2015 | Unicredit Group

Merchant Analytics

Interactive, Dataviz, Experience, and Big Data

An interactive solution that visualizes geolocalized data about transactions on both Unicredit POS and cards recorded day by day inside the Italian merchants.


In the summer 2015, UBIS, the IT company within Unicredit Group, contacted Accurat to imagine and conceptualize a digital product that could leverage the value of data related to daily transactions recorded from thousands of Italian stores, gathered from the bank’s credit cards and its digital points of sale (POS). The goal was to transform a large amount of raw and unstructured data into useful information to inform the bank’s commercial efforts. Some topics were considered particularly interesting: mapping the evolution of Unicredit’s POS penetration in the Italian market, monitoring commercial performance of different merchant categories, analyzing peaks of churn rates, and allowing potential future data monetization.

Accurat imagined, designed, and developed a scalable and functional web-based software platform that interacts with Unicredit’s data structure and back-end technologies. During several months of collaboration with UBIS, Accurat’s data visualization designers, interaction designers, and front-end and back-end developers translated business inputs into an articulated and multi-layered tool developed with the most modern web technologies.


The application provides quick visual access to an always-updated stream of transaction data that can be explored by geography, time, single merchant, or merchant categories. To allow a wide range of internal users to perform a complete and critical analysis of this data, Accurat designed a simple interface based on an interactive map, a customizable time frame, and a list of suggested merchants based on their performance. The geographical exploration allows for the visualization of data at different spatial granularities; starting from an overview of the entire country, users can navigate to more specific areas, down to the district level. During the navigation of the different geographical levels, transactions can be viewed as an aggregate for the selected area. With a similar logic, the selected time frame can be modified freely at any zoom level thanks to a standardized calendar, enhancing the analysis around the transactional data and enabling the visualization of day-to-day trends. At any time, the information visualized in the application can be filtered by merchant category. The color-coding and the additional information on the merchant’s ATECO code (the Italian ISIC code) help to group the data and allow exploration of the transactions at a specific geographical level (a specific city, for example) by comparing its product categories. To allow users to dive even deeper in the dataset, Accurat imagined and designed a “Merchant Summary” section of the tool; this view collects all the useful data around a specific merchant (type of merchant, type of account, group that owns the merchant) and enables the comparison between its transactions and the ones performed on average at other merchants of the same brand or of the same product category.
The analysis is by design split between two separate exploration environments: ‘Acquiring’ (what the bank can discover from its points of sales) and ‘Issuing’ (what the bank can discover from its credit card clients); this clear separation enables different types of analyses on the collected data that are translated into clear visualizations for both the number and volume of transactions.
DETAIL - Acquiring amount
DETAIL - Issuing amount
DETAIL - Total amount


When dealing with Big Data, unexpected drawbacks can easily arise if the scope of the project isn’t clearly defined and kept in check during development; these risks increase when working with multiple data sources, continuous streams of information, and different types of users. This is why Accurat’s team approached the design and development of the application with flexibility, based on continuous product iterations that allowed for multiple reviews with all stakeholders involved. This approach led to a constant and controlled redefinition of the requirements of the application during development, to ensure that both the technological and the design efforts followed a clear vision and ensured added value. Accurat’s analysts and dataviz designers started by developing simple, static data visualizations with real data from the cities of Rome, Milan, and Palermo to identify the tangible operations that could be performed, and proceeded based on concrete observations rather than hypotheses. At the same time, Accurat’s interaction designers and front-end engineers developed the first concept into a more complete tool by working with a limited set of simulated transactional data that served the prototype from an in-house temporary back-end, without dealing with complex integration tasks before testing how the user interface worked with real users. Once the actual usefulness of the analyses and the effectiveness of the interface were tested and approved, the two processes converged in the creation of the final tool. As the platform gained its skeletal structure, the “Suggestion” module, a predictive search engine with the capability of creating multiple comparisons with various benchmarks, was also added.

Machine learning techniques are used to create a ‘Suggestion List’, that is now widely used within the organization as an instrument to foster quick discovery of opportunities for direct monetization: this feature automatically highlights performance trends, both positive and negative, of geographical aggregates and single merchants in order to develop possible commercial strategies.


'The “Merchant Analytics” tool is the first instrument Unicredit utilized with embedded data visualization principles that help present large amounts of data in a clear and flexible way without asking its user for any programming or statistical skills. The application is built entirely on widely adopted open source technologies, making it easy to expand and maintain by the bank’s internal IT teams.''
Giorgia Lupi
Gabriele Rossi
Simone Quadri
Paolo Corti
Pietro Guinea Montalvo
Alex Piacentini
Marco Vettorello
Tommaso Zennaro
UX/UI Interactive Data
Visual Storytelling

Interactive, Dataviz, Storytelling, and Experience

Dataviz, Editorial, and Experimental