Viewpoint paper

Transform customer experiences and relationshipsThree disruptive forces combine for breakthrough innovation

Table of contents

1 Reshaping the business landscape

2 Force 1: Data management

5 Force 2: Decision support

7 Force 3: Agile services

9 Optimizing customer relationships

9 For more information

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Reshaping the business landscapeAlthough many companies have reached a high level of business intelligence (BI) and information management (IM) maturity, there is a sense that transformational business breakthroughs have not yet been realized. The next phase of investment in BI and IM is likely to deliver these long-awaited breakthroughs. To realize that value in the shortest time, today’s enterprises must prioritize future investment to fully leverage three powerful and emerging disruptive forces:

• Data management

• Decision support

• Agile services

Noonedoubtsthescaleofpotentialbenefitsthatcanbegainedfrom appropriately leveraging BI and IM. But why is it an uphill struggle to reach them? Many potential explanations exist for this anomaly, and most organizations have grappled with some or all of these issues.

Numerous data silos persist, adding complexity and cost to the BI/IM environment and hampering cross-functional analysis and business insight. Analytic tools are too complex for adoption company-wide. Many organizations have not fully integrated analytic insights into business processes to facilitate timely decision-making where it matters most. At the same time, business and IT groups often struggle to create solutions that meet enterprise needs.

Even as companies with mature BI and IM programs address these barriers to success, the business landscape continues to evolve. For many organizations, business needs are so dynamic that aging data warehouse and BI technologies simply cannot keep pace. New and emerging technologies also are changing the face of BI. Searchtechnologiesnowenableeffectiveanalysisofunstructuredcustomer data. New analytical platforms such as Vertica enable organizations to assess and understand massive volumes of data in real time.

Mapping dynamic business processes, BI, and information strategies is an ever-growing challenge. Traditional marketing programs that used infrequently refreshed batch data have been replaced by real-time marketing requirements. Yesterday’s structured data sources thatfitconvenientlyintoadatawarehousearejoinedbysemi-andunstructured data sources such as call notes, email, call center voice recordings,video,andsocialmediadatasources—noneofwhichfitwithin the traditional data warehouse.

Today,westandataninflectionpointwherebreakthroughbusinessvalue from BI and IM investments can be unlocked—while also meeting the new challenges of rapidly changing business processes and requirements. By harnessing the power of emerging destructive forces—data management, data support, and agile services—forward-looking organizations can leverage this approach to enhancecustomerrelationships,financialfunctions,andsupplychain performance to improve product and service development, reduce risk, and ensure regulatory compliance.

HPresearchsuggestsasignificantamountof“blockingandtackling”must occur before companies can unlock transformational business breakthroughs. The question remains: How do you prioritize investments to realize those breakthroughs faster? One approach is to continue to invest deeply in the familiar obstacles to BI and IM success, such as data integration, consistency, and quality.

Anotherapproachistoapply“surgical”investmentsintheseareasinthecontextofaspecificbusinessdomain(forexample,customeror risk) and then move rapidly to investments in the emerging disruptiveforcesthatarecombiningtocreateaninflectionpointofuniqueopportunitytodrivetransformationalbusinessbenefits.

Overtheyears,similarinflectionpointshaveemerged,eachresulting in functionality leaps that unlocked new business benefits.Forexample,enterpriseresourceplanningandcustomerrelationship management applications brought rigor to back- andfront-officebusinessprocessesandcreatedacommon“datalanguage”fortheenterprise.Large-scaledatawarehousetechnologies brought together this cross-functional enterprise data for unprecedented new business insights.

Although value clearly was created by investments in these respective disciplines,thebreakthroughinnovationsemergedfromthe“whitespace”betweentheseadjacentdomains.Thisexampleof“readingbetweenthelines”istheLawofDiminishingReturnsinaction.

Did you know?The HP and TBR BI Trends Study (April 2011) revealed that the top three barriers that affect an organization’s ability to achieve its objectives with BI are data integration, data consistency, and data quality.

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Unlockingthetransformationalbusinessbenefitscanbe accelerated by exploiting three emerging disruptive forces that will reshape business:

• Datamanagement—Leveragingsemi-andunstructureddataandintegratingitwith“traditional”structureddatasourcesinyourcustomer data warehouse

• Decision support—Real-time decision support and pattern-based strategies embedded in business processes

• Agile services—Agile information management and analytical platforms and new delivery models to speed time to value

You must also decide where to apply these disruptive forces within your organization. HP research suggests that the customer domain is where many companies are prioritizing their investment. In this paper, HP will consider a representative retail banking customer named Mary. What can a retail bank know about Mary today using only traditional BI and IM processes and technologies?

• Mary holds a personal transaction account with Bank X. She is a rising executive and earns $135,000 per year. Her salary increased 16 percent last year.

• The bank knows Mary is likely to be a valuable customer over the long term, with many up-sell opportunities for a mortgage, a brokerage account, and credit cards.

• Mary has logged several service complaints, using email and call center channels to seek resolution.

• The bank’s records indicate Mary’s service complaints were successfully resolved by a customer service manager after her issues had escalated over a number of weeks.

• Recently, Mary’s debit card was refused by a merchant due to a system issue at the bank. For Mary, this was a great inconvenience and possibly the last straw in her banking relationship.

That is all useful, accurate information—but it fails to create acompleteviewofMaryasacustomer.Leveragingthethreedisruptive forces of data management, data support, and agile services, the bank could build a complete and accurate customer profileandserveMarymuchmoreeffectively.Tofullyappreciatethese three disruptive forces, it may help to examine how each can be used to better understand and serve Mary.

Force 1: Data managementUbiquitous information access and the integration of heterogeneous data types/sources are driving new requirements for data management. Organizations now seek to harness exploding volumes of data—both unstructured and structured—to leverage externally based data and to manage the complete information lifecycle.

The onset of new social media technologies has fundamentally changedthewaypeoplecommunicate.Informationflowsbetweenfriendsandfamilyefficientlyandinstantaneously,toanylocation,via numerous devices. Consumers now also use social media to communicate with and about companies that provide them with products and services.

Channels for these types of communications go well beyond social media networks to include blogs, forums, and chat rooms. The people communicating through these new channels are also information consumers, and they expect to interact with product and service providers via these channels. What they do and say online illustrates their lifestyle choices, buying preferences, and brandperception—potentiallyvaluablecustomerprofiledatafor companies seeking to gain their wallet share. Recognizing the value of these new sources of social data and leveraging them appropriately is a relatively new challenge for many organizations.

Did you know?The HP and TBR BI Trends Study (April 2011) revealed that integrating and analyzing unstructured and semi-unstructured data is a top priority for many companies during the next two years. Doing so has a positive impact on driving customer value, sales, and performance while also helping resolve important business challenges:

• Optimizing customer relationships

• Improving product/service deployment

Did you know?Using the information customers provide in email and call center complaints and share with their social network, you can:

• Correlate service history, product usage, and transaction patterns to identify service dialogue opportunities

• Improve overall customer satisfaction and loyalty

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Even before the explosion in social media-based customer data, companies struggled to leverage other forms of semi-unstructured and unstructured content—including call notes, email text, and audiofiles.Companieshaveoftenneglectedthesesourcesofcustomer data, focusing instead on the more straightforward task of integrating their structured customer transaction data.

Consider the following scenario: A new source of unstructured data is created when a consumer speaks to a customer service representative in a call center. The representative types notes during the call. These notes contain valuable information about the customer, such as a complaint, service issue, or feedback about a product. The data is collected by internal applications.

But what happens next? How will those unstructured call notes be used in the future?

• Is that information made available to the customer service department when it responds to an email from the same customer soon afterward?

• Doestheinformationaffecttargetedbanneradswhenthecustomer logs in to the vendor’s website?

• Willacallcenterrepresentativemakeamoreappropriateoffertothat customer as a result of this new information?

• Does the information lead to better understanding of brand perception and future product or service design?

Incorporating social intelligence

Social intelligence is an emerging marketing approach that extrapolates valuable information from social network interactions andotherlargedataflows.Tofullyrealizetheexceptionalpotentialof social marketing intelligence, organizations must understand, prioritize, and leverage data from various social media sources. At the same time, they must still incorporate other unstructured media as discussed previously.

Most companies will need to modify their technical environment to support social intelligence initiatives. For example, more customer data must be available at the point of contact, enabling targeted marketingatanyendpoint.Significantfirst-moveradvantagesexistforcompaniesthatexecuteaneffectivesocialintelligencestrategybefore their competitors.

Listening—the start of a social intelligence program

At the dawn of social media, some organizations, mostly those with a strong brand identity, began listening to their customers online—crudelyatfirstbysimplybecomingmembersofrelevantcommunities and searching out content related to them. By listening, a company hopes to better understand what customers feel about the organization, whether customers have service problems,andhowcustomersinfluenceotherconsumers.

The answers to all these questions can be found in a customer sentiment analysis, which attempts to make sense of vast quantities of unstructured data. This analysis is a key component of any social intelligence program.

Converting text data into useful customer insights

Most information-processing techniques, including search engines, assume that data is factual. Although there is now a large body of data that includes opinion, the standard tools have no way to assess that opinion in a meaningful way. As a result, recent and emerging research focuses on techniques to evaluate opinion in user-generated content. The goal of sentiment analysis is to determine the attitude, opinion, emotional state, or intended emotional communication of a speaker (video or audio) or writer (writing text).

Integrating and acting upon social media data

Many forward-thinking companies have taken their online listening effortstothenextlevel.Theyhavefoundthatalthoughlisteningtoconsumers yields good information, integrating that information with other customer data sources creates deep insights that drive better marketing and business decisions company-wide.

Did you know?One customer’s negative purchasing or service experience can be broadcast to many people across numerous channels—including aggregator sites that display all exchanges between participants, positive and negative. Any negative posting has the potential to sway any/all chat participants. Rather than lose any participants, companies can use social intelligence to stop a problem before it occurs by applying proper segmentation strategies, enhanced by adding social value.

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Managing the information lifecycle, merging with BI

With new external and internal data sources, growth in unstructured data, and everyday use of online services, corporate data volumes are doubling every 18 months. In most organizations, these prodigiously growing data volumes continue to be stored in silos, managed by disparate groups representing numerous business needs. The result isasignificantandgrowingdatamanagementchallenge.

To reverse this trend, forward-thinking companies are opting for tighter alignment between data management and the business purposes their information serves.

Twenty-firstcenturyeconomiesarewitnessingtheemergenceofdata-drivenbusinessmodels.“Consumerization”ofinformationcreation,access,andusageisnaturallyleadingto“co-creation”product/servicedevelopmentmodels,foreverredefiningtherelationshipsbetweenbuyersandtheirsuppliers.Leadingcompanies are beginning to monetize the knowledge they glean from corporate information in ways never before seen.

But to deploy and capture the value of these new business models, a more structured, corporate-wide approach to managing data is required. The Data Warehouse Institute (TDWI) calls this emerging disciplineunifieddatamanagement(UDM).AccordingtoarecentsurveybyTDWI,only11percentofrespondentcompanies“reportcoordination(ofcorporatedata)athighorveryhighlevels.”But90percent of the companies surveyed believe they will need to be at moderate or higher levels of coordination within three years.

McKinsey & Company reports that 70 percent of all U.S. market value created over the past three decades resulted from the application of “knowledgeskills.”Today,companieswiththehighestconcentrationsof knowledge workers can expect three times the return per employee, comparedtocompaniesthatdoapoorerjobofcollaboratingandexploiting information assets. Clearly, data management practices—governance, quality assurance, metadata management, and the like—are essential to thriving in today’s economy.

But how does UDM tie to business intelligence? The answer lies in the business outcomes:

• Recognize that information has a lifecycle—from creation to archival to disposal—and information needs to be rationally managed throughout that lifecycle.

• Ateachstageofitslifecycle,datahasa“fitnessforpurpose;”timeliness and access characteristics vary accordingly.

• Dataoffersvaluethroughoutthelifecycle—frombusinessvaluecreation to cost avoidance.

• BI’s deep analytics opportunities depend on the right access to the right high-quality, well-managed data at the right time.

Did you know?According to the HP and TBR BI Trends Study (April 2011), data integration, data consistency, data quality, and speed are the top barriers that affect an organization’s ability to achieve business objectives with BI. Today, enterprise-wide data governance/quality/integration is the top BI/IM/analytics technology trend.

Did you know?To be effective, a consumer segmentation strategy must be able to differentiate between customers using more than their demographic and purchasing profiles. The segmentation strategy must also measure potential value based on social networking. The ability to discriminate between consumers enables customized treatment based on existing and predicted engagement patterns with any product or affinity product. Customized treatment also incentivizes consumers to advocate other products to their social network while providing new ways to cross-sell and up-sell products or services.

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Today, consumers create information with every transaction and interaction and expect the ability to access information anywhere, anytime, and from any device or location. This information is of considerable interest to service providers—they can use it to precisely target each consumer, and ultimately sell more services. This fact is true for wireless service providers, healthcare service providers, and many others.

The bottom line is that information is extremely valuable at every stage of its life cycle, which places greater importance on unifying data management with business intelligence capabilities.

Understanding better data management

To fully appreciate the importance of data management, it may help to examine how this disruptive force can be used to better understandandserveMary,ourfictionalbankingcustomer.

All of Mary’s historical data—structured, semi-structured, or unstructured—must be available and combined with external sources of data—social media, blog content—to enable a complete understanding of her transaction history, interactions with your call centers, visits to your websites, and communicating via any other channels.Foranenterprisetooffertargetedproducts,providebetter service, and increase Mary’s loyalty, all these data types must be integrated. A critically important decision is how best to manage the large volumes of new data sources throughout their lifecycle. Afinancialinstitutionshouldaskitselfwhatitneedstoknowaboutthe customer, how long that data should be kept, and how best to retire that data.

As noted earlier, Mary has complained about the customer service she received from the bank and was displeased with the way the bank’s call center manager resolved her issues. Deeper analysis of unstructured call data shows that even though previous complaint incidents were closed and coded resolved, Mary told the bank that the pattern of service issues she had recently experienced was causing her to consider other options. Mary has also voiced this sentiment on social media sites and her blog, which has a large audience.

Force 2: Decision supportThe emergence of real-time analytics and pattern-based strategies areredefiningthenatureofdecisionsupportwithintheenterprise.Critical imperatives for this second disruptive force include providing BI and analytics to the masses, managing the consumerization of IT, leveraging operational BI and analytics, and focusing on BI and analytics adoption and innovation.

Managing new demands

Business is changing faster than ever before. Economic volatility, demanding customers, regulatory constraints, new competitors with new business models, and the unrelenting pressure for improvedfinancialperformancehaveuppedtheanteforthespeedand quality of fact-based decision-making.

Consumers have new demands. They behave in real time. They constantly receive information and use it to make near-instantaneous decisions. Marketing to these customers requires systems that can continually receive, process, and output data.

Employees have new demands. Within an organization, any employee interacting with customers needs access to all customer information all the time. Using this information, employees can change a conversation, modify a tactic, or extend a new product offer—whateverittakestoensurecustomersatisfaction.

Thisnew“real-timereality”notonlyrequiresreal-timeinformationbut also real-time execution at the moment of truth for both the consumer and employee. The underlying IT environment must enable marketers to execute social media and online marketing programs in real time, as well as reach customers on their device of choice.

It must enable risk-management professionals to identify and avert fraudulent behavior of customers or employees. It must enable supply chain managers to anticipate demand and synchronize supply, production, and logistics to meet it. Widespread use of intuitive search capabilities on the desktop and mobile phone have familiarized a generation of professionals with constructing search and analytic logic—and they expect that ease of use to extend to the enterprise business processes they manage.

Responding with BI and IM

Old assumptions about the design and execution of business analytic functions misalign with new customer and employee demands. The traditional cycle of understanding business requirements,designing,andthenbuilding,testing,andrefiningnew analytical techniques is proving too cumbersome for today’s fast-paced, ever-changing business landscape. Complicating the matter is the scarcity of advanced analytical skills, which makes scaling the old model to meet the new demands virtually impossible.

Business intelligence was designed to support strategic decision-making through the analysis of historical data. Traditionally, the data used for marketing to customers has been batched, where groups of transactions are collected over a period of time. Batch results are produced, analytics are executed, and reports are reviewed to guide management decisions and create new marketing programs. Batch processingisanefficientwaytoprocesshighvolumesofdata,butineffectiveforleveragingsocialmediainrealtime.ExistingBIplatforms, analytical databases, and tools were created with those requirements in mind. But today, two emerging trends—real-time analyticsandpattern-basedstrategies—areredefiningtraditionalBI.Sobusinesseswonder,“Whatdowedonow?”

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A growing trend calls for moving analytics out of the back room and into the hands of line-of-business professionals. To meet the demand for intuitive tools that any business worker can use, BI and analytics vendors are building social capabilities and easy-to-use searchtechniquesintotheirtools,chieflyasameanstocomplementor enrich the analytic experience and mask underlying complexity.

This technology evolution has the potential to change the rules for how fact-based decisions are made in large enterprises. Although the need for rigorous, statistically based analysis will remain, complementary capabilities will be added. These will include analytical roles and processes that reside in the organization’s functional areas This new hybrid analytical process will require an entirely new process for definingbusinessquestions,assemblingthenecessarydatasets,and testing and deploying richer analytical insights.

Following a forward-thinking BI strategy enables companies to manage both disruptions simultaneously— while also paving the waytoadaptivebusinessprocessescomprisingworkflowsthatcanrapidlyreconfigurebasedondetectedpatterns.

Integrating operational BI with analytics

The value of a business decision increases with its proximity to a business event—the closer to real-time, the better. So today, companies are looking for a new solution that enables them to systematically integrate information gathered through predictive models with business processes—for example, move to operational business intelligence (OBI) and analytics, which operate in close to real time.

OBI tightly links three competencies: the continuous monitoring ofoperationalprocesses;theanalysisofdatapatterns,includingrecognitionofexceptionstothosepatterns;andproactiveactionstaken in response to those patterns and exceptions.

Companies implementing operational BI must bring together deep predictive analytics using historical data to develop predictive models. Then they must integrate business processes and business rules with the analytics. By combining business processes and rules with what is known about customer behavior, OBI can capture business events as they happen and take action at the point of the event.

A business event can be an external condition such as a customer action or an environmental change, an internal condition such as a system or equipment failure, or a combination event.

Events occur at all hours of the day and night. All events must be monitored, and the system must determine if any given event is an exception (for example, requires action), which creates a high volume of data. After the data is in the system, businesses can apply real-time analytics against the information.

Although the concept of OBI has been considered for several years, it is now a trend. Today, the OBI domain is better known and understood, so the technology is being more widely deployed. The nature of today’s always-on marketplace means companies need to respond faster to customer requests and problems. Another factor that contributes to OBI’s growing favor is the ever-connectedness of companies with their partners and suppliers. In our business environment,always-onconnectivityisnolongeragoal;itisarequirement for success.

Did you know?The HP and TBR BI Trends Study (April 2011) indicates that real-time data management, analytics, and event processing help companies keep pace with the changing environment and affects strategic decisions. Real-time BI helps resolve the business challenges of optimizing:

• Customer relationships, reducing business risk, and ensuring regulatory compliance

• Financial management functions

• Supply chain performance

Did you know?IDC recently defined a decision management framework that stretches beyond strategic decisions to include operational and tactical decisions and provides the foundation for the next generation of decision support.

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Adopting innovative processes

Muchmorethanfindinginformation,innovativeanalyticsisthethoughtful,creativemethodologyfordefininganewbusinessprocess.Itislessaboutskillstrainingandmoreaboutinnovation;adopting innovative BI and analytics requires an all-new process.

For many organizations implementing advanced analytic techniques,thetypicalcycleincludesknownstages.Thefirststageischaracterizedbyearlyenthusiasmforthepossibilitiesandaflurryof experiments that validate the potential of the new solution. While creatingapositiveinitialimpact,theseeffectsareoftenlimitedinterms of scope and duration.

During the next stage, the organization struggles to establish the process and skills required to scale the advanced analytics capability across the enterprise. Doing so involves connecting the analytical insights to the business processes where value can be created. It also means establishing an analytic innovation center (AIC) that connects business leaders with colleagues who understand the data environment and how to use analytical tools in a formal process of discovery, testing, validation, and deployment.

The analytics evolution mirrors the adoption and life cycle of business intelligence tools, which we have seen occur during the last 15 years. Centralized BI competency centers were created to speed training, adoption, and governance of the ever-expanding use of BI tools for basic analysis, reporting, and visualization. Although the analytics for the masses trend clearly changes the shape of this function, there is much that can be gained from using BI competency center principles to create an AIC.

Anticipating needs with decision support

So how might an organization leverage decision support to better serve Mary? To anticipate and respond to Mary’s needs, you must be able to detect subtle changes in her patterns of product use, service incidents, channel preferences, sentiments, and intentions. Further, these insights must be gained as close to the point of interaction as possible, and actions must be taken in near-real time to drive desired behaviors.

For example, when Mary calls a call center, a customer service representative should be able to see the full context of Mary’s recent interactions with the bank, and recommended actions should be proposed to the agent and Mary during that interaction. That action mightbedifferentthanonepredeterminedinamarketingcampaignand instead focus on ensuring recent issues were completely resolved in Mary’s mind.

In addition, because Mary often uses her smart phone to access her financialaccounts,sheexpectstoaccessalltheinformationsheneeds, whenever she needs it, and receive only relevant and timely offersthroughthatchannel.

GivenMary’sinfluentialblogandherpreviouscustomer-serviceproblems, she should receive the best possible service to ensure she becomesandremainsanadvocateforthebank,notjustapotentialcross-sell target.

Force 3: Agile servicesToday’s business users want to access and analyze information when and how they choose, with limited IT interaction. However, most established analytics processes—running on monolithic general-purpose enterprise data warehouse (EDW) platforms—offernosupportforthistypeofself-serviceenvironment.Toboostproductivity and user success, BI communities must accommodate moreflexibleanalyticapproaches.Thatmeansadoptingmoreagileanalytical platforms.

Data warehouse (DW) appliances have had both positive and disruptive impacts on the BI marketplace. Time to solution has improveddramatically,andtangiblebusinessbenefitshaveincreased. The DW appliance has also heralded the emergence of analytical platforms, which provide further improvements in agility and time to value.

BI“mashup”productscancreateavirtualizedDWenvironmentthatenables business users to achieve greater agility and development speed—coupled with orders of magnitude greater productivity.

Did you know?A mashup is an application that combines data, presentation, or functionality from two or more sources to create new services. Mashups make existing data more useful for personal and professional use. Typically easy to use, mashups offer fast integration with existing systems, and they frequently use open APIs and data sources to produce enriched results that were not necessarily the original reason for producing the raw source data.

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Although the self-service trend began with the introduction of appliances,information—as-a-serviceandcloud-basedofferingsare now overshadowing on-premise analytic platforms. This enables businesses to use OpEx funds to rent the capacity they need rather than undergo lengthy CapEx procurement.

Companies must weigh their desire to own and control their BI and IMenvironment—coupledwiththeirabilitytodifferentiatefromcompetitors—againstthepotentialbenefitsofleveragingcloud-based BI solutions. The push for cloud deployment often comes not from IT but from business decision-makers frustrated by lengthy implementationprojectsandinflexiblesystems.

For companies not interested in having a core competency in IT and BI—and prefer to focus on their core competencies—cloud-based BI solutions are particularly attractive. In a cloud environment, software upgrades are made without interruption to system availability.Otherbenefitsincludesavingsonimplementationand product upgrade costs. The pay-per-user/per-month pricing structure typical of many cloud deployments can be particularly attractive;however,outsourcingtheBIenvironmentmeansaccepting an external vendor’s product road map and knowing that your competitors may be using the same vendor. After you work from the same standardized capabilities as your competition, there islittleroomfordifferentiation.Shouldyouchoosetocustomizeheavily, you may lose any long-term cost savings, as compared with an on-premise BI implementation.

Security and data privacy

Mobile BI, analytics for the masses, cloud, and social network analysisallhavesignificantunderlyingsecurityand/ordataprivacyimplications. In fact, many of these trends compromise traditional security best practices. As sentiment and social networking analysis continue to gain popularity, we can expect the consumer protection bodies to express security and privacy concerns.

Traditional IT data security primarily has been based on a perimeter model, where some form of physical or network barrier shields the corporate environment. Access to the environment is controlled and readily audited by a permission model, so any external attempts to access the environment can be detected and prevented. The growth in mobile/remote services has necessitated the implementation of a VPN (virtual private network) and other mechanisms to provide extended access to the corporate environment, which is still protected by a permission/encryption model.

The emergence of cloud computing puts much of the established security infrastructure at risk. Arguably, private clouds are extensionsofthesecurenetworkmodel,offeringtangiblebusinessbenefitsintermsofcostandflexibility.Deliveringpubliccloud solutions with robust and auditable security is a primary requirement. Provisioning BI and analytical services on sensitive corporate and/or customer data from the cloud will necessitate the highest level of security.

As companies invest in social media analytics, technology is emerging to enable personal management and marketing. After these capabilities become widely available, companies will be required to consistently manage all consumer data and carefully observe opt-in/opt-out consumer preferences.

The U.S. Federal Government is also currently reviewing its position on data privacy. It likely will enforce a model that restricts social mediaspam—amovethatwouldhavesignificantimplicationsonU.S. consumer data management.

Ensuring satisfaction with agile services

HowwouldmoreagileservicesaffectMary,ourfictionalconsumer?

When it comes to customer relationships, information security and privacy are vitally important issues. Clearly, using insights gained about Mary’s sentiments and intentions from public social media and combining them with other customer information has great potentialtobenefitMaryandthebank.Acarefulreviewofcustomerprivacy and data use policies is required—while keeping a watchful eye on emerging regulatory restrictions. Clarifying the way in which customer information can be used to improve Mary’s experience will go a long way toward mitigating her privacy and security concerns.

Whether customers like Mary will be better served by companies who leverage BI and IM capabilities in the cloud depends on each individual company and the reasons for moving away from on-premiseinstallations.Someorganizationsmayfindthatclouddeployment enables faster response to changing customer needs. Othersmayfindthatthecloudenablesthemtoalwaysusethelatest versions of key BI and IM tools rather than wait for lengthy implementations or upgrades to be completed on premise. Finally, someorganizationsmaysimplyfindthatthevalueandriskassociated with customer information is so high that it makes cloud implementations a last-resort option.

Did you know?The HP and TBR BI Trends Study (April 2011) shows that cloud computing for BI and analytics is a growing BI technology trend for resolving various business challenges: optimizing customer relationships, reducing business risk, and ensuring regulatory compliance; and optimizing supply chain performance.

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Optimizing customer relationshipsIn today’s rapidly evolving marketplace, 65 percent of a business’ revenues come from existing customers. By increasing the loyalty and satisfaction of existing customers, a company can expect to raiseprofitsfrom25percentto80percent.Soaprimaryobjectiveof every business today is optimizing customer relationships—and that requires a complete understanding of the customer.

Asmanyorganizationsarefinding,traditionalBIandIMsystemsrevealonlycertainaspectsofthecustomer;muchremainsunseenand unknown. How can you meet your customers’ needs if you know only part of their story?

As we learned from the Mary vignette, a traditional BI/IM solution provides important but limited opportunities to deliver the optimal customer experience. Exploiting the disruptive forces and trends discussed here will enable you to transform the customer experience, improve the customer’s sentiment about your organization, and turn customers into advocates.

For more informationTo learn how HP Business Intelligence solutions can help you achieve connected intelligence and outcomes that matter, contact your HP representative or visit us at hp.com/go/bi.

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4AA3-8174ENW,CreatedNovember2011;UpdatedJune2012,Rev.1