A managerial movement is now picking up steam: organizations are applying business analytics to gain insights to determine good decisions and best actions to take. This topic was once the domain of “quants” and statistical geeks developing models in their cubicles. Today, applying analytical methods is on the verge of becoming mainstream.

One way I draw my conclusion about this emerging movement is that there is much discussion about the topic. Articles in IT magazines and websites about analytics of all flavors, such as segmentation analysis, are increasingly prominent. Debate is always healthy, and some IT analysts view applying analytics as a fad or something that is way overvalued. Others, such as leading proponents of analytics like authors Tom Davenport of Babson College and Jeanne Harris of Accenture, claim that an organization’s achievement of competencies with analytics provide a competitive edge.

Predictive analytics is one type of analytical method that is getting much attention. This is because senior executives appear to be shifting away from a command-and-control style of management – reacting after the fact to results – to a much more anticipatory style of managing. With predictive analytics, executives, managers and employee teams can see the future coming at them, such as the volume and mix of demands to be placed on them. As a result, they can adjust their resource capacity levels and types, such as the number of employees needed or spending amounts. They can also quickly address small problems before they become big ones. They can transform their mountains of raw data into information to test hypotheses, see trends and make better decisions.

Analytics as Sustainable Competitive Advantage

For the last few decades, many executives and strategic consulting firms have followed the framework of the popular Harvard Business School professor, Michael E. Porter. Porter has advocated three types of generic strategies. Notice that with today’s clock-speed and technology-driven markets and economies, each generic strategy is vulnerable.

  1. Cost leadership strategy. This is accomplished via improving process efficiencies, unique access to low-cost inputs (e.g., labor and materials), vertical integration or by avoiding certain costs. But today, other firms using lean management techniques and data analysis methods can quickly lower their costs.
  2. Differentiation strategy. This is accomplished via developing products and/or services with unique traits valued by customers. But today, there can be imitation or replication of products and services by competitors (e.g., smartphones) or changes in customer tastes.
  3. Focus strategy. This is accomplished via concentrating on a narrow customer segment with entrenched customer loyalty. But today, broad market cost leaders or microsegmenters can invade a supplier’s space and erode its customers’ loyalty.

So, how will an organization gain a competitive edge? In my opinion, the best defense is agility with quicker and smarter decision-making. This is accomplished by achieving competency with business analytics that can provide a long-term sustaining competitive advantage. It means creating an organizational culture for metrics and analytics.

Resistance to Change and Presumptions of Existing Capabilities

Some organizations may believe that because they hired or trained employees with analytical skills they have fulfilled the need to be analytical. But there are misconceptions as to what analytics is really about. To demonstrate this, consider the following true example from one of my colleagues.

A large department store retailer accepted a brief meeting with my co-worker for possible clarification about how analytics can increase profit lift from individual customers. The company’s president, chief marketing officer and head of customer analytics attended. They were somewhat impatient because they were confident they already had an effective program in place - many of their customers used a loyalty card at the checkout counter.

My colleague described that with access to each customer’s profile (e.g., age, address, gender, etc.) and their purchase history, a real-time analytics system could substantially increase the probability that a customer will actually respond to an offer, deal or intervention – and determine when he or she would be likely to do so. The first answer comes from data mining and the latter from forecasting – two of the many components of business analytics.

After the presentation, the head of customer analytics concluded that the company was already using appropriate techniques. My co-worker then took a risk. The day prior to the meeting he went to one of the retailer’s stores and purchased travel-size shampoo and toothpaste using his loyalty card. But he repeated the identical purchase a second time. In the meeting he placed both receipts on the table, and turned them over. One receipt had a discount offer for a feminine hygiene product. The other receipt’s discount was for cat food. My male colleague has no pet. The chief marketing officer asked the head of customer analytics for an explanation. The answer was, “Those were among the hundred high-profit-margin products that are being promoted this month.”

In this example, there was no true connection to the individual customer. And the checkout register did not have sufficient technology to access customer-specific deals in real time. The three executives had a kind of an epiphany and are now piloting a store entrance kiosk where customers can swipe their loyalty cards and receive personalized discounts and offers.

This is a substantial improvement from the checkout register method. The kiosk knows what specific discount or deal to offer through statistical analysis of different customer behaviors (e.g., Amazon.com’s message: “Others who bought what’s in your shopping cart also bought X.”). The retailer described above had a 1.8 percent response rate to register receipt offers that increased to 30 percent with the real-time store entry kiosk.

Geeks are Chic

The point of this article is not about quants and statistics jockeys being smart. The intended takeaway is that statistical analysis, data mining and forecasting with a goal of application and optimization is within reach – and some organizations that may think they are applying these methods are only just starting to develop them.

It may be that the ultimate sustainable business strategy is to foster analytical competency among an organization’s work force. Today, managers and employee teams do not need a doctorate in statistics to investigate data and gain insights. Anyone can be chic.

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