Advanced Analytics Help You Nurture Customer Service Excellence

Superior customer service is an ethos that pervades the best companies. Everybody who comes into contact with the customer must go that extra mile to make sure customers’ needs come first. Likewise, everyone in the back office must stand ready to swing into action to fulfill orders, resolve technical issues, correct billing anomalies, and generally ensure an all-around great customer experience.

If you’ve been paying attention to business management gurus these past 30 years, you’ve had the foregoing philosophy drummed into your heads. Chances are good that you’ve bought their books, attended their courses, and paid them big bucks to give pep talks at your corporate retreats. Leading management consultants have also brought you up to speed on what exemplar corporate case studies have done to become 100% customer-focused.

In other words, you’re in the choir and would greatly appreciate it if you weren’t being preached at quite so often. If you’re managing a company of any size, what you really want to know is how you can transform your organization into one of these customer-centric juggernauts without the secular equivalent of a religious conversion. Do you really need to subscribe to any particular consultant’s holy writ — or can you simply identify your more customer-focused employees, hold them up as shining examples, and encourage them to share their best practices with colleagues? Can you nurture superior customer service practices that spring organically from your current operations, while at the same time supporting these efforts by encouraging operational personnel to apply the latest information technologies (IT) in new and creative ways?

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Social Business Intelligence: The Knowledge Management Connection

Business intelligence (BI) has always had a “pipeline” orientation—in other words, a primary focus on the one-way flow of data, information, and insights from “sources” (e.g, your customer relationship management systems, enterprise data warehouses, and subject-area data marts) to “consumers” (e.g., you). 

But we all know that this pipeline orientation—also known as “simplex” information transfer—doesn’t describe the predominant flow of mission-critical intelligence in our lives. Quite often, the most important insights are those that issue from other people’s heads, not from our companies’ data marts. Many real-world intelligence flows are full-duplex, many-to-many, and person-to-person in orientation. This fundamental truth will continue to drive the spread of “social” architectures in core BI and advanced analytics.

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Findings From Forrester Wave: Customer Service Analytics Empower The Predictive Process

Bill Band and I have just published the latest Forrester Wave on customer relationship management (CRM) customer service solutions. We included 19 vendors in this in-depth market study and evaluated their solutions against 196 criteria. As you can well imagine, it took time and a fine-toothed analytical comb to compile the research and double-check the facts before we scored these sophisticated product suites. 

One of the key findings from this Forrester Wave is that a growing range of CRM vendors have incorporated deep analytics features into their customer service capabilities. Most provide embedded, out-of-the-box business intelligence (BI) features such as reporting, query, online analytical processing, dashboarding, scorecarding, and key performance indicators prebuilt to support their customer service applications. That’s no surprise, because these core BI features enable enterprises everywhere to keep track of how well they’re providing customer service across diverse CRM interaction channels and to identify opportunities to improve satisfaction, retention,  upsell, agent productivity, and other key metrics. 

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Social Media Analytics: You Will Still Need Actual Analysts In The Loop

Social media analytics is one of the most exciting new frontiers in business intelligence (BI). As I noted in a recent blog post, it refers to the application of BI tools, such as reporting, dashboarding, visualization, search, event-driven alerting, and text mining, to information that originates as messages streaming from social media such as Twitter and Facebook. 

Forrester sees growing adoption of social media analytics across the entire customer relationship management (CRM) life cycle. This makes perfect sense, because social media are where customers spend more and more time, voice more unvarnished sentiment, and interact with a growing range of trusted commercial enterprises in addition to their friends and families.

Recognizing this trend, enterprise CRM professionals everywhere have incorporated social media into their public relations, product management, marketing, sales, and customer service processes. In addition to establishing their brands’ presence in the leading social media communities, companies have implemented tools to support continuous listening and engagement with customers, prospects, and the world at large through these channels.

Listening and engaging via social media involves much more than BI dashboards to monitor mentions on Twitter and the like. It may also require tight integration with the company’s CRM, enterprise data warehouse (EDW), business process management (BPM), business rules engine (BRE), complex event processing (CEP), predictive analytics and data mining (PA/DM), text analytics (TA), social network analysis (SNA), and other key tools and platforms. We often refer to this cluster of technologies as enablers for “social CRM.”

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Agile Data Warehousing: Do You Scrum?

When IT professionals speak of “agile development,” they could be referring to any of countless overlapping schools of thought. It’s best to tread lightly and keep an agile mind to find some hybrid or innovative approach that specifically meets your needs.

By most accounts, this term “agile” refers to any approach that straddles the razor’s edge between traditional top-down development and sheer adhocracy. Agile approaches attempt to speed the development process while enabling rapid shifts in development priorities to meet changing user requirements.

Agile approaches involve incremental, iterative, collaborative development among cross-functional teams consisting of IT professionals and business users. Under agile development, self-organizing teams refine requirements and craft modular solution components as they go. Teams hold regular checkpoint status meetings to review work in progress and reprioritize tasking. At every point in an agile program, the team develops useful prototypes that can stand alone as production-grade business technology systems, or can function as building blocks for larger, more complex, multifunctional systems. Documentation plays catchup to the work being performed, rather than constraining it through imposition of fixed, top-down specifications.

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Those Nasty “Hairball” Analytics Projects: Usually There’s A Data Warehouse At The Core

Sometimes I feel like Ol’ Doc Jim.

As Forrester’s lead analyst on data warehousing (DW), my core job often involves diagnosing enterprise analytics practitioners’ DW aches and pains. I try to cultivate a reassuring bedside manner and give them something for both immediate and long-term relief from their problems.

I receive all Forrester customer inquiries on DW matters, many of which are from IT practitioners who have hit a wall of intractable technical, operational, or vendor-related issues. Those sessions usually involve me probing for the source of the IT practitioner’s DW-relevant woes. If all of these issues could be isolated to the DW itself, my life would be much easier. But customers’ DW concerns are often tangled into stubborn knots of business intelligence (BI), master data management (MDM), data integration, data governance, business process management, IT service management, and other critical infrastructure, operations, and application issues. Often a seemingly DW-based problem such as poor-performance queries reveals that the root cause is somewhere else entirely, and the DW itself is the least of their problems.

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Social Media Analytics vs Social Network Analysis: Is There A Real Difference Or Are You Seeing Double?

As an industry analyst, I’m part of the professional class that delights in defining standard marketplace terminology. More than that, many of us spend our working lives coaxing industry to march under marketing banners aligned with our pet definitions.

Yes, indeed, each analyst likes to feel that his or her marketecture terminology should rule school. Last month I did a Forrester podcast on a topic that’s extremely hot right now: leveraging the power of social media and social networks to manage your brand, drive marketing and sales campaigns, and manage ongoing customer relationships. In that session, I discussed the role of analytics in social media for multichannel customer relationship management (CRM).

My initial impetus for the podcast was to spell out the chief distinctions between two terms that, on first glance, appear almost synonymous: social media analytics and social network analysis. During the podcast I also trucked in another related closely related term—social media monitoring—and even alluded to social intelligence and other phrases that have gained currency.

What follows, for those of you who don’t listen to podcasts, or can’t find them, is the gist of what I said on this topic: 

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EMC Acquiring Greenplum, Paving the Way to Emergence of Virtualized Cloud Data Warehousing

Suddenly but not surprisingly, EMC has jumped headlong into the data warehousing (DW) market via strategic acquisition. Now that the deal is in the works, it’s clear that EMC perceived a “low-hanging plum” in one of its established DW partners, and simply made the right offer at the right time.

The EMC/Greenplum deal signals that the DW market is probably moving into a new round of consolidations. Just as Oracle acquired Sun in part to offer fully one-stop integrated DW appliances (i.e., Oracle Exadata over Oracle Sun hardware), EMC comes from the other end of the telescope: acquiring a DW software vendor to layer on top of its hardware and possibly leverage its other software technologies at a later date. In this way, EMC now becomes one of the few DW appliance vendors that can provide a reasonably full stack of hardware and software from its own portfolio.

When I say “reasonably full” in this context, I’m referring to the hardware (EMC is storage, but will still rely on third-parties to provide the server and interconnects), database (Greenplum has extended/customized BizGreSQL), query planning/optimization, data-loading, and workload management (Greenplum has built its own technology in those three areas). In this regard, EMC/Greenplum will be one of several “integrated one-stop hardware/software stack” DW appliance vendors on the market: IBM, Oracle/Sun, and HP are others. Interestingly, all of these vendors also supply hardware to rival “hardware-less” DW appliance vendors, and it’s likely that EMC/Greenplum, like these companies, will offer its own optimized DW-appliance stack while offering an equivalent degree of hardware optimization for partners.

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Commercializing Enterprise-Grade Hadoop: Tools For Harnessing Petabyte Analytics

Hadoop is riding the hype wave right now. You’ll find many IT professionals who know just enough about Hadoop to be dangerous in a cocktail party setting, but not enough for their own comfort to respond to grilling from the chief technology officer or the geekier business executives.

If you’re slightly bewildered by all the buzz over this new technology with the funny-sounding moniker, you’re not alone. The official story is that Hadoop was the name of the inventor’s kid’s stuffed elephant. However, for most IT professionals, it could easily be an acronym for "Heck, Another Darn Obscure Open-Source Project." The fact that Hadoop, managed by Apache, includes subprojects with similarly opaque names — such as Pig, Hive, Chukwa, and ZooKeeper — contributes to the queasy feeling that this is an untamed menagerie of squealing beasties.

And if you’ve pegged Hadoop as an advanced analytics initiative to mine petabytes of unstructured information, prepare for further bewilderment. The Apache Hadoop project states that it develops open-source software for “reliable, scalable, distributed computing.” Yes, that’s true, but the better-informed among you may be puzzling over the linkages that people often draw between Hadoop, in-database analytics, and MapReduce.

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SAP Acquiring Sybase, Rocking The Analytics Market With Deep Synergies

SAP’s acquisition of Sybase was one of those deals that just makes sense on so many levels for both parties. 

SAP is of course one of the dominant brands in enterprise applications, and it also has strong business intelligence (BI), data integration (DI), business process management (BPM), and service-oriented architecture (SOA) offerings. But, until this bombshell announcement, SAP had been lacking some key solution components that it needed to compete more effectively with the other dominant IT brands—specifically, with Oracle, IBM, and Microsoft. 

Unlike these rivals, SAP had been lacking an enterprise-grade database management system (DBMS) product of its own (no—the open-source MaxDB doesn’t qualify). Likewise, SAP has had no complex event processing (CEP) tools for truly real-time analytics and transactional computing. Furthermore, SAP had been lacking any in-database analytics features that would allow partners and customers to execute data mining, text analytics, and other advanced analytics features in its data warehousing (DW) platform. Also, though SAP has mobile access middleware in its NetWeaver platform, it has not been able to offer customers a truly world-class mobility-enabling toolset. 

Enter Sybase, a venerable and diversified IT brand that brings all of these key capabilities—and then some—to its soon-to-be corporate parent. This is as important an acquisition for SAP as Business Objects was two years ago. It will prove just as pivotal a move for fending off aggressive encroachment by Oracle into SAP core accounts. 

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