Forecasting Sales
Our consultants forecasted new site sales based on the data and sales of the operating companies. In addition, we built predictive models and used location analytics. Our findings indicate a 72.35% probability that the investments in new companies are getting a high return.
Online Conversation
Our analysts developed an understanding of the relevant online conversation and discovered discussion patterns within the set of conversations. This in-depth “competitive analysis” focused primarily on conversations with patients and caregivers. The data included posts from Twitter, blogs, forums, online news, and open Facebook groups.
Algorithm
A major real estate company hired us to forecast the sales revenue derived from properties. Therefore, our analysts built predictive models to examine the research question and hypothesis. As a result, our findings indicate a 93.82% probability that property sales revenue will improve.
Chargeback Detection
A major technology firm asked us to measure the risk of friendly fraud (aka chargeback fraud). Therefore, we developed an analytical algorithm and data modeling set to predict the customers most likely to request a chargeback. In addition, we provided analytical algorithm software module(s) in Python. The analytic algorithms and data modeling allowed our clients to show the prediction result.
Fraud Detection
A major bank asked us to study fraud indicators. Therefore, we built models using algorithms and machine learning. In addition, we provided more predictive capabilities that could identify and mitigate fraud. As a result, our client could reduce fraud.
Sales
A global manufacturing firm hired us to predict its sales per day. Therefore, we created a module in python that produced a prediction model. As a result, we found that the model provided high accuracy and precision of sales. In addition, the model provided the probability of sales per day.
Customer Churn
Our consultants identified customers at risk of leaving by building predictive models. In addition, we performed churn analyses for the utility industry by analyzing the data, including addresses, gas consumption, products, contract terms, and more. Additionally, we worked on a “Churn Probability” number as accurately as possible. Thus, our findings suggest a 59.93% probability that customers will leave.
Financial Markets
A global investment bank asked us to develop macroeconomics and financial markets outlooks. Therefore, we incorporated these macroeconomics and financial markets outlooks in the tactical asset allocation for its assets. As a result, we could help our clients generate tremendous economic benefits.