Steffen Quistgaard, a Senior Marketing Specialist at re:member, a trademark of Entercard, one of Scandinavia’s leading credit market companies, noticed an unusual increase in users bouncing off re:member’s credit card application form. Despite having traditional analytics tools like Google Analytics and their custom data warehouse, these tools were not providing a complete picture of the user journey. The main challenge was that these tools could not visually show what went wrong. The issue was particularly noticeable with traffic arriving from affiliates, which are sites that compare different types of credit cards. Despite being a significant traffic source for re:member, the high bounce rate from these users was a concern. Google Analytics could show what was happening on re:member’s application form, but it couldn’t explain why affiliate traffic was bouncing.

Intertop, a well-established shoe retailer in Ukraine with 114 stores in 25 cities and an online operation that attracts 3.5 million monthly visits, was facing challenges with its ecommerce model. Despite its high growth and rapidly increasing traffic, the company was struggling to simplify the customer journey on its website, increase conversion rates, speed up the introduction of user experience changes, and mitigate risks of damaging the site experience. To address these issues, Intertop engaged CRO and UX agency Turum-burum. The agency initiated their Conversion Rate Optimization program based on step-by-step interface enhancements, a model they call ESR: Evolutionary Site Redesign. The goal was to anchor all proposed website changes on analytical proof and confirm them through A/B testing, a proven way of minimizing risk of rolling out changes that impact the shopping experience.

Vimcar, Germany’s leading fleet management software, was experiencing rapid growth and needed to rebrand their regional websites for Germany and the UK to keep up with their expanding product and feature list. However, they were concerned that the changes might disrupt their funnels, leading to a temporary reduction in page views, leads, and sales. The site’s heavy focus on product and features was suspected to be making it harder for users to book a demo and get started. The team wanted to make the experience more customer-centric but needed hard data to identify what elements to change. The slow pace of traffic from its B2B customers made A/B testing a slow process, taking up to three months to gather enough data to act. They needed solutions in place right away.

Gavin, the founder of Yatter, a lead generation agency, faced several challenges in improving the conversion rates of various websites. One of the main issues was with his personal site, myfunnelacademy.com, where after switching from Clickfunnels to WordPress for more flexibility, the conversions dropped significantly. Another challenge was with an ecommerce store for car parts. Despite the Facebook ads for the store generating a 5x ROI, Gavin suspected the landing page was not fully optimized. He also worked on a stem cell therapy website, where despite having high-value visitors, the conversion rate was low. Lastly, he faced a challenge with a personal training and nutrition planning website, faultlessfitness.com, where too many options were causing choice paralysis.

Credit is a vital component of any financial system. However, for banks and other financial institutions, making credit available to those who need it can be a complex process. The credit scoring models used by financial companies often fail to reflect the actual financial behavior of individuals. In France, banks and lenders often base their lending decisions on outdated socioeconomic factors, such as marital status, which may not accurately represent the financial stability of a customer. Algoan, a fintech startup, aimed to make credit scores more transparent and representative of the applicants’ actual financial behavior rather than demographic signifiers. Launched in 2018, Algoan sought to provide financial institutions with a digital platform designed to improve their lending decisions by making them faster and more relevant to the circumstances of each customer. However, to achieve this, Algoan needed an infrastructure that balanced power, stability, and security.

AppsFlyer, a leading mobile attribution and marketing analytics platform, was faced with the challenge of providing real-time access to raw datasets for over 100,000 users. The platform, which processes more than 80 billion events daily, was experiencing a 10% monthly growth in traffic. This rapid growth necessitated a service that could scale to match the exceptional demand. Additionally, AppsFlyer's internal teams required access to the raw data for analytics to inform various aspects such as product performance and research and development. The company needed a data warehouse that could scale quickly while providing real-time access to data with high availability.

Apxor, a company that helps mobile app companies grow and retain subscribers, was facing challenges with its existing cloud provider. The company needed to process 1.5 billion data points daily to help its clients develop personalized user experiences. However, the infrastructure of their previous cloud provider was causing deployment issues. Additionally, the company was struggling with managing its infrastructure, which was taking away valuable time from its DevOps team that could have been used to focus on new product road maps. Apxor also needed to improve app discoverability and user retention for its clients, as users typically decide within 30 to 60 seconds whether they want to keep an app.

Ascend Money, one of South East Asia's largest fintech businesses, was facing challenges as it expanded its customer base and ventured into new markets. The company initially operated using physical infrastructure and on-premises workforce productivity applications. However, as the business grew, its technology leaders began to explore options to improve value for money, reduce the maintenance load on in-house team members, enhance data management and analysis, and foster more effective collaboration. The company was also looking for ways to reduce its infrastructure spending and become more efficient and cost-effective. Ascend Money needed a solution that would allow it to optimize its operations, lower costs, and improve performance.

The Athena Breast Health Network, a collaboration of breast cancer experts, healthcare providers, researchers, and patient advocates, was conducting the WISDOM Study, a clinical trial comparing personalized breast cancer screening to standard annual screening in 100,000 women. The study aimed to determine whether personalized screening that incorporates individual risk assessments is as safe and effective as the standard approach. However, the team faced a challenge in securely and conveniently providing access to mammograms and other breast health information. Additionally, they needed to improve study recruitment and retention, as the process of requesting records by phone or mail was inconvenient for participants.

The Institutes RiskStream Collaborative™, a large enterprise-level blockchain consortium in the risk management and insurance industry, faced a challenge with the carrier to carrier data sharing of a first notice of loss (FNOL). This process was resource-intensive and time-consuming, leading to increased claims cycle time and handling costs. The consortium needed a solution that would facilitate early and accurate notice of loss data exchange between the relevant carriers’ claims systems. The solution had to be intuitive, easily blend into existing business processes and environments, and run on a platform that could be set up quickly and easily updated. It also needed to allow users to create, update, and match loss records with another network participant in a permissioned manner, privately sharing information and reducing data reconciliation issues between disparate data systems.

The IT technical champion at a global manufacturing company was aware of the organization's security vulnerabilities despite having no existing compliance issues. The team was limited by budget constraints, only able to afford one penetration test per year. This was a significant challenge as the company's attack surface was expanding due to their growing IoT footprint. The organization needed a solution that could identify and address these vulnerabilities effectively and efficiently, without requiring significant resources or disrupting their operations.

A teaching hospital, despite having a diligent IT team that tracked security updates and promptly patched critical issues using industry-leading tools, found itself in a precarious situation. The team was confident that they had patched a critical vulnerability, known as ZeroLogon, months earlier. They even had reports from Qualys and Microsoft DISM, both industry-leading tools, to back up their claim. However, when NodeZero exploited this supposedly patched vulnerability in under a day on several of their Active Directory domain controllers, the IT team insisted it was a false positive. NodeZero, on the other hand, had evidence of a detailed attack chain showing each step taken to get credentials, escalate privileges, and gain administrative rights to Active Directory. This discrepancy led to the hospital reapplying the patch and repeating the NodeZero autonomous pen test.

The national homebuilder, with a large and mobile IT estate, was a potential target for cybercriminals due to its dispersed workforce and heavy reliance on cloud services. The company was not consistently capturing, analyzing, and correlating security logs, leaving it vulnerable to attacks without any visibility. There were also concerns about the company's compliance with GDPR and PCI DSS requirements. The company needed a security capability that would enable it to monitor and protect important data and assets round the clock. However, with a small team, the company lacked the resources to build this capability in-house and needed a security partner to provide support and expertise.

A private healthcare organisation in the UK, which processes large volumes of sensitive patient data, was targeted by a sophisticated type of malware. The malware aimed to harvest employee credentials and exfiltrate data. The organisation was already using Redscan’s Managed Endpoint Detection and Response service to protect its data beyond the level of security offered by traditional perimeter solutions. However, the malware attack posed a significant threat to the organisation's operations and the security of patient details. The challenge was to quickly identify, investigate, and respond to the attack to minimise operational disruption and prevent patient details from being stolen.

Kettering Health, a healthcare network supporting 13 medical centers, over 120 outpatient locations, and more than 30,000 users, was in the process of implementing layered defenses and aligning with NIST security controls when an aggressive rollout of the EPIC Electronic Health Records system fully consumed all IT resources. This resulted in a stall in cyber hygiene activities, leading to configuration drift. The situation was further exacerbated by the COVID-19 pandemic, which forced the IT staff to pivot from routine maintenance to address new security challenges presented by a mobile and remote workforce of first responders. The complexity of the situation, coupled with scarce resources, made it difficult to reestablish cyber hygiene. Limited visibility into the status of security tools, time-consuming manual analysis, and an overwhelmed IT network team further complicated the situation.

IMVU, the world’s largest avatar-based social network, was facing challenges with its aging on-premise data platform. The company wanted to enhance and re-architect their platform to support advanced analytics and Machine Learning use cases. However, with an exponentially growing data volume and a monolithic Hadoop architecture, the IMVU team was struggling to efficiently utilize user-generated data. The existing infrastructure limited innovation and capacity for advanced analytics. IMVU’s analysts lacked the tools to rapidly generate business-critical reports on customer in-game behavior at scale. They were working with historical data in batches, which resulted in late reports, inaccurate assumptions about customer in-game purchases, slower sales, and loss of profit. The analytics team also lacked a test environment to efficiently check analytics assumptions. The platform was powered by a 90-node on-premise Hadoop cluster, which was not cost-efficient and resulted in high costs and low efficiency.

InMarket, an omnichannel marketing platform, was grappling with an inefficient legacy data platform that was unable to handle the growing volume of real-time location data collected from multiple sources. The platform, built using 50 AWS nodes and 400 bare metal nodes managed by Apache Mesos, was processing over 5 billion events daily. However, it was plagued with delays, bottlenecks, and inefficiencies. The platform's job success rate was a mere 40%, with 60% of Apache Spark jobs being randomly aborted in the system. This led to developmental delays, inaccurate timeline projections, and a significant reduction in InMarket's ability to attract marquee brands, thereby slowing down revenue growth. The time taken to hand off a data pipeline from data scientists to data engineers and then to operations for deployment in production was estimated to be up to twelve months, which was unacceptable given InMarket's business model.

Nitrio, an AI company specializing in sales optimization, was facing significant challenges with its Natural Language Processing (NLP) platform. The platform relied heavily on manual rules and heuristics-based models, which led to bottlenecks and scalability issues, hindering Nitrio's growth. The existing platform was unable to ensure the required level of accuracy for sentiment analysis of rep-to-lead messages, resulting in a significant number of messages being outsourced to a third party for manual analysis. This not only increased service costs but also created further bottlenecks and scalability issues. The platform's infrastructure demonstrated tight coupling between services, increasing their dependencies and negatively impacting team performance, causing data quality and consistency issues. Nitrio's platform was designed to efficiently analyze inbound rep-to-lead messages to extract their intent and collect useful data about every sales representative's performance. However, the reliance on manual processes and the inability to ensure 95% certainty in message intent identification were major setbacks.