Any large problem being tackled today has an emphasis on testing and experimentation. For everything from space exploration to finding cures for diseases, the solution to any large problem has scientific experimentation in its roots; the approach to marketing planning, execution and analysis should be no different.
For modern marketers, trying to get a regular, quality message out to an audience is a complex problem. A positive intent to educate and promote can be misunderstood or lost in the fray. Effective campaigns here and there are achievable but a constant string of ‘wins’ on many different media is often more luck than preparation. There are too many factors to consider and not enough time to dissect and action all the data. Making the right decision for countless segments is no simple feat. This is an especially difficult task for ‘lone’ marketers assigned to different regions, products or channels.
Given this environment, it is necessary for marketing departments to find a new way of organizing their efforts. Minimizing failure and mistakes is critical in permanent media (print, television, events, etc) as large audience sizes and static messaging allow little room for testing or experimentation. Focus groups and market analysis can help, but are generally reserved for larger brands with the budget to allocate on expensive accoutrements. However, a growing amount of marketing is taking place on dynamic media with an ability to innovate to smaller, targeted segments.
By definition, the scientific method “is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge”; this should be the aim of all modern marketers. Unfortunately, these ideas can be unintentionally suppressed by the limitations of traditional marketing processes.
The sheer amount of marketing data being collected and stored by an organization can be vast. With so many technologies available to the modern marketer, it can be hard to decide which to use and easy to get lost in all the information. A scientific approach is critical for answering the key questions that evade marketers. Iterations of four essential elements are crucial for the scientific method:
• Characterizations (observations, definitions, and measurements of the subject of inquiry)
• Hypotheses (theoretical, hypothetical explanations of observations and measurements of the subject)
• Predictions (reasoning including logical deduction from the hypothesis or theory)
• Experiments (tests of all of the above)
The current trend in many marketing organizations today is an exclusive push towards key performance indicators (KPI’s). While these are very important metrics which determine short-term success or failure, they rarely provide insight for the next campaign. In addition to these KPIs, a new learning system needs to be in place to promote experimentation. Leadership needs to promote a culture and environment of shared learnings. With every campaign concept, marketing should ask ‘what is our hypothesis?’.
Short-term successes are important, but proving a hypothesis true or false can be much more valuable to the larger organization’s marketing efforts. A simple cycle of four similar stages can significantly add value:
• Plan & Review (innovate and build on what has been done before)
• Hypothesize (what is being done differently and what is expected to occur? What will be the evidence proving the hypothesis true or false)
• Execute (run the campaign and collect evidence)
• Report (document the outcome of your hypothesis for future planning efforts)
Many organizations are already using a similar cycle but unfortunately, the most common missing piece is the hypothesis. This first step of creating a hypothesis, is a required component for kicking off a more scientific approach; it is only through comparing real results to that hypothesis that insights can be found.
For large organizations, additional enablement systems can be used for organizing hundreds of global marketers’ learnings. However, this process can be simplified to a simple tracking spreadsheet for small to mid-sized companies. Regardless of company size, the modest task of requiring a hypothesis is a fundamental change that starts a series of benefits:
1. Properly proving a hypothesis (either true or false), requires a set of corresponding evidence (metrics). These metrics are often a compliment to the usual success KPI’s mandated by leadership, such as revenue attribution and return on investment. This helps to identify and champion different types of successes (short & long term).
2. A hypothesis requires perspective. Often called a ‘placebo group’ in pharmaceutical testing, control groups help to ensure that an audience segment is not inadvertently biased. A hypothesis cannot be found true or false without some comparison. This gives strength to the results. It creates data integrity.
3. The result of the hypothesis itself (true or false) is also curated. The outcomes of hundreds of marketing initiatives are available for gathering further insight beyond simple ROI measurement. Inefficiencies and duplicated efforts are reduced. Successes can be replicated.
4. Finally (and perhaps most beneficially), this procedural change creates alignment in the organization. Disparate people and technologies can come together towards a common goal. Marketers can see the past attempts and collaborate to prove or disprove each other’s hypotheses – all towards better understanding their customer. This ‘human’ benefit can be incredible for large enterprises seeking motivation.
To view this from another angle, consider the earlier analogy of finding the cure for a disease. Epidemiology is one of the most complex fields of study and naturally, it leverages scientific methodology to find answers. It takes decades of research by thousands of people to find cures for the toughest diseases. Answers come from potentially millions of hypotheses, a collection of learnings that cannot be amassed by a single person. Further, these efforts are reinforced by a second, larger group of supporters who do not work within the scientific process, but who are empowered to fundraise and champion the goal due to an alignment of objectives and expectations.
It is in this way that organizations benefit from answering their most complex marketing questions; with a scientific approach, insights gain a relevancy and longevity they might not otherwise have. Internal and external learnings can be shared and acted on over time and can be used to build support and alignment for a common goal.
In the future, experts are predicting corporations will have standard data scientist positions. McKinsey estimates that “there will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”
As data scientists become more common, organizations will have to adapt processes and leadership to attract and incentivize them. An easy way for companies to start down this road is to sprinkle a little bit more science into their marketing.