When it comes to optimizing the implementation of existing marketing strategies, what exactly are the technical procedures that encompass the management of a marketing campaign? Is there a concept, a term that adequately quantifies the process of categorizing all the conditional relationships and events that populate the sum of all user interactions on a web site. What are the methods utilized in organizing and interpreting those results in context of defined goals and performance targets? The above considerations involve the application of techniques and procedures utilized in the field of data science.
What is Data Science? Another way to to say Applied Statistics.
Data Science is a blanket title applied to data interpretative analysis that differs broadly across industries. In digital marketing, and here at Wirefreesoft, we generally are engaged in a set of steps and practices outlined in the following process.
First and foremost we begin by outlining clear objectives as to why and what data will be collected and establishing ideas about insights or discoveries that might be gleaned. Depending on the size and scope of a digital marketing directive, a vast array of dimensions and measurement characteristics are accumulated and stored in products like Google AdWords and Analytics. Knowing what to look for and accurately retrieving the relevant and necessary data points is key. This data will provide the backbone to trace the relationships between the categories and groups that attribute to the particular aspect of the marketing campaign that has been assigned for study. It will begin to form the basis for the formatting and modeling process.
After defining the objectives and collecting all relevant data, it is important to explore the sets and become familiar with the dimensions, segments and the metrics involved in their measurement. This exploration points out any obvious anomalies or outliers and supports the proper organization of the data. Often an unanticipated pattern or trend can come into focus redirecting the original ideas of how to approach the directive and the format. The data is then filtered to more granular levels that lead to the development of a practical model for analysis.
The next step is the analysis phase. Now that clean data sets have been organized into an intelligible format the modeling process can begin. Facts are established so as to formulate hypothesis’s relative to the stated objectives. Observations derived from mathematical testing are weighed and compared sifting through differing model outputs until a consistent conclusion comes into focus. This is then considered in light of the over all history and knowledge of the marketing campaign to ultimately ask …… does this conclusion make sense.
The process concludes with the implementation of a new strategy or adjustments applied to current tactics. If necessary a story supporting and explaining the model outputs will be incorporated into a report or proposal. This will illustrate and tie together the findings and insights derived from the data science process into an understandable format related to the clients knowledge base.