Fiber AI
Fiber AI’s Challenges and Needs
Fiber AI is a pioneering SaaS company that offers AI-powered outbound marketing automation to its customers. One of their key challenges is providing easily accessible and expandable analytics and insights on their automated campaigns to empower their customers to better understand their campaign success metrics, and launch better campaigns. To maximize the efficiency and effectiveness of Fiber AI's product, they needed a scalable solution to deliver these insights. They required a solution that could:
Efficiently analyze large amounts of marketing campaign data for each customer.
Provide actionable insights for automated marketing strategies.
Offer customizable analytics insights tailored specifically for each customer.
The Solution
Fiber AI's customers need to understand the performance of their automated marketing campaigns in order to determine where to focus their marketing efforts. To achieve this, Fiber AI used Upsolve to aggregate marketing and campaign data from various sources for analysis. They then utilized Upsolve to build an analytics dashboard that displays key campaign performance metrics and trends, which was then natively embedded in their SaaS platform. Since each of Fiber AI's customers is unique, they needed to provide customizable analytics insights for each customer. Normally, this would require significant engineering effort, but Upsolve was designed to dynamically adapt its analytics to each customer’s data and business requirements. With Upsolve, Fiber AI was able to automatically customize the end-user facing analytics for each of their customers.
“Upsolve takes away the analytics burden from our development team, enabling us to laser-focus on delivering Fiber’s core offerings. Tasks that would take months are now accomplished at lightning speed.” - Aditya Agashe, Co-Founder & CEO, Fiber AI
Upsolve's Impact - Significant time and resource reduction
By using Upsolve, Fiber AI eliminated the need to build an in-house solution from scratch. They were able to reduce the timeline for delivering customer-facing analytics by two months. Not only were they able to build analytics within a short timeframe, but they also gave their engineering team and end-users the superpower to customize these analytics to suit their specific needs.