Data Science Solutions

We have the domain expertise in driving strategic decision-making with our data science and AI technologies. Our advance data science accelerators uncover hidden patterns in the data, and provide intelligent and predictive insights. Our deep knowledge of machine learning, natural language processing, advanced statistical, and mathematical decision science helps significantly improve the business bottom line, reduce risk, boost customer satisfaction, and win in a globally competitive marketplace. We take pride in delivering successful experiences in a broad range of industries such as manufacturing, FMCG, telecommunications, banking, hospitality, healthcare, and others.  

Proof of value

Evoke provides leading-edge data science solutions to provide innovative insights, predict growth and revenue opportunities. Our multi-disciplined data scientists can help enterprises create analytic solutions that extract valuable insights, improve understanding of problems, and predict future outcomes.

We have built the right ecosystems and advanced analytics capabilities to help enterprises derive the accurate insights, improve business performance, mitigate risk and enhance growth. To find out how we can help your business with our data science solutions, schedule a free consulting session.

Leverage our expertise for your next data science project.

Evoke Content

Evoke Content combines advanced analytics and text mining capabilities from machine learning and artificial intelligence to turn information into actionable insights.

Evoke Predict+

Leverage Evoke Predict+ to improve profits, explore new opportunities, and revenue streams. We develop predictive models that can accomplish a variety of business intelligence tasks.

Evoke Interactions

Evoke Interactions help you solve and automate a number of business tasks across multiple functional areas. Deploy AI chatbots as virtual agents or assistants to replicate your best customer service agent.

Data Science Case Studies

Forecasting Case Study

Overview

Our client, one of the fortune 100 company wanted to improve their existing BI heuristic forecasting model by utilizing advanced statistical modeling technique. They were keen to forecast their sales for multiple locations, drive improvements in inventory management and decision-making process.

Challenges
  • Multiple seasonality present in the data.
  • Uncovering impact of holidays, seasonal and weather factors.
Solution

We built a forward-looking model focused on weekly/monthly sales forecasting, the following modules were implemented:

  • EDA - Understand data to identify the right techniques to build forecasts.
  • Benchmarking - Create baseline accuracies that the models have to beat.
  • Modeling - Use different time series models.
  • Confidence - Use multiple metrics to understand the model's coverage of the data.
Benefits
  • 95% accuracy in forecasting with a 10% variation from the predictive forecast number.
  • Ability to find the trend and pattern of sales.
  • Capability to plan for production and capacity.
nerd girl

Customer Churn Case Study

Overview

A leading manufacturer wanted to understand customer churn and the drivers that contributed to their business. In addition, the client wanted to build machine learning algorithms to proactively reduce churn. The client wanted a machine learning solution to analyze multiple data sources, understand the impact, and to identify the key reasons behind the churn.

Challenges
  • To define and measure the customer lifetime value during the active stage of a customer relationship.
  • Reduce customer churn rate by knowing the reasons of the customer leaving the company.
Solution
  • Designed a predictive and stable model based on the client’s internal data.
  • Leveraged historical data to model and understand transactions impact on churn.
  • Uncovered early warning signs of customer churn, enabling successful customer retention before they decided to leave the company.
  • Applied best practices in model selection and testing to build an effective churn model to analyze the monthly account portfolio.
  • Modeling: Used a different classification and regression models.
Benefits
  • The solution helped the client identify their high-risk customers and proactively reach out to reduce churn.
  • Helped clients deploy timely retention campaigns.
  • Marketing teams are able to improve marketing strategies to retain customers.

nerd girl

Download Case Studies

Technology Stack

  • ethereum
  • ethereum
  • ethereum
  • ethereum
  • ethereum
  • ethereum
  • ethereum

Contact Us

Leverage our expertise for your next data science project.

Let's Talk