It is essential to know how to carry out marketing actions within the commercial strategy. In marketing we talk about predictive models or predictive analytics to the tactics deployed to analyze where we should invest or which is the most profitable public and markets. Predictive analytics are also used to recognize what risks we face. In this article we will tell you more about marketing actions and their relationship with predictive analytics.
Marketing actions: creating a prediction model
Regarding marketing actions, predictive analytics combine the handling of a large volume of data for the elaboration of models through information technologies .
With the amount of data and information that surrounds us, the key today to manage all this is multidisciplinarity . A data engineer will be the one to develop a predictive behavior model combining different areas of knowledge such as:
- Information technology, which allows us to understand data structures, create algorithms and program objects.
- Marketing, accounting, management tools.
- Statistics for modeling, machine learning and mathematics.
It is worth mentioning that the figure of the data scientist , the person in charge of processing and analyzing the data, is crucial in marketing actions that include predictive models. And it is that the data scientist is the one who shapes the data obtained from the investigations so that the user of the model can understand and work with it.
Types of predictive model as a marketing action
Engineers who develop predictive models by analyzing the data do so by relating the variables that they have previously identified. There are two usual models:
- The regression : analyzing variables that can be quantified, such as units sold stock or the price of the product. A result is expected that is also accountable.
- The classification : in these models, the predicted result is not quantifiable. Aspects such as customer consumption intentions on a product are dealt with in a probabilistic way.
Data generation and marketing actions
A large amount of data is required for a prediction model to work and be reliable. Prediction models are based on the observation and analysis of all these data.
Thus, it is about handling huge amounts of data, so the analysis of these is quite complex. However, what is really difficult is to detect which are the variables that provide information and are useful : those variables that fit more effectively to that set of data.
What types of data are used for predictive analytics?
For predictive analysis, the historical data that the company has stored is necessary, as well as the information that is collected day by day. The way to collect this data and to be able to generate a model will be decided based on what the statistical needs are, shuffling data, both internal and external.
Within the so-called data mining, data analysis tools are used to identify the patterns that operate in this data.
Marketing actions: what decisions do we make after the analysis?
Predictive analysis is very useful in different aspects. On the one hand, it helps us to make the most optimal decision at any given time, avoiding risk as much as possible. This risk can be opaque in a mere observation of the data and that is why a predictive model is a great tool in this regard.
Through the predictive model we can also predict user and customer behavior : with this we gain knowledge of our public and we reverse it for our benefit. We will also reduce costs, since the process optimizes resources by gaining in simplicity.
Predictive models are a great tool to analyze different aspects in a company. They allow us to better understand the target audience and in this way we can simplify processes thanks to the knowledge that data mining provides us. One of the marketing actions that provide the greatest value, without a doubt.