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  • 7 January 2021

Predictive analysis for marketing strategies

Predictive analysis for marketing strategies

Predictive analysis for marketing strategies 620 350 3rdPlace

written by Marco Belmondo (Chief Marketing Officer at Datrix group)

What does predictive analysis mean? This term refers to the use of different types of forecasting models, in order to predict the value that a variable will assume in the future, with a certain degree of confidence. Predictive Analysis seems to be a very technical concept, nonetheless it is more and more a fundamental part of business processes, both for large companies and smaller ones. According to the Big Data & Business Analytics Observatory of the Politecnico di Milano, three out of four of large companies and about 40% of small and medium-sized enterprises use predictive analysis in at least one business function (such as marketing, finance, production). However, it is quite clear that these types of analysis can be perform in very different ways, in simple or complex terms. Meanwhile, the skills needed to develop effective predictive models are also constantly evolving.

Marketing was the function that first began to approach predictive analysis activities and, nowadays, many companies use these models to improve offline and online marketing activities, as well as systems of lead generation. Marketing has in fact the primary need to understand which customer behavior is and, even more, which are customers’ future desires. Exactly for this reason, predictive analysis can play a central role. Let’s see why.

Marketing Applications of predictive analysis

Let’s go concrete. Which are the questions that a predictive analysis on customers data can answer? In order to get an answer to that, you need to understand which are the applications of predictive analytics in Marketing.

First of all, a good Marketing strategy must be based on data and, most of all, it should be aware of how to use data in day-by-day Marketing decisions. Some examples of Marketing Analytics questions are:

  • Through which channels, my customers reach my brand / product / store? This doubt is obviously valid both online and offline.
  • What is the most common customer journey? In other words, which are the steps that the customer takes from knowing the brand to purchasing a product?
  • Why do some customers churn the brand?
  • Who are the most profitable customers? In other words, which are the population groups that are most likely to transform themselves from lead to prospect up to customers?

These issues can be translated, in the field of predictive analysis, into specific applications. Let’s go through some examples:

  • Churn Prediction: an application of great importance is the ability to predict the abandonment rate of your customers and therefore, at the level of individual customers, calculate the probability of the individual customer to stop purchasing the company products / services. To do this, it is necessary to develop predictive models that are able, analyzing purchasing behaviors, to associate the single probability in an effective way.
  • Micro-segmentation: the idea of splitting your customer base into small groups of similar customers is well known in traditional marketing theory. Thanks to Big Data, statistical models and in some cases using Machine Learning, it is now possible to achieve a more advanced segmentation of customers, which associates a single probability of a new purchase to the single customer or prospects.
  • Optimization of advertising investments: talking about advertising activities, predictive analysis can play a relevant role as well. First of all, predictive models can support the choice of advertising channels to be used, secondly, a data-driven approach can lead to optimizing the advertising spaces to be purchased and purchase times. To achieve these results, it is necessary to choose the right data sources, more than choosing the right predictive models. In some cases, apparently unrelated variables (for example the weather) can play a key role in predicting the timing of a purchase.

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Predictive analysis in Marketing: technologies and skills

So far, in this article, we mentioned the applications and use cases that predictive analytics can have in the Marketing field, from the acquisition of new leads to customer loyalty. The benefits that can be drawn are numerous and it is very important, in the development of new models, to constantly monitor the right metrics. To conclude, it should therefore be emphasized that starting to use predictive analysis in the Marketing field is not trivial. First of all, it is necessary to have the right technologies, for example statistics or data science software.

Secondly, it is necessary to have specific skills. For example, you need to be able to choose the right model, to prepare data, to visualize data and so on. You can decide to hire professional roles, such as Data Scientists or Data Analysts, or create specific partnerships with consulting companies. In 3rdPlace, a tech company of Datrix Group, you will find people who know how to analyze data, develop adequate models, and interpret the results. Among the predictive marketing solutions, the invitation is to deepen DataLysm powered by 3rdPlace.

Eventually, what matters the most is the ability to develop a data-driven corporate culture, in which different business people are more and more ready to base their decisions on data-driven insights.

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