10 Predictive Marketing Applications

10 Predictive Marketing Applications

The Digital Environment is constantly evolving and it’s reaching the most diverse areas, and Marketing is no exception. We hear about social media or digital marketing, different concepts that complement each other and above all help several business areas to promote, communicate or sell their products or services. 


In recent years, Digital Marketing has invaded most business areas and many already consider that if a product or service is not on the internet, it’s as if it doesn’t exist. Just as the consumer/client is on the internet, the products or services should be too. But the main questions is: do the products and services reach the exact consumers/clients that the business area has in mind? We hear about segmentation, but is Digital Marketing able to reach the right segment? Or are we being driven by mass patterns and investing in this type of platform is becoming obsolete?


Due to technological development (use of interactive software and faster and more accessible computers), it’s becoming easier to access large volumes and large data variations, which after cross-referencing, can provide valuable information to companies and organizations. Predictive analysis allows us to identify trends; predict behaviours; understand the real needs of customers, promote decision-making based on trustworthy data, and improve business performance.


Thus, to redirect Digital Marketing actions and reach the desired segment, Predictive Marketing emerged.


It is common knowledge that when a company or organization knows their clients and potential customers in depth, their preferences and needs, it is able to not only anticipate their needs but also to meet their expectations about a given product or service faster and more effectively. Predictive Marketing is precisely the process of boosting data search and using it strategically to create a strong relationship with the target audience. Several authors explain this concept as a perfect alliance between the data from Analytics and segmentation. In other words, it is a way of defining market strategies using the massive information universe that brands have at their disposal nowadays, the so-called “Big Data”. To that end, companies increasingly use artificial intelligence, machine learning, data mining, among others.


Predictive analysis is able to use data, algorithms and machine learning techniques to try to predict future scenarios. The goal is to post statistical and historical data to decide the best steps that will ensure business success.


The use of predictive analysis makes decisions at least 80% more assertive because a predictive model provides detailed data on the production process, maps sales possibilities, helps control customer behaviour, and gathers external information such as worsening/improvement of economic conditions or the price of raw materials, which may impact the results.


Therefore, in general, Predictive Marketing enables the improvement of information and knowledge about customers and potential customers; the creation of targeted and impacting digital marketing campaigns; the increase of customer loyalty and the reduction of conflict points. In essence, Predictive Marketing is the combination of marketing and data science.


Next, we present the top 10 applications of Predictive Marketing:


  1. Predicting the ROI of digital campaigns

It is possible to simulate the ROI of a digital campaign before executing it, since every company has, at some point, already performed this operation taking into account previous results. Thereby, based on the statistics obtained, it is possible to point out the best targeting for each digital investment. Most importantly, those mathematical models have to be alive and a part of an evolutionary cycle.


  1. Suggesting an investment based on goals

Predictive Marketing allows not only the prediction of results (according to the actions and the results), but it can also go the opposite direction, indicating the appropriate investment according to the established goals.


  1. Consumer profiling to help Direct Marketing

Predictive Marketing also ensures that businesses can analyse which audiences can be engaged in each campaign. Therefore, there will be a customized and adequate investment for each cluster.


  1. Predictive Lead Scoring

Companies, organizations and institutions need to be able to rank the leads according to the conversion potential in order to direct efforts and investments. We can thus rationalise the costs and perform a more personalized work.


  1. Contextual Offer

In social media, it is very common for an ad to be displayed systematically to a potential customer who viewed a product or service but did not purchase it. The repetition tactic is often not enough and, therefore, there needs to be a contextual offer to understand which potential customer’s purchasing cycle presents more chances of conversion. We can, for example, figure out their browsing behaviour and make it simpler and more direct or present complementary products or services.  


  1. Churn Predictor

The “Churn Rate” is a metric for customer loss and usually applies to recurring services. The likelihood of losing a customer can also be calculated by mathematical models that consider variables related to the profile and data regarding their behaviour on the internet.


  1. Contextual Data Enrichment

While recognizing the importance of information about potential customers to companies, the forms used for capturing leads must be simple, clear and direct because if they are too long, they can drive away potential leads. So, another application of Predictive Marketing is to improve the list of information about each contact, without them having to provide these data too extensively.


  1. Spotting anomalies in the user’s purchasing cycle

Browsing and usability issues sometimes prevent potential customers from reaching data collection forms or closing a sale. The most important thing is to analyse and understand why certain groups, which have high conversion rates, are not completing a certain step in order to conclude the sale.


  1. Detecting fraud and Security

Nowadays, cybersecurity is a growing concern. Therefore, predictive analysis can help prevent losses arising from fraudulent activities before they occur. By combining various detection methods (such as business rules, anomalies, predictive analysis, link analytics, etc.), companies can achieve greater precision and better predictive performance.


  1. Predictive Marketing System

There are some predictive intelligence companies in the market, in addition to big names like IBM or Salesforce. Many of these companies offer intuitive, user-friendly and affordable predictive intelligence software. This software ends up diminishing a barrier which is sometimes imposed on small businesses that do not have the budget to invest in marketing.  


Márcia Monteiro

MA Marketing Course Leader

LSDM – London School of Design and Marketing


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