, and for Windows Server.  Right now the service is available as a cloud service in a by-invitation beta that just entered its third release.  … Why would Microsoft be so bullish on technology that is open source, Java-based and largely Linux-facing in pedigree? Most likely it’s because Microsoft runs Bing. By some counts, Bing and Yahoo Search (which is Bing-powered) together have about 30% search market share and Turner announced in his keynote that Bing is now leading Google in search relevance. (ZDNet)
Adding Second-Tier analysis To Harness Big Data: The real challenge with Big Data is in going from individual siloes of data analytics to a bigger picture that successfully and meaningfully puts those analytics into the full-enterprise context. It’s how we map these analytical islands to each other that ultimately provides the support we need for improved quality in our decisions. … It is important that business and operational metrics be aligned to improve decisions and help ensure business survivability.
— A useful second-tier analysis effectively describes key business functions or processes and business assets, and then correlates operational reports and metrics to them. For example, linking accounts receivable to technology assets to operational practices and metrics can help expose significant enterprise risk. (SC Magazine)

Forbes: The Age of Big Data / A Looming Talent Gap For Data Scientists / Why Companies Are Spending More on Analytics

The Age of Big Data: “…Big Data has the potential to utterly transform the relationship that individuals have with institutions, customers with companies, patients with the healthcare system, students with universities, and voters with government. And that means once it has fully penetrated society and industry, the Big Data revolution may very well prove a turning point in our economic – and ultimately, cultural – history as great as the electronics revolution. . . perhaps even as great as the first and second Industrial Revolutions.”
–“Why? Because once the relationship of individuals to institutions transforms, the benefits to the individual consumer, citizen, patient and student will be profound.” (Forbes)
How a Looming Talent Gap Will Crush Enterprise Hopes for Big Data: “’A lot of companies don’t know how to find data scientists, and don’t understand data science,’ … ‘These enterprise companies can’t implement a proper data analytical solution because they have no data talent.'”
— “Part of the problem is an overall lack of big data skills in the United States. In May 2011, the McKinsey Global Institute laid out the numbers: ‘By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.’” (ReadWriteWeb)
Big Data Security Is Inevitable: “There’s been a fair amount of discussion about the fact that security analytics is becoming a big data problem. … If you think that enterprises recognize these trends, boning up on Hadoop, Cassandra, and NoSQL, and hiring data scientists to tag along with security analysts, think again.  There’s a growing security skills shortage that will preclude these activities before they even start.(Network Worlds)
Big Data Holds Big Promise for Government: “Big data has the potential to transform the work of government agencies, unlocking advancements in efficiency, the speed and accuracy of decisions and the capability to forecast, according to a separate report from MeriTalk.”
–“…the Centre for Advanced Spatial Analysis (CASA) at University College London is combining data from London’s Oyster cards – used to pay for public transport – and Twitter messages. Tube-travel patterns are regular: people who enter the system at one station tend to leave it at a particular other one. Twitter messages reveal a city’s structure and its activity.” (Smart Data Collective)
Why are companies spending more on analytics despite cutbacks elsewhere? “Analyst Dan Vesset, author of IDC’s “Worldwide Business Analytics Software” report, credits ‘attention-grabbing headlines’ about big data, rather than the data stockpiles themselves, with helping to put business analytics on the agenda of senior executives. Goodnight seems equally dubious, saying big data is the hot new topic ‘because people got tired of talking about the cloud.'” (InformationWeek)

Analytics Market Grows in 2012

Analytics Market Grows in 2012

“The global market for business analytics software grew roughly 14 percent in 2011, fueled by pervasive hype about ‘big data’ as well as new technological innovations, according to a report unveiled by analyst firm IDC yesterday. Between now and 2016, the business analytics market will have a compound annual growth rate of 9.8 percent, reaching US$50.7 billion, IDC said.” (Global Financial Network)
— “As part of that overall business analytics segment, the data warehousing platform software portion represented the fastest growth, at 15.2 percent in 2011 compared with 2010. IDC also pegged analytic application growth at 13.3 percent last year from 2010, and BI and analytic tools at 13.2 percent last year from 2010.” (Information Management) (more…)

Analytics and the Nimble Corporation

Brian Sommer at ZDNet has an excellent three part blog series about nimble verses the ossified companies and how each will use big data analytics technology. (Mr. Sommer defines ossified companies cease to develop and innovate and becoming stagnant or rigid in their ways.)
Brian says ossified companies could care less about responding to their market, therefore analytics will be a waste of time and money for them. Brian writes, “They are so rigid in their world view, their processes and business practices that they choose to ignore the very suggestions that could save their firms. They’ve not only ossified, they’ve turned deaf, too.”
What are the characteristics of an ossified company? Brian says they lack “leadership, vision, a continuous change capability and a culture that rewards risk-taking and change over risk avoidance and slavish adherence to ever growing obsolete processes.”
On the flip side of the coin, the nimble companies will live and die by analytics technology because responding to market conditions is part of their DNA.

A nimble firm experiments. Thomas Edison tried something like 6000 attempts at creating a long-lasting light bulb. Edison would have never been allowed 1% of those at most companies. A nimble firm can refine their analytics to isolate experimental results from those of other markets. The insights from these experiments will guide the eventual rollout of game-changing new solutions/processes/etc.

A nimble firm has many current hypotheses about the market. They use analytics to test, prove/disprove and refine these.

A nimble firm, and this is most important, can scale fast. When they see a new market opportunity, they test, refine and then use explosive energy to seize the awaiting market opportunity. These firms can exploit a new market opportunity with incredible speed and precision. They are not only capable of change, but, they can change their entire firm almost overnight.

 I highly recommend reading all three posts.
The Ossified Organization Won’t ‘Get’ Analytics (part 1 of 3)
How Tough Will Analytics Be in Ossified Firms? (Part 2 of 3)
Analytics and the Nimble Organization (part 3 of 3)

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