How Revolut is finding value in the Big Data they are collecting.

Revolut markets itself as the most convenient way for a customer to manage their finances by promoting services such as spending analytics and saving vaults. However, Revolut’s biggest convenience is that all banking can be done online from their mobile app at any time without having to physically travel to a bank within their 9am to 5pm working hours. This neobank is non-existent without their customers and they are constantly updating their services with new FinTech opportunities for them. It is clear that Revolut is doing something right, as of 2021 they have 12 million customers worldwide (Revolut, 2021). But how do Revolut know what customers want?

With over 12 million customers worldwide it is hard to imagine the amount of data Revolut collects on a daily basis. They use Exasol, the relational database management system to store and process this data (Exasol, 2019). The value Revolut extracts from these user data sets and how they use it is what makes their business model so successful. For Revolut to find this value, data mining needs to take place. Tan et al (2019) describe data mining as “the process of automatically discovering useful information in large data repositories”, this is done through searching the datasets for useful patterns that would have gone unnoticed. These useful patterns or insights are then used to improve Revoluts services. 

Revolut using three types of data mining to find value in data:

1. Association Rule Mining

2. Cluster Analysis Mining

3. Anomaly Detection Mining. 

Association Rule Mining extracts the most interesting patterns and finds relationships between variables, this is also known as the market analysis technique and is useful for cross selling. Revolut uses this to find relationships in what customers are purchasing and where they are shopping to improve their customer experience and offer rewards and discounts related to their shopping habits. 

Cluster Analysis Mining finds groups in unstructured data that share common traits and can be used for problem solving. In relation to Revolut, this type of mining is used to segment problems that may be occurring for some users and solve these problems. It also separates users into groups which can be used for marketing purposes when testing out new services Revolut may be implementing. 

Anomaly Detection finds deviations in regular data that is collected. Revolut uses this type of data mining to detect card fraud. Revolut will store profiles on customers spending and behaviour, anomaly detection will distinguish irregular transactions or activity which will then block this transaction until the customer can verify it on the mobile app. This prevents the customer from having to block their card and wait for a new one with a regular bank. 

By improving customer experience and providing new services to customers, Revout is not only providing value to their company but monetizing this value from Big Data. 

It is evident that Revolut is utilizing Chaffey and Smith (2017) 5S goals of digital marketing strategy, in particular the ‘serve’ and ‘save’ approach.  Serve implies adding value through providing customers extra benefits online, this can be seen throughout the neobanks tactics as all business and communication takes place online only. But also through the insights they are finding in data which is benefiting the customer daily. They are also saving costs to both the business and the customer by trading this way. 

Chaffey, D, and Smith, P.R., (2017).. Digital marketing excellence : planning, optimizing and integrating online marketing. 5th Edu. Abingdon: Routledge. 

Exasol (2019) Revolut achieves true data democratisation with Exasol. Available at 

https://www.exasol.com/revolut-achieves-true-data-democratisation-with-exasol/ [Accessed 09 February 2020].

Revolut (2020) A better way to handle your money. Available at: https://www.revolut.com/en-IE [Accessed 09 February 2020].

Tan, P., Steinbach, M., Kumar, V, and Karpatne, A. (2019). Introduction into data mining. 2nd edn. Harlow: Pearse Education. 

Thanendran, A. (2018). ‘How we use machine learning to protect you from fraud’, Revolut Blog, 13 November. Available at https://blog.revolut.com/how-we-use-machine-learning-to-protect-you-from-fraud/ [Accessed  08 February 2020].