Transformation through Analytics: Need of the Hour
Businesses today face a volatile macro environment and a demanding customer base. Consumers demand more relevant offerings, consistently positive experiences, better communication, and faster service delivery. To respond to this need – and to deal with emerging threats – businesses need to be more agile. Agility is informed by leveraging the huge, ever-growing amount of data your company processes and stores.
For many organizations, this exponential increase in the volume, type, and flow of data represents a great opportunity. To take full advantage of it, however, requires making a commitment to analytics. It’s the information gleaned from analytics – rather than the raw data itself – that will be a deciding factor in the success or failure of organizational changes.
Decision makers who effectively extract information for actionable insights can capitalize on opportunities they may otherwise miss. Those that cannot may ultimately find themselves acting on outdated information, failing to keep up with the fierce competition of the business world. In time, this can result in an increasingly difficult – and possibly failing – business environment. Organizations are just beginning to realize the potential of data analytics. It’s in a transitional stage, shifting from:
• Initiative to imperative • Enterprise data to Big Data
• Organizationally-focused to industry-transforming
A new, holistic approach is needed to realize the transformative power of analytics, a power that turns information into insight and insight into business impact. To this end, AbsolutData, a leading Analytics & Research firm, and Alteryx, a leading analytics software provider, conducted a survey of thought leaders across multiple industries with the goal of understanding the current status of analytics in their organizations.
Companies collect many different types of customer data
Every customer interaction creates data, and this data provides deeper insight on customer behavior, attitudes, and opinions. This can be leveraged to improve customer relationships, thereby gaining a competitive edge. The survey results show that traditional data sources still dominate, but several new areas of insight are emerging.
Customer analytics data sources used by companies for making decisions
The vast majority of companies use Customer Analytics today
Through the data they collect, organizations are listening to what customers have to say. They then use these insights to implement customer-driven marketing strategies that increase profit and improve customer loyalty.
Do companies use Customer Analytics when making business decisions?
69% 69% 61% 49% 41% 31% 30% 17% 6% Customer demographics Primary/Research Data POS/transaction data Customer interaction data Social media Loyalty card data Complaint data Recorded voice calls Others
82%
18% Yes
Analytics contributes significant insight for strategic operations
Customer Analytics is used primarily for customer-focused Sales & Marketing activities, but they aren’t limited to that realm. Many companies also use these insights to make product or service portfolio decisions and to determine the optimal distribution channels.
How Customer Analytics Benefit Companies:
Three Major Challenges Inhibit Analytic Decision Making
Despite the fact that analytics are being widely used, organizations still struggle will three main areas – the challenge of integrating large volumes of dissimilar data types; a lack of skilled people to execute their customer analytics strategy; and the problem of defining and calculating ROI for analytics strategies.
We’ll consider these challenges individually. 69% 63% 46% 62% 60% 49% Be2er Customer Acquisi<on
Enhanced customer sa<sfac<on
Increased loyalty
Improved product/service design
Op<mized marke<ng/channel
Challenge #1 - Integrating Large Volumes of Dissimilar Data Types
Why do companies face this challenge? According to the businesspeople surveyed, it’s a multi-faceted issue, with the separation of resources and departments and the sheer amount of information that needs to be processed leading the list.
Challenges faced during implementation of analytics
With the advance in computing and anayltics, why is the collection and analysis of data – even widely different types of data – a problem? There are three primary causes:
• There is no unified point of reference, no “absolute source of truth”. Marketing, Product Management, Operations, and other departments use different data sources to answer similar questions, thus creating fractured and unclear results.
• Time is wasted thanks to inadequate data processing resources. Data processing units that cannot handle at least 1 TB of information are usually too slow and underpowered to produce the quick, comprehensive results needed.
• New data sources are difficult to integrate. Unstructured but valuable data - such as social media and call center logs – may be lost if they do not work with the existing analytics setup. This means that any reports arising from analysis in which this data in not included is incomplete and possibly flawed.
43% 39% 38% 37% 23% Siloed departments, each with separate
data resources
Integra<ng massive amounts of data
Integra<ng disparate customer data types
Conver<ng data into ac<onable insight
Case Study – Understanding Marketing Across Various Media Channels
A building is only as good as its foundation; and insight is only as good as the data that prompts it. Hence, before looking to build a strategy - before tuning in to that actionable insight – you must get ‘all the right data’.
One recent instance of this is a 4-billion-dollar retail giant that wanted to understand the impact of various marketing activities across different media channels: TV, radio, print, direct mail, Internet, and email. This data was broadly distributed; some was within the company, among several internal departments, and some was outside the company, in industry stakeholders and media vendors.
A substantial amount of time was initially spent educating various stakeholders about the desired outcome from this exercise. After data collation and the creation of a data-mart, predictive models were created which improved the ROI from marketing expenses.
Getting a grasp on data is not that easy:
• Today’s data comes from multiple channels. Knowing which data matters, using each group in an integrated way, and acting upon the congregated whole of the information requires time and effort at nearly every stage of its creation and consumption.
• Businesses don’t have much choice when looking at the channel-agnostic, multi-screen and increasingly complex behaviour of today’s consumer.
• Data flow will continue to increase. It is vital for companies to get a handle on the situation sooner rather than later.
By 2020, Data is Forecasted To Reach 40 Exabytes per person
Challenge #2 - A Lack Of Workers Skilled In Customer Analytics Strategy
Close to 90% of organizations lack industry-leading skills needed to execute their customer analytics strategy.
Current Levels of Executive Analytics Strategy Skills
Why is there such a lack of skilled analytics strategists?
• Users in various departments do their own department-oriented analytics, learning only the skills they need to perform this task. They get the answers they need, but there is a lack of an overall strategist company-wide.
• Limited availability of IT staff or resources with specialized skill sets can cause delays. • 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 – Hiring Specialized Professionals Rather than A Single Analytics Expert
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 three individual professionals to provide for each of those three needs rather than trying to find one person who would meet all three needs at once. 12% 38% 42% 8% 1%
Industry leading, with a mastery of advanced analy<cs and business domain knowledge
Advanced, for crea<ng workflows using all sorts of predic<ve and spa<al analy<cs
Basic, for repor<ng and modifica<on of exis<ng analy<c workflows Limited, for genera<ng reports only
With analytics tools in the hands of the subject matter experts in the individual departments, a culture of rapid organizational decision making was created. This approach also allowed the analytics center to be scaled virtually at will, which is considerably more cost-effective.
Why does the struggle to find “one perfect expert” hamper businesses? • There is a very limited number of available experts.
• Due to limited personnel and financial resources, building internal capabilities is difficult. McKinsey projects a potential shortfall of 1.5 million data-savvy managers and analysts in the US alone.
• Companies are compelled to define their operating model based on two functions – the level of requirements to be met and the organization’s current internal capabilities.
Analytics resourcing has evolved to meet the rising industry demand. Instead of using a centralized approach of hiring one all-knowing expert, organizations are now approaching a more compartmentalized approach, focusing on recruiting several professional with unique and specific skills.
The Centralized Approach of the Past:
• 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, and an associated difficulty in scaling up to meet expanding business needs.
Today’s Scalable, Dynamic Solution:
• 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.
Challenge #3 - Defining And Calculating ROI For Analytics Strategies
The fundamental truth of business is that you need to bring in more than you spend. Looking at Return on Investment statistics can help you determine the efficacy of your marketing initatives; poorly-performing ideas can be scrapped and substituted with something better. Yet, organizations struggle with defining and calculating ROI for analytics. According to our survey:
Why is this hard to quantify? Careful planning is required to maximize ROI. An organization needs to painstakingly plot its analytics journey to reap its maximum benefit. A customized approach is required, based on the analytics maturity of the organization and its current analytics capabilities.
Case Study – A 3-Year Analytics Journey Highlights Tailored Results
Organizations today have increasingly complex business models with unique value propositions, strengths, and weaknesses. To apply a “one size fits all” approach to analytics is not wise. This is exemplified in the story of a client who went on a 3-year-long analytics journey, with the goal of identifying an ideal analytics operating model for their business.
The client started with ad-hoc analytics projects, gradually developed campaign execution capabilities (primarily focusing on high volumes and great accuracy), and finally evolved to
9%
10%
43%
38% Return is less than investment
Return is equal to investment
Return is more than investment
Today, the organization considers analytics indispensable to its marketing and strategy functions. Tailored approaches to analytics are required. What works for one company may not work for another, since company size, organization, location, and purpose vary. The best strategy will highlight the current analytics maturity of the organization and the types of problems that it needs to solve.
Measuring Return on Investment Accurately
Companies that adopted “data-driven decision making” achieved 5 to 6 percent higher productivity rates. (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 every line item in a corporation’s profit & loss statement. With the advent of Big Data and enhanced data processing technologies, the analytics community is leading the innovation curve on new, impactful methods and business processes.
Analytics Success Drives Better Results!
Some of the world’s leading companies have repeatedly leveraged analytics to efficiently process data and to achieve competitive differentiation. For example, a leading American e-commerce corporation amassed nearly $5 billion in revenue in only eight years. They performed this feat by being an early adopter of analytics throughout their decision making processes, particularly in studying customer data to drive repeat purchases. And the power of analytics in is daily use for other companies. Currently, about 38,000 Procter & Gamble managers use analytics every day to understand “What Happened”, “Why”, and “What to do” for their 300 brands in 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
Analytics Shakers1 vs. S&P 500
Transformation is a constant process of optimizing and refining data sources, learning from the previous outcomes, and applying things learned, eventually leading to changing how the organization achieves its goals. In an era of relentless competition, organizational leaders realize that investment in analytics technology, employee training, and external resources must continue.
Analytics Investment Continues
Summary
No one will disagree that there is access to more data than ever before, and that this is continually and rapidly increasing. Organizations are fighting hard to utilize this in the best way possible, but are facing big challenges along the way. These challenges vary from struggling with volumes of data to not having the right skillset to devise and implement an effective analytics strategy. Not being able to measure the return-on-investment on analytics is also a recurring problem. However, companies who have implemented analytics with moderate success have shown shown superior business performance. This will continue to motivate others to transform their businesses thorough analytics. Successful implementation of analytics is now one of the most desirable outcomes for business, but it requires continued effort and investment to gain competitive advantage.
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