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User behavior analytics for the use of mobile banking applications in Latin America and the Caribbean
From Mobile First to (almost) Mobile Only. This is how people throughout the region are behaving. Selecting increasingly more banking transactions and consultations from your cellular phone. Including payments. In 2019, according to e-marketer, 28.2 million individuals in Latin America process proximity payments via cellular phones, and it is expected that by 2023, this number will reach 48.3 million persons.
Now, ¿Do we know exactly what users want and how the future app should be? In our I&D sector, Infocorp addresses the following challenge: understand, imagine, and project how the new mobile experience should be in the next few years. A research and development ideation process develops a roadmap towards a new version of mobile banking.
No data
The point of departure was to understand the current use of banking applications by clients. The outcomes cannot be anticipated without knowing the real behavior of the average user at the moment of opening a bank app. Some of the questions formulated include:
- What determines the user behavior with its application?
- Do all app users act the same way?
- How much does the behavior vary according to use, client profile and segment?
- What relationship is established for the use of previous interactions with bank channels (customer journey)?
We believe these factors should have already been researched, but after analyzing several behavior reports from consulting companies all around the world, no specific answers were detected for these questions providing at least reliable information for Latin America, Central America or the Caribbean.
Unlike the behavior from other markets in different regions of the world, studies disclosing this level of detail in these regions are very scarce or partial thus a new alternative is considered: development of our individual analysis.
Origin of results
Since our IC Banking platform is installed in a large banking database providing services to hundreds of thousands of users in our regions, our team decided to access our own source: analyze audits conducted in our platforms to provide the objective grounds from where a strategic vision must be established.
Complying with data protection laws from each country, our team was only interested in large numbers, not people. The team carried out large consultations of databases between millions of records, intermediate databases, cubes, data warehouse, and dedicated dozens of hours of research and exploration of impersonal information providing great statistical value.
What did we find?
How easy is to define it and how difficult was to find it. The main variable affecting the largest use of application can be defined as the “client relationship with the bank.”
This relationship ensures different behaviors, and therefore different application uses. It is not the same to analyze the behavior of a client who collects payroll payments from the bank, possibly holding a mortgage loan and two or three additional products from the same bank, than an omniproduct client associated with the bank only because the credit card offers a very convenient loyalty plan.
Said relationship (that broadly speaking could be associated with the number of products or types of products the client used) are the variable explaining the different behavior clusters that can be foreseen if our team uses them as a source of information in intelligent learning algorithms.
The first paradigm change originated this way, the application must not be conceived as a bank application but it must be from “the client.”
Our team must develop an application that “understands the client and its context”, and informs, notifies and prioritizes the information the client is interested to consider based on the relationship established with the bank.
It is not possible to offer each client the best experience with only one mobile application. In turn, the future app will be 100% personalized and predictive.
It is not enough to know how users behave with their bank app. An optimal experience required the understanding of a much deeper issue. ¿What is the relationship of people with their Smartphone? ¿Where do they spend more time once they turn it on? These studies lead us to develop a design which is a lot more similar to social network feed, highly intuitive and intelligent, through an algorithm deciding what benefits or offers it shows.
The foregoing considers the work of the innovation and technology teams from the banks, to know their demands and specificities, and to develop an adequate road map for each one.
