Our clients are people and organizations with ambitious missions. They want to unleash the power of data for their cause or business. We help them envisage, model and deliver on their mission.
Our goal is to set realistic expectations with our clients, and then not to just meet them, but to exceed them — preferably in unexpected and helpful ways. We strive to make data and science simple to understand and easy to act on, and we stand side-by-side with our ambitious clients to make it happen. Here are some recent examples.
In this whitepaper on data warehousing, while we talk about the best practices, we also take a look at the most common pitfalls that enterprises usually realise, later on in their data warehousing implementation journey. Read and stay ahead of the curve with Prescience.
Product Notes and Whitepapers
Vida for Financial Services, an advanced data analytics based solution by Prescience, helps organizations to bring their data to life. It helps users to search and discover relevant data about companies and sectors from various public and private sources for analysis. The solution uses advanced technologies to process large, heterogeneous and unstructured data sets at an extremely fast rate.
Currently, decision makers struggle with limited information while onboarding partners or setting up a new store. Vida, an advanced data analytics based solution by Prescience, leverages machine learning and artificial intelligence to understand the key factors that influence the performance of channel partners.
The company runs approximately 150 promotions a year. There was a pressing need to leverage the “active” seller segment to improve net revenue. Various modelling techniques were used to calculate the propensity score
Prescience offers an AI-based solution that helps retailer to connect with customers better and give them a delightful shopping experience.
The client runs a loyalty program for its UK customers with the help of a third party loyalty program provider. The key challenge that the client faced while executing the loyalty program was to capture the incremental sales and revenue the program was bringing to the business. We found out a propensity score for each user using machine learning.