Everyone is talking about data science and how it is changing the world, but what is data science? we are going to tell you all you need to know about data science and the applications in marketing.
What is Data Science?
First of all, is important for the reader to understand the concept of data science. Will Van der Aalst (2016) states that “For this aim, we defined Data Science as an interdisciplinary field aiming to turn data into real value” which allows predicting the behavior of a specific market and all the different stages it goes through. In these terms, Data Science is able to identify the result of a process, the effect that causes that result, the future reactions the result will follow and what is the best case scenario this process will turn out in.
Taking into account the above, Data Science must go through a 5 stage cycle, that includes the correct data capture from the source, the storage of data, in order to get it into an efficient process so we can analyze and report it. All these, so we can make valid and strong conclusions.
Following the process is shown in Figure, first of all, you will need to collect the data by entry, and extraction. Second, store it by data architecture in which is define the data requirements. Then, process it through data mining and, modeling, this stage could happen before or after the store stage, depending on the data. After processing the data, the next stage is to analyzed data by statistics principals, doing predictive analysis, regression analysis, text mining, and qualitative analysis. Finally, data presentation delivering insights, generating reports, and making decisions or recommendations.
By completing the Data Science cycle, you should be able to answer this questions, What happened? Why did it happen? What will happen? What is the best that can happen?.
Segmentation: Clustering to Make Data-Based Decisions
In Marketing, to develop a strategy to enter a market with specific characteristics, we need to segment the potential customers and understand their needs. Data science could help us develop predictive and descriptive methods by using different techniques of clustering that give us leverage information about the segment. For example, the ward method a hierarchical method in which you get the group of clusters in a hierarchy, by using a dendrogram (tree-like diagram) and the K-means algorithm which forms groups based on distance from the centroid. We can apply these methods of clustering by using R.
Alternatively, we could use attributes different from the demographic characteristics(e.g. Age, weight, gender) to make conclusions about the study population using distinct clustering methodologies from the given above. With the constant digital transformation and methods to understand the infinitive data given by the consumers, it is necessary to learn how to apply methodologies based on the use of new type of data to explore different segments.
Following the previous idea, based on mathematical models, we can use the techniques of neural networks. These models primary purpose is to understand how a user has more affinity to segments, which are defined by attributes of the study population, transferring information from one node to another in the neural network. It is important to identify the possible variables of confusion during the study in order to assure that the conclusions are bias-free and are exclusively made over the attribute (directly related to the segment) in study.
Social Media Data Analysis: Studying Human Interaction Online
As we already know, digital data is derived from a lot of sources and social media is particularly characterized for collect information based on human interactions from content that is shared by users. With data science we could collect data and analyze this type of information, understanding the behavior of users and aiming to structure a marketing strategy that satisfies people needs. Some of the marketing strategies could be using an advertising plan, A/B testing and content marketing.
Social media is useful to understand the facts that affect a customer decision while purchasing a product, which helps the analyst to identify what information does the user needs in order to make a conversion. An example of this is, using A/B testing to establish in an ads strategy, which is the most effective and profitable ad for your business goal.
- Will Van der Aalst (2016). Process Mining
- Derrick S. Boone et Michelle Roehm (2002). Evaluating the Appropriateness of Market Segmentation Solutions Using Artificial Neural Networks and the Membership Clustering Criterion.