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Transformation through analytics white paper by absolutdata & alteryx

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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

Veröffentlicht in: Technologie, Business
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Transformation through analytics white paper by absolutdata & alteryx

  1. 1. Transformation Through Analytics
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 13. Appendix Thanks San Francisco | Chicago | New York | London | Dubai | New Delhi | Bangalore | Singapore