Data Scientist Geeks are Chic
A managerial movement is now in motion and picking up steam. It is the application of business analytics for organizations to gain insights to determine good decisions and the 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 to draw my conclusion about this emerging movement is that there is much chatter and debate about the topic. Articles in IT magazines and websites about analytics of all flavors, such as correlation and segmentation analysis, are increasingly prominent. Debate is always healthy. Some IT analysts view applying analytics as a fad or fashion or way overvalued. Others claim that an organization’s achievement of competencies with analytics will 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 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 hypothesis, see trends, and make better decisions.
Analytics as the only 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 S. Porter. Porter has basically advocated three types of generic strategies. Here they are, but notice that with today’s real time data access and technology-driven markets and economies, each generic strategy is vulnerable:
- Cost leadership strategy. This is accomplished via improving process efficiencies, unique access to low-cost inputs (e.g., labor, materials), vertical integration, or avoiding certain costs. Think Walmart. But today other firms using lean management techniques and data analysis methods can quickly lower their costs.
- 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., smart phones) or changes in customer tastes.
- Focus strategy. This is accomplished via concentrating on a narrow customer segment with entrenched customer loyalty. Think Tiffany jewelry. But today broad market cost leaders or micro-segmenters 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 the executives must create an organizational culture for metrics and analytics.
Resistance to change and presumptions of existing capabilities
Some organizations may believe because they hired or trained employees with analytical skills that they have fulfilled the need to be analytical. But there are misconceptions as to what analytics is really all about. To demonstrate this, here is a true experience of one of my past work 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. This is because they were confident that they already had an effective program in place since 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, discount or intervention – and when. The first answer comes from data mining and the latter from forecasting – two of the many components of business analytics.
After the brief presentation with only a few minutes of the scheduled meeting left, 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 immediately 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 colleague, a male, did not have a 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 quickly access in real time customer-specific deals. The three executives experienced an epiphany. The result as a plan to pilot 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. What I omitted from this example is how the kiosk knows what specific discount or deal to offer. That requires statistical analysis of different customer behaviors (e.g., Amazon.com’s website message: “Others who bought what’s in your shopping cart also bought X”).
Data scientist geeks really are chic
The point of this article is not really about quants and statistics jockeys being stylish and smart. My point is that applying statistical analysis, data mining and forecasting with a goal of optimization is now in reach – and some organizations that may think they are applying these methods are only just starting to develop them.
I believe 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 doctorates degree in statistics to investigate data and gain insights. Commercial software tools are designed for the casual user. Anyone can be chic.
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