Will The 4 Big Fads in Analytics Actually Stick?

By Gary Cokins, Feb 03, 2015

I was recently a presenter at an analytics conference where a speaker in the customer marketing tracks said something that stimulated my thinking. He said, “Just because something is shiny and new, or is now the ‘in’ thing, it doesn’t mean it works for everyone.”

That got me to thinking about some of the new ideas and innovations that organizations are being exposed to and experimenting with. Are they fads and new fashions or something that will more permanently stick? Let’s discuss a few of them:


Visualization software is a new rage. Your mother said to you when you were a child, “Looks are not everything.” Well, she was wrong.

Viewing table data visually, like in a bar histogram, enables people to quickly grasp information with perspective. But be cautious. Yes, it might be nice to import your table data from your spreadsheets and display them in a dashboard! Won’t that be fun? Well it may be fun, but what are the unintended consequences of reporting performance measures as a dial or barometer?

A concern I have is that measures reported in isolation of other measures provides little to no context as to why the measure is being reported. Ideally dashboard measures should have some cause-and-effect relationship with key performance indicators (KPIs) that should be derived from a strategy map and reported in a balanced scorecard. KPIs are defined as monitoring the progress toward accomplishing the 15-25 strategic objective boxes in the strategy map. The strategy map provides the context from which the dashboard performance indicators (PIs) can be tested and validated for their alignment with the executive team’s strategy.

Business analytics

Talk about something that is “hot.” Who has not heard the terms Big Data and business analytics? If you raised your hand, then I am honored that I am apparently the first blogger you have ever read. Business analytics is definitely now established as a permanent managerial wave.

I am biased towards this because my 1971 university degree was in industrial engineering and operations research. I love looking at statistics. So do television sports fans who are now provided “stats” for teams and players in football, baseball, golf and every kind of televised sport. But the peril of business analytics is they need to serve a purpose for problem solving or seeking opportunities.

The analytics thought leader James Taylor advises, “Work backwards with the end in mind.” That is, know why you are applying analytics. Experienced analysts typically start with a hypothesis to prove or disprove. They don’t apply analytics as if they are searching for a diamond in a coal mine. Instead, they first speculate that two or more things are related or that some underlying behavior is driving a pattern seen in various data.

Customer Lifetime Value (CLV)

The savvy C-suite executives understand that the only goal of sales and marketing should not be just to grow sales and increase market share. It should be to grow the more profitable sales. They realize that below the gross product-related gross profit margin line in the profit and loss financial income statement high demanding customers (e.g., requiring special treatment, frequently returning goods, substantial price discounts) have higher “costs-to-serve” compared to low demanding customers. Hence, customer profitability analysis using activity-based costing principles are pursued. And when customers are viewed as an investment rather than as an expense, the customer lifetime value (CLV) equation calculates forecasts of multiple years as a discounted cash flow.

But the peril is one should not just rank the highest CLV customer to the lowest one and then devote marketing and sales budgets and efforts to the highest CLV ones. The critical resource allocation rule should be that the incremental revenues from a customer should exceed the incremental effort and cost (e.g., deals, offers, discounts) to increase those sales. A low CLV customer may provide a relatively higher profit lift from an offer or deal compared to a high CLV customer. Marketing ROI measurements are needed that are rank-ordered by customer.

Rolling financial forecasts

The dirty little secret that most everyone knows is that the annual budget has become a low value-added exercise. Budgets quickly become obsolete.

Today there is too much volatility, sand-bagging cost center managers, and baked-in inefficiencies based on the past year’s baseline spending that budgets are typically incremented from. The CFO’s shiny new toy is driver-based budgets. This advanced spending projections method relies on modeling parameters from sales forecasts (i.e., the primary independent variable) and from recent past period calibrated per-unit-level cost consumption rates of business processes and the work activities that belong to them – think activity-based costing (ABC). And once you can model the future projected headcount, equipment capacity, and spend levels for the budget, why not re-model them at more frequent intervals (e.g., quarterly, monthly) and abandon the budget process altogether?

But the perils are poor forecasts, unreliable cost consumption rates, and neglecting to include sufficient spending for capital, strategic and risk-mitigating projects. No wonder people say that the budget is a fiscal exercise done by the accountants that is future volume-insensitive and disconnected from the executive team’s strategy and enterprise risk management (ERM) plans.

Fads or fashions?

Are these fads and fashions or the real deal? Are managers attracted to them as the shiny new toys that they must have on their resume for their next bigger job and employer? My belief is these four “hot” managerial methods and tools are essential. But they need to be thought through and properly designed and customized; and not just slapped in willy-nilly just to have them as shiny new toys.

About the author

Author photo

Gary Cokins (Cornell University BS IE/OR, 1971; Northwestern University Kellogg MBA 1974) is an internationally recognized expert, speaker, and author in enterprise and corporate performance management (EPM/CPM) systems. He is the founder of Analytics-Based Performance Management LLC He began his career in industry with a Fortune 100 company in CFO and operations roles. Then 15 years in consulting with Deloitte, KPMG, and EDS (now part of HP). From 1997 until 2013 Gary was a Principal Consultant with SAS, a business analytics software vendor. His most recent books are: Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics and Predictive Business Analytics. Gary is also a member of the IIA Expert Network.