Growth in the digital payments industry is accelerating rapidly as people adopt alternatives to traditional banking channels. This generates large quantities of data, allowing us to learn even more about how people make purchases and move money around, especially in largely cash-economy parts of the world and amongst demographics that have a small digital footprint.
Uulala’s innovative analytics algorithm is designed to track all activities carried out by users within the Uulala app, from purchases to searches to transfers, allowing the app to adapt to user needs and preferences in real time.
Putting Big Data to Work
Back in the day, computers could only do exactly what they were programmed to do. They could collect data, but it took humans to sort the data, analyze it and then make predictions accordingly.
Now, with the application of analytic techniques like predictive analysis and data mining businesses can use algorithms that rapidly sift through vast amounts of data, seek patterns and make predictions based on those patterns. They can then be applied in real time to a wide range of activities, creating a user-specific experience in a variety of platforms, from digital entertainment to mobile payments.
What is Big Data?
Big Data refers to data sets with very large, very complex or very rapid information which can’t be analyzed sufficiently through conventional relational databases. The data comes from a variety of inputs: sensors, social media, searches, audio, video, networks and applications.
Paying bills and buying products, reading articles, transferring funds, using social media: Online activity creates billions of pieces of information every day. As mobile coverage and the Internet become ever more pervasive through smartphone technology, data is becoming available from previously unreachable groups of people and corners of the globe.
Who Uses Big Data Analytics?
There is really no limit on where to apply big data analytics. Health agencies can use an algorithm designed to look at the spread of diseases, Amazon can use one to tailor purchase recommendations, and conservationists can even use it to save endangered tigers from poachers.
Netflix is a very successful example of the advantageous application of data analytics. They have developed an impressive recommender system to show relevant content to users, keeping them interested and making them less likely to look for entertainment options elsewhere.
Using a combination of algorithms to decode user preferences and recommend the right content, Netflix not only keeps customers but gains exposure for the $6 billion worth of content they’ve either purchased or created. This increases the subscriber retention rate, reportedly saving the company $1 billion annually.
Google’s search engine is likely the most long-standing, expert example of the application of big data analytics
Google’s algorithms are designed to ‘perceive’ the meaning behind the words and phrases typed into their search engine. These algorithms scour the internet for synonyms, similar subjects and related information, and then rank the results.
Google’s search engine takes in over 200 different signals to process patterns, uses semantics to understand what someone actually meant when they typed in a search, and sorts through and returns the results in a split-second.
Big Data and Uulala
Uulala’s primary client base, the global unbanked and underbanked, lives largely off the financial grid. This leaves a gap in information about the demographic’s purchasing patterns and financial needs. Uulala’s analytics algorithm can not only personalize the user experience, but will generate a database of information about a population that previously had little or no financial data available, all while helping to elevate them out of a cash-only economy.
Learning the Market: Unbanked and Underbanked Users
According to the 2015 U.S. census, 9 million households in the U.S. were unbanked, and a further 24.5 million were underbanked. In Latin America, this number is much higher: in 2014, nearly half of Latin Americans had no bank account, meaning there is little to no formal information available about their spending behaviors or financial needs.
Mobile network coverage and smartphone use outstrip traditional banking access in both the United States and Latin America, making mobile payment apps a realistic place to start for unbanked and underbanked users entering the financial world.
Applying Analytics to the Uulala Experience
App users generate a wide stream of information as they pay bills, send and receive remittances, buy goods, choose digital content, and withdraw funds.
Uulala’s analytical algorithm crunches this data in real time with attributional and predictive analysis, allowing it to do a number of things:
- Predict preferences and target personalized digital entertainment and product offers.
- Tailor in-app service offers to individual users, such as micro-credit and rewards tokens.
- Build a database of user preferences and activities.
- Predict fraud and increase security within and between platforms.
Uulala uses predictive algorithms to track activity within the app, aiding its goal of global financial inclusion by getting a realistic picture of the current financial abilities and needs of its current and potential clients.