Developing the Right Data Strategy for Your Organization

When it comes to making actionable use of data, there is no single playbook or set of common practices that apply universally to all businesses, CIO Journal Columnist Randy Bean says. “Organizations would be well served to break from accepted dogma and apply fresh thinking as they consider how best to align their resources, capabilities, and people to make wise use of their data,” he writes.

Combining consumer data to solve big problems

This Forbes article includes some great examples of business models evolving to make money and improve our lives through mining and sharing data. The businesses are not making money immediately but are investing in data capture, analytics and partnerships to start capitalizing on their data for long term revenue.

Key points in the article:
  • Your data combined with those of thousands of other people can tackle bigger problems such as cutting your company’s health care budget or sparing the nearby utility from building another power plant.
  • Smart-thermostat maker Nest Labs has quietly built a side business managing the energy consumption of a slice of its customers on behalf of electric companies.
  • In wearables, health tracker Fitbit is selling companies the tracking bracelets and analytics services to better manage their health care budgets, and its rival Jawbone may be preparing to do the same.
  • These companies are capitalizing on the terabytes of data they collect from consumers and, to an extent, on the largesse of taxpayers. State governments have increased the money–from $1.3 billion in 2003 to $6 billion in 2012–allocated to helping utilities manage energy demand, according to the U.S. Energy Information Administration.

See full article -

How can we leverage our data either directly or with business partners/customers to generate revenue and solve big problems?

Start-ups are now offering BI solutions to our middle market customers

We know we have data which we can leverage as an asset. We also can derive great insights or predictions for our customers. In this article it describes what start-ups are doing to build a winning BI business model in this middle market. I like the intercept/middleware model mentioned near the end of the article. CDF is looking at potentially playing in that space or partnering with other companies that offer services to dealers and capture additional data from the dealers.

Great analysis of the 2014 Gartner BI Quadrant – winners and losers

The Gartner report was released a few months ago. I was looking for an objective review of their quadrant and who was gaining ground and who was losing ground. I found this excellent summary with insightful commentary.

  • Gainers: Tableau, Qlik and Spotfire. Losers: Microsoft, MicroStrategy, SAP and Oracle.
  • We don’t need more tools right now but we do need to keep an eye on the future for when we might want to retire a tool or move more solutions to a tool we already have.
  • Hope you find the analysis informative

    The Industries Plagued by the Most Uncertainty

    It’s a cliché to say that the world is more uncertain than ever before, but few realize just how much uncertainty has increased over the past 50 years. To illustrate this, consider that patent applications in the U.S. have increased by 6x (from 100k to 600k annually) and, worldwide, start-ups have increased from 10 million to almost 100 million per year. That means new technologies and new competitors are hitting the market at an unprecedented rate. Although uncertainty is accelerating, it isn’t affecting all industries the same way. That’s because there are two primary types of uncertainty — demand uncertainty (will customers buy your product?) and technological uncertainty (can we make a desirable solution?) — and how much uncertainty your industry faces depends on the interaction of the two.

    Demand uncertainty arises from the unknowns associated with solving any problem, such as hidden customer preferences. The more unknowns there are about customer preferences, the greater the demand uncertainty. For example, when Rent the Runway founder Jenn Hyman came up with the idea to rent designer dresses over the internet, demand uncertainty was high because no one else was offering this type of service. In contrast, when Samsung and Sony were deciding whether to launch LED TVs, which offered better picture quality than plasma TVs at a slightly higher price, there was lower uncertainty about demand because customers were already buying TVs.

    Technological uncertainty results from unknowns regarding the technologies that might emerge or be combined to create a new solution. For example, a wide variety of clean technologies (including wind, solar, and hydrogen) are vying to power vehicles and cities at the same time that a wide variety of medical technologies (chemical, biotechnological, genomic, and robotic) are being developed to treat diseases. As the overall rate of invention across industries increases, so does technological uncertainty.

    Consider the 2×2 matrix below. The horizontal axis plots each industry based on technological uncertainty, measured as the average R&D expenditures as a percentage of sales in the industry over the past ten years. The vertical axis plots each industry’s demand uncertainty, measured as an equal weighting of industry revenue volatility, or change, over the past 10 years and percentage of firms in the industry that entered or exited during that same time period. Although these are imperfect measures, they identify the industries facing the highest, and lowest baseline levels of uncertainty.

    Business Choice Awards 2014: Business Intelligence

    Some might call it an oxymoronic term, but Business Intelligence can put an organization on the path to data-driven decisions. Here are the ratings of BI tools used by PCMag’s more corporate readers.

    In today’s big data world, every big business needs a software or service designed to retrieve, analyze, and report data to provide insight into business operations and customer satisfaction. Anything worth collecting is worthy of a report, especially if that report can help improve your business and increase revenues, decrease costs, or both. That report can be a dashboard that shows a 30,000-foot view and lets you drill down to determine the significance of individual factors. And the software and services that provide those kinds of reports are called Business Intelligence (BI) tools.

    Naturally, that’s the topic for this edition of the PCMag Business Choice Awards. For more than 25 years, we have been augmenting our hands-on, labs-based product reviews (which receive our Editors’ Choice Award when worthy) with our Readers’ Choice Awards, in which PCMag readers rate the products and services they use the most. The Business Choice Awards extend the Readers’ Choice Awards by garnering feedback about the hardware, software, and services our readers deploy, administer, maintain, and use in a work environment.

    Want to participate in future surveys with other PCMag readers? Click here and sign up for the Readers’ Choice survey email list to receive invitations.

    Our survey asked respondents to rate their overall satisfaction with the BI products they use or manage and the likelihood they would recommend them to others. In addition, we inquired about their satisfaction with technical support, and the overall reliability of the solution.

    If you select, deploy, or administer the products in our Business Choice Awards, or if you advise or manage people in these roles, then you know how critical it is to choose the right products. The results of the PCMag Business Choice Awards survey are invaluable when doing so. And on the next page we’ll reveal just who to turn to when you’re thinking your company needs some serious business

    Business Intelligence Tools

    Business Intelligence tools are typically developed and sold by big name players in database and business software. This is because it isn’t just about the reporting, it’s also about the ability to find and consume data, most of which is housed in databases built by behemoths.

    In this year’s survey, four companies received enough responses to be included. Looking at overall satisfaction, IBM Cognos takes the lead followed by Microsoft BI at 6.7, Oracle at 6.6, and SAP BusinessObjects at 6.4.

    Business Choice 2014: Business Intelligence - Overall Scores

    Reliability, in this case the ability of the software or service to run every day and provide consistent results (click for the full table of scores below), follows the trend of overall satisfaction. IBM Cognos leads the way with a score of 7.3 followed closely by Oracle at 7.2 and Microsoft BI at 7.1. Trailing well behind is SAP BusinessObjects at 6.8.

    Looking at tech support, only Microsoft BI had a reasonable number of customers requiring tech support with 15 percent. IBM Cognos and Oracle trail behind at 21 and 24 percent, respectively. SAP BusinessObjects brings up the rear with a full 30 percent of survey respondents requiring tech support. Tech support ratings are also relatively low, with Microsoft receiving a high of 7.6 out of 10.

    Speaking of abysmal, SAP BusinessObjects had the highest tech support needed, the lowest tech support rating, the lowest reliability, and the lowest overall satisfaction.

    We asked respondents to answer this critical question: “How likely are you to recommend your business tools provider to a colleague?” Here, we again see that no one is very likely to recommend their business tools provider to a colleague. IBM Cognos again leads with a 6.9 followed by Oracle and Microsoft at 6.7, and SAP BusinessObjects with a 6.4.

    That same question is used to calculate the Net Promoter Score (NPS), which is certainly the most fascinating statistic here. The negative numbers tell it all: No one likes their BI tool. Even our winner, IBM Cognos, has a strongly negative NPS at -14, meaning that customers are more likely to steer colleagues away from IBM Cognos than recommend it. It’s unclear from our survey why this is. My 25+ years as an industry analyst tells me it is because no matter how you slice it, business intelligence is tough stuff. There’s a lot of back-end integration required, followed by a lot of report customization. None of these solutions are as turnkey as their marketing materials would lead you to believe and that makes for some pretty unhappy customers.

    From these results, we could arguably say that businesses might not need BI tools, but if your office does feel the need, we can say that IBM Cognos is the service to try.

    Related Story See all survey results for Business Choice 2014: Routers.

    Business Choice Winners: BUSINESS INTELLIGENCE Business Choice seal IBM Cognos

    IBM says that the Cognos software (which it acquired in 2008) will make any operation “top-performing and data driven” and our readers place IBM Cognos, in all its iterations from Express to Enterprise, at the top of the heap of available BI solutions.


    For the 2014 Business Choice series, we emailed survey invitations to community members, specifically subscribers to our Readers’ Choice Survey mailing list. The surveys are hosted by SurveyMonkey, which also performs our data collection. This survey was in the field from June 16, 2014 to July 14, 2014.

    Respondents are asked to rate their business intelligence tools provider. They are asked multiple questions about their overall satisfaction with the solution, as well as experiences with technical support within the past 12 months.

    Because the goal of the survey is to understand how the BI tools compare to one another and not how one respondent’s experience compares to another’s, we use the average of the BI tools’ rating, not the average of every respondent’s rating. In all cases, the overall ratings are not based on averages of other scores in the table; they are based on answers to the question, “Overall, how satisfied are you with your business intelligence tools provider?”

    Scores not represented as a percentage are on a scale of 0 to 10 where 10 is the best.

    Net Promoter Scores are based on the concept introduced by Fred Reichheld in his 2006 best seller, The Ultimate Question, that no other question can better define the loyalty of a company’s customers than “how likely is it that you would recommend this company to a friend or colleague?” This measure of brand loyalty is calculated by taking the percent of respondents who answered 9 or 10 (promoters) and subtracting the percent who answered 0 through 6 (detractors).

    If you would like to participate in PCMag’s monthly Readers’ Choice surveys and to be eligible for our monthly sweepstakes promotion

    “Global SaaS-Based Business Intelligence (BI) Market 2014-2018″

    New Software market report from TechNavio: “Global SaaS-based Business Intelligence (BI) Market 2014-2018″

    Boston, MA — (SBWIRE) — 07/25/2014 — The term BI refers to applications and technologies that are used for gathering, providing access to, or analyzing information about a company’s operations. BI help organizations to acquire comprehensive knowledge of the factors affecting their business; for example, metrics on sales, production, and internal operations. Some of the BI tools and applications include query reporting, analysis tools, data mining tools, and data warehousing tools. SaaS-based BI refers to the deployment of BI applications as a web-based subscription service. SaaS-based BI is operated by a third-party organization as opposed to the installation and maintenance of an On-premise business system. In the pay-per-use model, BI is hosted by a third party from a remote location, and the outsourcing company pays according to the usage. Hence, enterprises employing SaaS-based BI can shift their focus from frequent software updates, maintenance, continual trouble-shooting, and increasing storage requirements, and concentrate on their core business activities.

    View Full Report Details and Table of Contents

    TechNavio’s analysts forecast the Global SaaS-based BI market to grow at a CAGR of 30.93 percent over the period 2013-2018.

    Covered in this Report

    The Global SaaS-based BI market can be segmented on the basis of deployment models such as Private cloud, Public cloud, Community cloud, and Hybrid cloud.

    TechNavio’s report, the Global SaaS-based BI market Market 2014-2018, has been prepared based on an in-depth market analysis with inputs from industry experts. The report covers the Americas, and the EMEA and APAC regions; it also covers the Global SaaS-based BI market landscape and its growth prospects in the coming years. The report also includes a discussion of the key vendors operating in this market.

    Key Regions
    • Americas
    • EMEA
    • APAC
    Key Vendors
    • IBM Corp.
    • Microstrategy Inc.
    • Oracle Corp.
    • QlikTech International AB
    • SAP AG
    • TIBCO Software Inc.
    Other Prominent Vendors
    • Actuate Corp.
    • Birst Inc.
    • Bime
    • Cloud9 Analytics
    • GoodData Corp.
    • Indicee Inc.
    • Host Analytics Inc.
    • Jaspersoft Corp.
    • Kognitio
    • PivotLink
    • SAS Institute Inc.
    Key Market Driver
    • Increasing Volume of Information Generated in the Enterprises.
    • For a full, detailed list, view our report.
    Key Market Challenge
    • Presence of Many Open-source BI Vendors.
    • For a full, detailed list, view our report.
    Key Market Trend
    • Growing Demand for Mobile BI.
    • For a full, detailed list, view our report.
    Key Questions Answered in this Report
    • What will the market size be in 2018 and what will the growth rate be?
    • What are the key market trends?
    • What is driving this market?
    • What are the challenges to market growth?
    • Who are the key vendors in this market space?
    • What are the market opportunities and threats faced by the key vendors?
    • What are the strengths and weaknesses of the key vendors?

    You can request one free hour of our analyst’s time when you purchase this market report. Details are provided within the report.

    Companies Mentioned in this Report: IBM Corp., Microstrategy Inc., Oracle Corp., QlikTech International AB, SAP AG, TIBCO Software Inc., Actuate Corp., Birst Inc., Bime, Cloud9 Analytics, GoodData Corp., Indicee Inc., Host Analytics Inc., Jaspersoft Corp., Kognitio, PivotLink, SAS Institute Inc.

    About Fast Market Research

    Fast Market Research is a leading distributor of market research and business information. Representing the world’s top research publishers and analysts, we provide quick and easy access to the best competitive intelligence available. Our unbiased, expert staff is always available to help you find the right research to fit your requirements and your budget. For more information about these or related research reports, please visit our website at

    Browse all Software research reports at Fast Market Research

    Read more:

    Big data a winner in the World Cup

    ON the exhibition floor of the Mobile World Congress in Barcelona in February, one of the stand-out displays was a large TV screen on which the tactics of the German football team were being analysed.

    Enterprise systems company SAP was demonstrating how an application called Match Insights could gather data before and during a match and use it to influence the coach’s tactical decisions while the game was on.

    Most saw the demo as a marketing exercise. But when Germany won the World Cup, systematically outplaying opponents with superior tactics, the data game became very real.

    According to SAP, the journey started last year when national team general manager Oliver Bierhoff found that players were happiest communicating with each other on digital platforms. He commissioned SAP to develop an application that could facilitate the exchange of information, including data about opponents. SAP Match Insights was then developed in collaboration with the German team.

    “This data can be converted to simulations and graphs that can be viewed on a tablet or smartphone, enabling trainers, coaches and players to identify and assess key situations in a match,” said Manoj Bhoola, a director at SAP Africa.

    “SAP Match Insights synchronised data from scouts with the video footage from the pitch to make it easy for coaches to identify key moments in the game.”

    The impact on the outcome of the World Cup is not as easy to quantify, but it’s given “big data” its biggest showcase yet.

    “Big data is an incredible resource for coaches and players to contextualise information and draw well-informed conclusions to optimise training and tactics,” said Simon Carpenter, chief customer officer at SAP Africa. “It’s high time to make this type of information accessible to sports journalism and the fans as well.”

    German soccer may have discovered big data, but it’s a path well worn by large enterprises.

    “We have been doing it all along,” said Desan Naidoo, managing director for Southern Africa of global analytics company SAS. “But some of the aspects have changed. If you look at the volume and variety of structured and unstructured data, ranging from social networks to text and video, that has definitely changed. Ninety percent of all data ever created have been created in the past two years.

    “This is unbelievable in itself. But now the requirement from clients to have access to this data has moved from running data through models for 18 to 24 hours to wanting access in minutes or seconds.”

    And it’s not enough merely to analyse the data that are formally collected in organisational systems. “We’ve had to tap into social media data. We’ve had to restructure the way we do analytics to cope with the volumes. We’ve had to look at hardware changes and infrastructure such as in-memory analysis.”

    The latter refers to loading relevant data into live memory so that it can be processed on the fly, providing usable information in seconds. A typical example is a customer going to a bank for a home loan: it can now run a risk profile and give an answer while the individual is waiting.

    “In the past, if you based that risk profile on all the data sources the bank has, it would have taken hours,” said Naidoo.

    “Having access in-memory means you can click a button and run a risk profile accessing all that data instantaneously. On top of that, analytics today can predict how that customer will behave, rather than being merely reactive, as in the past. That’s what big data means today.”

    Goldstuck is founder of World Wide Worx and editor-in-chief of Follow him on Twitter @art2gee,

    This article was first published in Sunday Times: Business Times

    How Vertical Markets will Drive Big Data

    Although the article is bit dated, this 2012 report from Gartner does a good job of defining the vertical market opportunities that existed in the emerging world of Big Data.  As you can see in the first graphic below, all vertical markets had some interest in Big Data in 2012, with Education and Transportation having the highest percentage, and Insurance and Banking having the lowest.  That always seemed a big backwards to me, but the core idea is that Big Data grew at different rates in each vertical industry because Big Data delivered different values for each vertical.

    how to chose a hard drive

    Gartner’s take on Big Data opportunity by industry in 2012.

    About the same time as the Gartner report, Reuters provided some data that predicted the rapid growth of Big Data, as shown in Figure 2.  For the most part, we’re on track with the Reuters numbers and should see the 45% annual growth that Reuters predicted, if you look at other analyst reports and take my own experience into consideration.

    how to chose a hard drive

    In 2012, Reuters predicted the rapid growth of Big Data.  For the most part, we’re on track with these numbers.

    More recent data includes an IDC study that predicts the market for Big Data to reach $16.1 billion in 2014, growing 6 times faster than the overall IT market. “IDC includes in this figure Infrastructure (servers, storage, etc., the largest and fastest growing segment at 45% of the market), services (29%) and software (24%).”

    Most interesting was one prediction that the adoption of analytics-as-a-service will accelerate, and “ready-made analytics in the cloud” will make the use of Big Data more attractive and compelling.  This would include the ability to define common vertical analytics that will be sold as part of Big Data technology, using cloud-based platforms as the way to distribute these analytics to end user organizations.  That’s my assertion here, and seems to be an emerging and rapidly growing pattern.

    Finally, IDC predicts that cloud infrastructure will be the fastest-growing sub-segment of the Big Data market, with a 2013-2017 CAGR of close to 50%.  This means that cloud is driving Big Data, and vice versa.  We’ve seen this happen for the last few years now.

    So, where am I going with all of this?  Moving forward, factors that will drive Big Data adoption include:

    ·  The use of public cloud-based platforms.  This will remove much of the risk of adopting Big Data systems, even amongst the most frugal vertical industries.

    ·  Vertical market-focused features within Big Data systems, which will make the use of Big Data much more valuable in specific verticals.  Despite what the Gartner report said in 2012, the majority of adopters now appear to be banks and other financial services firms, as being the largest users, with healthcare showing the highest percentage of growth.

    ·  The ability to leverage cloud platforms as a single point of distribution for vertically-oriented features and facilities.  This includes schemas to support specific vertical industries, as well as pre-built analytical services that focus on specific vertical processes and analytical requirements.

    I’ve already encountered examples of all the above factors.  There are now analytical services that focus on providing information about quality of care in the healthcare vertical.  These services leverage local patient care data along with industry data provided by other data providers that allows healthcare providers to determine true quality of care information, as well as determine how their organization stacks up against other healthcare providers.  This is all part of a canned report, and data set, specifically built for the healthcare vertical and is just one of hundreds of pre-built cloud-delivered analytics that are offered within vertical markets.

    Most IT shops don’t think in these terms yet.  However, end users continue to discover pre-existing and well-defined analytical services that exist in clouds, services that will finally allow them to get at the information they need.  Much like the drive of “shadow IT” around cloud usage in the last few years, the pressure from end users toward pre-built and vertically-oriented Big Data analytics will force IT to provide access to these services.  As a result, more reinvestment will go back into developing these services by the Big Data and cloud technology providers, and these tools will become even more valuable and useful.

    This is happening now.  Big Data continues to get, well, bigger, and cloud computing seems to be driving its use.  The growth of specific vertical market features provided by Big Data systems, and typically delivered and enabled by the public cloud, will be what drives the use of Big Data going forward.  The business case is just too compelling, and technology just too easy to leverage and deploy to make any other prediction.

    Delving into customer thoughts: TEXT ANALYTICS provides insights

    Text analytics is a powerful tool that so far has been used most extensively for analyzing customer-related information. “The tools are business-friendly and the content—once parsed—is easy to process,” says Leslie Owens, VP of research at Forrester. “The leading vendors have found the most adoption in social listening and are well established, with robust cloud-based solutions in this area.” Other applications for text analytics are also growing, and predictions for the worldwide market indicate that it will increase from under $2 billion at present to nearly $5 billion by 2019.

    Developing strategies

    Oberweis Dairy evolved from a family dairy business based in Illinois. Founded in the early 1900s, Oberweis still offers home delivery of milk and ice cream in the Midwest and in several other states throughout the country. In addition, the company operates about 50 retail dairy stores that sell its products, and recently opened several restaurants that are co-located with its stores.

    To provide detailed insights into its operations, Oberweis decided about five years ago to deploy a business intelligence (BI) solution. Prior to that, the company had used consulting services for some of its analytics support, but wanted to bring that function completely in house. After considering a variety of analytic tools, Oberweis selected the SAS analytics platform. One of the early uses of SAS was to provide analyses that would help develop more effective marketing strategies.

    In addition to carrying out numerous quantitative analyses to support that goal, Oberweis also began using SAS Text Miner to analyze notes taken by the customer service representatives in its call center. “Our home delivery business had begun to slow down,” says Bruce Bedford, VP of marketing analytics and consumer insight at Oberweis, “and we did not have a handle on the attrition rate by segment.” Oberweis decided to explore the issue by analyzing the content in its customer relationship management (CRM) system.

    Using Text Miner helped identify one particular promotional strategy—six months of free delivery—that was correlated with a higher attrition rate. “When the promotional phase was over, customers called in to object to having a new charge on their bills,” recalls Bedford, “and sometimes had even forgotten that the offer was temporary.” Information from the CRM system revealed details about the customers’ dissatisfaction. As a result, Oberweis modified the offer to be in the form of a discount on delivery. “Even though this change was very slight, it was effective,” Bedford says. “The customers no longer experienced such a noticeable discontinuity in the nature of their service, and retention was improved.”

    The CSR resource

    The ongoing process of text analytics also provides some serendipitous insights. “We use customer complaints as an indicator for how we can improve our products and services,” Bedford says. “At one point, customers were complaining about the milk in a variety of ways, such as ‘cream on top,’ or ‘milk looks funny.’ SAS helped us group the comments despite the different terminology that the customers were using.” The graphics that present a visualization of the text data generated a new category and a spike. “We discovered that one batch of milk had been produced that was not homogenized. Once an adjustment in a machine setting was made, the problem was solved,” explains Bedford.

    Over time, it became evident to Oberweis that the customer service representatives were a resource that could be used for more than customer service. “We realized that the call center could also be the primary point of contact when products were launched,” says Bedford. Oberweis identified a goal for each representative that related to cross-selling or upselling, and tied it to the employee’s bonus program. “The reps were able to use the CRM system to document the products sold and their value,” he continues. “This approach was very useful because the reps did not have to change the way they worked, yet we could capture the new information. The information is fed into the SAS repository for analysis.”

    An interesting bonus was that the customer service representatives, now renamed marketing representatives, have become more engaged and are serving as the vanguard for new product introductions. “The outcome has been very positive,” Bedford says, “and we were able to create this change without having to re-engineer our proprietary database to accommodate the new information. Although we are a small company, SAS has really helped us function as a large one by allowing us to use powerful analytics to leverage our information more effectively.”

    Analytics, applications and adjustments

    The use of text analytics for customer-related information has been so dominant that its use for applications tends to be overshadowed, but those also are continuing to grow. Another relatively common use of text analytics is for fraud detection. “One European government was able to reduce tax-related fraud by 98 percent simply by noting the contents of certain fields on a form,” says Fiona McNeill, global product marketing manager at SAS. “Others are building knowledgebases by consolidating service notes into a searchable format and analyzing the contents.”

    Root cause analysis relies on text analytics to understand scenarios in which manufactured parts are failing. “The text related to insurance claims and servicing logs associated with manufacturing can help pinpoint the underlying causes of failure in a way that quantitative data alone cannot,” McNeill adds.

    It is important to consider in advance what an organization will do with the information. “One insurance company analyzed incoming queries and realized that its website was not clear enough,” McNeill says. “The company then modified its site and substantially reduced the number of queries. However, if an organization is not prepared to make adjustments, then they might want to reconsider the role of their analysis.”

    Fine-tuning for customer retention

    The Medicare & Retirement division of UnitedHealthcare (UHC) serves nearly one in five Medicare beneficiaries. It offers a full range of products and services, and is the country’s largest provider of Medicare Advantage plans, Medicare Part D prescription drug plans and Medicare Supplement insurance. Many of its products carry the AARP name.

    To determine whether UnitedHealthcare is meeting the needs of its customers, the company has used a variety of software tools to measure its success. “We use a mix of quantitative and qualitative data,” says Tom Allenburg, VP of data analytics and marketing solutions for UnitedHealthcare Medicare & Retirement. “However, we were underutilizing the information that is captured in free form text during call center interactions.” The information was being stored, but not analyzed.

    UHC selected Mu Sigma to support a number of analytics initiatives. “Leveraging text was a new venture for us,” Allenburg says, “and we wanted to partner with an organization that had both the professional expertise and a set of powerful analytic tools.” Mu Sigma’s text mining engine, muText, analyzes text in structured and semi-structured content to evaluate customer comments.

    One of UHC’s priorities was to identify customers who were likely to let their policies lapse. “The more we understand customers’ feelings and experiences, the better we can develop ways of retaining them as subscribers,” Allenburg adds. “If someone felt negatively about an interaction with our customer service area, we wanted to be able to find out why.” UHC had used both quantitative and qualitative data to derive a predictive model about policy lapses, but wanted to incorporate the call center notes to improve its accuracy.

    In fact, UHC was able to show that bringing in the call center log notes provided lift, and improved its ability to identify those likely to lapse. “A model won’t lower the lapse rate, but since we are now able to better target the customers who are at risk, we can develop plans to reach out to those customers,” Allenburg explains. In addition, the analysis allows training activities with the call center representatives so they can be alert for certain key phrases. “If we know that certain phrases are associated with lapse, we can improve the customers’ interactions upfront and improve retention,” he adds.

    Better communication

    Over time, additional applications for text analytics have been developed. UHC is using voice recognition to convert audio to text and then analyze it. “This process allows us to ensure that certain statements required for compliance purposes are being made during the calls,” Allenburg says. In addition, UHC is using text analytics to improve interactions with customer service representatives during open selling season. “Some of the newer reps were not clearly conveying a sense of urgency during these limited time periods, to ensure that a prospective customer would make a decision by the deadline,” he notes. “We were able to analyze the conversations and make positive suggestions for better communication.”

    The use of text analytics allows a much greater degree of personalization, which is a strong trend in marketing. “Traditional demographics and transactional data only get you so far,” Allenburg says. “Two people who are demographically similar might have very different reactions to a product, a service or an interaction. It all comes down to the individual, and personalizing our response to meet their needs.”

    The desire to know what customers are saying in near real time is one of the most frequently mentioned drivers for implementing text analytics, whether the comments are coming from a CRM system or on the Internet through various social media channels. “Many large companies have years of focus group information that has not been analyzed,” says Mike Feldner, regional head of client services at Mu Sigma. “More broadly, everyone is contending with the explosion of data, and trying to decide what the right tools are. Since companies can now answer questions that they could not in the past, they are having to change their mindset about what is possible.”

    Mu Sigma’s text mining application is integrated into its Decision Sciences workbench, muRx, which provides a guided method for data exploration, modeling, analysis and reporting. “We offer both a platform and professional services,” Feldner says, “but our services and products are transparent, so the clients know exactly how their data is being transformed and modeled.” The outcome of the decision process is a set of conclusions and recommendations on which the company can act.

    What’s next?

    Several trends can be anticipated for text analytics over the next few years. “Specific text analytics capabilities, such as sentiment analysis, will mature,” says Leslie Owens of Forrester. “Right now, the analytical models are at varying levels of maturity, depending on the uniqueness of the topics or the source material itself.” In addition, better integration with traditional business information systems will bring a more comprehensive understanding of the context of the comment, such as the store the customer visited or what they bought.

    “All the characteristics of an interaction between a customer and a company need to be put together to form a complete picture that supports the right action to be taken,” Owens adds. Finally, big data (see sidebar following or on page 13, KMWorld, Vol 23, Issue 7) will take on an increasingly important role as the burgeoning volume of data puts greater pressure on creating effective automatesystems for interpreting unstructured information.


    Big data is unquestionably a driver for the increased use of text analytics. “In particular, big customer data is more available than ever before,” says Brian Koma, VP of research, enterprise feedback management practice leader at Verint Systems. Verint provides solutions for capturing and analyzing customer data, security intelligence and fraud/risk/compliance information. The volume of unstructured information in social media customer records, voice recordings, chat sessions, e-mail interactions and open-ended comments in surveys is growing rapidly. “Most of this information is underutilized or in some cases, not analyzed at all,” Koma says.

    Yet most organizations are still listening to customer data in silos, because different channels evolved at different times, and fully integrating the data poses many problems across an enterprise. Being able to answer the “why” behind the “what” is where text analytics shines. “Highly sophisticated companies are putting together the structured and unstructured information to track, profile and segment customers,” Koma says, “but the integration process remains a challenge, both organizationally and technically.” However, by overcoming those barriers, companies have the best shot at proactively detecting patterns and fully understanding their customers.

     The volume of big data and the speed with which it is flowing into organizations provide a great opportunity for real-time monitoring and response. “Traditional transactional data has a time lag (for example, billing information shows up only once a month), but you can see text data right away,” says Ranjan Mishra, president and senior partner of ESS Analysis, a consulting firm that specializes in analytics. “The question is, can you act on it right away? The best companies are those that do the ?best job of integrating disparate data to provide actionable information. Otherwise the decision-making is siloed too. Without this ?integration, it is difficult to get the right kind of information in front of the CIOs and CFOs.