Business problem

Nowadays, the process of finding the ideal spot where to place a shop is still largely driven by human experitse, painful and long. The variety and heterogeneity of available data and the fragmentation of the market causes losses related to opportunity costs and the fact that most spaces remain unoccupied for longer times they should.

Dribia's solution

We are helping our US client ARetailSpace to build an intelligent retail recommender leveraging data from a variety of open and private datasets. We are in charge of designing, implementing and testing all the algorithmic and data technical exploitation of the product. This includes the analysis of datasets, creation of data products, recommendation engines and user experience analysis.

"Dribia played a key role in building our data products and strategy. We have been extremely impressed with their agility, innovation and professionalism."

Scott Bagby

CEO and Co-Founder @ A Retail Space


Business problem

The company-client interactions are usually registered in a CRM (customer relationship management). When properly exploited, these data are very valuable as they allow for client targeted offers and improve the sales department performance. The input of new data to CRM in a company with a number of salespersons is decentralized. The lack of a central entity that controls the CRM inputs, speeds up the addition of data by salespersons, but it may also have a negative impact in the quality of these data. Typos, duplicated registers, or a lack of normalization are shortcomings that may complicate or even prevent from the exploitation of these data. The challenge is to design a tailor-made, intelligent algorithm that improves current and future CRM data quality, keeping it a decentralized system.

Dribia's solution

Together with the Digital Dept. of Simon, we designed and implemented an algorithm that uses similarity measure techniques applied to textual data in order to normalize, clean and disambiguate company’s CRM data. We have also built the web app integrated to the CRM that allows salespersons to benefit from the algorithm results autonomously. The impact of the algorithm-application is an improvement of the commercial data quality, very valuable information both for the Sales department as for the other company departments.

"The method, transparency, and compromise of Dribia has lead to a very successful project."

Alba Porta

Head of Digital @ Simon


Business problem

So far, destinations without an established direct and regular connection have been difficult to reach with collective transport. Yet, technology and the availability of major data sources provide an accessible tool to assess the real demand for such off-schedule routes without coverage.

Dribia's solution

For our UK client Zeelo Ltd., a start-up offering “pop-up” coach services to specific events, we designed, trained, and validate an intelligent algorithm that predicts general demand for collective transport. It crosses different geo-localised data sources (traffic control, social media, search engines) and generates a ranking of profitable routes and their optimal trajectory. This algorithm has allowed the company to scale and has contributed to Zeelo's major achievement of raising 4.35M Eur.

"Dribia managed to turn our high level ideas into concrete algorithms that address our needs. Their commitment, thoroughness, and transparency have convinced us from the outset."

Daniel Ruiz

CTO @ Zeelo

Business problem

Real estate search portals are consistently trying to offer the best, most unique information to their users and search engines. As the 5th largest portal in the US, collects a massive amount of anonymous data from on-site user behavior, however data at the scale of hundreds of millions of interactions is challenging to aggregate and summarize. With a comprehensive understanding of both the demand and supply sides of real estate availability, can offer more personalized recommendations that better address user needs and inventory targeting.

Dribia's solution

We used data from user site searches as a proxy for housing demand, geo targeted to specific locations and cross referenced with listing availability (almost 3 million records) in each US market. Using this data, we created an adequate but valuable KPI that defines areas where housing demand might lead to fluctuations in pricing, and the velocity of those price changes, based on the anticipated inventory of listings versus the real-time search behavior.

"Our partnership with Dribia has provided valuable tools that can empower key customers with accurate timeframes of the potential risks and opportunities in their real estate markets."

Grant Simmons

VP of Search Marketing @


H2020 research project

European citizens are increasingly worried about their data sovereignity. We produce more and more data each day but are unable to control it. Where does it go? With whom are we sharing it? Who does it serve? The DECODE project tries to build the social, technological and legal tools for European citizens to share data for the public good in innovative ways.

Dribia's solution

Dribia is part of the DECODE consortium, formed by more than 14 partners from 6 countries across Europe to provide its expertise in data innovation. We coordinate the development of the project pilots in Barcelona, their technological integration as well as being in charge of the development of the projects' App using SoTA criptography and distributed technologies developped by our partners.

Àrea metropolitana de Barcelona

Public problem

Promoting the use and adoption of data-driven policies is a strategic goal for many innovative institutions. To reach a long-lasting, overarching change and a large impact by the use of these policies, one must combine a mix of technical, organizational and strategic perspectives. A correct alignment of teams, technologies to be used and objectives to be fulfilled can only be achieved by defining and implementing a clear roadmap with concrete actions to reach the desired goals.

Dribia's solution

Dribia has helped AMB in establishing a data strategy for the inclusion of quantitative criteria into urban regulations encoded in the ambitious Urbanism Director Plan (PDU) that is to affect 3.2 M people. Such an innovative vision on the use of data differentiates the PDU from other similar initiatives, and demonstrates the need for interdisciplinarity in the definition of urban policies.

"Combining Dribia's technical expertise with our urbanistic knowledge, we have jointly defined concrete tasks to add a dynamical layer of tools and quantitative indicators to our urban planning tools."

Xavier Mariño

Coordinator of PDU @ BCN Metropolitan Area


Business problem

Retaining your client base is a key factor for success, both in the online and offline sectors. Companies always have finite resources to invest into their commercial effort, and thus it is very important to know where to better place them for maximum efficiency.

Dribia's solution

We have worked alongside Cecot to explore the information available about their clients. Jointly with their commercial department, we have extracted patterns from the data and we have created a predictive algorithm that supports them in prioritizing fidelization efforts.

"Client retention is one of the key challenges any company needs to face. Alongside Dribia, we took a step forward in our commercial strategy and we also opened new venues for innovation in other areas."

Josep Casas

Director Comercial @ Cecot


Business problem

Shared micro-mobility services are key players in a modern network of sustainable means of urban transportation. Citizens benefit greatly from a comfortable and flexible experience, but operators face new challenges regarding logistics and maintenance. How do we manage rapidly changing needs and maximize user engagement while limiting costs and resources?

Dribia's solution

For the folks at Reby, a start-up that manages a fleet of shared electric kick scooters, we created an algorithm that computes how the demand is distributed in the city. We use open data from the city and private usage data to predict when and where a kick scooter will be needed. This allows us to design a relocation strategy that maximizes fleet performance and optimizes distribution routes.

"Dribia's expertise enabled us to exploit the full potential of our data and allowed us to become a data-driven company from the very start. They quickly understood our poblems and needs, and build a powerful and solid algorithm, capable of scaling to many cities."

Kiran Thomas

Founder @ Reby


Business problem

Newcomers to a city often have to go through a cumbersome research to find a neighborhood to live in that best suits their interests and needs. Nowadays, most relevant data is publicly available. However, users actually look for easier and more straightforward ways and tools that help them in making that decision.

Dribia's solution

For our Lithuanian client,, we designed a recommender algorithm that suggests a neighborhood in New York City on the basis of users’ responses to nine short questions – about, e.g., security, rent, noise level, or commuting time. This matching system has been featured in the New York Times and will be applied in other cities.

"Dribia has excelled with their transparence, commitment, and outstanding quality work."

Sarunas Legeckas



Business problem

Users of digital sales outlets (websites or applications) only turn into buying clients if they find the product they look for within a reasonable time span. The question is how to guide them to the right kind of product. One way to achieve this is to create algorithms that incorporates their interests and search history.

Dribia's solution

For our client 500 Markets, a Barcelona-based developer of collaborative e-marketplaces, we have designed and implemented an algorithm that increases the visibility of relevant products. It does so on the basis of a client’s connectedness with other users, their preferences and their search history. This has increased their interaction and retention rate.

"Working with Dribia has opened our eyes about the possibilites of working with data. Their involvement has been key for the success of this project."

Minty Ascención

Project manager @ 500 markets


Educational problem

Today's youth must develop an interest in science, technology, engineering, and mathematics (STEM) to cover tomorrow's need for talent in a technological and digital society. Given its importance, initiatives are needed that present STEM – and its consequent applications, such as data science – in an appealing, concrete, and engaging manner.

Dribia's solution

Dribia is a founding member of an educational project called “Beepath”. It allows students to conduct experiments involving human mobility through a mobile application that tracks human mobility. Dribia accompanies the analysis of the accumulated data. Various private foundations have so far financed the project.

"Without Dribia's team this project would have been impossible. We really appreciate their commitment and professionality."

Josep Perelló

professor @ University of Barcelona