OPINION26 June 2014

From battle to business: how operations research could aid marketing

Operations research (OR) achieved acclaim during World War II as a multidiscipline, scientific approach to solving war-related operational problems. Jerry Thomas of Decision Analyst argues that marketing could benefit from its structured approach.


An OR team might be made up of a psychologist, a medical doctor, a mathematician, and a historian, and OR investigations followed rigorous scientific protocols and used mathematical concepts and methods.

From battle to business

One famous story of operations research success involved an analysis of Allied bombers returning from missions over Europe. In order to identify where to place additional armour on aircraft, the military analysed the location of shrapnel damage and bullet holes in returning bombers. Operations researchers were brought in at the last minute to do a “confirmatory” analysis, but recommended that additional armour be placed on bombers everywhere except the places with damage or bullet holes. They realised that analysing damage to returning bombers involved a sampling error: It was the bombers that did not return that needed extra protection — and they needed it in the most vulnerable places (i.e. the places not damaged on the returning aircraft).

After the war, the promise and practice of operations research moved into industry: Ford Motor Co. hired 10 young U.S. Army Air Force officers to bring advanced operations methods to its business. This team transformed the managerial systems and methods at Ford and helped publicise the benefits of operations research and quantitative analysis.

The concept of a multidiscipline team in OR has tended to fade away over the years as the glitter of advanced quantitative techniques has garnered most of the attention. Despite all of the mathematical advances and software improvements, the multidiscipline team approach should not be forgotten. The value of different educational and experiential backgrounds and different viewpoints in solving complicated problems is time-tested and proven.

In practice

The utilisation of OR/MS methods sinks to its nadir in the marketing domain, despite the development in recent decades of a branch of OR/MS devoted to marketing (namely marketing science). But OR offers a varied and robust analytical toolkit. Some of the widely used OR techniques include linear programming, nonlinear programming, dynamic programming, integer programming, Markov chain analyses, structural equation modelling, and Bayesian statistics. These models and methods can answer profound marketing questions.

Here are some examples:

Optimal restaurant density

Let’s suppose a restaurant chain would like to know the number of retail stores to build in a particular designated marketing area (DMA) to maximise return on total investments within that area. At first this might seem like a straightforward task, but an optimisation model would need to consider the following variables across DMAs:

a.   Warehousing, distribution and supply chain costs
b.   Managerial efficiency, overhead and related costs
c.   Operating costs (labor, utilities, taxes, etc.)
d.   Advertising efficiencies (the more restaurants, the bigger the ad budget)
e.   Media advertising costs
f.     Positioning, marketing strategy, and advertising themes and messages
g.   Promotion efficiencies (the more restaurants, the bigger the budget)
2.   Optimal Distribution System.

Optimal distribution system

How about if a coffee company wants to create a distribution system that maximises profitability within given DMAs. The coffee company can deliver directly to the store (DSD) or ship coffee to the food retailers’ warehouses that in turn move the product from warehouse to retail shelves (i.e., warehouse distribution). What are the major variables to consider across DMAs?

a.   Out-of-stocks. What level of out-of-stocks is associated with DSD versus warehouse distribution?
b.   What is the shelf space (number of facings) and shelf position implications of DSD versus warehouse?
c.   What warehouses, trucks, employees, and infrastructure are required to support DSD versus warehouse distribution, and what would be the comparative costs?
d.   What is the tradeoff between spending more of the budget on media advertising with warehouse distribution versus better in-store merchandising and control with DSD?

Optimal product line

What if an automotive manufacturer wants to create an optimal product line to help it succeed over the next 20 years. What variables might be considered in creating an OR optimisation model?

a.   What is the range of market conditions the manufacturer might face over the next 20 years?
b.   What are the probabilities of these market conditions or states?
c.   What are the long-term trends in fuel prices? Fuel types? Technological probabilities?
d.   What are the boundaries of consumer acceptance, given extreme scenarios?
4.   Optimal Positioning and Advertising Messaging. What if an Internet dating service wants to optimise its television advertising? An optimal solution would involve some of the following variables:
a.   What is the architecture of target-audience possibilities? Demographic? Attitudinal? Behavioral?
b.   What are the strategically viable positionings?
c.   What are viable themes, messages and taglines?

In this example, some qualitative research would be used to help define the range of possibilities (positionings, themes, messages, taglines, colors, imagery, etc.). Survey research would be employed to provide a first approximation of target-audience definition. The final optimisation model would involve choice modelling experiments among the broadly-defined target audience to identify a set of optimal solutions, which would also precisely define corresponding optimal target audiences. 

The Challenge

The great challenge facing marketing executives at all levels is how to make better decisions that maximise long-term returns on marketing investments. Rarely are these major decisions simple and obvious, even when they appear to be. As the examples in this article suggest, many complex and interacting variables must be understood and modelled to find the ultimate answer.

Operations research, combined with marketing research, can be a valuable ally in the search for long-term optimal solutions. So, strap on your parachute, put on your goggles, and fly your business on the right course at the right altitude — with armor in the right places.

Jerry Thomas is president and chief executive of Decision Analyst Inc.