PagesJaunes.fr, the French equivalent of the YellowPages, is a leader in local advertising and information on web, mobile, and print, generating hundreds of millions of queries each year. The quality and relevance of results is a top priority for PagesJaunes. Category managers are responsible for maintaining the quality and relevance of the directory by creating the pertinent associations between terms and categories. The challenge was to improve user experience without increasing workload. The client wanted a solution that would help them measure and improve customer satisfaction, help Category Managers automatically detect and correct problematic queries, and optimize the quality of results to improve customer satisfaction.

Parkeon, a global supplier of parking and transit systems, wanted to leverage the vast volumes of data they had access to regarding city drivers' habits. They aimed to design a powerful parking availability prediction B2C application that could provide reliable predictions of parking availability and enrich the parking meter data to create greater intelligence. The challenge was to turn the parking meter data and geolocalized data into accurate predictions that could be used in a user-friendly mobile application.

The healthcare industry is grappling with a high rate of patient no-shows, with studies indicating that 5-10% of scheduled patients miss their appointments. This has a significant impact on the financial health of healthcare organizations and their ability to care for other patients. Primary care physicians lose an average revenue of $228 for every no-show, and the lost revenue for specialists is even higher. When a patient misses an appointment, overhead costs including staffing, insurance, and utilities are not reimbursed. Cancellations with primary care physicians also impact the number of necessary specialist referrals those physicians can make. Combined, these factors contribute to significant revenue loss for physicians. To help minimize the occurrence of no-shows and thus reduce associated costs, Intermedix decided to develop and operationalize a no-show predictor that would assist office managers in scheduling appointments.

BGL BNP Paribas, one of the largest banks in Luxembourg, had a machine learning model in place for advanced fraud detection. However, the model remained largely static due to limited visibility and limited data science resources. The business team was keen on updating the model but faced challenges due to lack of access to data projects and the data team. The challenge was to harness a data-driven approach across all parts of the organization. The bank needed a solution that would democratize access to and use of data throughout the company, without compromising data governance standards.

A major healthcare provider in the UK was struggling with staffing inefficiencies, leading to physician overwork, patient dissatisfaction, and high costs. The hospital's staffing process was largely manual and based on the number of available beds, which did not allow for efficient allocation of staffing hours. This lack of data-driven decision making was impeding the hospital's ability to deliver optimal care and retain the best doctors. The hospital sought a technical solution that would enable it to model patient inflows on a small scale and recommend staffing schedules based on patient demand forecasting.

Infopro Digital, a crossmedia company, wanted to offer more advanced targeting options to its advertising customers. Instead of basic category targeting, they wanted to leverage the user’s navigation path and behavior to more accurately target those who may be interested in a particular ad. This advanced targeting required experienced technical teams to handle a vast data lake. However, Infopro Digital’s marketing teams needed to be able to handle the queries and most of the day-to-day work themselves without the help of IT every time. The marketing teams had some prior knowledge of processing data using Microsoft Excel, but they were frustrated by its computing and speed limitations. Infopro Digital also wanted to develop any new processes and skills within the company to keep costs and production delays low.

The customer, a major hospital in Western Europe, was facing challenges in accurately measuring physician and healthcare organizations' performances due to uncoordinated heterogeneous data sources, irregular and poor quality data, insufficient risk-adjustment of results, and lack of automation in physician profiling processes. They were seeking to embrace an Accountable Care Organization (ACO) model to improve clinical outcomes and compete on cost. Some clinical processes, like prescribing expensive or unnecessary drugs or recommending longer hospital stays than needed, were costly and detrimental to patient care. The customer estimated that administering the wrong care at the wrong time represented upward of $1.6M loss per year, a problem that they believed could be solved with accurate physician profiling.

Showroomprivé, an e-commerce retailer specialized in flash sales, faced challenges in targeting their marketing emails. Until 2016, the team selected the target audience for these marketing emails manually based on what they know about the brand. However, this approach presented several challenges. Brands often have overlapping or broad audiences, which meant touching some prospective buyers multiple times, while others not at all. This also meant casting out a wide net, potentially sending emails to people who were not interested in that particular brand. The ultimate goals of the project was for the marketing team to be completely autonomous in targeting and sending these emails.

Malakoff Humanis, the leading non-profit group health insurer in France, was facing growing challenges in keeping up with customer demands and providing quality customer service. The company offers supplementary health, welfare, and pension contracts to companies, employees, self-employed individuals, and single-payer individuals. It covers healthcare reimbursements in addition to the French social security and guides clients in their choice of care establishments. The company has a dedicated data science and analytics department led by a Chief Data Officer. The data department is comprised of four main branches, each in charge of Data Science and Analytics, Data Governance, Data Architecture and Cloud, and AI and Data Visualization. However, the company was struggling to effectively manage customer claims and improve telephone customer assistance.

Coyote, a European leader in real-time road information, uses IoT-based devices and mobile applications to warn drivers of traffic hazards and conditions. The company collects extensive data on the different uses of its community, such as mileage, time spent on the road, or the number of alerts issued by the community members. Initially, Coyote started with predictive analytics for improving their customer retention. However, they wanted to leverage the value of their vast data sources and implement a data-driven strategy at the heart of their core products and services. They aimed to improve road safety using IoT devices.

Action, Europe’s fastest growing non-food discount retailer, faced a significant challenge in managing the vast inflow of data from its over 2,300 stores across 11 countries. The company needed to track various aspects such as consumption patterns, product placement, and supply chain disruptions, which varied according to local, national, and international trends. The existing architecture was not sufficient to handle the data efficiently and provide accurate forecasting models for demand and sales in new and existing markets. The company also faced issues with data access and quality, costly and complex processes, lack of visibility and control, and operationalization and business impact. The use of Excel for gathering, sorting, manipulating, and modeling data was proving to be a bottleneck for the speedy and efficient deployment of data analytics and models.

JK Lakshmi Cement, a decades-old manufacturing firm in the cement industry, was facing significant challenges in improving operational efficiency and accelerating analytics reporting. The company was bottlenecked by a lack of data- and tech-savviness, with only a few people tasked with building reports for the entire organization. This limited the number of reports that could be created, hindering the company's ability to make data-driven decisions. The team was also struggling with scarce and underutilized data experts, and their data processes lacked operationalization and the ability to make a strong business impact. They were in need of a platform that could boost the efficiency of its coders and allow for cross-team collaboration with line-of-business users.

Trainline, Europe’s leading independent train travel platform, was facing a challenge in monitoring and improving their marketing acquisition. With paid campaigns running 24/7 and users interacting with those ads around the clock, static dashboards were no longer sufficient. The company needed a dynamic, real-time data tool for accurate marketing insights. They had invested in many different services and solutions to sustain their growth, but these were not always easy to manage. The company decided to build a centralized, global, real-time dashboard to get a global understanding of their marketing acquisition. The challenge was to start a big data project from scratch, ensuring that the technical team ended up with a tool that allowed them to improve and upgrade their own skills while also satisfying the marketing department’s requests quickly and efficiently.

Voyage Privé, a boutique vacation retailer, faced the challenge of creating personalized offer displays for its customers. The company needed to expand the range of customer signals that could be captured and analyzed to offer travel options that were appropriate for their members. This required a software solution that could capture and make sense of large amounts of data, develop effective customer segmentation, and implement a new non-rule-based approach for analyzing incoming and historical data. The end goal was to increase customer satisfaction by providing users with personalized offer selections while simultaneously boosting the total transaction value by customer.

A large national media organization wanted to provide high-quality recommendations for users of their app. Their goal was to target consumers with content that they would actually be interested in based not only on what they previously consumed, but how exactly they interacted with topics in which they previously expressed interest. For example, if someone chose to listen to a report on Topic A but then fast forwarded through much of the piece (as opposed to actually listening to the piece in its entirety), the app should take that activity into account for future recommendations. However, with a very small team and limited resources, the organization wanted to accomplish this in a scalable way. Not only would the system have to be mostly or entirely automated, but the team itself would have to be able to build the recommender easily in a way that would allow for quick tweaks and adjustments in the future.

In 2017, LINK Mobility, Europe’s leading provider of mobile communications, decided to scale up their data efforts for handling internal requests and externally with customers. Their primary offering is mobile messaging services, sending more than 6 billion messages a year worldwide carrying invoices, payments, and vouchers, associated with various services. They produce a lot of data and saw an opportunity to expand their offerings to provide more data-driven insight to customers surrounding the delivery and performance of their messages and services. They were looking to expand to customer dashboards as well as the ability to take action based on that data. However, with just a one-man data science team at the beginning of the project, they needed to be able to get up and running quickly and easily. They also needed to find a tool that would allow them to scale up data requests coming from inside the company as well as to be flexible enough to provide data insights to customers without having to use two different tools or platforms to cover their various needs, use cases, and data types.

DAZN, a subscription sports streaming service, was looking to grow their business in existing and new markets. They wanted to enable their small data team to run predictive analytics and machine learning projects at scale. They also wanted to find a way to allow data analysts who were not necessarily technical or experienced in machine learning to contribute in meaningful ways to impactful data projects. The goal was to support an underlying data culture with advanced analytics and machine learning at the heart of the business.

OVH, a global provider of hyperscale cloud services, was facing challenges with its dashboarding system. The business analysts responsible for disseminating data and insights to inform the commercialization and optimization of the website were spending more than 80% of their time on data preparation for the dashboard. The existing dashboard only provided basic, high-level metrics and did not combine different data sources for a complete view. This necessitated ad-hoc analysis, for which the analysts had little time. Additionally, the ETL process for the dashboard presented concerns for the data architects around data and insights quality, as there was a lack of transparency around exactly what data was being transformed and how.

Technical Safety BC, an independent, self-funded organization, oversees the safe installation and operation of technical systems and equipment across British Columbia, Canada. Conducting physical assessments is costly, and false positive inspections can result in significant opportunity costs each year. Those same resources could be better allocated within the safety system; therefore, finding a way to more accurately predict hazards is of high strategic value to the organization, and it creates greater safety benefit to the public. Technical Safety BC was looking to find more high-hazard sites while operating at the same resourcing level by introducing more sophisticated machine autonomy in the risk assessment process. Some of the challenges faced included: uncoordinated heterogeneous data sources; data quality; speed of collaboration; and training challenges in the use of machine-recommended predictions.

Heetch, a French company founded in 2013, has grown quickly to 250 employees united around one goal: making mobility more accessible by offering a smooth user experience. The company has gathered troves of data from drivers, passengers, global operations, and more since its launch, yet they struggled to scale their ability to actually leverage that data. Five years in, data warehouse costs were spiraling out of control, and performance was suffering as the amount of data grew. The company needed to find a solution that would allow anyone across the organization to work with large amounts of data while also ensuring optimized resource allocation.

La Mutuelle Générale, a French insurance company with over 70 years of experience in the market, serving over 1.4 million customers and 8,000 enterprise clients, and generating more than €1.1 billion in turnover annually, was facing a challenge in customer acquisition. The competition in the insurance industry is fierce, with organizations all vying to capture the same type of customer. The cost of acquiring a new customer has significantly increased in recent years. To address this, La Mutuelle Générale sought to develop a decision support tool for sales to aid their understanding and prioritization of prospects based on their likelihood to convert and their potential value compared to their cost of acquisition.

MandM Direct, one of the largest online retailers in the United Kingdom, faced a significant challenge as they grew rapidly. With over 3.5 million active customers and seven dedicated local market websites across Europe, the company delivers more than 300 brands annually to 25+ countries worldwide. Their accelerated growth meant more customers and, therefore more data, which magnified some of their challenges and pushed them to find more scalable solutions. The two main challenges were getting all the available data out of silos and into a unified, analytics-ready environment and scaling out AI deployment in a traceable, transparent, and collaborative manner. Initially, the company's first machine learning models were written in Python (.py files) and run on the data scientist’s local machine. However, as the number of models in production increased, the team quickly realized the burden involved in maintaining models.

Provincie Noord-Holland (PNH), a province in the Netherlands, embarked on an initiative to become a more data-driven organization. However, they faced challenges in determining the necessary steps to achieve this goal, including the required technology and expertise, setting up experiments, and implementing new processes. They also faced unique challenges as a public sector organization, such as the need to consider regulations and societal impact when conducting experiments and working with data. Additionally, they had to work within a closed IT environment, limiting their access to data science tools. They also realized the need for data scientists and technology to help them succeed with their data science initiatives, and the importance of being both data and business-driven to generate positive performance and encourage buy-in among organization-wide stakeholders.

Buildertrend, a leading construction project management software company, was looking to disrupt the residential construction industry by leveraging data science to improve business operations and make residential contractors more efficient. They were seeking a data science platform that could enhance speed and agility in their data-to-insights process, enable company-wide collaboration on data projects, and empower their data scientists with the right tools and resources. The company was also keen on automating repetitive tasks, improving documentation practices, and increasing the amount of data included in their models. One of their key use cases was churn reduction, where they aimed to efficiently target at-risk accounts to drastically reduce churn.