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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

Monthly Archives: March 2012


The Data Analytics of Baseball’s Opening Day

opening day 150x150 The Data Analytics of Baseballs Opening DayOK, all you data geeks and baseball fans . . . “Moneyball“ has been on DVD and Blue Ray for a few weeks. How many times have you watched it in anticipation of April 4? You know, Opening Day for Major League Baseball and the start of a very special season for the Boston Red Sox, who will celebrate the 100th anniversary of Fenway Park on April 20.

And we expect the analytics discussion to have a banner year in the field of baseball, especially in the aftermath of “Moneyball’s” popularity.

Focusing on Opportunities & Outcomes = More Wins

“Moneyball,” the story of the Oakland Athletics General Manager Billy Beane, who pioneered the use of player statistics and analytics to scout players, maximize payroll, and utilize talent that was often overlooked or undervalued, shows the value of an analytics plan in action.

Beane worked with a lean budget (like so many of us in technology and business) that was dwarfed by the budgets of big-market teams such as the New York Yankees and the Red Sox. His story is a helpful reminder that the competitive landscape doesn’t always favor the big players anymore. Star power doesn’t decide who wins the game. There are many more factors to consider.

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Big Data = Moneyball for Your Company

Money Ball 150x150 Big Data = Moneyball for Your CompanyIn every business, people have to analyze information in real time to arrive at the right decisions.

The problem is that very few people can correctly solve important business problems in real time. In fact, most people can’t even solve these problems if they have unlimited time to do so, according to John Lucker (@johnlucker), a principal at Deloitte Consulting and James Guszcza, senior manager at the firm.

So to avoid becoming overwhelmed, decision makers rely on their gut feelings when weighing various factors, the authors say.

But people are pretty biased about the ways they make decisions – biases that can sabotage efforts to unlock the value in all the data their businesses collect. That’s because the human mind just hasn’t evolved enough to allow us to make the kinds of business decisions we have to make every day, according to Lucker and Guszcza.

“The human brain is very bad at juggling decisions,” Lucker told an audience at GigaOM’s recent Structure:Data conference.

And it’s not easy for businesses to overcome some of those biases, according to the author of the GigaOm article. For Lucker, however, companies have to overcome the “human factor” if they want to successfully drive organizational change. The way to do that is by using statistical analytics and predictive models, he says.

Take for example, “Moneyball,” the story of how Billy Beane of the Oakland A’s used analytics to identify undervalued baseball players – a story that has far-reaching implications for many industries. The premise behind “Moneyball” is that the team could do a better job acquiring new players by using a computer-generated analysis of the pertinent data than the scouts could do using their gut-feelings.

“Moneyball” is a early example of “workforce intelligence,” according to Lucker and Guszcza. And analytics can bridge the gap that often exists between workforce-related data sources and the business issues to which they should be applied. For example, they say predictive models are being built so HR managers can make better hiring decisions.

However, it’s just not enough for Billy Beane to have the data to back up his decisions, he also has to win over the Oakland A’s execs to change the way they run the team, according to Lucker. Similarly, Lucker says while you have to have the data, you also have to establish a clear link between your corporate strategy and what you’re doing with big data and analytics. If not, the new processes just won’t work.

Next Steps:

  • Join us for a complimentary webcast on Tuesday, September 18 at 11 a.m. Eastern to learn how Beeline leverages the TIBCO Spotfire analytics platform to create an Intelligent Workforce Analytics solution. In this webcast, Syed Mahmood, Sr., Product Marketing Manager of TIBCO Spotfire, will discuss the challenges faced by executives and professionals and how Spotfire analytics platform is used to extract valuable insights from big data sets. Martin Matula, Technical Product Manager of Beeline, will share the company’s award winning application for contingent workforce management.
  • Check out our complimentary “5-Minute Guide to HR Analytics” to learn how analytics can transform the perception of HR as a cost center to a strategic ally for business leaders.


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Why Analytics Teams Don’t Collaborate and How to Fix It

collaboration 150x150 Why Analytics Teams Dont Collaborate and How to Fix ItOne of the best things about contemporary analytics tools is that they enable teams of data scientists and other decision makers to brainstorm about business challenges that companies face and identify approaches for solving them.

For example, maybe a big box retailer has experienced a drop in sales for a particular set of stores or across specific categories. Well, data scientists and business leaders can use analytics tools to identify the likely causes for the drop-off as well as approaches for improving sales.

The beauty of collaboration is that individuals offer different perspectives and ways of attacking problems. Sometimes internal debates can lead to vicious arguments when people feel passionately about particular positions. Still, collaboration can bring great results when intelligent, even-handed executives are charged with making the best decisions based on the input that’s been presented to them.

While this kind of teamwork sounds great in theory, unfortunately, it doesn’t always work out in the real world. As Forbes contributor Ron Ashkenas points out in a recent blog post, one of the ways that teams typically react to group challenges is through “cooperation.”

Under this scenario, each person develops and implements his own plan and then shares what he’s doing with the group. While there’s some degree of joint dialogue, “the focus is still on individual actions rather than a collective strategy,” Ashkenas says. Each member of the team – or subgroups if the teams are large enough – often takes a different approach to solving a shared challenge.

Let’s say an automaker is looking to increase sales by 5% for a particular quarter. Through the use of analytics, one dealership might determine that it makes sense to create a set of sales incentives for recent college graduates or members of the military. Another dealership, however, might see the benefit of creating trade-in offers for owners or lessees of vehicles of rival car makers. Each approach is aimed at achieving a similar result – lifting sales – but the dealerships use different tactics. This is hardly a collaborative effort.

While different approaches to fostering collaboration tend to work well depending on the organization’s cultural fit, here are four styles that can help any company:

  1. Champion collaboration. It’s critical to have senior leadership regularly and openly communicate the value and importance of collaboration. This includes highlighting how constructive debate can yield fresh ideas and more effective ways to solve problems. Tangible examples and storytelling are great ways to emphasize this.
  2. Agree to disagree. It isn’t collaboration if team members all nod their heads in agreement when a senior executive presents an idea. Even if it’s a solid idea, it should be dissected and turned on its side to explore possible cracks and ensure that it’s the best available method.
  3. Disregard chain of command. To truly promote a culture of open dialogue, people must believe they can speak freely without fear of retribution. Company leaders aren’t looking for “yes” men or women. They want people who stand behind their convictions and offer the organization alternative views to help shape multi-dimensional decision making.
  4. Make it fun. Strategic projects are serious stuff. Still, it’s important to inject some humor into project meetings and keep the mood loose. A bit of levity will go a long way to keeping the project team fresh and help strengthen the bond among its members.

Next Steps: Download this complimentary “5-Minute Guide to Business Analytics,” and learn how user-driven “analytic” or “data discovery” technologies help business and technology users more quickly uncover insights and speed action.

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The Role of Advanced Analytics in CRM

LouJ 150x150 The Role of Advanced Analytics in CRM The following post is syndicated from Lou Jordano’s (@LouJordano) recent guest post on the Sage SalesLogix Journal (@SageSalesLogix):

In a recent interview with Gregory Piatetsky-Shapiro, founder and editor of the KDNuggets knowledge discovery community, we learned that while analytics is much easier when we predict the behaviors of inanimate objects such as asteroids and viruses, it’s also quite relevant for predicting and responding to customer behavior.

And with the recent courtship of advanced analytics and CRM (customer relationship management), companies are now more poised than ever to sense their customers’ needs and respond in real-time. In this post, we’ll explore the role of advanced analytics and its relationship to CRM.

Moving Beyond Gut Feelings & Canned Reports

With the explosion of sales and marketing channels, it takes much more time and effort to find out which activities are working and which activities are not.

Yes, you have reporting, but that’s an after-the-fact exercise. Today’s competitive landscape requires us to equip our marketing and sales teams with real-time information and allow them to analyze customer and sales data without restrictions – and without IT’s help. Asking questions of the flood of data available allows us to innovate and activate.

A Practical Example – Marketing Manager to Marketing Maven
Take this example – a marketing manager is segmenting lists for an email campaign about an upcoming major product release. To show the campaign’s effectiveness and to create her action plan for lead flow, she needs to know some key stats about the customers and prospects on her lists.

Armed with questions and a powerful analytics dashboard, our marketing manager can ask a question like:  what is the projected profitability for each former customer we’re emailing?

That single question could mean the difference in the offer, as well as which lists of customers to email and even the testing involved in the campaign.

She is also able to access data from across the organization in a single application (e.g. recent conversations with the support team or account manager; recent events the customer attended; and even visits to the resource center on the company website). Using this data, she can create a data mashup and get the whole story regarding what the company knows about this prospective customer or group of customers.

More than likely, she’ll have more questions as she digs into the data. And that’s when she’ll really understand the benefits of integrating the CRM system with an advanced analytics solution. She can paint a complete picture of the known facts and work through the unknowns in a deductive manner. She can run if/then scenarios and present them to her team of decision makers and potentially define her next step up with the company.

Empowering your CRM users with an analytics solution allows them to make discoveries not found in spreadsheets and canned reports.

The Customer Service Difference
CRM’s original purpose was to help us manage customer data – to document customers’ buying habits; to store their contact info; to create a better service experience; and to grow that valuable long-term relationship. Without this system in today’s competitive, global marketplace, most companies would be dead in the water.

However, managing and storing that data is just not enough to keep us a step ahead of the competition. We need to sense and respond to our customers’ support calls and answer their questions as quickly as possible. After all, that’s what they expect.

Armed with analytics and an empowered service staff, you can create a sense-and-respond organization.

Let’s look at another scenario.

Relationship Revival & Revenue Protection
Mr. Brown calls in and angrily tells you of his billing issue. He also lets you know he’s been a loyal customer for 12 years. “What are you going to do about it?” he asks, or rather demands. With your CRM system, you can access his record, but it may take some digging – not to mention more angry words from Mr. Brown.

If your CRM solution is equipped with an analytics dashboard, your CSR can access the answers to common customer scenarios in a predetermined dashboard. With this one resource, your customer service rep is empowered with data – almost instantly.

If, indeed, Mr. Brown is a 12-year supporter of your product, the CSR can take an immediate step toward retaining his business – offering a significant discount or extended service, for example. If the facts show he’s a more recent acquisition and he’s called support 10 times in 12 weeks, the rep could simple relay his support options and offer a sincere apology.

The Bottom Line
The key to being a sense-and-respond organization is to query, understand and act on relevant data quickly – without IT intervention. The advantages are shorter decision cycles and the ability to take action in real-time, which result in better margins, better productivity and happier customers.

Next Steps:

Are you a software applications vendor? Then we invite you to view our on-demand webcast: “5 Reasons Why You Should Add Visual Analytics To Your Applications.”


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The Future of CRM Analytics is Already Here

CRM customers 150x150 The Future of CRM Analytics is Already HereThanks to the growing use of smartphones and social media channels, customers are more empowered than ever. And that means it’s more difficult for companies to retain their best customers and attract new ones.

According to a recent Accenture study, 85% of consumers who posted comments about negative online experiences with particular retail companies found other companies to meet their needs.

This is where the intersection of customer relationship management (CRM) and analytics can make a dramatic impact. As decision makers look for more effective ways to understand their customers’ needs, preferences, attitudes, and behaviors, more companies are applying analytics to their CRM efforts.

To make the most effective use of customer insights, companies are increasingly drawing upon a blend of customer data inputs from a variety of sources, including comments that reflect intent or attitudes in social channels. They’re also examining structured feedback that’s shared via online surveys and other voice-of-the-customer-type formats, as well as behavioral information that customers share on websites and in other channels.

Some companies are blending customer data and analytics with operational (e.g. contact center, ERP) and transactional data in order to gain a 360-degree view of the customer. Having a complete picture of the customer is critical in today’s competitive landscape. Companies that demonstrate that they listen closely to their customers and provide targeted offers and support based on customers’ needs and preferences are more likely to satisfy customers, strengthen customer loyalty, retain existing customers and attract new ones.

Still, gaining complete views of customers can be fraught with challenges. Siloed data within various channels and functions make it even more daunting for decision makers to develop complete pictures of their customers. These silos are often guarded by lines of business leaders who are reluctant to share customer data with other parts of the enterprise.

To help break through these barriers, it’s extremely useful to have a C-level executive champion these efforts and regularly communicate the business benefits of sharing customer data among different parts of the business. This includes identifying which products and services customers currently use and then pinpointing the most logical cross-sell or upsell opportunities. Companies can start these efforts with small pilot projects and then share the results with other business units and functional leaders to help gain acceptance.

Another way to break past these fiefdoms of customer data ownership is by changing the compensation and rewards structure within companies. For example, most line of business or functional leaders (e.g. online, mobile) are compensated and offered incentives for meeting or exceeding the business results within their areas of responsibility. However, companies that reward executives to share customer data across business lines can also enable business leaders to share in the spoils of these efforts.

Companies that have more complete views of their customers are much better positioned to identify and act on cross-sell and upsell opportunities successfully. In addition, the right mix of CRM and analytics can also provide decision makers with meaningful insights into which marketing campaigns are succeeding or failing and why.

Next Steps: Download this complimentary “5-Minute Guide to CRM Analytics,” and learn how agile analytics technology can deliver critical value to executives and front-line marketers.

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Using Analytics to Stay on Top of the Regulatory Landscape

global analytics4 150x150 Using Analytics to Stay on Top of the Regulatory LandscapeRegulatory, data protection, and privacy requirements are constantly changing across different countries and regions, making it extremely challenging for finance, operations, and risk management executives to stay abreast of the ever-changing regulatory environment.

Many companies struggle to keep pace with local changes to consumer rights, cross-border data transfer restrictions, tax obligations, and a raft of additional regulatory requirements.

This includes the onus on Fortune 500 multinationals to keep up with the constant changes that are occurring in the regulatory environment on a global basis.

The challenges to keep up with regulatory modifications have become more acute following the global financial crisis that started in 2007. As Deloitte notes in a report on the topic, the aftermath of the crisis has resulted in extensive regulatory reform – not just in the US but in countries around the world. The Deloitte report focuses on steps that regulators can take to use analytics to collect, aggregate, analyze and share information about the health of the financial system and its participants.

Decision makers for financial services companies can use visual analytics tools to help identify and act on real-time changes in the global regulatory landscape so they can continue moving forward with business strategies instead of staying mired in prolonged market analysis.

This kind of paralysis-by-analysis is not only for valid financial services companies but for any type of organization that operates in a highly-regulated industry. Business leaders need tools that can help them make quick and precise decisions. They don’t have time to get bogged down in the regulatory quagmire.

Still, the nature and scale of these regulatory shifts shouldn’t be taken lightly. This is especially true for business leaders who have active roles in research or product development/product rollout where regulatory or compliance requirements in a particular geography can suddenly derail ambitious marketing plans.

For example, biotechnology and pharmaceutical professionals who are concerned with regulatory updates within their areas of focus are increasingly using web analytics and other types of discovery tools to stay abreast of the latest policies and enforcements that may affect their areas of research or product development.

It’s imperative that they keep up to speed with the ever-changing regulatory climate. A May 2011 study found that US regulatory approvals for biologic drugs and biotechnology products made from human or animal proteins have nearly doubled over the past decade.

Intuitive and visual analytics tools can make the job of tracking the evolving regulatory landscape much easier and more effective.

Next Steps: To learn more about this topic, check out our 5-Minute Guide to Analytics for Financial Planning & Analysis.



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The Data Analytics of March Madness

MARCH MADNESS 150x150 The Data Analytics of March MadnessPredictive analytics can be used for a wide variety of applications, including matching the right offer to the right customer at the right time using data from customer transaction history, customer needs and preferences, as well as customer lifecycle status.

Last week, many data scientists and other college basketball enthusiasts found themselves making extensive use of statistics and analytics tools for altogether different purposes: to gain an edge on fellow March Madness bettors in their efforts to make correct picks in their NCAA tournament pools.

Even with the help of analytics, making the right picks isn’t easy: there are an astounding 9.2 quintillion possibilities for the possible winners in a 64-team bracket in the NCAA tournament. By expanding the field to 68 teams in 2011, the odds of picking all of the bracket winners has increased to 147.57 quintillion to 1.

One approach to picking the winners involves the use of analytics tools to help determine the statistical likelihood of certain tournament seeds beating other types of seeds (e.g. #13 versus #4). GigaOM and BusinessWeek blogger Derrick Harris notes that he makes his picks using a tool called BracketOdds created by University of Illinois computer science professor Sheldon Jacobson.

BracketOdds lets you know the probability of any combination of seeds making it to a given round in the tournament. For the Final Four, the most likely seed combination this year is 1,1,2,3. The odds against this combination occurring is only 16.08 to 1. As Harris notes in his blog, the odds of each of the top seeded teams making the Final Four is 48.7 to 1, so the chances of a #2 seed and a #3 seed making the mix is three times more likely to occur.

Of course, sentimentality and bias often factor into the NCAA picks made by data scientists and other gamers. One analyst I spoke to this week says he includes a range of variables with his team selections, such as a comparison of team records, the strength of each team’s regular season schedule versus its opponent, game location and the proximity to each school’s campus/fan base, the statistical likelihood of “sleeper” teams (e.g. overlooked 11th seeds) to advance in specific rounds of the tournament, etc.

But when I pressed him on which team he picked to win, he confessed his partiality for the University of Missouri. A #2 seed in the West region, Mizzou is known for its guard tandem. And it’s his alma mater.

But in the end, Norfolk State became just the fifth #15 seed in tournament history to knock out a #2 seed (Mizzou), shortly followed by Lehigh’s upset win against #2 Duke. Statistically unlikely to occur? Absolutely.

The odds of a #15 seed beating a #2 seed are 25 to 1. Finding this type of information isn’t always easy. Decision makers in business sometimes encounter similar challenges. Indeed, it’s critical for business leaders to be able to get at the type of information they need when they need it.

So how did Norfolk State pull off its historic upset? In part, by scoring a highly efficient 1.34 points per possession.

Cinderella teams have pulled off upsets in previous tournaments but they’ve rarely advanced into the final rounds. In fact, in the history of the tournament, only a small number of teams seeded lower than #8 has actually made it to the final rounds. Just two #14 seeds have reached the Sweet Sixteen (Cleveland State in 1986 and Chattanooga in 1997).

Meanwhile, a #12 seed has made it to the Elite Eight just once (Missouri in 2002) while an #11 seed has reached the Final Four just three times (LSU in 1986, George Mason in 2006 and Virginia Commonwealth in 2011). And in perhaps the greatest upset in championship game history, the #8 seeded Villanova Wildcats stunned #1 seed Georgetown 66-64 on April 1, 1985.

Improbable, but it can happen.

Next Steps:


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Changing the Mindset About Analytics

problem solving 150x150 Changing the Mindset About AnalyticsThe beauty of analytics is that it allows analysts and decision makers to take new approaches to problem solving. This includes exploring new ways of examining data as well as new types of data sets that can be applied to making better business decisions.

A prime example of this is the way that some companies are beginning to look differently at supporting growth strategies. Historically, most companies have taken product-centric approaches to business. That is, if we can produce “X” product units this quarter, we will hit our revenue and profit targets.

More recently, forward-thinking companies are taking more customer-centric approaches to growth whereby management is placing greater focus on optimizing the customer experience since ultimately “customers are the true source” of a company’s revenues. This is an important distinction for data scientists who are trying to help corporate decision makers identify business opportunities, minimize operational disruptions and drive improved business performance.

Collaboration with peers and organizational leaders is another way for data analysts to look at analytics in new ways. Joel Rubinson, the former chief research officer of the Advertising Research Foundation, and Judah Phillips, a former analytics director for Monster and Reed Elsevier, have created the Analytics Research Organization (ARO) in an effort to help data scientists share ideas and approach analytics from new angles.

For its part, ARO plans to focus on applying analytics and research to business challenges such as increasing revenue and profit, and creating value. According to an article on the group by, Rubinson and Phillips plan to use crowdsourcing to determine ARO’s priorities and activities.

Meanwhile, the continuous eruption of big data from a variety of structured and unstructured data sources, including social channels, sensors, contact centers, web site activity, etc., is also helping data scientists raise new questions regarding their approaches to analytics as well as discover new market opportunities. recently posted an article highlighting the ways analysts and others can use a combination of big data, social channels, and collaboration to unearth new approaches to problem solving. Or, in some cases, borrow ideas from other industries.

For example, the article points to a major toilet paper manufacturer that was trying to figure out how best to package its rolls. An expert in the beverage industry suggested a design that’s similar to fridge-pack soda boxes, where drinks drop and then roll to the entrance of the box for easy access.

Of course, there have been other examples of companies borrowing ideas from other industries to solve product, process, and other problems. Still, it underscores how data scientists can shake up their traditional approaches to problem solving and use analytics to embrace other ideas, whether they’re old or new.

Next Steps:

  • Tweet us and let us know how you’re using analytics to shake up your traditional approaches to solving problems.
  • Sign up for our webcast “Capture HR Workforce Data To Drive Strategic Decisions” and learn how you can deploy HR analytics to make your people and processes more effective.
  • Sign up for this webcast to find out how European life science companies are using analytics to identify new opportunities and detect emerging trends. Maybe you’ll find a way to apply their ideas to your company.

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The Data Analytics of St. Patrick’s Day

St Patricks Kiss Me Im Irish 2012 The Data Analytics of St. Patricks DayTomorrow, almost half of Americans will don green shirts to avoid being pinched, seek out some green grub and celebrate a saint who wasn’t Irish by birth.

On an unrelated note, we think a few of these Irish-for-a-day folks will seek out iPad 3s on their way to their favorite pubs or one of the many famous St. Paddy’s day parades.

Born in Roman Britain, St. Patrick, who is considered the patron saint of Ireland, became Irish by force in the early 400s. He was sold as a slave in 403 and escaped in 409. He later returned to Ireland to spread the message of Christianity. The anniversary of his death has been celebrated by the Irish for more than 1,000 years. A traditional celebration includes a visit to church in the morning and a feast of beer, bacon and cabbage (an Irish tradition) in the afternoon.

What’s with the Shamrock?

According to Irish legend, St. Patrick used the plentiful plant to explain the holy trinity on which Christianity is founded. Four-leafed clovers, which can’t be shamrocks by botanical definitions, have been associated with luck since well before this holiday – some references say Eve was holding one when she and Adam left the Garden of Eden. Another legend says the lucky clover repels snakes, but that didn’t come from Ireland either (there are no snakes in Ireland).

Data Geek Lesson – Data quality is a must or you could end up with myths and legends like snakes in Ireland.

More American Than Irish?

The American version of this holiday is believed to have begun on March 17, 1762 when Irish soldiers who served in the British military marched through the streets of NYC “to reconnect with their Irish roots.” In 1848, several Irish Aid societies (concerned about Irish immigrant patriotism and rights as more than a million Irish immigrants came to America during the famous potato famine) joined forces to create a single St. Patrick’s Day parade in New York City. Today, this parade is the world’s “oldest civilian parade and the largest in the US,” according to

Are American Irish Luckier?

Today, Americans of Irish descent make up about 11% of the population, just behind people of German ancestry, who make up more than 15% of the US population. The Irish-Americans defy the odds in luck in education, income and home ownership in comparison to the overall US population. As you can see in our infographic:

  • 70% of Irish Americans own homes
  • Just 10% lack health insurance
  • They make 22% more money
  • They are more educated  92% have at least high school diplomas and nearly a third have bachelor’s degrees or higher

Data Geek Lesson: It’s impossible to know whether Irish Americans are luckier because we aren’t starting with the right question  What is the definition of luck as it applies to this data set?

How Green is St. Patrick’s Day?

With gas going for $4 per gallon in many areas, it’s exciting to note that the green paper impact of St. Patrick’s Day is $4.55 billion (the highest in the history of the holiday). We also dove in to see what other green analytics we could find. Here’s a quick list:

  • The color green is not traditionally lucky in Ireland. The original color designated for the date was blue. However, the argument for green won out as the Americans popularized the holiday. No one knows for certain where the “wear green” tradition comes from, but a few guesses are  it’s because Ireland is called the “Emerald Isle,” the shamrock St. Patrick championed, and an urban legend that the leprechauns (or protectors of gold) would pinch you if they saw that you were not wearing the color of spring;
  • Green beer (which about 20% of Americans will seek out) is an American tradition. Until the 1970s, Irish pubs were required to close for the religious holiday;
  • Saving green is a top priority for businesses and even sports teams as we saw in the box office smash “Moneyball.” And taking an analytical approach has gone from geek to chic as we saw in this Information Management article.

Next Steps:

  • Tweet us your favorite St. Patrick’s tradition.
  • Sign up for our March 22 webcast featuring Claire Schooley, senior analyst at Forrester Research, and Dan White, product manager for Spotfire, and learn how to use analytics to spot more opportunities in HR to make people and processes more effective.
  • Check out our 5-Minute Guide to HR Analytics to learn how analytics can transform the perception of HR as a cost center to a strategic ally for business leaders.
Amanda Brandon
Spotfire Blogging Team

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Filed under: Data Analytics


Using Analytics to Create a Sense-and-Respond Organization

FollowCustomers1 150x150 Using Analytics to Create a Sense and Respond OrganizationMany business forecasters believe that the most successful companies in the future will be those that develop sense-and-respond type approaches for listening to and responding to customer needs and preferences. This includes the use of predictive analytics to anticipate and address customer issues before they’ve reached out to contact centers.

As R “Ray” Wang (@rwang0), principal analyst and CEO of Constellation Research Inc., points out in a recent Forbes article, a growing number of companies are shifting from transactional systems to engagement technologies to help improve interactions with customers and to provide better and more relevant customer experiences across the various customer channels.

The drivers behind this are simple: as companies seek new ways to grow revenues and expand their customer bases, they’re devoting greater attention to delivering solid, multichannel customer experiences. The quality of customer experience can have a profound impact on an organization’s business performance.

According to The Business Impact of Customer Experience report from Forrester Research (@forrester), improvements in the experiences delivered to customers can generate more than $1 billion in revenue for wireless carriers and hotels. This can be measured by a customer’s willingness to do more business with a company, the likelihood of switching their business to another company and their willingness to recommend a company to others.

When companies strive to become more customer-centric, one of the things that many organizations need to improve on is proper tracking of customers as they move from one channel (e.g. web) to another (voice). As it stands, too many companies struggle to do this. Rather, they make customers feel as if they’re working with multiple companies instead of a single organization.

This leads to customer frustration and churn. Plus, when customers have poor experiences with companies, they often share their frustrations with thousands of other people on Facebook and other social channels, resulting in lost business opportunities.

As companies gain experience in sensing and responding to customer needs, they can also use analytics to help identify potential customer issues before customers reach out to their contact centers for help. By reaching out to customers proactively, companies can strengthen customer trust by demonstrating that they’re proactively looking out for their customers’ best interests.

For example, let’s say a wireless carrier notes that a customer is about to reach his monthly data limit. An alert can be sent to a customer to let him know that he’s about to reach his limit before additional fees are assessed.

Companies that make greater use of analytics to identify and respond quickly to customer needs and preferences will be able to leapfrog their competitors and position themselves to be more successful.

Next Steps: Download our complimentary 5-Minute Guide to CRM Analytics to learn how agile analytics technologies can help companies answer critical questions about their customers and deliver more value to executives and front-line marketers.

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