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 to help clients truly scale analytics and derive value from their digital investments.
Customer Journey Optimization Analytics
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:
- Hyper-personalized recommenders
- Propensity models at the granularity of individual customer, channel and product
- Omni-channel campaign measurement and analytics
- Customer Lifetime Value models
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
Sales Performance Analytics
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:
- Sales Territory Planning to create optimal allocation plans at the intersection of Sales Account Managers, markets and products
- Goal setting and incentive program design to better align sales performance criteria to key business outcome KPIs
- Sales Playbooks with specific engagement actions that are customized to the client, channel and product context
- Sales exception scenarios with the ability to do a data-driven root cause analysis
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:
- Start with a “no customer service” motto - need for Customer Service indicates broken product/process which need to be fixed at source
- Business processes need to be aligned better to customer needs
- AI and Tech need to be at the heart of everything
Incedo helps clients build world class customer service through 5 key initiatives:
- Voice of Customer - Use NLP + AI to extract insights from unstructured VOC data. Drive priorities from VOC
- Fix at Source - Use Predictive Models to prevent occurrences and Drive cross - functional approach to fix root causes
- Personalize Channel mix - Use micro-segments and propensity models to design channel mix. Build Resolution Rate guardrails
- Automate Service - Build out Self-Serve and Machine-Serve as predominant channel. Design chatbots from customer’s point of view
- Deploy Flexible Cloud based architecture - Build micro services based architecture to meet evolving customer needs
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.
Enterprise Model Performance Monitoring (eMPM) Framework
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:
- Business Definition: Understand the business context and KPIs to be impacted, and sharply define the business problem to be solved
- Diagnostic & Hypothesis Setting: Develop hypotheses on root causes & key levers for digital transformation through internal data analysis and external benchmarking
- Customer and Operational Flow Analysis: Deep-dive into customer and operational process flows, to identify detailed levers for each business use case
- Digital & Analytics Solutioning: Develop digital & analytics solution approach evaluating different options, impact v/s cost tradeoffs and deliver final recommendations
- Agile Implementation: Iteratively, deliver on end-to-end analytics platform implementation including AI/ML engine, data platform and Tech/Process integration
- Change Management: Re-design business processes, help change organizational structure and build new teams for successful execution and adoption
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.
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