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March 2012                                                                                 Document   M20




RESEARCH NOTE
THE BIG RETURNS FROM BIG DATA


THE BOTTOM LINE
Nucleus found organizations can earn an incremental ROI of 241 percent
by using Big Data capabilities to examine large and complex data sets.
One driver of high returns on Big Data was the ability to improve business
processes and decisions by increasing the types of data that can be
analyzed. Another driver of high returns was the ability to monitor the
factors that impact a company, such as customer sentiment, by scouring
large external data sources such as social media sites.



Nucleus has determined that enterprises increased their average ROI on analytics
by 241 percent when they used Big Data to become a more Analytic Enterprise. An
Analytic Enterprise improves its competitiveness and operating results by
continuously expanding its use of analytics (Nucleus Research m17 – The stages of
an Analytic Enterprise, February 2012). There are four stages in the evolution of
an Analytic Enterprise, and Big Data plays an important role in this transformative
path.

                           1400%

                           1200%
    Return on Investment




                           1000%
                                                                                        Predictive
                           800%
                                                                                        Strategic
                           600%                                                         Tactical
                           400%                                                         Automated

                           200%

                             0%
                                   Automated   Tactical   Strategic   Predictive

Enterprises with strategic deployments earn an average ROI of 968 percent by
deploying analytics across the organization to align daily operations with senior
management’s goals. Predictive deployments achieve higher returns by tapping
into what is commonly referred to as “Big Data,” data sources that are large,
contain a broad variety of data sets, are available on-demand, and change rapidly.




Corporate Headquarters                                                      Nucleus Research Inc.
Nucleus Research Inc.                                                       NucleusResearch.com
100 State Street
Boston, MA 02109
Phone: +1 617.720.2000
March 2012                                                                                              Document M20

Predictive deployments also reach beyond the traditional limits of internal
enterprise data to the Web, customers, vendors, and partners.

BIG DATA, BIG RETURN S
When Nucleus validated the benefits of Big Data, benefits achieved by end users
included:
      Increased productivity. A major metropolitan police department achieved an
       863 percent ROI when it combined its criminal records database with a national
       crime database created by a major university. The combination of national
       trends, local crime-related data, and predictive analytics enabled the police
       department to allocate its law enforcement assets more effectively and reduce
       crime rates.
      Increased margins. An ROI of 942 percent was earned by a major
       manufacturer which used Big Data capabilities to examine purchasing and cost-
       related data in all of its vendors’ databases, leading to vendor consolidation
       and reduced cost of goods sold.
      Increased revenues. Revenues can be increased when Big Data is used to
       rapidly detect changes in consumers’ activities and preferences. For example,
       an organization optimizing online campaigns can track click streams and data
       gathered from all customer touch points to continuously monitor and fine tune
       their programs, resulting in increased revenues.
      Reduced labor costs. A major resort earned an ROI of 1,822 percent when it
       integrated shift scheduling processes with data from a national weather
       service, enabling managers to avoid unnecessary shift assignments and
       increase staff utilization.


BENEFITS OF BIG DATA
Nucleus analysts identified four key drivers for high returns on Big Data
investments. First, Big Data enabled organizations to examine large volumes of
structured and unstructured data, such as large data sets captured by customer
loyalty programs and call centers. Second, Big Data improved decision making by
rapidly delivering data and conclusions while the information was still valuable.
Third, companies improved decision making by combining their own data with
acquired large data sets, such as geospatial data. Finally, Big Data capabilities
enable the scouring of Web-based data for tasks such as monitoring and detecting
changes in customer sentiment.

Big Data enables analysis of vast data sets
By dramatically expanding the volume of data that can be examined in an analytics
deployment, Big Data capabilities enable employees to detect conditions that
impact a large number of transactions, but are unobservable without a dedicated
analytics effort. For example, an automobile manufacturer that examines parts
purchases by its servicing subsidiary can detect design flaws or quality problems
before they become a public relations or branding crisis. Another source of on-
premise Big Data is information gathered from customer loyalty programs. By
examining the purchasing habits and behaviors or thousands of members in a
loyalty program, retailers can improve their product offerings and price points,
leading to higher revenues and margins. Through standard touch points with
customers, partners, and vendors, many enterprises already have collected Big
Data sets even though they may not know it. Large telecommunications providers




© 2012 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited.
NucleusResearch.com
                                                                                                                       2
March 2012                                                                                              Document M20

can use Big Data to reduce customer churn by continuously examining their
customers’ interactions related to billing, purchases, and call center activity to
anticipate which customers are considering switching.

Big Data also improves decision making by aggregating and analyzing large
volumes of unstructured data. Although many organizations accumulate large
quantities of data in the form of handwritten notes, e-mails, and voice recordings,
this data is typically unavailable for analysis because most analytical tools are
designed for highly structured data such as financial information. Big Data
analysis tools enable organizations to collect and examine this unstructured
information to detect desired operational trends or conditions. For example, an
airline could examine recordings of call-center interactions in order to identify the
best practices that lead to higher customer retention rates when standard service
capabilities are disrupted.

Big Data accelerates decision making
By rapidly sifting through such large volumes of information, Big Data enables
organizations to identify problems or opportunities while something can still be
done. For example, many consumers are likely to tweet or blog about a product
long before they share their opinions with a call center representative. With
sentiment tracking tools, organizations can get a read on customer sentiment far
faster than relying on call centers or focus groups. Customer churn prevention also
requires timely reactions based on the accurate view of leading metrics. Many
large telecommunications providers examine churn statistics on a weekly or
monthly basis. Although such reporting may successfully identify customers at risk
for churn, this information only becomes available after the customer has already
switched. Big Data assets can reduce churn costs by continuously monitoring
billing databases to identify at-risk customers and sending them offers designed to
retain them.

External Big Data sources make proprietary data more valuable
Nucleus found that organizations were able to improve decision making when Big
Data sets were created by combining proprietary data sets with externally available
Big Data sets, such as geospatial data or meteorological information. For example,
a car manufacturer could identify local customer preferences or climate-specific
quality problems by adding geospatial data to the information gathered by onboard
sensors. Lending institutions that purchase publicly available credit data and
integrate it with their customer lists can improve the effectiveness of their
marketing campaigns and improve the quality of their loan portfolios.

Big Data enables Web-based sentiment monitoring
Social media is a common source of Big Data used to improve decision making,
mainly through sentiment tracking of brands, products, and other events
representing the company’s public face. Nucleus found many organizations
monitored customer sentiment by tracking the results of specific key word
searches, such as “our brand, need to return phone.” Tracking was also performed
by identifying individual user profiles on social networking sites capable of wielding
influence on sites such as Twitter or Facebook. By tracking such individuals, two
core benefits were achieved. First, those individuals were closely observed to
detect shifts in customer sentiment. Second, by proactively providing superior




© 2012 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited.
NucleusResearch.com
                                                                                                                       3
March 2012                                                                                              Document M20

service to such individuals, companies could ensure that such influencers had a
good opinion of their company.


CONCLUSION
Although Big Data may seem overhyped, technology buyers should set aside their
skepticism and consider making investments that enable the analysis of large and
complex data sets. When analytics capabilities are applied to large data sets,
whether they are associated with the enterprise, social media, the customer
audience, or the partner ecosystem, employees become capable of insights they
can’t make by examining traditional data sources. The scouring of the Web for
important shifts in customer sentiment, the use of acquired credit-ratings data for
loan portfolio improvement, and the analysis of warranty databases for the
detection of potential product failures are all examples of benefits from Big Data
that have significant bottom-line impact, yet are unavailable from less mature
analytics deployments.




© 2012 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited.
NucleusResearch.com
                                                                                                                       4

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The Big Returns from Big Data

  • 1. March 2012 Document M20 RESEARCH NOTE THE BIG RETURNS FROM BIG DATA THE BOTTOM LINE Nucleus found organizations can earn an incremental ROI of 241 percent by using Big Data capabilities to examine large and complex data sets. One driver of high returns on Big Data was the ability to improve business processes and decisions by increasing the types of data that can be analyzed. Another driver of high returns was the ability to monitor the factors that impact a company, such as customer sentiment, by scouring large external data sources such as social media sites. Nucleus has determined that enterprises increased their average ROI on analytics by 241 percent when they used Big Data to become a more Analytic Enterprise. An Analytic Enterprise improves its competitiveness and operating results by continuously expanding its use of analytics (Nucleus Research m17 – The stages of an Analytic Enterprise, February 2012). There are four stages in the evolution of an Analytic Enterprise, and Big Data plays an important role in this transformative path. 1400% 1200% Return on Investment 1000% Predictive 800% Strategic 600% Tactical 400% Automated 200% 0% Automated Tactical Strategic Predictive Enterprises with strategic deployments earn an average ROI of 968 percent by deploying analytics across the organization to align daily operations with senior management’s goals. Predictive deployments achieve higher returns by tapping into what is commonly referred to as “Big Data,” data sources that are large, contain a broad variety of data sets, are available on-demand, and change rapidly. Corporate Headquarters Nucleus Research Inc. Nucleus Research Inc. NucleusResearch.com 100 State Street Boston, MA 02109 Phone: +1 617.720.2000
  • 2. March 2012 Document M20 Predictive deployments also reach beyond the traditional limits of internal enterprise data to the Web, customers, vendors, and partners. BIG DATA, BIG RETURN S When Nucleus validated the benefits of Big Data, benefits achieved by end users included:  Increased productivity. A major metropolitan police department achieved an 863 percent ROI when it combined its criminal records database with a national crime database created by a major university. The combination of national trends, local crime-related data, and predictive analytics enabled the police department to allocate its law enforcement assets more effectively and reduce crime rates.  Increased margins. An ROI of 942 percent was earned by a major manufacturer which used Big Data capabilities to examine purchasing and cost- related data in all of its vendors’ databases, leading to vendor consolidation and reduced cost of goods sold.  Increased revenues. Revenues can be increased when Big Data is used to rapidly detect changes in consumers’ activities and preferences. For example, an organization optimizing online campaigns can track click streams and data gathered from all customer touch points to continuously monitor and fine tune their programs, resulting in increased revenues.  Reduced labor costs. A major resort earned an ROI of 1,822 percent when it integrated shift scheduling processes with data from a national weather service, enabling managers to avoid unnecessary shift assignments and increase staff utilization. BENEFITS OF BIG DATA Nucleus analysts identified four key drivers for high returns on Big Data investments. First, Big Data enabled organizations to examine large volumes of structured and unstructured data, such as large data sets captured by customer loyalty programs and call centers. Second, Big Data improved decision making by rapidly delivering data and conclusions while the information was still valuable. Third, companies improved decision making by combining their own data with acquired large data sets, such as geospatial data. Finally, Big Data capabilities enable the scouring of Web-based data for tasks such as monitoring and detecting changes in customer sentiment. Big Data enables analysis of vast data sets By dramatically expanding the volume of data that can be examined in an analytics deployment, Big Data capabilities enable employees to detect conditions that impact a large number of transactions, but are unobservable without a dedicated analytics effort. For example, an automobile manufacturer that examines parts purchases by its servicing subsidiary can detect design flaws or quality problems before they become a public relations or branding crisis. Another source of on- premise Big Data is information gathered from customer loyalty programs. By examining the purchasing habits and behaviors or thousands of members in a loyalty program, retailers can improve their product offerings and price points, leading to higher revenues and margins. Through standard touch points with customers, partners, and vendors, many enterprises already have collected Big Data sets even though they may not know it. Large telecommunications providers © 2012 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited. NucleusResearch.com 2
  • 3. March 2012 Document M20 can use Big Data to reduce customer churn by continuously examining their customers’ interactions related to billing, purchases, and call center activity to anticipate which customers are considering switching. Big Data also improves decision making by aggregating and analyzing large volumes of unstructured data. Although many organizations accumulate large quantities of data in the form of handwritten notes, e-mails, and voice recordings, this data is typically unavailable for analysis because most analytical tools are designed for highly structured data such as financial information. Big Data analysis tools enable organizations to collect and examine this unstructured information to detect desired operational trends or conditions. For example, an airline could examine recordings of call-center interactions in order to identify the best practices that lead to higher customer retention rates when standard service capabilities are disrupted. Big Data accelerates decision making By rapidly sifting through such large volumes of information, Big Data enables organizations to identify problems or opportunities while something can still be done. For example, many consumers are likely to tweet or blog about a product long before they share their opinions with a call center representative. With sentiment tracking tools, organizations can get a read on customer sentiment far faster than relying on call centers or focus groups. Customer churn prevention also requires timely reactions based on the accurate view of leading metrics. Many large telecommunications providers examine churn statistics on a weekly or monthly basis. Although such reporting may successfully identify customers at risk for churn, this information only becomes available after the customer has already switched. Big Data assets can reduce churn costs by continuously monitoring billing databases to identify at-risk customers and sending them offers designed to retain them. External Big Data sources make proprietary data more valuable Nucleus found that organizations were able to improve decision making when Big Data sets were created by combining proprietary data sets with externally available Big Data sets, such as geospatial data or meteorological information. For example, a car manufacturer could identify local customer preferences or climate-specific quality problems by adding geospatial data to the information gathered by onboard sensors. Lending institutions that purchase publicly available credit data and integrate it with their customer lists can improve the effectiveness of their marketing campaigns and improve the quality of their loan portfolios. Big Data enables Web-based sentiment monitoring Social media is a common source of Big Data used to improve decision making, mainly through sentiment tracking of brands, products, and other events representing the company’s public face. Nucleus found many organizations monitored customer sentiment by tracking the results of specific key word searches, such as “our brand, need to return phone.” Tracking was also performed by identifying individual user profiles on social networking sites capable of wielding influence on sites such as Twitter or Facebook. By tracking such individuals, two core benefits were achieved. First, those individuals were closely observed to detect shifts in customer sentiment. Second, by proactively providing superior © 2012 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited. NucleusResearch.com 3
  • 4. March 2012 Document M20 service to such individuals, companies could ensure that such influencers had a good opinion of their company. CONCLUSION Although Big Data may seem overhyped, technology buyers should set aside their skepticism and consider making investments that enable the analysis of large and complex data sets. When analytics capabilities are applied to large data sets, whether they are associated with the enterprise, social media, the customer audience, or the partner ecosystem, employees become capable of insights they can’t make by examining traditional data sources. The scouring of the Web for important shifts in customer sentiment, the use of acquired credit-ratings data for loan portfolio improvement, and the analysis of warranty databases for the detection of potential product failures are all examples of benefits from Big Data that have significant bottom-line impact, yet are unavailable from less mature analytics deployments. © 2012 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited. NucleusResearch.com 4