2015 | Unicredit

UniCredit Zooms In on ‘Merchant Analytics’

Business Intelligence, Visual Analytics, and Location Intelligence

Data can be an invaluable resource for insight to improve business—if there’s a system to wrangle it. UniCredit S.p.A—a Milan–based Italian global banking and financial services company—lacked one in 2015. Accurat stepped in to build a platform that functioned as a magnifying glass on bank–related goings–on, across the country or within a single brick–and–mortar location.


In 2015, UBIS—the company’s IT banking division—possessed massive stores of data related to transactions at storefronts and bank locations across Italy. They were interested in exploring ways to transform this data into insight with the specific goal of learning more about the market penetration of their point–of–sale (POS) systems. With this info, they wagered they could monitor performance across merchant categories and reduce churn. They contacted Accurat to repackage their raw material into a structured system that leveraged visualization to engage employees at all levels of the organization.


Multiple data sources, continuous streams of information, and a diverse userbase are all potential hurdles in the development of a data–based system. To avoid missteps, we established a flexible work plan that involved regular communication with representatives at UniCredit. In meetings with stakeholders, we continuously adjusted the scope of the project based on feedback. This approach ensured that we remained aligned with our clients on the design and technological requirements of a shared vision, obviating the need for “buy–in” at any stage.

We began the process of designing visualizations using real data from UniCredit locations in Rome, Milan, and Palermo. Familiarizing ourselves with these numbers allowed us to make educated assumptions about how it could be used to convey the insight that UniCredit’s team was searching for. Meanwhile, interaction designers and front–end engineers refined conceptualizations progressively by testing them with simulated transactional activity. Working with dummy data, we were able to test the tool’s features with real users before beginning the drawn–out process of integration.

Design and development converged in the finalization of the platform’s features. We used machine learning techniques to create a “Suggestion” module: a predictive search tool that serves up recommendations based on benchmark comparisons.
Transaction analysis lives in two separate environments on the platform: “Acquiring,” where details on POS locations are, and "Issuing," which has information about credit card clients.
A map view of POS activity.


The Merchant Analytics suite visualizes data received daily from credit card and POS transactions as well as branch locations. Credit card swipes and POS sales can be filtered to show the activity of a single merchant or business category, while branch activities are sortable by client type and transaction category (i.e. ATM withdrawals, teller interactions). A map–based interface gives users the ability to survey the entire country, as well as drill down to specific storefronts. Setting parameters for time with a calendar tool, UniCredit employees can home in on specific hours of the day, or observe trends over time.

The deployed platform reflects UniCredit’s input on how to make a product for the unique needs of their employees. The resulting interface is simple—easy to grasp for all types of users—based on intuitive, interactive maps, customizable timelines, and suggested filters for key KPIs.

To allow users to dive even deeper in the transactions dataset, Accurat designed a “Merchant Summary” section of the tool: this view collects all pertinent data about a specific merchant—i.e. its type of business or its holding group—and enables comparison between it and others in the same category or umbrella brand. To analyze the performance of individual bank branches, users can compare traffic data and service offerings.



With Merchant Analytics, UniCredit expanded the role of data visualization at the company beyond the domain of data scientists and analysts. It represented a push by the company to incorporate its principles into all types of workflows. As an added bonus, it’s built entirely on recognizable, open source technologies, making it an easy product for UBIS to maintain and build upon. The Suggestion List feature, which highlights performance trends in set locations, has become an indispensable tool within the organization to identify untapped opportunities for investment.
Paolo Corti
Giorgia Lupi
Pietro Guinea Montalvo
Alex Piacentini
Simone Quadri
Gabriele Rossi
Marco Vettorello
Tommaso Zennaro
UX/UI Interactive Data
Visual Storytelling
Dataviz, Experience, WebApp, and Mobile App Design
Interactive, Dataviz, Storytelling, and Experience


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