Statistical models and algorithms that dangle the possibility of performance breakthroughs make some prospects especially tempting. Marketing, for example, has always been tough to quantify because it is rooted in psychology. But now consumer products companies can hone their market research using multiattribute utility theory—a tool for understanding and predicting consumer behaviors and decisions.
Similarly, the advertising industry is adopting econometrics—statistical techniques for measuring the lift provided by different ads and promotions over time. Wal-Mart, for example, insists that suppliers use its Retail Link system to monitor product movement by store, to plan promotions and layouts within stores, and to reduce stock-outs.
The distributors, in turn, use that information to help retailers optimize their mixes while persuading them to add shelf space for Gallo products. Culture is a soft concept; analytics is a hard discipline.
Nonetheless, analytics competitors must instill a companywide respect for measuring, testing, and evaluating quantitative evidence. Employees are urged to base decisions on hard facts. And they know that their performance is gauged the same way. Human resource organizations within analytics competitors are rigorous about applying metrics to compensation and rewards.
Senior executives also set a consistent example with their own behavior, exhibiting a hunger for and confidence in fact and analysis.
At Yahoo, Progressive, and Capital One, process and product changes are tested on a small scale and implemented as they are validated. That approach, well established within various academic and business disciplines including engineering, quality management, and psychology , can be applied to most corporate processes—even to not-so-obvious candidates, like human resources and customer service.
It was also expensive to develop, and that increased the risk. In this case, Bezos trusted his instincts and took a flier. And the feature did prove popular when introduced. Analytical firms hire analytical people—and like all companies that compete on talent, they pursue the best. Since his arrival, Yu and his team have been designing and building sophisticated supply chain systems to optimize those processes.
Established analytics competitors such as Capital One employ squadrons of analysts to conduct quantitative experiments and, with the results in hand, design credit card and other financial offers. These efforts call for a specialized skill set, as you can see from this job description typical for a Capital One analyst :. Ability to quickly learn how to use software applications.
Experience with Excel models. Some graduate work preferred but not required e. Some experience with project management methodology, process improvement tools Lean, Six Sigma , or statistics preferred. Other firms hire similar kinds of people, but analytics competitors have them in much greater numbers.
Capital One is currently seeking three times as many analysts as operations people—hardly the common practice for a bank. Good analysts must also have the ability to express complex ideas in simple terms and have the relationship skills to interact well with decision makers. Of course, a combination of analytical, business, and relationship skills may be difficult to find.
When the software company SAS a sponsor of this research, along with Intel knows it will need an expert in state-of-the-art business applications such as predictive modeling or recursive partitioning a form of decision tree analysis applied to very complex data sets , it begins recruiting up to 18 months before it expects to fill the position.
In fact, analytical talent may be to the early s what programming talent was to the late s. Unfortunately, the U. Some organizations cope by contracting work to countries such as India, home to many statistical experts.
That strategy may succeed when offshore analysts work on stand-alone problems. But if an iterative discussion with business decision makers is required, the distance can become a major barrier. Competing on analytics means competing on technology. And while the most serious competitors investigate the latest statistical algorithms and decision science approaches, they also constantly monitor and push the IT frontier. The analytics group at one consumer products company went so far as to build its own supercomputer because it felt that commercially available models were inadequate for its demands.
Companies have invested many millions of dollars in systems that snatch data from every conceivable source. Enterprise resource planning, customer relationship management, point-of-sale, and other systems ensure that no transaction or other significant exchange occurs without leaving a mark. But to compete on that information, companies must present it in standard formats, integrate it, store it in a data warehouse, and make it easily accessible to anyone and everyone.
And they will need a lot of it. For example, a company may spend several years accumulating data on different marketing approaches before it has gathered enough to reliably analyze the effectiveness of an advertising campaign.
That information allows Dell to fine-tune its promotions for every medium in every region. Business intelligence tools allow employees to extract, transform, and load or ETL, as people in the industry would say data for analysis and then make those analyses available in reports, alerts, and scorecards. The popularity of analytics competition is partly a response to the emergence of integrated packages of these tools.
The volumes of data required for analytics applications may strain the capacity of low-end computers and servers. Many analytics competitors are converting their hardware to bit processors that churn large amounts of data quickly.
Most companies in most industries have excellent reasons to pursue strategies shaped by analytics. Virtually all the organizations we identified as aggressive analytics competitors are clear leaders in their fields, and they attribute much of their success to the masterful exploitation of data.
Rising global competition intensifies the need for this sort of proficiency. Western companies unable to beat their Indian or Chinese competitors on product cost, for example, can seek the upper hand through optimized business processes. Companies just now embracing such strategies, however, will find that they take several years to come to fruition.
The organizations in our study described a long, sometimes arduous journey. The UK Consumer Cards and Loans business within Barclays Bank, for example, spent five years executing its plan to apply analytics to the marketing of credit cards and other financial products. The company had to make process changes in virtually every aspect of its consumer business: underwriting risk, setting credit limits, servicing accounts, controlling fraud, cross selling, and so on.
On the technical side, it had to integrate data on 10 million Barclaycard customers, improve the quality of the data, and build systems to step up data collection and analysis. In addition, the company embarked on a long series of small tests to begin learning how to attract and retain the best customers at the lowest price.
And it had to hire new people with top-drawer quantitative skills. Much of the time—and corresponding expense—that any company takes to become an analytics competitor will be devoted to technological tasks: refining the systems that produce transaction data, making data available in warehouses, selecting and implementing analytic software, and assembling the hardware and communications environment.
You apply sophisticated information systems and rigorous analysis not only to your core capability but also to a range of functions as varied as marketing and human resources. Your senior executive team not only recognizes the importance of analytics capabilities but also makes their development and maintenance a primary focus.
You hire not only people with analytical skills but a lot of people with the very best analytical skills—and consider them a key to your success. You not only employ analytics in almost every function and department but also consider it so strategically important that you manage it at the enterprise level.
You not only are expert at number crunching but also invent proprietary metrics for use in key business processes. You not only use copious data and in-house analysis but also share them with customers and suppliers. You not only have committed to competing on analytics but also have been building your capabilities for several years.
And, of course, new analytics competitors will have to stock their personnel larders with fresh people. Existing employees, meanwhile, will require extensive training. They need to know what data are available and all the ways the information can be analyzed; and they must learn to recognize such peculiarities and shortcomings as missing data, duplication, and quality problems.
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The company has even created a revenue opportunity model, which computes actual revenues as a percentage of the optimal rates that could have been charged. Customers notice the difference in every interaction; employees and vendors live the difference every day.
Our study found three key attributes among analytics competitors:. 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 modeling 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. Capital One, for example, conducts more than 30, 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.
Progressive employs similar experiments using widely available insurance industry data. The company defines narrow groups, or cells, of customers: for example, motorcycle riders ages 30 and above, with college educations, credit scores over a certain level, and no accidents. For each cell, the company performs a regression analysis to identify factors that most closely correlate with the losses that group engenders. It then sets prices for the cells, which should enable the company to earn a profit across a portfolio of customer groups, and uses simulation software to test the financial implications of those hypotheses.
With this approach, Progressive can profitably insure customers in traditionally high-risk categories. Other insurers reject high-risk customers out of hand, without bothering to delve more deeply into the data although even traditional competitors, such as Allstate, are starting to embrace analytics as a strategy. 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.
UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. 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. These programs operate not just under a common label but also under common leadership and with common technology and tools.
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. 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. In traditional companies, departments manage analytics —number-crunching functions select their own tools and train their own people.
Some analytics competitors apply the same enterprise approach to people as to technology. Although most of the analysts are embedded in business operating units, the group is centrally managed. 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 turn, apply their expertise in certain decision-analysis techniques to such new areas as competitive intelligence.
A companywide embrace of analytics impels changes in culture, processes, behavior, 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. All others bring data. But we found that these lower-level people lacked the clout, the perspective, and the cross-functional 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. 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 practice. The analysis-versus-instinct debate, a favorite of political commentators during the last two U. For now, analysis seems to hold the lead.
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