Data driven marketing decision framework

Marketing professionals face ever tougher strategic questions. Even more, these reoccur with rising frequency. Ironically, at the same time they are flooded with more and more data. In theory this should aid their decisions, but in reality rather the opposite is the case.

Data driven marketing decision framework

Marketing professionals face ever tougher strategic questions. Even more, these reoccur with rising frequency. Ironically, at the same time they are flooded with more and more data. In theory this should aid their decisions, but in reality rather the opposite is the case. For instance, only 0,5% of gathered data by organisations is being analysed and used for decision making1. Fact is, large amounts of data may require complex analysis approaches and additional investments in order to get the answer one is seeking. As a consequence, marketing professionals are too often paralysed when facing available datasets, or purchase redundant analysis services in order to be on the safe side.

We would argue, however, that a marketing professional is not required to understand the math and algorithms of a certain analysis, as long as he understands which overall analysis approach provides an answer to his marketing challenge. What follows is a “Marketing Analysis Decision Framework” and an “Analysis Decision Grid”, which function as guides for choosing an appropriate analysis approach based on the marketing strategy one is pursuing and the challenge facing.

In general, there are four analysis types: descriptive analysis, diagnostic analysis, predictive analysis and prescriptive analysis. These defer from one another based on their individual research focus:

  • Descriptive analysis provides an explanation of the current state of affairs by taking a novel look at the past data. Furthermore, it is used for hypothesis definition in regard to future business problems and opportunities.
  • Diagnostic analysis identifies factors that caused past events and lead to current events, as well as how these relate to one another. Understanding such factors allows an adaptation of current actions.
  • Predictive analysis envisions what will happen in the future in regard to individual factors. In its core it provides an understanding of how likely it is that something will occur under certain conditions and with a certain level of reliability.
  • Prescriptive analysis identifies the right actions needed today, in order to address future challenges and take advantage of upcoming opportunities. The identified actions are based on a computed estimation of how the market will behave, as well as at the same time taking into account company internal factors such as available assets or willingness to take risk.

Although, these analysis types are well established in the analytics literature, they often lack an applicable portrayal, such as their connection to the overall marketing strategy. With our framework (Item 1) we attempt to close this gap, by providing marketing professionals with an intuitive graphic for identifying what type of analysis they may need.

The four analysis types, are mapped based on the overall marketing strategy one is pursuing and the sought for output. Marketing strategy is broadly divided in two categories: defence strategies, for instance, preserving current sales rates; or offense strategies, an example of which would be venturing into new business fields. While the sought for analysis outputs can either be new insights or action recommendations. In addition, the framework takes into account that the analysis types have different complexity levels, which impact the time and investment they may require.

Taking this idea even further, we recognise that marketing professionals face concreate challenges when pursuing their strategies. Even more, their need for analysis originates from the need to solve a distinct challenge they are facing. In this regard we matched individual marketing challenges with the analysis types to develop an “Analysis decision grid”, aiming to provide an easy way for selecting the most appropriate analysis.

It is important to note that all of the analysis types can be executed on different levels, from the level of an individual product to the level of a whole category. Furthermore, even within each analysis type the analysis sophistication can widely vary, especially when it comes to prescriptive analytics, where automated or half-automated services are nothing unusual.

It is clear that the need for data based decision making is and will continue to grow. Unfortunately, in the process of gathering relevant insights marketing decision makers too often lose focus of the key issue they want to solve and get lost in the admittedly complex world of analytics. Hence, our recommendation is to use the marketing challenge facing as a compass for applying analytics, rather than to be guided by the large extent of possibilities analytics and business intelligence offer. We hope the two provided models enable you to do just that. If you have additional questions regarding data driven marketing decision making do not hesitate to contact us.

 

1 The data made me do it (2015); Antonio Regalado; MIT Technology Review; Source: https://www.technologyreview.com/s/514346/the-data-made-me-do-it/