IKEA, a global home furnishing chain, was grappling with an outdated and siloed data process. The company was manually managing large amounts of data from various sources, services, and agencies, which was labor-intensive and inefficient. This data included not only the usual localized website and sales data but also crucial customer data from the IKEA Family Loyalty program. The data team aimed to disseminate this data across the organization, allowing other departments such as finance and e-commerce to gain access. However, the existing process made it challenging to turn this vision into reality. The company needed a solution that would establish data and facts as the foundation to accelerate and enhance IKEA’s overall growth.

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.

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.

IKEA Austria was facing challenges in consolidating data from various sources, which was crucial for understanding the needs of its customers and preparing for future growth. The company was dealing with multiple data sources and using the services of several agencies, which led to data silos and unavailability of data. Data quality was also a significant issue, with different KPIs and naming conventions used for campaigns on different channels, making reporting on campaign performance extremely difficult. The company was also facing challenges in terms of data accessibility, with global teams at IKEA having to wait for days, even weeks, for the information they needed.

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.

Carter’s, a leading children’s clothing brand in America, was facing a significant challenge in managing and analyzing vast amounts of performance marketing data from various sources, including Google SA 360 and Facebook. The team was overwhelmed with the manual task of collecting data using spreadsheets, a process that was not only laborious but also considered low-value. The need for a more efficient approach to manage and process data was evident, as the team sought to understand customer interactions with their campaigns and improve future performance. The goal was to examine specific data points to gain insights into potential and existing customers' behavior.

The Philadelphia 76ers, a top NBA franchise under Harris Blitzer Sports & Entertainment (HBSE), faced a significant challenge in managing their complex data ecosystems. These ecosystems included online advertising platforms, ticketing systems, and audience engagement data. The data team was tasked with managing and aggregating 130 diverse data sources into an Excel spreadsheet, a process that was chaotic, overwhelming, and time-consuming. It took the team 10 hours a week just to extract the data and up to a few weeks to connect to a new data source. The urgency of this task was further amplified by the fixed timelines of the events-driven industry, necessitating the ability to pull and manage data quickly.

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.

Dassault Systèmes, a global software company, relied heavily on LinkedIn lead generation forms (LGF) to acquire new customers. They ran high volumes of campaigns from over 20 different markets. The challenge was to convert this campaign information into usable datasets for the sales team, which involved manually collecting and transforming the data. Each dataset had to be downloaded, formatted with tracking, and imported into Adobe Campaign, a process that took more than 10 days to complete. This time-consuming process hindered the company's ability to move quickly. Additionally, the lead forms contained personally identifiable information (PII), which required prioritizing customer security, adding to the complexity of handling the data. With such demanding data complexities, Dassault Systèmes needed a faster and more efficient solution to save time and produce usable, accurate data.

Digital Matter, a digital marketing agency, was grappling with the challenge of managing multiple cross-channel campaigns on various marketing platforms for its clients. The team heavily relied on data to set benchmarks, budgets, and ROI, but the process of gathering and analyzing this data was chaotic and manual, lacking a cohesive system. The consultants at Digital Matter were spending a significant amount of time collecting data and creating static reports, many of which were outdated by the time of the next extraction. The manual process also increased the risk of human error, which further fueled uncertainty in their ability to derive accurate, numbers-driven insights.

Fashionette, a leading retailer specializing in designer handbags for German-speaking markets, was facing a significant challenge in managing and integrating data from over 40 sources. The company's data integration tools were not keeping pace with the rapid business growth. The process of logging into various tools, downloading reports, and manually copying numbers into sheets was tedious and time-consuming. The company sought a solution that would integrate all the data into a single, sophisticated platform that would serve departments across the organization. The goal was to gain a comprehensive view of their entire advertising activities, enabling them to analyze and strategically increase ROI, ensuring the company's continued growth and success.

Havas Middle East, a global agency, had developed a client-facing data platform called Automated Data Analytics Manager (ADAM). ADAM was designed to handle client onboarding, target setting, and reporting. However, the platform lacked advanced tools to automate updated reports across various ad platforms. The team had to manually export CSV files to create reports, a process that was slow, stressful, and prone to errors. They also found it challenging to keep up with the constant changes from various APIs. These issues prompted the team to search for a more advanced platform that could improve data integration and analysis, enabling them to serve clients more quickly and strategically.

Infobip, a global leader in omnichannel communication, was struggling with manual data collection processes. The growth marketing team was spending valuable time gathering information for lead acquisition and conversion reporting from Google Analytics and various ad platforms such as Facebook, LinkedIn, and Google. As a global company, Infobip’s marketing analytics required two advertising accounts with Facebook in different currencies, making it difficult to gain a holistic view of their advertising spend. The lack of a data platform that could automatically pull the data and provide reports led the team to rely on outdated spreadsheets to merge the aggregated data. This process required the support of Infobip’s business intelligence (BI) data engineers, who invested 10 to 15 hours a month just to consolidate and turn the information into usable datasets. The company needed a data integration solution that would eliminate such manual efforts, that could collect data from website analytics and ad platforms and push it to the company-wide BI tool Qlik Sense.

Mediahub, a leading global media planning and buying agency, was grappling with the stress of creating data reports and analyzing performance for its global clients. The agency's growing data was scattered across various advertising platforms, making it difficult to manually keep up with API changes that required constant monitoring and updating. These API connectors often broke, causing delays and requiring data engineers to spend time and effort fixing them. Despite having a full-time engineer to develop an in-house solution, Mediahub needed to automate the data acquisition and management process to alleviate the frustration and inefficiencies.

ODLO, a global retailer specializing in sports clothing, was facing a significant challenge in tracking and analyzing the performance of their marketing campaigns across various online channels and international retail stores. The team was manually collecting data from disparate marketing tools, a process that was not only time-consuming but also prone to errors. The lack of a consolidated data solution made it difficult to clean, prepare, and report the data, thereby weakening the team's ability to accurately predict return on investment (ROI). The absence of a unified platform for data management was hindering their operational efficiency and decision-making capabilities.

Seven.One Entertainment Group, a German-based innovator in the ad tech industry, was managing over 100 marketing campaigns involving siloed sources of data, including TV, Google and Bing search ads, print, and social channels. The team was manually gathering and breaking down the information using spreadsheets and PDF files, a process that was not only time-consuming but also made it difficult to examine results quickly. Reports were only issued once a week rather than daily, and the company lacked an accurate, overall view across online and offline campaign strategies, leading to uncertainty in decision-making.

Superbet, a leading sports betting and gaming operator, was facing challenges in measuring the effectiveness of their marketing efforts due to the lack of a dedicated marketing data team. They were using various platforms for their operations, but tracking and updating across these platforms was proving to be time-consuming and at times, impossible due to limited resources. The company operates across multiple geographies, with localized campaigns in place across various channels. Therefore, being able to track, analyze, and improve their marketing programs was crucial for their continued growth. The challenge was to find a solution that could streamline their process, improve the speed and accuracy of their marketing data and analysis, and save time.

UPC Switzerland, a dominant player in the telecommunications sector, was struggling with the collection and understanding of large amounts of data from various channels and platforms. The team lacked an automated platform to manage the information, resulting in a tedious process of copying and pasting sales and marketing performance data into individual spreadsheets. The company wanted to issue accurate, updated reports to understand campaign performance rather than spend time and resources on manual data processes. The challenge was to find a solution that could automate and streamline the data management process, enabling the team to focus on analyzing the data and making informed decisions.

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.

Colgate-Palmolive's Online Acceleration Center of Excellence in London was focused on transforming its digital offering in markets throughout the EU. However, reporting on key digital advertising parameters was largely manual. Local media teams logged into various ad platforms, collected data, and inserted them into Excel templates to create bi-weekly reports. These reports were then sent to brand managers in respective markets and the local agency teams, with no automated flow of information. The process was slow and time-consuming, involving over 25 people in the EU region. Furthermore, the data was practically out of date by the time it arrived with the analysts and decision makers. The reports were distributed via email, clogging up inboxes and increasing the possibility of a data discrepancy.

The Philadelphia 76ers, a part of Harris Blitzer Sports & Entertainment’s (HBSE) portfolio, had a data ecosystem consisting of over 130 data sources. Managing and expanding this data infrastructure was becoming increasingly difficult and time-consuming. The process of connecting to a new data source could take weeks, severely limiting the bandwidth of the data team. The fixed timelines surrounding live events made flexibility and agility of data operations paramount. The existing data consolidation procedure was not scalable and was preventing the team from fully focusing on uncovering the insights the data could provide.

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.

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.