Cloudera

Marketing to prospects (direct mail, telemarketing, and email) is expensive. Targeting incorrectly can hurt your brand, leaving prospects feeling spammed. Traditional techniques are not very sophisticated, resulting in low response rates which in turn leads to high cost-per-lead/acquisition numbers.

In the data-driven world, marketing technology companies like Numberly are increasingly dependent on their ability to deliver valuable insights from diverse, large datasets to maintain a competitive edge. The greatest return on marketing and advertising spend is achieved by delivering precisely targeted, relevant, and timely messages. To maintain its position as a global leader, Numberly faced the challenge of including, processing, and reconciling large and diverse datasets, such as static data from CRMs and streaming sensor data. They needed to make this data accessible for data science and analytics in near real-time.

The global information technology services company, which provides one of the largest e-commerce platforms in the world, was facing challenges due to the exponential growth in data volume resulting from increased internet transactions. The company needed to improve the relevance of information for discovery and required a semantic search engine to power the search function for all applications on its platform. This was crucial to understand user intent through search context and improve the relevance of results. The company also needed to create a modern architecture framework to enable better searches, replicate data, and perform experimental customization analytics. The challenge was to move data efficiently to allow effective searchability, a task the company was struggling with. The platform also needed to handle increased search traffic and data volumes expected in the future, while adhering to high security and compliance standards and avoiding high costs.