According to our recent survey on customer analytics: 37% of organizations struggle with converting their customer data into actionable insight. If this sounds familiar, download this white paper and get the details about how organizations: Put ROI metrics in place that encourage the sharing of customer data between departments Recruited the right talent, with skills matched to specific line-of-business needs Adopted best practices for predicting customer behavior and analytic decision making

1. Transformation Through Analytics

2. Transformation through Analytics: Need of the Hour
Businesses today are facing a volatile macro environment and a demanding customer base.
―Cash rich, time poor‖ consumers are demanding more relevant offerings, experiences,
communication and service delivery. This requires businesses to be agile and respond quickly to
emerging opportunities and threats. Businesses need to achieve this by leveraging the large and
growing volume of data stored.
Every organization today recognizes that this exponential increase in the volume, velocity and
variety of data represents a great opportunity. What they don‘t always fully grasp is how
analytics should be applied to turn that data into the kind of insight that will enable them to
develop analytics into a competitive advantage in today‘s dynamic marketplace.
Advanced analytics will be a deciding factor that determines whether organizations succeed or
fail. Those able to effectively extract information for first-hand top insights can capitalize on
virtually endless opportunities. Those that cannot master the data may ultimately find themselves
playing catch-up—or, worse, simply cease to exist.
Analytics is a transformational phenomenon, and organizations are just beginning to realize its
potential as the role of analytics shifts from:
Initiative to imperative
Enterprise data to Big Data
Organizational focus to industry transformation
Realizing the transformative power of analytics requires a new, holistic approach that turns
information into insight and insight into business impact. In this context, AbsolutData, a leading,
consulting-oriented, Analytics & Research firm, in partnership with Alteryx, a leading analytics
software provider, conducted a survey of thought leaders across multiple industries to understand
the current status of analytics across their organizations.
The findings have been quite interesting.

3. Companies collect many different types of customer data
Data captured from every customer interaction provides deeper insight on customer behavior,
attitudes, and opinions which can be leveraged to improve customer relationships and gain
competitive edge. The survey results show that traditional data sources still dominate, but several
new areas of insights are emerging.
Customer analytics data sources used by companies for making decisions
The vast majority of companies use Customer Analytics today
Organizations are listening to what customers say through their data. Organizations use these
insights to implement customer-driven marketing strategies that improve revenue and customer
loyalty.
Companies using customer analytics for making business decisions
69%
69%
61%
49%
41%
31%
30%
17%
6%
Customer demographics
Primar/Research Data
POS/transaction data
Customer interaction data
Social media
Loyalty card data
Complaint data
Recorded voice calls
Others
82%
18%
Yes
No

4. Analytics contributes significant insight for strategic operations
Customer Analytics is being used primarily for customer-focused Sales & Marketing activities.
But, many companies also use these insights to make product/service portfolio decisions and
determine the optimal distribution channels.
Benefits companies get from customer analytics
But Three Major Challenges Inhibit Analytic Decision Making
Organizations struggle with integrating large volumes of disparate data
Organizations struggle with integrating large volumes of disparate data
Organizations lack industry leading skills to execute their customer analytics strategy
Organizations struggle with defining & calculating ROI for analytics
69%
63%
46%
62%
60%
49%
Customer acquisition/retention
Enhanced customer satisfaction
Increased loyalty
Improve product/service design
Optimize marketing/channel
Design/improve channel strategy

5. Challenge#1
Organizations struggle with integrating large volumes of disparate data:
Challenges faced during implementation of analytics
Implications
No Single source of truth: Marketing, Product Management, Operations, and other
departments use different data sources to answer similar questions
Lack of Adequate Tools: Time is wasted using tools that cannot process TBs (1000
GBs) of data
New Data sources are difficult to integrate: Unstructured, but valuable data such as
social media and call center logs cannot be used
Case Study
A building is only as good as its foundation; and insight is only as good as the data. Hence,
before looking to build a strategy, before getting that actionable insight out, it is of prime
importance to get ‗all the right data‘. Clients face these challenges day in and day out and one
recent instance is of a $4B retail giant who wanted to understand the impact of various marketing
43%
39%
38%
37%
23%
Siloed departments, each with separate
data resources
Integrating massive amounts of data
Integrating disparate customer data types
Converting data into actionable insight
Collecting relevant customer data

6. activities across various media – TV, Radio, Print, Direct Mailers, Digital, Emails. Most of this
data was available in silos across various internal departments, industry stakeholders as well as
media vendors.
A substantial amount of time was initially spent to educate the various stakeholders about the
desired outcome from this exercise and hence get them ‗on-board‘. After data collation and
creation of a data-mart, predictive models were created which improved the ROI from their
Marketing spend.
Getting a grasp on data is not that easy:
Today‘s data comes from multiple channels. Knowing which data matters, using them in
an integrated way and acting upon them is not easy
Businesses don‘t have much choice when looking at the channel-agnostic, multi-screen
and increasingly complex behavior of today‘s consumer
IDC‘s Digital Universe Study (sponsored by EMC), December 2012 estimates that between 2010
and 2020, data stored is expected to grow by ~50X to 40K Exabytes
~5,200 GB
for every man, woman and child
by 2020

7. Challenge#2
Close to 90% of organizations lack industry leading skills to execute their customer analytics
strategy
Current Skill-set to execute analytics strategy
Implications
Line-of-business users must do their analytics: Limited availability of IT staff/resources
with specialized skill sets can cause delays. Users in the various departments must learn
analytics tools in order to get the answers they need.
Scaling up of analytics operations is diffcult: Skilled resource shortages, access to data,
and overly complex analytics remain a barrier to greater usage.
Case Study
While setting up an Insights Hub at a world‘s leading genealogy company, the analytics director
asked, ―Where do I get trained statisticians who understand my business and can make business
decisions?‖ She soon realized that it was easier to find separate professionals to provide each of
the three above mentioned ‗needs‘ rather than trying to find people who would meet all three
needs at once.
12%
38%42%
8%
1% Industry leading, with a mastery of advanced analytics and
business domain knowledge
Advanced, for creating workflows using all sorts of predictive and
spatial analytics
Basic, for reporting and modification of existing analytic
workflows
Limited, for generating reports only
None

8. With analytics tools in the hands of the subject matter experts in the individual departments, a
culture of rapid organization decision making was created. This approach also allowed the
analytics center to be scaled at wish, and is also considerably more cost-effective.
The Struggle of finding the ‘Scientists’ continues to haunt the organizations:
Building internal capabilities is difficult due to limited resources. McKinsey projects a
potential shortfall of 1.5 Million data-savvy managers and analysts in the US alone.
This is compelling companies to define the right operating model, which is a function of
two elements:
a. Level of requirements
b. Current internal capabilities
To meet the rising industry demand, Analytics resourcing has evolved to meet the rising industry
demand. From a centralized approach of hiring ‗know-it-all‘ professionals, organizations are
now approaching a more disaggregated approach focusing on specific skills.
Past approach (Centralized):
Organizations hired highly educated analysts with 10+ years of work experience and
Techno-Functional and Domain knowledge.
This approach has failed due to a lack of adequate resources, high costs & difficulty in
scaling up.
Current Approach (Disaggregated):
Specialization & segregation of specific skills: Domain Expert, Project Manager & Data
Scientist
This approach is succeeding due to the availability of sophisticated analytics tools that
are easier to learn, as well as the ability to deploy the ―right‖ skills at the ―right‖ stage of
the project.

9. Challenge#3
Organizations struggle with defining & calculating ROI for analytics
Implications
Careful planning is required to maximize ROI: An organization needs to carefully plot
its analytics journey to derive the maximum benefit. A customized approach is required
based on the analytics maturity of the organization & its current analytics capabilities.
Case Study
Organizations today have increasingly complex business models with unique value propositions,
strenghts & weaknesses.
To apply a ―one size fits all‖ approach to analytics is sub-optimal. A recent example is of a client
who went on a 3 year analytics journey to identify the right analytics operating model for itself.
The client started with ad-hoc analytics projects, gradually developed campaign execution
capabilities (high volumes, & extremely sensitive to accuracy), and evolved to managing
complex strategic projects for a global audience.
Today the organization considers analytics indispensable to its marketing and strategy functions.
Tailored approaches to analytics are required depending on the current analytics maturity of the
organization and the types of problems that analytics needs to solve.
9%
10%
43%
38%
Return is less than investment
Return is equal to investment
Return is more than investment
Don't know

10. Measuring Return on Investment accurately
Companies that adopted ―data-driven decision making‖ achieved 5-6 % higher productivity
(2011 study of 179 companies by professors at MIT and Wharton)
A 2011 Nucleus Research of 60 analytics-related ROI case studies found that for every dollar
invested in technologies such as Business Intelligence and predictive analytics, organizations get
back an average of $10.66.
Predictive analytics has proven capabilities in adding value to each and every line item in a
corporation‘s Profit & Loss statement. With the advent of Big Data and better data processing
technologies, the analytics community is leading the innovation curve on new methods and
business processes where it can have an impact.

11. Analytics Success Drives Better Results!
Some of the world‘s leading companies have leveraged analytics to process data and to achieve
competitive differentiation again and again. For example, a leading American e-commerce MNC
reached $5B in revenue in only eight years by being an early adopter of analytics throughout its
decision making processes, and analyzing customer data to drive repeat purchases. And, 38,000
P&G managers (30% of workforce) use analytics every day to understand ―What Happened‖,
―Why‖ and ―What to do‖ for their 300 brands across 180 countries.
AbsolutData‘s study of Analytics Shakers and S&P 500 index reveals that companies that
invested heavily in advanced analytical capabilities outperform the S&P 500 index. They were
also able to recover quicker from economic downturns faster than their peers.
Analytics Shakers1 vs. S&P 500
Transformation is a constant process of optimizing and refining data sources, learning from the
previous outcomes, and applying that learning to transform how the organization achieves future
goals. In an era of relentless competition, organizational leaders realize that investment in
analytics technology, employee training, and external resources must continue.

12. Analytics Investment Continues
Summary
There is no doubt in anyone‘s mind (or databases) that there is access to more data than ever
before and this is continually on a rapid increasing curve. Organizations are fighting hard to
utilize this in the best way possible to have an impact on their bottom-line but are facing big
challenges in doing so. These challenges vary from struggling with volumes of data, to not
having the right skill-set to devise and implement an effective analytics strategy, to not being
able to measure the return-on-investment on analytics. However, the companies who have
implemented analytics with moderate success and shown superior business performance continue
to inspire the others to transform thorough analytics. Successful implementation of analytics is
now the holy-grail to the management, which requires continued effort and investment to gain
competitive advantage.

13. Appendix
Thanks
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