In this article, I tried to give a brief summary of Thomas H. Davenport’s Competing on Analytics article published on HBR. You can check the reference section for the entire article.

The business world is facing an enormous data bombardment and has to use it. Business processes seem to be the only way to differentiate in today’s world where many companies offer similar products and use very similar technologies.

Companies that compete using analytics have started to gain serious competitive advantage in these processes.

As Thomas H. Davenport, author of The AI Advantage book, points out, although many companies recognize the importance of analytics, only a handful of companies can become experts in using it. Companies that compete using analytics are leaders in many Industries such as consumer products, finance, retail, travel and entertainment.For example, analytics has been instrumental to Capital One, which has exceeded 20% growth in earnings per share every year since it became a public company. Wallmart’s social genome project tracks social media conversations and tries to automatically predict which products to buy based on people’s online conversations.

To identify characteristics shared by analytics competitors, Thomas H. Davenport and two colleagues from Babson College’s Working Knowledge Research Center studied 32 organizations focusing on quantitative and knowledge-based analysis. 11 of these organizations were completely analytics focused, meaning top management had announced that analytics was key to their strategies; they had multiple initiatives under way involving complex data and statistical analysis, and they managed analytical activity at the enterprise (not departmental) level.

In their articles, they reveal the practices and characteristics of these statistical experts. They describe the fundamental changes that other companies that want to compete in the quantitative field must experience.

Using analytics in their work, they identified three characteristics of companies that compete:

1. Widespread use of modeling and optimization

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.

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.

2. An enterprise approach.

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.

3- Senior executive advocates

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. 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.”

References: Harward Business Review

Competing on Analytics

The New Science of Winning

Author: Thomas H. Davenport, Jeanne G. Harris, 2007

Veri Stratejisi-MediaCat