AI and Data - Incedo Inc.

HCP engagement in the new era

The engagement strategies for pharma representatives to connect with HCPs (Health Care Professionals) were already in a state of transformation. Covid-19 has only accelerated this process. With fewer HCPs now preferring in-person meetings and with the advent of new technologies, there has been a steady rise in the use of various digital channels like email, social, virtual connects etc. In the evolving landscape of medical engagement, a mix of approaches have emerged. The pandemic brought a big increase in using online and digital methods, especially video calls and emails. According to a 2022 research, before the pandemic, these methods made up only 3% and 14% of medical contacts with HCPs, but in 2022, they make up up 22% (7-fold increase) and 25%, respectively. All of these developments have compelled the drug companies to reimagine their engagement strategies, maintain a healthy relationship with the HCPs and use the right channels for making the required impact on the HCPs.

Personalization – The need of the hour:

With different technologies at their disposal and HCP preferences differing, pharma companies have realized that they have to change their marketing and engagement tactics to meet the engagement needs of each doctor. Each HCP’s expectation from the interaction is different and the education required for each of them is largely dictated by the patient cohort the HCPs serve. The same research highlights that a significant 61% of physicians pinpoint greater personalization as the key factor that sets apart and enhances the value of medical engagement. The interest points of HCPs differ and their response to channels differs. So does the response of HCPs to various incentives offered by pharma companies. For example, in one of the recent analyses executed using the Incedo Lighthouse^TM Platform, we found that the Pediatricians (PD) respond to nutritional rebates much more than their Non-PD counterparts.

Personalization, and sometimes hyper-personalization, therefore is the central theme of customer engagement across domains. HCPs now prefer to be connected to a digital platform of their choice e.g. mobile, email, social, call activity, etc. This behavior may differ across various HCP segments e.g. across therapeutic areas, affiliation, years of experience, and geography, apart from the patient cohorts they serve.

With response data now available to the drug companies, it is possible to derive insights on the HCP preference by various cuts such as segment, sub-segment, geography, etc. The HCP preference and behavior patterns shed light on how receptive they are to digital engagement. This data analysis is leveraged by organizations to analyze the context and content for a digital interaction to derive Next Best Action by answering critical questions related to the message and channel strategies.

By harnessing the Machine Learning capabilities of the Incedo Lighthouse^TM platform alongside the AWS Personalize solution, the process of automatically categorizing users based on their preferences in various HCP segments becomes seamlessly achievable. This intelligent user segmentation not only enhances engagement with marketing campaigns but also boosts retention through personalized messaging. Ultimately, such precision in targeting leads to an improved return on investment for your marketing expenditures helping pharmaceutical companies understand High Impact Channels using Incedo Lighthouse^TM with the power of cloud capabilities.

In one of the recent deployments of Incedo Lighthouse^TM at a pharmaceutical company, the client wanted to understand the most impactful channels for engaging with the HCPs. This was also driven by the CMO agenda to understand the profitable marketing channels. Understanding the segments and sub-segments of HCPs through the segmentation models powered by the Incedo Lighthouse^TM platform, analyses were done for different therapeutic areas. The marketing investment was translated into input variables specific to the channel and the impact was measured on the overall sales leveraging the regression models built and operationalized in the AWS Sagemaker integrated with Incedo Lighthouse^TM platform. The insights from the models developed on the cloud were used to derive the contribution of different channels on both the baseline and promotional sales. Using this, the ROI of various channels was determined. In this case, at a broad level, every marketing dollar spent gave an extra 40 cents as a return. Also, it was understood that among all the channels, digital channels were underinvested and the HCPs responded best to them.

The Incedo Lighthouse^TM platform helps you better connect with HCPs using tools like the KPI Tree and Cohort Analyzer. You can figure out which HCPs did not get enough attention but responded well, and which ones got too much attention without responding. By digging deeper into affiliations and hospitals, you get practical insights to create specific and effective strategies for better engagement with HCPs. What sets these models apart is their development, training, validation, and deployment via AWS Sagemaker, a cutting-edge cloud platform that amplifies the power of machine learning. This cloud integration not only ensures the robustness of the analytical tools but also underscores the commitment to harnessing advanced technology for optimizing HCP engagement strategies.

Incedo Lighthouse^TM goes a step further with advanced visualization capabilities by leveraging the cloud, allowing the generation of response curves for each channel and providing additional drill-down options. These features are crucial for simulating the performance of HCP cohorts and channels, helping to identify the break-even dollar spend. The integration of Amazon QuickSight into the platform transforms complex data into easily understandable visual insights, contributing to better decision-making and boosting operational efficiency in the pharmaceutical industry. By applying optimization algorithms from Incedo Lighthouse^TM pre-built accelerators and leveraging Amazon QuickSight, organizations can craft a channel strategy that optimizes HCP engagement across different mediums while minimizing investment.

Faster and better decisions with AI-driven self-serve Insights

Incedo Lighthouse^TM with Self-Serve AI is a cloud-based solution that is creating a significant business impact in Commercial Effectiveness for clients in the pharmaceutical industry. Self-serve entails empowering them with actionable intelligence to serve their business needs by leveraging the low-code AI paradigm. This reduces dependency on data scientists and engineers and makes faster iterations of actionable decisions and monitoring their outcomes by business users.

As internal and external enterprise data continues to grow in size, frequency, and variety, the classical challenges such as sharing information across business units, lack of a single source of truth, accountability, and quality issues (missing data, stale data, etc.) increase.

For IT teams owning diverse data sources, it becomes an added workload to ensure the provisioning of the enterprise-scale data in requisite format, quality, and frequency. This also impedes meeting the ever-growing analytics needs of various BU teams, each having its own request as a priority. Think of the several dashboards floating in the organizations created at the behest of various BU teams, and even if with great effort they are kept updated, it is still tough to extract the insights that will help take direct actions to address critical issues and measure their impact on the ground. Different teams have different interaction patterns, workflows, and unique output requirements – making the job of IT to provide canned solutions in a dynamic business environment very hard.

Self-service intelligence is therefore imperative for organizations to enable business users to make their critical decisions faster every day leveraging the true power of data.

Enablers of self-service AI platform – Incedo Lighthouse^TM

Our AWS cloud-native platform Incedo Lighthouse^TM, a next-generation, AI-powered Decision Automation platform t arms business executives and decision-makers with actionable insights generation and their assimilation in daily workflows. It is developed as a cloud-native solution leveraging several services and tools from AWS that make the journey of executive decision-making highly efficient at scale. Key features of the platform include:

Customized workflow for each user role: Incedo Lighthouse^TM is able to cater to different needs of enterprise users based on their role, and address their specific needs:
- Business Analysts: Define the KPIs as business logic from the raw data, and define the inherent relationships present within various KPIs as a tree structure for identifying interconnected issues at a granular level.
- Data Scientists: Develop, train, test, implement, monitor, and retrain the ML models specific to the enterprise use cases on the platform in an end-to-end model management
- Data Engineers: Identify the data quality issues and define remediation , feature extraction, and serving using online analytical processing as a connected process on the platform
- Business Executives: Consume the actionable insights (anomalies, root causes) auto-generated by the platform, define action recommendations, test the actions via controlled experiments, and push confirmed actions into implementation
Autonomous data and model pipelines: One of the common pain points of the business users is the slow speed of data to insight delivery and share action recommendation, which may take even weeks at times for simple questions asked by a CXO. To address this, the process of insights generation from raw big data and then onto the action recommendation via controlled experimentation has been made autonomous in Incedo Lighthouse^TM using combined data and model pipelines that are configurable in the hands of the business users.
Integrable with external systems: Incedo Lighthouse^TM can be easily integrated with multiple Systems of Record (e.g. various DBs and cloud sources) and Systems of Execution (e.g. SFDC), based on client data source mapping.
Functional UX: The design of Incedo Lighthouse^TM is intuitive and easy to use. The workflows are structured and designed in a way that makes it commonsensical for users to click and navigate to the right features to supply inputs (e.g. drafting a KPI tree, publishing the trees, training the models, etc.) and consume the outputs (e.g. anomalies, customer cohorts, experimentation results, etc.). Visualization platforms such as Tableau and PowerBI are natively integrated with Incedo Lighthouse^TM thereby making it a one-stop shop for insights and actions.

Incedo Lighthouse^TM in Action: Pharmaceutical CRO use case:

In a recent deployment of Incedo Lighthouse^TM, the key users were the Commercial and Business Development team of a leading Pharma CRO. The company had drug manufacturers as its customers. Their pain point revolved around the low conversion rates leading to the loss of revenue and added inefficiencies in the targeting process. A key reason behind this was the wrong prioritization of leads from conversion propensity and total lifetime value perspective. This was mainly due to manual, human-judgment-driven, ad-hoc,, static, rule-based identification of leads for the Business Development Associates (BDA) to work on.

Specific challenges that came in the way of the application of data science for lead generation and targeting were:

The raw data related to the prospects – that was the foundation for f for e predictive lead generation modeling – was in silos inside the client’s tech infrastructure. This led to failure in developing high-accuracy predictive lead generation models in the absence of a common platform to bring the data and models together.
Even in a few exceptional cases, where the data was stitched together by hand and predictive models built, the team found it difficult to keep the models updated in the absence of integrated data and model pipelines working in tandem.

To overcome these challenges, the Incedo Lighthouse^TM platform was deployed. The deployment of Incedo Lighthouse^TM in the AWS cloud environment not only brought about real improvements in target conversions but also helped transform the workflow for the BDAs. By harnessing the power of Data and AI, as well as leveraging essential AWS native services, we achieved efficient deployments and sustained service improvements.

Combine the information from all data sources for a 360-degree customer view, enabling the BDAs to look at the bigger picture effortlessly. To do so effectively, Incedo Lighthouse^TM leveraged AWS Glue which provided a cost-effective, user-friendly data integration service. It helped in seamlessly connecting to various data sources, organizing data in a central catalog, and easily managing data pipeline tasks for loading data into a data lake.
Develop and deploy AI/ML predictive models for conversion propensity using Data Science Workbench which is part of the Incedo Lighthouse^TM platform, after developing the data engineering pipelines that create a ‘single-version-of-the-truth ’ every time raw data is refreshed. This was done by leveraging the pre-built model accelerators, helping the BDAs sort those prospects in the descending order of their conversion propensity, thereby maximizing the return on the time invested in developing them. The Data Science Workbench also helps with the operationalization of various ML models built in the process, while connecting model outputs to various KPI Trees and powering other custom visualizations. Using the Amazon SageMaker Canvas, Incedo Lighthouse^TM enables machine learning model creation for non-technical users, offering access to pre-built models and enabling self-service insights, all while streamlining the delivery of compelling results without extensive technical expertise.
Deliver key insights in a targeted and attention-driving manner to enable BDAs to make the most of the information in a short span of time. Incedo Lighthouse^TM leverages Amazon QuickSight, a key element in delivering targeted insights, that provides well-designed dashboards, KPI Trees, and intuitive drill-downs to help BDAs and other users make the most of the information quickly. These tools allow leads to be ranked based on model-reported conversion propensity, time-based priority, and various custom filters such as geographies and areas of expertise. BDAs can double-click on individual targets to understand deviations from actuality, review comments from previous BDAs, and decide on the next best actions. QuickSight seamlessly integrates with Next Gen Stats apps, and offers cost-effective scalable BI solutions, interactive dashboards, and natural language queries for a comprehensive and efficient user experience. This resulted in an increased prospect conversion rate due to data-driven automated decisions leveraging AI that are disseminated to BDA in a highly action-oriented way.

Complexity of decision making in the VUCA world

In today’s VUCA (Volatile, Uncertain, Complex and Ambiguous) business environment, the decision makers are increasingly required to make decisions at speed, in a dynamic and ever evolving uncertain environment. Contextual knowledge including cognizance of dynamic external factors is critical, and the decisions need to be made in an iterative manner employing ‘test & learn’ mindset. This can be effectively achieved through Decision Automation Solutions that leverage AI and ML to augment the expert human driven decision-making process.

Incedo Lighthouse^TM for Automated Decision Making

Incedo Lighthouse^TM an AWS cloud native platform has been designed and developed from the ground up to automate the entire process of decision making. It has been developed with the following objectives:

Distill signal from noise: The right problem areas to focus on are identified by organizing KPIs into a hierarchy from lagging to leading metrics. Autonomous Monitoring and Issue Detection algorithms are further applied to identify anomalies that need to be addressed in a targeted manner. Thereby, effectively identifying crucial problem areas that the business should focus its energy on, using voluminous datasets that are updated at frequent intervals (typically daily).
Leverage context: Intelligent Root Cause Analysis algorithms are applied to identify the underlying behavioral factors through specific micro-cohorts. This enables action recommendations that are tailored to specific cohorts as opposed to generic actions on broad segments.
Impact feedback loop: Alternate actions are evaluated with controlled experiments to determine the most effective actions – and use that learning to iteratively improve outcomes from the decisions.

Incedo Lighthouse^TM is developed as cloud-native solution leveraging several services and tools from AWS that make the process of executive decisions highly efficient and scalable.

Incedo Lighthouse^TM implements a powerful structure and workflow to make the data work for you via a virtuous problem-solving cycle with an aim to deliver consistent business improvements through automation of 6-step functional journey of Problem Structuring & Discovery to Performance Improvement to Impact Monitoring.

Step 1: Problem Structuring – What is the Problem?

In this step, the overall business objective is converted into a specific problem statement(s) based on Key Performance Indicators (KPIs) that are tracked at the CXO level. The KPI Tree construct is leveraged to systematically represent problem disaggregation. This automation enhances the decision making process by enabling a deeper understanding of the issue and its associated variables. Incedo Lighthouse^TM provides features that aid the KPI decomposition step, such as KPI repository, self-serve functionality for defining the structure of KPI trees and publish those with latest raw data automatically.

Step 2: Problem Discovery – Where is the problem?

Here the objective is to attribute the anomalies observed in performance, which are significant deviations from the performance trend, to a set of customers / accounts / subscribers. Incedo Lighthouse^TM provides features, which are a combination of rule-based and anomaly detection algorithms, that aid in identifying most critical problem areas in the KPI trees, such as Time Series Anomaly Detection Non-time series Anomaly Detection, Cohort Analyzer and Automated Insights.

Step 3: Root Cause Analysis – Why is there a problem?

Once the problem is discovered at the required level of granularity, identification of the root causes that drive the business performance becomes critical. To automate the root cause identification for every new or updated data set the Root Cause Analysis must be packaged into a set of pre-defined and pre-coded model sets that are configurable and can be fine-tuned for specific use case scenarios. Incedo Lighthouse^TM enables this using pre-packaged configurable model sets, the output of which is presented in a format that is conducive for the next step, which is, action recommendations. These model sets include Clustering, Segmentation and Key Driver Analyzer.

Step 4: Recommended Actions

However sophisticated the algorithms are, if the workflow stops at only delivering the insights using anomaly detection and root cause analyzer etc, it would still be a lost cause. Why? Because the executives are not supported with recommendations to take corrective, preventive or corroborative actions based on the insights delivered. Incedo Lighthouse^TM incorporates the Action Recommendation module that enables the actions to be created at each cohort (customer microsegment) level for a targeted corrective or improvement treatment based on its individual nuance. The Action Recommendation module helps define and answer questions for each cohort: What is the action, Who should be the target for the action, and When the actions should be implemented and state the Goal of the action in terms of KPI improvement target.

Step 5: Experimentation

Experimentation is testing various actions on a smaller scale, and being able to select the optimal action variant that is likely to produce the highest impact when implemented on full scale. Incedo Lighthouse^TM has a Statistical Experimentation engine that supports business executives to make informed decisions on actions to be undertaken. Some of the key features of the module are: Choice of the experiment type from the options such as A/B Testing, Pre vs. Post etc., Finalization of the target population and Identification of the success metrics and define targets.

Step 6: Impact Monitoring

Post full scale implementation of actions, through their seamless integration into organization’s operating workflows, tracking their progress on an ongoing basis is critical for timely interventions. Our platform ensures that the actions are not merely implemented but are continuously monitored for their impact on key performance indicators and business outcomes.

A two-way handshake is required between Incedo Lighthouse^TM and the System of Execution (SOE) that is used as an operations management system to continually monitor the impact of the actions on ground. Incedo Lighthouse^TM covers the following activities in this step – Push Experiments/Actions, Monitor KPIs, and Experiment Summary.

Incedo Lighthouse^TM in AWS environment

Infrastructure to host the Incedo Lighthouse^TM platform plays an important role in the overall impact that the platform creates on business improvements through better and automated decision making. In cases where the clients are already leveraging the AWS Cloud, the Incedo Lighthouse^TM implementation takes advantage of the following AWS native services that provides significant efficiencies for successive deployments and ongoing service to the business users. A few of the AWS Services prominently used by Incedo Lighthouse^TM are:

AWS Compute: AWS provides scalable and flexible compute resources for running applications and workloads in the cloud. AWS Compute services allows the companies to provision virtual servers, containers, and serverless functions based on application’s requirements, and enable pay for what you use, making it a cost-effective and scalable solution. Key compute services used in Incedo Lighthouse^TM are: Amazon EC2 (Elastic Compute Cloud), AWS Lambda, Amazon ECS (Elastic Container Service) and Amazon EKS (Elastic Kubernetes Service).

AWS Sagemaker: There are various ML models that are the brain behind various modules in Incedo Lighthouse^TM the Anomaly Detection, Cohort Analyzer, Action Recommendation and Experimentation etc. All these models are developed, trained, validated and deployed via AWS Sagemaker.

AWS Glue: The large amount of frequently updated data in various source systems that is needed by the ML models is brought into the common analytical storage (data mart/ data warehouse/ data lake) etc. using AWS Glue jobs that implement ETL or ELT logic along with value add processes such as data quality checks and remediation.

Incedo Lighthouse^TM boosts effectiveness and efficiency of executive decision making with the power of AI. As a horizontal cloud-native platform powered by AWS, it is the key to achieving consistent business improvements across domains and use cases.

The financial services industry has undergone immense disruption in recent years, with fintech innovators and digital giants eroding the market share of traditional banking institutions. These new players are championing enhanced customer experiences through personalization as a common strategy to drive user adoption and build market share. In this cloud-centric narrative, we’ll explore the value of personalization in retail banking and how AWS services can be leveraged to empower this transformation with Incedo Lighthouse^TM.

The Value of Personalization for Retail Banking

The adoption of personalization strategies has become a central focus for banks to enhance customer experience and deliver significant business impact. This includes:

Building a Growth Engine for New Markets and Customer Segments
New-age fintech companies have leveraged data-driven products for expedited underwriting, utilizing data like bureau scores, digital KPIs, and social media insights to assess a prospect’s creditworthiness. Traditional banks must swiftly adopt similar data-driven approaches for faster loan fulfillment and to attract new customer segments. AWS cloud services can facilitate this transition at speed by offering scalable, flexible, and secure infrastructure.

Maximizing Customer Lifetime Value
To maximize the share of customers’ wallets, banks are now focusing on improved cross-selling, superior servicing, and higher retention rates. Next-generation banks are employing AI-driven, next-best-action recommendations to determine which products to offer at the right time and through the most effective channels. This shift involves transitioning from reactive retention strategies to proactive, data-driven personalized approaches.

Improved Risk Assessment and Mitigation Controls
Personalization is not confined to marketing; it extends to risk management, fraud detection, anti-money laundering, and other control processes. Utilizing sophisticated AI models for risk, fraud, and AML detection, combined with real-time implementation, is crucial to establishing robust risk defense mechanisms against fraudsters.The impact of personalization in retail banking is transformative, with opportunities to enhance experiences across all customer touchpoints. Incedo’s deployment of solutions for banking and fintech clients showcases several use cases and potential opportunities within the cloud-based landscape.

Building a Personalization Engagement Engine with AWS

A successful personalized engagement engine necessitates integrated capabilities across Data, AI/ML, and Digital Experiences, all hosted on the AWS cloud. The journey begins with establishing a robust data foundation and a strategy to enable a 360-degree view of the customer:

Data Foundation to Support Decision Automation
Traditional banks often struggle to consolidate a holistic customer profile encompassing product preferences, lifestyle behavior, transactional patterns, purchase history, preferred channels, and digital engagement. This demands a comprehensive data strategy, which, given the extensive storage requirements, may require building modern digital platforms on AWS Cloud. AWS services and tools facilitate various stages of setting up this platform, including data ingestion, storage, and transformation.
AWS Glue: A large amount of frequently updated data in various source systems that are needed by the ML models is brought into the common analytical storage (data mart/data warehouse/data lake, etc.) using AWS Glue jobs, implementing ETL or ELT logic along with value-add processes such as data quality checks and remediation.

AI, ML, and Analytics-Enabled Decisioning
Identifying the right product or service to offer customers at the ideal time and through the preferred channel is pivotal to delivering an optimal experience. This is achieved through AI and ML models built on historical data. AWS offers services like Amazon SageMaker to develop predictive models and gain deeper insights into customer behavior.
AWS Sagemaker: The brain of Personalization lies in the Machine Learning models that take advantage of the wealth of customer-level data across demographic, behavioral, and transactional dimensions to develop insights and recommendations to enhance the customer experience significantly, as explained in the use cases above.

Optimal Digital Experience
Personalization goes beyond data; it requires the right creatives and effective communication to drive customer engagement. AWS services for data integration and analytics enable A/B testing of digital experiences, ensuring the creation of best-in-class customer journeys.While data, AI, and digital experiences are the core building blocks of a personalized engagement layer, the orchestration and integration of these capabilities are essential for banks to realize the full potential of personalization initiatives. Building these capabilities from scratch can be time-consuming, but the AWS Cloud provides the scalability and flexibility required for such endeavors.
AWS Compute: AWS provides scalable and flexible computing resources for running applications and workloads in the cloud. AWS Compute services allow companies to provision virtual servers, containers, and serverless functions based on the application’s requirements, enabling pay for what you use, and making it a cost-effective and scalable solution. Key compute services used in Incedo Lighthouse^TM are Amazon EC2 (Elastic Compute Cloud), AWS Lambda, Amazon ECS (Elastic Container Service), and Amazon EKS (Elastic Kubernetes Service).

Turning Personalization into Reality with Incedo Lighthouse^TM and AWS

Building personalization capabilities is just the first step; embedding personalization recommendations into enterprise workflows is equally critical. This integration ensures that personalized experiences are not just theoretical but are actively implemented and drive customer engagement.

Incedo’s Lighthouse^TM solution for CX personalization accelerates the journey, offering an enterprise-grade solution that significantly reduces time-to-market for data/AI-enabled marketing personalization. It automates AI/ML-enabled decisions from data analysis to customer outreach, ensuring personalized offerings are delivered to customers at the right time. Incedo’s solution includes a prebuilt library of Customer 360-degree data lakes and AI/ML models for rapid implementation, supported by digital command centers to facilitate omnichannel engagement.

No matter where banking clients are in their personalization journey, Incedo’s solution ensures that they experience tangible benefits within weeks, not years. The implementation is complemented by a personalization roadmap that empowers organizations to build in-house personalization capabilities.

In the fast-paced world of banking, personalization is essential for acquiring new customers, maximizing their value, and retaining the best customers. Trust, combined with personalization capabilities, ensures traditional banks maintain their competitive edge against fintech players and digital giants.

In the ever-evolving landscape of financial services, personalization powered by AWS offers banks a strategic advantage in acquiring and retaining customers. Incedo’s Lighthouse^TM solution, hosted on AWS Cloud, enables rapid implementation and ensures that banks can quickly harness the benefits of personalization. This approach is not just a trend but a necessity for banks looking to stay competitive and provide a superior banking experience.

COVID-19 pandemic has brought a significant change in the way financial advisors manage their practices, clients and home office communication. Along with the data driven client servicing platforms, smooth transition and good compensation, advisors are closely evaluating their firm’s digital quotient to provide them the service and support in times of such crisis and if not satisfied, may look out options of switching affiliation during or post this crisis.

This pandemic is not a trigger rather has provided additional reasons for advisors to continue to look out for a firm that fits better in their pursuit of growth and better client service.

2018 Fidelity Advisor Movement Study says, 56% of advisors have either switched or considered switching from their existing firms over the last 5 years. financial-planning.com publishes that one fifth of the advisors are at the age of 65 or above and in total around 40% of the advisor may retire over the next decade.

A Cerulli report anticipates transition of almost $70 trillion from baby boomers to Gen X, Gen Y and charity, over the next 25 years. Soon, the reduction in the advisor workforce will create a big advice gap, that the wealth management firms will have to bridge by acquiring and retaining the right set of Advisors

We are observing a changing landscape of advisor and client population, mounting cost pressure due to zero commission fee and the need for scalable operations. COVID-19 has further accentuated the need for the firms to better understand the causal factors for changes in advisor affiliation, to optimize their resources deployed for engaging through the Advisor life cycle. The wealth management firms are increasingly realising that a one fit for all solution may not get optimal returns for them.

Data and Analytics can help the firms segment their advisors better and drive better results throughout the advisor life cycle. Advisor Personalization, using specific data attributes can deliver contextual and targeted engagements and can significantly improve results by dynamically curating contextual & personalized experiences through the advisor life cycle.

A good data driven advisor engagement framework defines and measures key KPIs for each stage of the advisor lifecycle and not only provides insights on key business metrics but also addresses the So What question about those insights. As wealth management firms collect and aggregate data from multiple sources, they are also increasingly using AI/ML based models to further refine advisor servicing.

Let us look at the key goals or business metrics for each stage of the advisor life cycle below and see how data and analytics driven approach helps in each stage of the life cycle.

Prospecting & Acquisition

To attract and convert more high producing advisors, recruitment teams should be tracking key parameters through the prospecting journey of the advisors so that they can identify:

What is the source of most of their prospective advisors; RIA, wirehouses, other BDs
Which competitors are consistently attracting high producing advisor
What % of advisors drop from one funnel stage to another and finally affiliate with the firm
What are the common patterns and characteristics in the recruited advisors

Data driven advisor recruitment process that relies on the feedback loop helps in the early identification of potential converts, thereby balancing the effort spent on recruited vs lost advisors. It also improves the amount and quality of the recruited assets.

For example, analysis of one-year recruitment data of a large wealth management firm revealed that prospects dealing with variable insurance did not eventually join the firm due to the firm’s restricted approved product list. Another insight revealed that prospects with a higher proportion of fee revenue vs the brokerage revenue increased their GDC and AUM at a much faster rate after one year of affiliation. Our Machine Learning Lead Scoring Model used multiple such parameters and scored a recruit’s joining probability and 1-year relationship value to help the firm in precision targeting of high value advisors. These insights allowed the firm to narrow down their target segment of advisors and improved conversion of high value advisors.

Growth & Expansion

A lot of focus during the growth phase of the advisor lifecycle is on tracking business metrics such as TTM GDC, AUM growth, commissions vs Fee splits. The above metrics however have now become table stakes and the advisors expect their firms to provide more meaningful insights and recommendations to improve their practices. Some of the ways, firms are using data to enhance advisor practice are by:

Using data from data aggregators and providing insights on advisor’s wallet share and potential investment opportunities
Providing peer performance comparisons to the advisors
Providing next best action recommendations based on the advisor and client activities

For example, our Recommendation Engine analysed advisor portfolio and trading patterns and determined that most of the high performing advisors showed similar patterns in Investment distribution, asset concentration, churning %. This enabled the engine to provide targeted investment recommendations for the other advisors based on their current investment basket and client risk profile. The wealth management firms are also using advisor segmentation and personalization models based on their clients, Investment patterns, performance, digital engagement, content preference and sending personalized marketing and research content for the advisors based on their personas thus driving better engagement.

Maturity and Retention

It is always more difficult and costly to acquire new advisors as compared to growing with the existing advisor base. The firms pay extra attention to ensure that their top producer’s needs are always met. Yet despite their best efforts, large offices leave their current firms for greener pastures or higher pay-outs. The firms run periodic NPS surveys with their advisor population which indicates overall satisfaction levels of the advisors, but they do not generate any insights for proactive attrition prevention. Data and analytics can help you identify patterns to predict advisor disengagement and do targeted proactive interventions.

For example, our attrition analysis study for a leading wealth manager indicated that a large portion of advisors over the age of 60 were leaving the firm and selling out their business. This enabled the firm to proactively target succession planning programs at this age demographic of advisors. Our analysis also indicated a clear pattern of decreased engagement with the firm’s digital properties and decreasing mail open rate, for the advisors leaving the firm. Based on factors such as age, length of association with the firm, digital engagement trends, outlier detection, our ML based Attrition Propensity model created attrition risk scores for advisors and enabled retention teams to proactively engage more with at-risk advisors and improve retention.

As per a study from JD Power, wealth management firms have been making huge investments in new advisor workstation technologies designed to aggregate market data, client information, account servicing tools and AI-powered analytics into a single interface. While the firms are investing heavily in technology, only 48% advisors find the technology their firm is currently using, to be valuable. While only 9% of advisors are using AI tools, the advisor satisfaction is 95 points higher on a 1000-point scale when they use AI tools. Advisors find a disconnect between the technology and the value derived from the technology.

This further necessitates the need for personalised solutions for advisors and an AI driven Advisor personalisation platform which provides curated insights to the firms. This helps in targeted & personalized services & support to advisors through the Advisor lifecycle, enabling optimal utilization of the firm’s resources and unlocking huge growth potential.

The firms that will understand the potential of data driven decision making for their advisor engagement and will start early adoption of such tools will thrive in these uncertain times and will emerge as a winner once the dust settles.

COVID-19 pandemic brought with it a complete disruption to the existing normal operating procedures in most of the industries. The unprecedented situation due to the pandemic has struck some of the business functions disproportionately hard. The most impacted functions in the companies however are those where the workforces relied heavily on “on the field” presence for the execution of their work compared to those functions which could easily be converted into a remote working setup.

From the Life Sciences industry standpoint, the drug promotion via Medical Reps (MR) falls into the prior category. Although the industry as a whole has seen rapid adoption of digital solutions across the workstreams in the ongoing decade, their marketing efforts to the Health Care Providers (HCPs) still heavily rely on the Face to Face (F2F) interaction of the Reps with the Physicians.

This status quo however has been challenged by the ongoing COVID pandemic, with the social-distancing norms in place. There are estimates of 92% drop in F2F HCP engagements in April 2020 compared to 6 months ago[1]. It is also estimated that in the new post-pandemic normal, the frequency of F2F engagements will shift as much as by 65% to quarterly/annual rather than the weekly/monthly norms prevalent pre-COVID². This is indeed a massive blow to the existing Pharma sales and marketing approach and has seen many of the companies rapidly scale up their digital engagement channels to fill the gap. The use of these digital channels for HCP engagement has seen a 2x increase from their pre-pandemic levels.[2]

The current COVID driven environment has several key implications for the Lifesciences organizations in their effort to meaningfully engage with HCPs.

Impact on Sales & Marketing Channel Mix – Restrictions on in-person meetings have lead to reduced access to HCPs, canceled/postponed training sessions, and canceled conferences and events, all of which were major marketing methods till now. Pharma and other Lifesciences companies have to accelerate their sluggish digital transformation initiatives and enable a true omnichannel digital experience for HCPs
Digital engagement channel optimization – The digital omnichannel push needs to account for varying pysician preferences for the type of digital channel engagement, based on factors like therapeutic area, demography, and personal preferences.
Personalized, contextual messages for better engagement– Physicians at the front lines have to balance innovation and efficiency while dealing with the increased pandemic workload. As a result, engagement and interaction frequency with HCPs have decreased abruptly. With this sudden shift, there is a need for communication to be crisp and contextual for it to be effective.

This brings us to an important question of how the Bio-Pharmaceutical companies should navigate the current shock concerning HCP engagement and what lies ahead for them. Pharma Commercial Teams would need a strategic HCP engagement approach that manages the immediate COVID situation as well as builds capabilities for the new digital-driven normal.

As the Bio-pharma companies scramble to optimize their marketing efforts in the current times, they need to formulate a strategy which tackles the problem in phases:

Now: Immediate Priorities to manage COVID situation (next 1-3 months) – Set of tactical initiatives and workarounds to the existing HCP engagement methodologies, meant to strictly tackle only the immediate priorities around COVID-19 impact
Next: Accelerate digital capabilities build-up to drive Omnichannel HCP engagement (in 3-6 months ) – Strategic initiatives to accelerate and deliver a highly engaging digital experience for HCPs. These will fundamentally help in shifting and realignment of biopharma omnichannel engagement capabilities in post-COVID realities.

(Now) Immediate Priorities to manage HCP promotions in COVID situation

As an immediate measure, Bio-Pharma companies need to evaluate the impact of COVID-19 on HCPs’ practice – Rx, patient counts, geographical impact, etc, and Field Reps access to HCPs. It is imperative that Biopharma companies create a COVID control room, which integrates external trigger impact data with internal data sources to truly assess the impact of COVID situation (and potentially other external triggers and shocks) on their sales & marketing plans.

As the COVID impact is quantified, bio-pharma can synthesize the same to adjust the tactical call plans for their promotional activities. The critical parameters to consider while making changes to the call action models would be:

Incorporate external COVID impact triggers at geo, HCP level
Defining and quantifying the digital affinity of physicians
Optimization of cross-channel (Digital & Rep) targeting frequency
Dynamic adjustments to the call-plan (digital mix, frequency) as the COVID situation evolves

(Next) Accelerate digital capabilities build-up to drive Omnichannel HCP engagement

Once the immediate priorities related to the pandemic are solved, companies can utilize the learnings and key insights from the pandemic times to further advance their digital engagement strategy. The evaluation of what went right and what were the misses in the earlier stage should also be used to formulate a long-term digital and omnichannel engagement strategy. There is also, a lot to learn from Digital-natives who have, highly effectively, leveraged digital channels to driven customer engagement.
Bringing these best-practices from Digital natives together with Bio-pharma context can help accelerate the digital transformation of the industries HCP engagement approach.

Best-practices and Learnings from Digital Natives	Lifesciences Ecosystem Context
Focus on differentiated HCP experience	Physicians have different interaction points, interests, and requirements including clinical content, CMEs, studies, samples, copay coupons, patient counseling material, etc. and hence differentiated experience enables engagement.
Volume and variety of data	Pharma has access to multi-dimensional physician data in terms of demography, preferences, prescription patterns, patient/payer mix profiles via claims, digital affinity to micro-segment physicians, and uncover preferences, behaviors, and personalized needs.
HCP/Customer Journey management and personalization	Advanced analytics and ML-based approaches can leverage the available data to predict intents, recommend interventions, and seamlessly deliver them via physician engagement platform and processes.
Omnichannel execution	Multi-channel interaction provides a foundation platform for delivering these experiences across digital as well as non-digital channels.
Measure, Learn & Improve	A/B testing driven digital engagement experimentation anchored on performance-driven, yet responsive targeting strategies.

To accelerate their digital transformation journey, biopharma companies need to inculcate these best practices into their HCP digital marketing capability. An integrated Digital Engagement solution will help biopharma companies create and deliver omnichannel personalized experiences for HCPs, by enabling real-time AI/ML-driven next-best-action recommendations and precision targeting strategies based on their preference and intents.

COVID pandemic is an unprecedented global event, which will radically alter our behaviors, expectations, and interactions. Earlier rules of engagement are now getting irrelevant at a pace that is faster than ever before. To maintain(and grow) their share of voice and engagement with HCPs, bio-pharma organizations can no longer afford to follow the “digital-addon” approach. They have to fundamentally re-design their HCP engagement framework, as a Digital-driven strategy, $to stay relevant, to stay ahead and keep growing.

[1] Sermo COVID-19 Survey Apr, 2020
[2] Sermo COVID-19 Survey Apr, 2020

While the reckless overextension of credit lines by lenders and banks was the root cause of the financial crisis of 2007-09 and it had the US primarily as its central point, this time the financial crisis has been caused by a virus with rapidly evolving geographical centers and covering almost the entire world. The banks though are in a catch 22 situation, they need to support the government’s lending and loan relief measures while also maintaining low credit loss rates and enough capital provisioning for their balance sheet. Effective risk management and credit policy decisioning was never as challenging for the banks as it is now in the post covid-19 world.

COVID-19 implications and challenges for banks and lending institutions

Sudden shift in risk profile of retail and commercial customers – The surge in unemployment, deteriorated cash flow for businesses, etc has led to a sudden shift in the credit profile of customers. The data that banks used to leverage before COVID might not provide an accurate picture of the consumer’s risk profile in the current times.

Narrow window of opportunity to re-define credit policies – Bank’s credit policies in terms of origination, existing customer management, collections, etc have been designed over years with a lot of rigor, market tests, design and application of credit risk models and scorecards, etc. The coronavirus has caught the bankers and Chief Risk Officers by surprise and there is a narrow window of opportunity to make changes in existing models and risk strategies. While a lot of banks had built a practice of stress testing for unfavorable macroeconomic scenarios, the pace and impact of coronavirus have been unprecedented. This requires immediate response from the banks to mitigate the expected risks.

Government relief programs like payment moratoriums – The introduction of payment holidays and moratorium programs are effective to take some burden off consumers but prevent the banks from understanding high risk customers as there is no measure of delinquency that banks can capture from existing data.

Four-point action plan and strategy to navigate through the COVID-19 crisis

Banks will need to go back to the drawing board, re-imagine their credit strategy and put in accelerated war room efforts to leverage data and create personalized risk decisioning policies. Based on Incedo’s experience of supporting some of the mid-tier banks in the US for post COVID risk management, we believe the following could help banks and lenders make a fast shift to enhanced credit policies and mitigate portfolio risk

Covid situational risk assessment – As a starting point, Risk managers should identify the distress indicators that capture the situational risk posed post Covid-19. These indicators could be a firsthand source of customer’s situational risk (e.g. drop in payroll income) or surrogate variables like higher utilization or use of cash advance facility on credit card etc. Banks would need to leverage a combination of internal and external parameters, such as industry, geography, employment type, customer payment behavior, etc. to quantify COVID based situational risk for a given customer.
Early warning alerts & heuristic risk scores based on a recent behavioral shift in customer’s risk profile – A sudden change in the financial distress signals should be captured to create automated alerts at the customer level, this in combination with a historical risk of the customer (pre-COVID) should go as a key input variable into the overall risk decisioning process. The Early warning system should issue alerts, alerting the credit risk system of abnormal fluctuations and potential stress prone behavior for a given account.
Executive Command Centre for COVID Risk Monitoring – The re-defined heuristic customer risk scores should be leveraged to quantify the overall risk exposure for the bank post COVID. Banks need to monitor the rapidly changing credit behavior of customers on a periodic basis and identify key opportunities. The rapid risk monitoring based command center should focus on risk across the customer lifecycle and various risk strategies and help provide answers to some of the following questions of the bank’s management team
- What is overall current risk exposure and forecasted risk exposure over short term period?
- How has the overall credit quality of existing customer base changed, are there any patterns across different credit product portfolios?
- What type of customers are using payment moratoriums, what is the expected risk of default of such customer segments?
- Quantification of the drop in income estimates at an overall portfolio level and how it could affect other credit interventions?
- What models are witnessing significant deterioration in performance and may need re-calibration as high priority models?
Personalized credit interventions strategy (Whom to Defend vs Grow vs Economize vs Exit) – To manage credit risk while optimizing the customer experience, banks should use data driven personalized interventions framework of Defend, Grow, Economize & Exit. Using customer’s historical risk, post COVID risk and potential future value-based framework, optimal credit intervention strategy should be carved out. This framework should enable banks to help customers with short term liquidity crunch through government relief programs, bank loan re-negotiation and settlement offers while building a better portfolio by sourcing credit to creditworthy customers in the current low interest rate environment.

The execution of the above-mentioned action plan should help banks to not only mitigate the expected surge in credit risk but also enable a competitive advantage as we move towards the new-normal. The rapid credit decisioning should be backed with more informed decision making and on an ongoing basis, the framework should be fine-tuned to reflect the real pattern of delinquencies.

Incedo with its team of credit risk experts and data scientists has enabled setting up the post COVID early monitoring system, heuristic post COVID risk scores and COVID command center for a couple of mid-tier US based banks over a period of last few weeks.

Learn more about how Incedo can help you with credit risk management.

The magnitude of the spread of the COVID-19 pandemic has forced the world to come to a virtual halt, with a sharp negative impact on the economies worldwide. The last few weeks have seen one of the most brutal global equity collapse, spike in unemployment numbers, and negative GDP forecasts. With the crisis posing a major systemic financial risk, effective credit risk management in these times is the key imperative for the banks, fintech and lending institutions.

Expected spike in delinquencies and credit losses post COVID-19

The creditworthiness of banking customers for both retail and commercial portfolios has decreased drastically due to the sudden negative impact on their employment and income. In case of continuation of the epidemic for a longer-term period, the scenarios in terms of defaults and credit losses for banks could potentially be much higher than as observed in the global financial crisis of 2008.

Need for an up-to-date, agile and analytics driven credit decisioning framework:

The existing models that banks rely upon simply did not account for such a ‘black swan event’. The credit decisioning framework for banks based on existing risk models and business criteria would be suboptimal in assessing customer risk, putting the reliability of these models in doubt. There is an immediate need for banks to adapt new credit lending framework to quickly and effectively identify risks and make changes in their credit policies

Incedo’s risk management framework for the post COVID-19 world

To address the challenges thrown up by the COVID-19, it is important to assess the short, medium and long-term impact on bank’s credit portfolio risk and define a clear roadmap as a strategic response focusing on changes to risk management methodologies, credit risk models and existing policies.

We propose a six-step framework for banks and lending institutions which comprises of the following approaches.

COVID Risk Assessment & Early monitoring Systems

Banks and lending institutions should focus on control room efforts and carry out a rapid re-assessment of customer and portfolio risk. This should be based on COVID situational risk distress indicators and anomalies observed in customer behaviour post COVID-19. As an example, sudden spike in utilization for a customer, less or no credit of salary in payroll account, usage of cash advance facility by transactor persona could potentially be examples of increasing situational risk for a given customer. In the absence of real delinquencies (due to moratorium or payment holidays facility), such triggers should enable banks to understand customer’s changing profiles and create automated alerts around the same.

Credit risk tightening measures

Whether you are a chief risk officer of a bank or a credit risk practitioner, by now you would have heard many times that all your previous credit risk models and scorecards would not hold and validate any longer. While that is true, it has also been observed that directionally most of these models would still rank order with only a few exceptions. These exceptions or business over-rides can be captured through early monitoring signals and overlaid on top of existing risk scores as a very short term plan. Customers with a low risk score and situational risk deterioration based on early monitoring triggers are the segments where credit policy needs to be tightened. As the delinquencies start getting captured, banks should re-create these models and identify the most optimal cutoffs for credit decisioning.

Personalized Credit Interventions

There are still customers with superior credit worthiness waiting to borrow for their financial needs. It is very important for banks to discern such customers from those that have a low ability to payback. To do this, banks require personalized interventions to reduce risk exposure while ensuring an optimal customer experience through data-driven personalized interventions. Banks need to help customers with liquidity crunch through Government relief programs, bank loan re-negotiation, and settlement offers while building a better portfolio by sourcing credit to ‘good’ customers in the current low rate environment.

Models Re-design and Re-Calibration

A wait and watch approach for the next 2-3 months period to understand the shifts in customer profile and behavior is a precursor before re-designing the existing models. This would enable banks to better understand the effect of the crisis on customer profiles and make intelligent scenarios around the future trend for delinquencies. There would be a need to re-calibrate or re-design the existing models. Periodic re-monitoring of new models would be a must, given the expected economic volatility for at least next 6-12 months period.

Model Risk Management through Risk Governance and Rapid Model Monitoring

There is an urgent need for banks to identify and quantify the risks emerging due to the use of historical credit risk models and scorecards through Model monitoring. While the risk associated with credit products has increased, the delinquencies have not yet started getting captured in the bank’s database due to the payment holiday period facility introduced by govt’s of most of the countries. In such a situation, it is critical to design risk governance rules for new models that may not have information related to dependent variables (e.g. delinquency) captured accurately.

Portfolio Stress Tests aligned with dynamic macro economic scenarios

Banks and lending institutions need to leverage and further build on their stress testing practice by running dynamic macro-economic scenarios on a periodic basis. The stress testing practice has enabled banks in the US to improve their capital provisioning and the COVID crisis should further enable banks across the geographies to use the stress tests to guide their future roadmap depending on how their financials would fare under different scenarios and take remedial actions.

The execution of the above-mentioned framework should ensure that banks and fintech’s are able to respond to immediate priorities to protect the downside while emerging stronger as we enter the new normal of the credit lending marketplace.

Incedo is at the forefront of helping organizations transform the risk management post COVID-19 through advanced analytics, while supporting broader efforts to maximize risk adjusted returns.

Our team of credit risk experts and data scientists has enabled setting up the post COVID early monitoring system, heuristic post COVID risk scores, and COVID command centre for a couple of mid-tier US based banks over a period of last few weeks.

Learn more about how Incedo can help with credit risk management.

Quantum Computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. Quantum computers perform calculations based on the probability of an object’s state before it is measured – instead of just 1s or 0s – which means they have the potential to process more data compared to classical computers.

Why do companies need to reimagine their customer service? And why do they need to learn from Digital Natives like Google, Amazon, etc.? That is because these digital natives are setting the standard for customer expectations – In a recent survey, when customers were asked which company would they want to take Telecom services from, 60% people responded Google or Amazon!
So what are the key differences in the way Digital Natives approach Customer Service?

Fix at Source – While traditional organizations look to call deflection to save costs, digital natives believe that customer service indicates a customer pain point that should be fixed “at source”.
Use Product Thinking and Tech to solve issues – Too many processes and policies at legacy organizations are driven by risk, legal and finance making them high friction. Digital natives, start with the voice of the customer to design the right customer experiences and use technology to manage risks
Put AI and Technology at heart of everything – Not as siloed solutions to micro-problems but for driving end-to-end orchestration of customer experiences

To build next gen customer service capabilities, Incedo recommends 5 key initiatives:

Use Voice of Customer to drive business priorities
Fix root cause at source using Product Design Thinking
Personalise Service Channel Mix
Leverage AI to increase machine and self-serve digital channels
Use Cloud based architecture to enable AI driven Customer Service at scale

Voice of Customer to drive business priorities

KPIs to optimize: NPS

Customers talk about products and service through multiple medium – they leave reviews on product pages, social media, App Store, etc., call care, write emails or escalate to senior management. Often, the focus of customer service teams is on “managing” these inputs – douse the fire if the review is negative. However, there is a wealth of information available in these customer inputs on what is working and what is not – the challenge is that there is a lot of noise and traditional approaches have been inadequate. Advanced NLP + AI techniques can help organizations extract very actionable insights from these VOC channels

Fix root cause at source using Product Design Thinking

KPIs to optimize: Calls/Incidents per Unit/Order

Most customer service issues require cross-functional approach and product design thinking to resolve at the root cause. For example, when faced with fraud most organization end up putting strong checks and balances in place that also add a lot of friction to genuine customer journeys. Digital natives, on the other hand approach it differently:

They build robust tech and AI based preventive and corrective mechanisms and continuously refine them
They take a ROI based approach – compensating customers for small ticket breaches rather than adding friction

Personalize Service Channel Mix

KPIs to optimize: NPS, CSAT

A recent study showed that digital channel leads to highest customer satisfaction for service. However, all customers are not equal and so are the issues they face. Personalization of service channel based on following key parameters is recommended:

Customer lifetime value – High LTV customers expect white glove treatment best provided by high-quality agents
Digital affinity – Forcing low digital affinity customers towards digital channels and vice-versa can lead to dissatisfaction
Anxiety Levels – Some issues cause high anxiety, channels with best resolution rates if the customer is reaching out for these issues

Leverage AI to increase machine and self-serve digital channels

KPIs to optimize: Resolution Rate, MTTR (Mean Time To Resolution), Operating Cost

What should you automate or move to self-service? The choice should be driven by volume and resolution complexity of issues. High volume, low complexity issues lend themselves well to self-serve channel whereas high complexity issues will require human touch. Design of chatbot and self-serve solutions should begin with design thinking of customer journeys

Use Cloud based architecture to enable AI driven Customer Service at scale

KPIs to optimize: Time to Market

The solutions and approaches outlined in the previous 4 initiatives require building real-time AI/ML models that evolve continuously. Traditional data and technology architectures cannot keep up with the velocity of change and volume of data. Cloud based architectures are key to solving this problem given inherent scalability and vast & growing libraries of reusable components.
However, transforming existing legacy architectures to cloud based is a daunting task. Organizations can follow a 2-speed approach to this transformation:

Speed 1: End to End Cloud transformation use case by use case
Speed 2: Building out the cloud architecture that can support multiple use cases and future needs

In conclusion, customer service as most organizations know it is transforming and Digital natives are at the forefront. Leaders of traditional organizations can drive this transformation by undertaking 5 key initiatives that put the customer at the heart of the service – to begin this journey a cross-functional empowered team that can own and drive these initiatives is recommended. It is either that or slow death as customers abandon sub-par experiences for better ones.

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