Data Science and Analytics
Next Generation AI/ML to power customer experience and operational efficiencies
Next Generation AI/ML to power customer experience and operational efficiencies
In today's world, chances are that you already are on the digital transformation journey. Even as digital investments continue, most CXOs still feel that the decision support engines are far from running at the speed and effectiveness that the best-in-class digital natives have been able to demonstrate.
There is clearly a need to redefine how organizations need to think of Analytics: hyper-personalization is required to attract and retain customers; AI/ML needs to be engineered to scale; rapid experimentation needs to become the de-facto approach for making product and service design choices.
Incedo brings a deep understanding of data science with data science consulting to help clients truly scale analytics and derive value from their digital investments.
We partner with clients to drive customer success throughout the customer lifecycle: from acquisition to growth and retention. At every stage, we leverage the Incedo CX Journey Optimization Platform to develop advanced ML models for driving the Next Best Action (NBA) recommendations with the goal of increased customer engagement and reduced drop-offs across engagement channels at every stage in the CX lifecycle.
Some key capabilities include:
We helped a mid-sized bank improve their digital channel mix from 1% to over 10%, by enabling personalized cross-sell/up-sell recommendations for retail banking products and helped a large Telco improve their campaign conversion rates for their B2B customers from 2% to over 6%
We partner with clients to develop insights on the needs, beliefs and motivations that drive customer behavior and decisions through their purchase journey. We use a mix of qualitative and quantitative approaches to help clients develop a 360-degree view of their customers including generating micro-segments and razor-sharp propensity models. We also help optimize marketing spend using advanced marketing mix modeling.
For a large Telecom service provider, we built micro-segments and advanced propensity models for their enterprise business. This formed the basis for personalizing their digital experiences and led to a 3-5x improvement in conversion metrics
Incedo works with clients to develop Sales Performance Analytics that helps the sales functions execute a data-driven approach across the entire sales lifecycle. We work closely with Sales Planning and Operations teams to deliver a comprehensive set of solutions. Some of the capabilities include all the way from descriptive to predictive work:
Our BI and Analytics teams delivered a 5% lift in sales of two key products for a leading LifeSciences company by deploying targeted sales playbooks with a combination of descriptive and predictive analytics.
We use advanced analytics and problem solving techniques to enhance performance of core operations for our clients including supply chain operations, network planning, customer service operations, etc.
Incedo's supply chain analytics capabilities focus on solving key problems in this space including demand/supply forecasting, inventory planning, logistics optimization, etc. using AI/ML and Technology approaches pioneered by e-commerce companies
Incedo specializes in helping organizations build next generation customer service by leveraging some of the key learnings from digital natives who approach customer service very differently. In particular:
Incedo helps clients build world class customer service through 5 key initiatives:
We helped in service assurance by building predictive network device maintenance models for a Telco’s wifi services.
We developed a demand sensing solution for a large Life Sciences major which consisted an ML based forecasting engine and a what-if scenario planner. This enabled better planning for channel inventories and contributing to maintaining high service levels.
We specialize in two areas of Risk Analytics - Credit Risk Modeling & Analytics and Operational Risk & Fraud Analytics. Drawing upon our experience from fintech and e-commerce companies, we have developed an Enterprise Model Performance Monitoring (eMPM) framework that leverages a portfolio of ML models that help implement an ROI based risk management approach to effectively balance customer experience and risk minimization.
With critical business decisions driven by AI/ML models, ongoing monitoring of models is critical to proactively evaluate whether internal factors (e.g. product changes) and external factors (e.g. changes in regulatory frameworks) call for the redevelopment or adjustment of models. Our eMPM tool lets you test and compare analytical models, generate necessary benchmarks and take decisions on adjustments, redevelopment or replacement.
We implemented an enterprise model monitoring framework for a banking client, covering marketing, risk and operations analytics models to monitor and proactively flag model risk along multiple dimensions. The framework has been adopted as a core audit component by the Bank's enterprise risk team.
We focus on delivering business impact for our clients on the most relevant problems for them. To do so, we start with the business context and work all the way to agile implementation of solutions. To ensure highest level of ownership and delivery quality, we typically follow the process outlined below:
We work across the Data Platform to AI/ML spectrum with our clients to solve business problems. We have deep expertise in data engineering, advanced BI, advanced analytics and AI/ML modeling & full life-cycle management. We deliver end to end problem resolution all the way from consulting to AI/ML model development including data engineering and model deployment into the existing tech stack of clients.
Register for a 30 minutes no-obligation Digital Journey assessment session.
At the end of this 30 min session, walk out with: