Reading Comprehension L2

Any company can generate simple descriptive statistics about aspects of its business - average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modelling to identify the most profitable customers-plus those with the greatest profit potential and the ones most likely to cancel their accounts. They pool data generated in-house and data acquired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimize their supply chains and can thus determine the impact of an unexpected constraint, simulate alternatives and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs relate to their financial performance. Leaders in analytics also use sophisticated experiments to measure the overall impact or "lift" of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example,
conducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back Capital One.

Analytics competitors understand that most business functions- even those, like marketing, that have historically depended on art rather than science-can be improved with sophisticated quantitative techniques. These organizations don't gain advantage from one killer app, but rather from multiple applications supporting many parts of the business-and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world's most rigorous practitioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly focused. Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influence the actions of people-assessing the likelihood of customer attrition and identifying sources of problems. The UPS Customer Intelligence Group, for example, is able to accurately predict customer defections by examining usage patterns and complaints.

When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts. UPS still lacks the breadth of initiatives of a full-bore analytics competitor, but it is heading in that direction. Analytics competitors treat all such activities from all provenances as a single, coherent initiative, often massed under one rubric, such as "information-based strategy" at Capital One or "information-based customer management" at Barclays Bank. These programs operate not just under a common label but also under common leadership and with common technology and tools. In traditional companies, "business intelligence" (the term IT people use for analytics and reporting processes and software) is generally managed by departments; number-crunching functions select their own tools, control their own data warehouses, and train their own people. But that way, chaos lies. For one thing, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indicators within an organization.

Furthermore, research has shown that between 20% and 40% of spreadsheets contain errors; the more spreadsheets floating around a company, therefore, the more fecund the breeding ground for mistakes. Analytics competitors, by contrast, field centralized groups to ensure that critical data and other resources are well managed and that different parts of the organization can share data easily, without the impediments of inconsistent formats, definitions, and standards. Some analytics competitors apply the same enterprise approach to people as to technology. Procter & Gamble, for example, recently created a kind of uber analytics group consisting of more than 100 analysts from such functions as operations, supply chain, sales, consumer research, and marketing. Although most of the analysts are embedded in business operating units, the group is centrally managed. As a result of this consolidation, P&G can apply a critical mass of expertise to its most pressing issues. So, for example, sales and marketing analysts supply data on opportunities for growth in existing markets to analysts who design corporate supply networks. The supply chain analysts, in rum, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence.

The group at P&G also raises the visibility of analytical and data based decision making within the company. Previously, P&G's crack analysts had improved business processes and saved the firm money; but because they were squirreled away in dispersed domains, many executives didn't know what services they offered or how effective they could be. Now those executives are more likely to tap the company's deep pool of expertise for their projects. Meanwhile, masterful number crunching has become part of the story P&G tells to investors, the press and the public.

A companywide embrace of analytics impels changes in culture, processes, behaviour, and skills for many employees. And so, like any major transition, it requires leadership from executives at the very top who have a passion for the quantitative approach. Ideally, the principal advocate is the CEO. Indeed, we found several chief executives who have driven the shift to analytics at their companies over the past few years, including Loveman of Harrah's, Jeff Bezos of Amazon, and Rich Fairbank of Capital One. Before he retired from the Sara Lee Bakery Group, former CEO Barry Beracha kept a sign on his desk that summed up his personal and organizational philosophy: "In God we trust. All others bring data!' We did come across some companies in which a single functional or business unit leader was trying to push analytics throughout the organization, and a few were making some progress. But we found that these lower-level people lacked the clout, the perspective, and the crossfunctional scope to change the culture in any meaningful way. CEOs leading the analytics charge require both an appreciation of and a familiarity with the subject.

A background in statistics isn't necessary, but those leaders must understand the theory behind various quantitative methods so that they recognize those methods' limitations-which factors are being weighed and which ones aren't. When the CEOs need help grasping quantitative techniques, they turn to experts who understand the business and how analytics can be applied to it. We interviewed several leaders who had retained such advisers, and these executives stressed the need to find someone who can explain things in plain language and be trusted not to spin the numbers. A few CEOs we spoke with had surrounded themselves with very analytical people-professors, consultants, MIT graduates and the like. But that was a personal preference rather than a necessary practise. Of course, not all decisions should be grounded in analytics - atleast not wholly so. Research shows that human beings can make quick, surprisingly accurate assessments of personality and character based on simple observations. For analytics minded leaders then the challenge boils down to knowing when to run with the numbers and when to run with their guts.

1. With reference to the above passage what does the phrase "uberanalytic group" mean?

  1. A centralized homogeneous group comprising of experts in analytics within the organization who provide their expertise to all
  2. A centralized group that draws upon expertise from multifunctional areas within the organization and provides their expertise to all
  3. A centralized group consisting of data analytics experts from within the organization who provide their expertise to all
  4. A powerful centralized group of crack analysts within the organisation who provide their expertise to all

2. Replace the phrase 'more fecund the breeding ground for mistakes' from the passage by selecting the most appropriate phrase without changing the meaning.

  1. An unsuitable environment where mistakes are bound to happen
  2. Fructiferous environment for mistakes
  3. A highly fertile ground for producing more errors
  4. Creating a high possibility of inaccuracy

3. According to the author, what is the leadership challenge for analytics minded leaders?

  1. The analytics minded leader has to be well versed in statistics and quantitative analysis in order to be effective
  2. The analytics minded leader should have experts in data analysis from top institutes such as MIT etc. to advise him
  3. The analytics minded leader should be able to strike a balance between data driven decisions and intuition
  4. The analytics minded leader should be able to lead teams of cross-functional experts

4. In the above passage, how does the author differentiate between 'Analytics competitors' and 'traditional companies' with regard to their strategy towards data management?

  1. Analytics competitors have a centralised, multi-functional approach to data management and encourage data sharing whereas traditional companies, have a departmental, multiple databases approach
  2. Traditional companies surround themselves with very analytical people from academia, consultants, and pass outs from institutes like MTTs but Analytics competitors hire the best analytical minds
  3. Analytics competitors use predictive modelling to identify the most profitable options whereas traditional companies use basic statistics
  4. Traditional companies appoint leaders with data management expertise but Analytics competitors train all employees in data analytics


  1. B
  2. C
  3. C
  4. C

1. Refer to the lines Procter & Gamble, for example … to its most pressing issue in the 4th paragraph of the passage.

2. After reading these lines in the 4th paragraph of the passage it becomes evident that – more the number of spreadsheets floats around a company - higher the chances of committing errors.

3. Refer to the last sentence of the 5th paragraph and the whole of the last paragraph of the passage. It has been clearly indicated that an analytics minded leader must strike a fine balance between data analysis and instincts (gut feeling).

4. Refer to the first few lines of the first paragraph of the passage. It has been clearly mentioned that although any company can generate simple descriptive statistics, analytics competitors look well beyond these simple statistics and use productive models to identify the most profitable options.