The client, a large public utility company based in the US, was facing challenges in establishing a common customer satisfaction framework for both residential and business customers. They needed to identify key metrics and determine the impact of relevant dimensions and factors influencing customer satisfaction. A significant challenge was accounting for low response rates among business customers. The company sought to assess and monitor customer satisfaction and performance drivers and benchmark brand health and performance.

A leading global PC manufacturer wanted to set up a completely automated system of analyzing reviews and ratings for self and competition, enabling actionable insights for product, marketing, and consumer research teams. The client wanted to understand top-rated products by different categories, create an automated solution to categorize unstructured customer reviews, and use NLP and sentiment analysis approach to create insights on behavior and brand perception.

The client, a US-based auto finance company, was approving all loan applications at the origination stage. They desired to build a new risk-based loan application analytics solution to better understand their existing customers and manage risks, profitability, and growth more effectively. The challenge was to profile, model, extract, and automate data into one repository for the loan origination process. This included statistical evaluation of the borrowers’ characteristics, risk-based pricing model based on the riskiness of the customer, and real-time decision-making application for monitoring performance, decision support, and automated scoring of customers.

The client, a leading devices and services company, was looking to understand the features and capabilities of IBM’s Bluemix and GE Predix offerings. These platforms offer cloud-based development and application scaling or infrastructure management for deploying and managing apps in the cloud. They also offer a Platform-as-a-Service (PaaS) that virtualizes applications through a combination of open source software and hardware. The client wanted a comprehensive analysis of these offerings, covering details such as overview, the target companies for these solutions, positioning of these solutions, pricing model, community/ analyst feedbacks, etc.

The client, a leading eCommerce company, wanted to understand the payment preferences in India, China, Russia, and Europe. The eCommerce and payments industry has seen significant changes over the years with the introduction of many new and innovative hassle-free payment methods. Electronic payments and digital transactions have become much more common. However, each country has specific payment preferences. The client wanted to analyze the payment industry for each country, understand the competitive landscape and emerging needs of the consumers, and evaluate current tools and technologies.

Blueocean Market Intelligence was tasked with providing a comprehensive market opportunity assessment for a prospective CCN technology developed by the client company. The challenge was that CCN is a new technology, not yet commercially deployed, so a similar and more current technology had to be considered to study the prospective market. The market size was calculated referencing a similar technology (CDN).

The Austrian National Tourist Office was facing the challenge of managing large amounts of data from internal and third-party sources, especially while overseeing multiple international marketing campaigns simultaneously. The existing IT infrastructure was decentralized and designed for evaluating financial data, which was not suitable for the dynamic marketing landscape. The data collection and transformation process was manual and time-consuming, involving extraction of data from Google Analytics and the social media management tool Sprinklr, and then pasting relevant numbers into spreadsheets to derive useful information.

reBuy, a reCommerce company based in Germany, was looking to expand further in Europe. To achieve this, they needed to uncover insights into consumer behaviour data. They used multiple online tools to accumulate valuable data that needed to be organised and analysed regularly. However, they faced challenges with the never-ending flood of incoming data. Manual data preparation was time-consuming and distributing the final reports was tiresome. They tried to tackle their problems with an in-house solution first, transferring data to their warehouse via custom-made APIs. However, this solution proved to be error-prone and the data was not always up-to-date. Simple changes required a complete system update, which was cumbersome and costly. As a result, reBuy started looking for an out-of-the-box solution.

MediaCom, a large global media agency, offers a variety of services to their clients - from strategic consultation to the execution of ROI-driven digital campaigns. In an ever-increasing digital marketing landscape, agencies are facing a growing number of challenges - from mastering each and every new platform, to generating powerful insights from marketing campaigns. Similarly, client expectations have been revised upwards; advertising partners are no longer content with a short Excel report detailing impressions and clicks. In this data-driven world, understanding user behaviour through correlations and attributions is crucial to evaluating a campaign's performance.

Fashionette, a German ecommerce company specializing in designer handbags, aimed to become a digital pioneer in its core markets. The management team envisioned smart data as the driving force behind this ambition to accelerate decision making, as well as further growth and innovation. The marketing strategy needed a highly sophisticated tool to truly realize such an ambitious goal. For its data-driven daily business, the marketing team needed to integrate over 40 different data sources. To paint a holistic picture of all advertising activities and to analyze their ROI, it was necessary to connect data from all these marketing tools within one system. Combining such data with sales and logistics KPIs was essential for the management team to continue to make the right decisions and lead the company in the right direction.

UPC Switzerland was facing several challenges in their marketing department. The manual collection of performance data from each campaign was time-consuming and prone to errors. There was no standardised approach to budget planning and forecasting, and the company was unable to efficiently connect data from different channels. This resulted in inconsistent and unreliable marketing reports.

iProspect Canada, a performance-focused media agency and part of the dentsu global network, was looking for ways to deliver greater value to clients, based on data. They were employing a lot of manual work on harmonizing data from various data sources, the first challenge was to standardize and automate their reporting processes. This process would include going to each of the platforms, extracting reports and CSV files, and combining them in Excel. This was very tedious and time-consuming, so they wanted to remove this manual segment and move towards a more efficient, automated data environment that would save time, but also let the data speak for itself. With a proper infrastructure in place, they would then need to work out how to get the most valuable insights from the data in the most efficient manner possible.

AnalytixLive, a U.S. data analytics company, was hit hard by the COVID-19 pandemic. The company, which provided revenue management services to the live entertainment industry, saw its business come to a halt as live events were cancelled worldwide. However, as live entertainment shifted to streaming video services and social media, the team saw an opportunity to support their clients with advanced analytics of the engagement data coming from these platforms. The challenge was to seamlessly acquire the new and diverse datasets, which would allow them to successfully pivot their business in this direction.

Digilant, a digital media company, was facing challenges in providing timely reports to its clients. The process was time-consuming and involved manually collecting data from various platforms, importing it into Google Sheets, and then creating client-facing PowerPoint presentations. This process was not only slow but also prone to errors, causing issues both internally and with clients. The team needed a scalable solution that could automate the collection of necessary client data and then visualize and structure it in reports.

Havas Middle East was facing challenges in data automation and client reporting. They developed a client-facing data platform named ADAM (Automated Data Analytics Manager) to resolve these issues. The goal was to create a standard way to pull data from all platforms and across all clients, and display it in a convenient way for clients to transparently see the results of each campaign and how they relate to agreed targets. Initially, reporting was done by exporting CSV files from various platforms. This process was not efficient and had too many steps, which generated regular data errors. They tried to create a data integration tool in-house, but this proved to be inefficient, as it was difficult to keep up with the changes of the various APIs.

Hotspex’s Media Ad-Ops team faced a challenge when it came to collecting campaign performance data from various ad platforms. Most of these were extracted manually, and used for different reports. It was time consuming and impacted the team's ability to focus its attention where it mattered most – identifying insights and optimizing media investment.

ODLO, a sportswear company with a wide retail network and an online store, was struggling with monitoring and analyzing marketing data across different channels and ad platforms. This was time-consuming and difficult, leading to insufficient control over daily marketing spend and difficulty in finding the right marketing mix and efficiently steering the allocated budget. The company's data management difficulties stemmed from the siloed marketing tool landscape, making it tough to collect, process and analyze marketing data. This environment is a mixture of Google Analytics, Google Ads, Facebook Ads, Bing Ads, and other popular platforms, while Microsoft Excel spreadsheets were used to assemble the data manually from all these sources and create reports. Meanwhile, other sectors in the company were building and analyzing transactional reports, drawing information from order management and ERP systems.

Mediahub, a global media planning and buying agency, was facing daily operational difficulties with handling ad performance data for multiple clients coming from multiple advertising platforms. The agency had employed a full-time data engineer to build an in-house solution with API connectors to fetch data from popular ad platforms and store it into the internal database. However, these APIs would often change or update, sometimes overnight and without prior warning, which would break these connectors. The data engineer had to spend a lot of time and effort in fixing them.

Vodafone Italy’s digital marketing team was facing a challenge in linking online prospect activities with lead, sales, and activation data handled offline by many call centers using different CRMs. In a highly competitive market, data reporting and analysis for decision making was often manual and time-consuming. The goal was to unify offline and online data sources and hence extract and analyze actual campaign performances quickly, easily and in a self-service way for various stakeholders.

Business was booming for Cosmonauts & Kings, a political digital agency. The Berlin-based agency was rapidly expanding, but their data infrastructure did not match the pace of growth. Their teams were spending too much time wrangling client data into spreadsheets and creating pivot tables to examine the data and produce reports. They craved a better solution to seamlessly connect and analyze the data from various advertising platforms.

RBFCU had previously implemented an internally-developed core banking system to store and maintain member account information. As the credit union’s membership and product offerings expanded, the back end soon grew to hundreds of tables storing hundreds of gigabytes of data. Because of the legacy design of the system, RBFCU’s ability to develop modern, member-centric applications was hindered and fitting new products and services into the system often took an excessive amount of time. Additionally, RBFCU’s team was tasked with completing many regulatory changes to keep the core system compliant, which further hindered them from focusing on developing productive software applications. Ultimately, a decision was made to convert to a third-party core banking platform from Fiserv. The decision was a logical one; however, credit union leadership soon realized that converting to the new platform would be a daunting task. The sheer enormity of the data to be converted would require dedicated resources to get the job done. This spurred the creation of a core conversion team, which was charged with converting legacy data into the new core banking system, developing conversion logic/business rules, identifying opportunities for data cleanup, and integrating newly selected ancillary software with the core system.

VinSolutions, a subsidiary of AutoTrader Group, was tasked with the challenge of integrating data from hundreds of dealership management systems into their core system. This included critical data such as the list price, along with various other pieces of metadata that determine how the vehicle should be listed. With listings on more than a million cars requiring updates every day, the scale of the challenge for VinSolutions approached big-data territory. The company also had to integrate its software into the dealership management system (DMS), the core software applications that automate car dealerships. This was a significant challenge as each dealership used their DMS in different ways, and VinSolutions had to work with a variety of DMSs, including custom-built ones.

Children’s Community Health Plan (CCHP) is a non-profit Health Maintenance Organization (HMO) affiliated with Children’s Wisconsin. CCHP coordinates health care services for over 140,000 adults and children across 28 counties through the state of Wisconsin’s Department of Health Services BadgerCare Plus (Medicaid) program and Together with CCHP (commercial) plans. It also administers Wisconsin’s Foster Care Medical Home Program (Care4Kids), a Medicaid program that coordinates the health care for youth in Out of Home Care in 6 southeastern Wisconsin counties. The state requires HMOs to have a minimum 95% State Medicaid EDI data submission acceptance and approval rate to avoid financial penalties. For this, CCHP has to send accurate EDI 837 files regarding members’ healthcare utilization comprising of records from TPAs and vendors. By ensuring a high EDI approval rate, CCHP remains Medicaid-certified vendor and enjoys better claim reimbursement and capitation rates. For many years, CCHP struggled to meet the 95% encounter data submission and approval rate for State Medicaid reimbursements because of errors and inconsistencies in the encounters from vendors and TPAs. Some common errors included missing the National Provider Identifier (NPI) on referring providers, invalid state code, and missing NPIs to the service location segment.

The government department had to manually extract and aggregate data from more than 1,000 transactions every day. This prevented the team from having an in-depth view of the medication wholesaler’s purchasing patterns and performing accurate cost analysis to utilize the monthly budget better. Similarly, PBMs sent monthly invoices comprising of prescription and billing information in a spreadsheet. However, these invoices had the same compatibility issue and couldn’t be called in the SQL Server database, making it challenging to aggregate usage data for drugs.

Ciena Corporation, a leading networking products and services provider, was facing challenges with the time-consuming process of manually extracting data from PDF purchase orders and feeding it to their in-house custom application. This manual data entry was laborious and prone to human errors, leading to delays in the sales transactions. The company receives customer requests for its products and services in the form of a Purchase Order via multiple methods, including emails. Order and account managers use these POs to create sales orders and initiate related business processes to complete the customer request. These POs are generally in PDF format and comprise of key information, such as customer name, price, currency, total value, parts, addresses, and more. Since every customer uses a different structure and layout for the PO, Order Managers have to read these files and enter this data manually into their ERP system to initiate a sales order transaction.

Aclaimant serves as a bridge among employees, employers, and insurance carriers to speed up the process of settling claims related to workplace incidents. The company receives claim forms from both businesses and insurance carriers in PDF format. These files are manually transcribed to extract pieces of information that are compiled into a standardized report in CSV and Excel formats. The final report goes into the Aclaimant’s proprietary system for updating records on all fronts, giving all parties a centralized view of the claim’s status, details, and progress. The main challenge for Aclaimant was the manual data entry of facts and figures in claim forms. Since the forms came in PDF format, it took hours to transcribe them and create Excel and CSV reports for loading data into the Aclaimant system and data warehouse for further processing. In addition, the changing claim values and other dimensions required updating the system, which meant manually transcribing and entering the revised information.

The IT contractor company was tasked with managing critical personnel data of government employees and processing it for reporting and analyses. The company had to manually download lengthy PDFs containing personnel information, sift through the entire document to extract key data fields, and pass it through SQL queries before transferring it to the personnel CRM system. This process was time-consuming and prone to errors, leading to bad data quality. It took an IT officer more than three hours to process a single personnel document, leading to delays and risk of bad data for analytics.

Cherry Health, a large Federally Qualified Health Center in Grand Rapids, Michigan, was facing substantial costs in managing data for its comprehensive electronic recordkeeping process. The institution used SQL Server Integration Services (SSIS) for data transfers, but the code-based environment required a specific skill set for performing data management tasks. The process of handling patient-related information was time-intensive and required manual effort. The forms were printed, shipped to a central scanning location, scanned, stored electronically in a PDF format, and then shredded and disposed of. Cherry Health sought a solution that could save its users from performing these steps and result in significant cost savings.

CRC Industries, a global provider of over 1,300 chemical specialty products, was facing challenges in moving its internal and external data assets from legacy systems to the cloud. The company lacked the resources and technical infrastructure to handle large amounts of data and speed up the entire process without compromising on data security and integrity. The company's IT team had performed data migration for companies CRC has acquired in the past, but it was always a complex, time-consuming process. The global manufacturer exchanged data between its different business units via Excel or text files, used a legacy system to analyze them, and performed data quality checks via SQL statements. However, the only way to load data in D3FO was through its built-in templates, which mainly supported the Excel format. This made it difficult to handle, import, and export data to and from D3FO.

Vanderbilt University Office of Investments (OOI) manages the university's $3.2 billion endowment. The funds are typically invested through multiple external money managers who have little or no communication among each other. In the uncertain economy, the OOI wanted to determine consolidated risk exposures of the endowment to better understand the impact of different macroeconomic and market shocks. This was a difficult task that often required manual entry because the investment data was scattered across multiple systems. The risk management team needed an application that could integrate and map data from multiple internal and external sources in a timely and repeatable manner and give accurate reports of the endowment's factor risk exposures.