What can marketing engineering learn from financial engineering?

With the emergence of “Big Data”, marketing will become more science than art. Big Data enables marketing science and the application of operations research techniques. The collection, analysis and application of this new data will enable marketing engineering to be much more like financial engineering. What can we learn from Financial Engineering? First let’s define financial engineering, using this definition from Wikipedia. Financial Engineering is a multi disciplinary field involving financial theory, the methods of engineering, the tools of mathematics and the practice of programming.  If you substitute the word marketing for financial, this definition would work for defining marketing engineering. Marketing engineering and marketing science depend upon mathematics, statistics, models taken from physics and applied math. Marketing engineering is the application of engineering methodologies and quantitative methods to marketing. The engineering aspect comes in when we creatively apply these scientific principles to design or develop new marketing  processes, and applications. It can be a major source or driver of marketing innovation. What marketing engineering can learn from financial engineering, is how to move from a more data driven but analog-based or qualitative system to a more digital and quantitative one. It is not so much about the ability to calculate an ROI, but rather the ability to predict an outcome. In other words, in finance, investment decisions are based on the concept you can predict the performance of a security.  People may have different methods for trying to predict a security’s performance, but all have the same objective. In finance, clearly some predict a security will move up in price, while others predict it will go down. That is what makes a market. Those who are better at predicting an outcome and thus applying their capital more efficiently are the winners. In a similar way, marketing is trying to improve its ability to predict the outcome of its decisions. Better marketing decisions create competitive advantage and better returns. Scanner data provided marketers a tool to predict how well a particular product would sell if it was discounted 10%, and how much space and where a product should be located on a shelf. This type of predictive analytics is being applied to an increasing amount of marketing applications, especially since large quantities of data are being collected from online transactions and other devices. This is why “Big Data” will drive an explosion in marketing engineering and companies that are interested in marketing innovation need to start making investments in this area now. At one point finance, particularly investment decisions were made on a data driven, but much more qualitative, subjective  basis, thus the emergence of the “stock picker.” For a long time, stocks were thought to take a “Random Walk” and thus be unpredictable. But modern financial theory has largely changed that type of thinking. In a similar way, since people believed human behavior was largely unpredictable, trying to predict how consumers might respond to various marketing stimuli was thought to be guest work at best. But marketing science and marketing engineering are changing that as well. While it may not be possible to predict any one person’s behavior, it is possible to statistically predict how large numbers of people will respond to a particular marketing activity or tactic. CMOs taking their department down a path to embrace marketing engineering can learn the following from financial engineers.

1. You need to make your marketing system as completely digital, quantitative and real-time as possible.
2. You need to be able to increase the number of outcomes for which you can predict, and increase the accuracy of those outcomes.  You want to develop an increasing level of confidence in your predictive capabilities, so you not only see opportunities first but feel confident in acting upon them before your competitors do.
3. You want to build systems that can continually analyze data and look for opportunities rather than having to do discreet analysis e.g. ad hoc quires or using one-off decision support models. This will probably include incorporating artificial intelligence and other advanced technologies.
4.  You want to make sure you invest enough in this new area as it will take new skill sets, possibly equipment and other resources.

Having a marketing engineering capability significantly improves a company’s ability to innovate its marketing.  It enables a marketing department to do more things in real-time and improve the return on its marketing investments.   Instead of “reinventing the wheel,” we can learn some best practices from those who developed financial engineering capabilities and apply them to developing a marketing engineering capacity and creating new products, processes and applications based on marketing engineering. Marketing engineering is a cornerstone of marketing innovation.