Charles Darwin relationship to marketing

What can marketing learn from Charles Darwin?

What can marketing learn from Charles Darwin? 780 441 nMerge

This week celebrates the 207th anniversary of Charles Darwin’s birthday. Darwin is best known for his theory of evolution and natural selection. Most people will know the quote, “it is not the strongest of the species that survives, not the most intelligent, but the most responsive to change.” I would say that marketing is going through one of those evolutionary leaps right now. Here is a scary fact the mortality rate for firms is now six times as high as the rate just 40 years ago… Yikes! When I started working in the marketing and marketing technology field in the 90’s things were simpler, print was king, the internet was formative, sending content to advertising channels a love/hate affair with ISDN direct (time consuming and frustrating) transfer between marketer and supplier, and the customer more remote and less connected. That has all changed now and is continuing to change at an exponential pace. If you don’t believe me, just check out Scott Brinker’s Marketing Technology landscape from 2011 to 2015 and you see the rapid pace of change at work!

The technology we use is rapidly advancing, changing, and pivoting. We are becoming increasingly mobile in the use of technology and we want everything now! Our use of data is refining what we know and don’t know about internal operations, customers and the external environment around us. The noise of digital marketing and advertising content is growing at the speed of light and our knowledge of the customer is becoming more refined. As customers ourselves our attention spans are reducing to less than a gold fish! This is probably part reason for why customers are wising up and blocking our attempts to communicate to them. So basically in our attempts to engage, equip and empower we are shutting down our customers and they are shutting us out. Not great!

So what does this mean for marketers? I would suggest that it means we have to become more disciplined, scientific, lean and agile in our approach. We need to implement processes such as validated learning and lean thinking. We need to change the focus from planned campaigns to scientific experimentation underpinned by solid research and data using agile methodologies. This sounds like the world of a scientist or academia however it’s actually quite simple. Good research underpins the basis of hypothesis generation, experimentation and theory generalisation. The basis of research is that it should be empirical, accessible and repeatable so that validated learning can be derived.

If you are lucky enough to have access to solid data or researchers who can do this work for you great but you still need to understand the process. The first step is to design your question. This is an important area. So many of us run to the solution or detail because we are so used to executing. It’s comforting. The action of surrounding yourself in detail gives the illusion of action. However even Albert Einstein said, “If I had an hour to solve a problem I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.”

When you are designing your question start documenting your assumptions that you hold true, false or unknown. This is a great start to iteratively test your assumptions. You need to test whether they truly are facts or are assumptions. This is also where you can start to figure out experiments to test your assumptions.

I sourced a diagram by Hedrick, Bickman & Rog in their book Applied Research Design: A Practical Guide for a research project I was involved in that succinctly illustrates the process of research design and data gathering.

Research Definition & Design Process

Hedrick, Bickman & Rog Research Definition and Design Process in their book Applied Research Design: A Practical Guide (1993)

This was a neat summary of an area that many tend to get lost in (hence why most jump straight to execute). You can see that the research design process is iterative so can neatly fit into agile process and iterative time-boxed sprints. The research definition and design process can also neatly fit into Agile DSDM’s Feasibility and Foundations phases. However in the interest of keeping brief, I am going to gloss over data gathering techniques and data analysis and summarize by saying that now is the time to understand what the data you have gathered means. Constantly ask yourself “so what?” What value has this data created for me in understanding what will make the customer change their behavior? How would they want to access this information and when would they be more likely to respond positively? In short what creates customer value and what is wasteful? What should I do more of and what should I do less of?

Finally, build your experiments using scientific method. By generating a hypothesis you are predicting what you think will happen. By testing your hypothesis and it’s underlying assumptions as time-boxed sprints you have now developed a system that will be responsive to change, confirming and/or disconfirming your data, your assumptions and your hypothesis. So just get going: define, design, test, learn, respond; do more, do less or pivot.

By spending time and energy on the question, by being lean and agile in your approach to the experimentation process you can now start to truly be responsive to change.

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