Business problem

The process of finding the ideal spot where to place a shop is still, nowadays, 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 non-occupied for larger times they ought to.

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

So far, destinations without an established direct and regular connection have been diffcult 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 (e.g., 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 1.35M 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 prices 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 @

Àrea metropolitana de Barcelona

Public problem

Promoting the use and adoption of data-driven policies is a strategic objective 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, used technologies and objectives to be fulfilled must 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 stablishing 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 diferentiates 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 sector. Companies always have finite resources to invest into their commercial effor, 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 optimize 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 suits their interests and needs best. Now, 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, orcommuting time. This matching system has been featured in the New York Times and is to 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, then, 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, Bkie – a Barcelona-based collaborative e-marketplace 2nd hand bikes – 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 has to 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 an 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