What is Contract Pull Through?

The pharma sales team engages in contracts with brands, hospitals, clinics, infusion centers, doctor offices, IDNs, ONA, GPOs, and other networks. These networks are often referred to as pharma accounts, and contracts are lined up to improve overall sales, market share, and profitability. Contracts with these accounts are based on various factors such as rebate percentage, formulary tiers, and performance-based fees.

Pharma’s gross contracted sales is a large multi billion dollar opportunity which is growing at a rapid pace. This is a big opportunity for the commercial team to boost sales with these accounts. Pharma companies analyze data on contracts, rebates, terms, and tiers to see how accounts perform. This enables them to identify accounts that are doing poorly and the ones that are doing exceptionally well.

We may define Contact Pull Through, as the analysis of –

  1. How much an account has purchased (sales) by contract program and by brand;
  2. How much they’ve received in discounts (rebates, chargebacks);
  3. How they are doing against their baselines; and
  4. Where are the opportunities to buy more and save more?

Why does pharma need to focus on Contract Pull Through?

Large and mid-size pharma companies have Contract Pull Through from the accounts, as the top-of-mind problem, as even increasing effectiveness by 2-5% would mean savings to the tune of millions of dollars.

Based on our experiences working with the client’s market access team – we realized that the organization delegated the task of supporting field pull-through entirely to its payer account managers. These executives reported spending 75% of their time creating and pulling reports, time that could have been better spent with customers or in more strategic dialogue with the field members.

The key stakeholders for Contract Pull Through are the field team members i.e., Business Engagement Managers (BEMs) and Healthcare Market Directors (HDs), who need to focus on Contract Pull Through for –

  1. Generating contract awareness and pull through for major providers in their ecosystems
  2. Creating awareness around the contracts/terms offered by pharma firms for the products
  3. Engaging with customers to show their historical performance and current performance

Some specific Contract Pull Through use cases the pharma account team focuses on include:

  • A portfolio purchasing summary of the account, which enables the account to understand how much volume the account has bought, and the savings received from pharma products
  • Product contribution at an account level, which enables the understanding of how much volume is coming from each pharma product
  • Contract eligibility of an account, to understand which contracts are available at a certain account

Other business insights that Contract Pull Through data may help pharma companies are around –

  • How is the account performing as compared to others, in their ecosystem/region?
  • Which is the account’s dominant payer, and how does that payer work at a national level?
  • How much can this account purchase to reach the next tier?

Use case deep dive: Portfolio purchasing summary of an account

A portfolio purchasing summary of the account is one of the critical use cases handled through Contract Pull data – what the Contract Pull Through team is looking for is to understand a particular account within a regional ecosystem, across all periods of the contract or for a particular period –

  • What has been the number of gross sales? Has that gone up or down compared to its last quarter?
  • What part of the total gross sales is contracted vs non-contracted? What part of sales is attributed to specialty pharmacy? What percentage of account savings is attributed to contracting?

Also, another key insight to look at for portfolio purchasing summary for the account is to identify the product mix/segment mix (GPO, 340B, NCCN, etc.) across the portfolio, and double click on which product/ segment contribution has gone up, or down over the previous periods, and how does it fare against its anticipated baseline numbers.

These insights help field teams understand account purchase volume, savings from contracted products, account performance compared to expectations, and identify opportunities for cost-saving purchases.

How to generate the pull through business insights from data?

To arrive at these insights- the key data elements that must be looked into are contracts, chargebacks, 867 Sales, non-contracted sales, rebates, terms and tiers, account hierarchy, and zip to territory-mapping data.

These data sets coming from different source systems are ingested, assimilated, and presented as output for improved decision-making –

  • Firstly, it needs to be ingested into cloud-based or on premise databases by using RPA tools like UIPath
  • The ingested data then goes through a set of data quality checks to ensure data is of expected quality.
  • The clean dataset then is transformed through the ETL process, where complex calculations through business-defined rules are applied, and
  • Presented through BI tools reports providing visualized/graphics and tabular data on gross sales, savings, performance, opportunities for an account, and other pull through insights.

Unlocking Insights with Incedo’s Data and analytics services on AWS environment

With broad and relevant expertise on data and analytics solutions on AWS cloud, Incedo offers Data and Analytics services in database transformation with data ingestion, data preparation, modernization, archival, integration with data warehouses, formation of data lakes in AWS, real time and operational analytics, business analytics, visualization and data governance. These services provide a holistic view of specific accounts in regional ecosystems, breakdown of sales components, and product/segment analysis. This empowers field teams to optimize their strategies and enhance Contract Pull Through effectiveness in the pharmaceutical industry.

AWS SageMaker provides an effective solution for creating an efficient data processing pipeline. Data was collected from various sources including contracts, chargebacks, 867 Sales, non-contracted sales, rebates, terms and tiers, account hierarchy, and zip to territory-mapping data. Once the data is ingested, SageMaker supports a set of data quality checks to verify that the data meets the expected quality standards, guaranteeing data integrity. After ensuring the data’s accuracy, it allows a transformation process involving complex calculations based on predefined rules.

Amazon QuickSight offers a powerful solution to present results through visually appealing reports and dashboards, employing Business Intelligence tools. Incedo’s strategic approach leveraged these capabilities to empower stakeholders in the pharmaceutical industry. This enables them to make informed, data-driven decisions and optimize their Contract Pull Through strategies effectively. With Amazon QuickSight, complex data is translated into comprehensible visual insights, facilitating better decision-making and ultimately enhancing the pharmaceutical industry’s operational efficiency.

Conclusion:

BEMs/ HDs pay significant attention to generating Contract Pull Through insights. Thus, large and mid-sized pharmaceutical companies should invest in a robust system to understand account performance, optimize rebates, and potentially save millions of dollars. This also aids in focusing on top accounts and renegotiating terms for underperforming ones.

In today’s fast-paced business landscape, where efficiency, accuracy, and cost savings reign supreme, organizations are increasingly turning to automation to streamline their financial operations. According to the Institute of Financial Operations & Leadership, only 9 % of Accounts Payable (AP) departments are fully automated today. However, – the Strategic Treasurer survey reveals that, by 2025 67%s of finance professionals anticipate their AP departments will be entirely automated. This transformation is driven by an emphasis on cost savings, with 73% of organizations stating that it is the primary motivator for shifting to fully electronic processing. This shift bolsters both an organization’s operational efficiency as well as its financial health.

In this blog, we will explore why and how automating the Accounts Payables process is vital and delve into the myriad benefits of IncedoPay in achieving this transformation. Further, we will elaborate how IncedoPay eliminates the need for upfront enterprise investments in infrastructure, hosting fees, and licenses. It addresses critical customer challenges by reducing capital expenditure (Capex) and operational costs, thanks to its deployment on the AWS cloud and its service model based on per-payment transaction costs.

The Need for AP Automation: The traditional AP process is manual and resource intensive, – involving tasks such as data entry, invoice verification, purchase order matching, and physical check handling. This manual approach is both time-consuming and error-prone, leading to delayed payments, inefficiencies, supplier frustrations and potential compliance issues. AP Automation can help overcome these challenges by:

  1. Streamlined Workflow: Automation of the AP process facilitates a seamless and well-structured workflow. Purchase orders & invoices are electronically captured, verified, and routed through approval workflows, reducing the risk of errors and fraud. This expedites payment cycles and enables organizations to take advantage of early payment discounts, optimizing cash flow.
  2. Enhanced Visibility and Control: Automated AP systems provide real-time visibility into financial data, offering better tracking of financial commitments. This transparency facilitates more informed decision-making about cash flow and liabilities while ensuring compliance with financial regulations and reporting requirements.
  3. Cost Reduction: Manual AP processes incur hidden costs, of which labor costs constitute the most significant portion. Automation eliminates these expenses, resulting in significant cost savings. Improved cash flows with visibility and control over the liquidity allows organizations to not only reduce costs but to enhance forecasting and spend management.
  4. Vendor and Supplier Relations: Timely and accurate payments foster positive relationships with vendors and suppliers. Automated AP systems streamline the processes effectively to ensure that payments are made promptly and accurately, reducing the risk of disputes and enhancing trust among business partners.

IncedoPay – Revolutionizing the Accounts Payable Landscape for Banks and Enterprises:

IncedoPay, a proprietary payments platform from Incedo Inc., empowers enterprises to automate the process of Accounts Payables (AP) by integrating multiple payment systems while maintaining control, governance, and payment approval. IncedoPay stands as a beacon of innovation and excellence in the realm of integrated payment solutions, tailored specifically for banks and their corporate customers. As a best-in-class platform, IncedoPay harnesses the power of automation to revolutionize every facet of the Accounts Payable (AP) lifecycle, elevating profitability, productivity, and the overall user experience.

IncedoPay redefines the banking experience by seamlessly integrating digital solutions. With self-service portals catering to various stakeholders (eg. bank and their corporate customers) and personalized payer & payee journeys, to offer unmatched convenience to users. Real-time visibility and payment tracking further enhance the overall user experience. The platform streamlines multiple payment methods, including RTP, ACH, PayPal, Zelle, virtual cards, checks, and prepaid cards onto a user-friendly platform. Through the consolidation of legacy systems, IncedoPay enhances user experiences, transforming the way businesses handle their financial transactions.

IncedoPay isn’t just a payment platform; it’s an innovation-led, secure, and efficient payment solution. It adheres to the most stringent industry standards. It empowers banks with proactive fraud reduction and regulatory compliance management by analysing transactional data such as vendor details, payment amount and terms etc to identify anomalies or suspicious activities. This includes sentiment analysis and transactional data analytics. Replacing legacy applications, boosts business performance and unlocks upsell/cross-sell opportunities through data-driven decision-making.

Elevating Payments with Innovation, Security, and Reliability with AWS

IncedoPay is powered by Amazon Web Services (AWS), a powerhouse in the realm of cloud computing. AWS empowers IncedoPay to redefine the digital payment landscape, offering high-performance, secure, and scalable solutions. In the online payment world, security is paramount. AWS Key Management Service ensures the security and compliance of IncedoPay’s data. Web Application Firewall stands as the first line of defense, guarding against threats and ensuring uninterrupted service. Combining Availability Zones, Auto Scaling, and Elastic Load Balancing, ensures business continuity and cost-efficiency. Elastic Kubernetes Service (EKS) allows IncedoPay to swiftly adapt to market demands and Relational Database Service (RDS) enhances resilience, scalability, and compliance. The platform optimizes performance through caching and continuously monitors and optimizes resources with Amazon CloudWatch.

With global supply chains, late payments, evolving regulations, uncertainties, and cybersecurity threats, risk and compliance management become paramount. IncedoPay adheres to the most stringent industry standards. It empowers banks with proactive fraud reduction and regulatory compliance management through the use of information, sentiment analysis, and transactional data analytics.

IncedoPay’s remarkable impact is clearly demonstrated through the key statistics derived from its wide range of customer deployments:

  1. Cost Reduction: IncedoPay has achieved substantial cost savings, with one of our customers experiencing an astounding 80% reduction in processing costs. Another one realized an impressive 37% reduction in operations costs.
  2. User Adoption: IncedoPay’s modular architecture, coupled with tailored campaigns, has significantly boosted user adoption rates. In one instance, digital payment usage soared by 2x, while new supplier outreach increased sixfold. The platform streamlines the process of onboarding new suppliers, capturing their payment details, alerting them about payments, and providing regular metrics on their business relationships.
  3. Enhanced Efficiency: The platform’s seamless integration and comprehensive payment options have streamlined legacy processes and significantly reduced processing times. This improvement is evident in the 70% reduction in support requests from suppliers experienced by one of our customers, enhancing business cohesiveness.

Automation is critical for organizations to optimize cash flow, reduce costs, enhance financial control, and strengthen relationships with key stakeholders. Embracing this change enables businesses to thrive in an increasingly competitive financial landscape. So don’t wait; start your journey toward a more efficient and prosperous financial future today.

The explosion of data is a defining characteristic of the times we are living in. Billions of terabytes of data are generated every day, and million more algorithms scour this data for patterns for our consumption. And yet, the more data we have, the harder it becomes to process this data for meaningful information and insights.

With the rise of generative AI technologies, such as ChatGPT, knowledge workers are presented with new opportunities in how they process and extract insights from vast amounts of information. These models can generate human-like text, answer questions, provide explanations, and even engage in creative tasks like writing stories or composing music. This breakthrough in AI technology has opened up possibilities for knowledge workers.

Built with powerful LLMs, Generative AI has taken the world by a storm and led to a flurry of companies keen to build with this technology. It has indeed revolutionized the way we interact with information.

And yet, in this era of ever-increasing information overload, the ability to ask the right question has become more critical than ever before.

While the technology has evolved faster than we imagined, its potential is limited by the ways we use it. And while there is scope for immense benefits, there is also a risk for harm if users don’t practice judgment or right guardrails are not provided when building Gen AI applications.

As knowledge workers and technology creators, empowering ourselves and our users relies heavily on the ability to ask the right questions.

Here are three key considerations to keep in mind:

1. The Art of Framing Questions:

To harness the true potential of generative AI, knowledge workers must master the art of framing questions effectively. This involves understanding the scope of the problem, identifying key variables, and structuring queries in a way that elicits the desired information. A poorly constructed question can lead to misleading or irrelevant responses, hindering the value that generative AI can provide.

Moreover, knowledge workers should also consider the limitations of generative AI. While these models excel at generating text, they lack true comprehension and reasoning abilities. Hence, it is crucial to frame questions that play to their strengths, allowing them to provide valuable insights within their domain of expertise.

2. The Need for Precise Inquiry:

Despite the power of generative AI, it is essential to remember that these models are not flawless. They heavily rely on the input they receive, and the quality of their output is heavily influenced by the questions posed to them. Hence, the importance of asking the right question cannot be overstated.

Asking the right question is a skill that knowledge workers must cultivate to extract accurate and relevant insights from generative AI. Instead of relying solely on the model to generate information, knowledge workers need to approach it with a thoughtful mindset.

3. Collaboration between Humans and AI:

Generative AI should be viewed as a powerful tool that complements human expertise rather than replacing it. Knowledge workers must leverage their critical thinking, domain knowledge, and creativity to pose insightful questions that enable generative AI to augment their decision-making processes. The synergy between human intelligence and generative AI has the potential to unlock new levels of productivity and innovation.

Think of Gen AI as a powerful Lego block, a valuable component within the intricate structure of problem-solving. It’s not a replacement but an enhancement, designed to work in harmony with human capabilities to solve a problem.

In conclusion, in the age of generative AI, asking the right questions is fundamental. Careful framing of queries unlocks generative AI’s true power, enhancing our decision-making. Cultivating this skill and fostering human-AI collaboration empowers knowledge workers to navigate the information age and seize new growth opportunities.

To say the payments industry is going through disruption is certainly not a hyperbole these days. The fundamental shifts in how commerce gets done have begun to impact the way payments have been done all these years. On the one side, the payments industry has seen the entry of diverse fintech players, including giants like Facebook and Tencent, in addition to the start-ups that are presenting increased competition for banks and corporations. On the other, the threat from fintechs is being further fuelled by rapidly evolving customer expectations, which continue to push the boundaries for the industry as a whole. It is increasingly apparent that the payments marketplace will look fundamentally different a decade from now. There will be new form factors, real-time infrastructure, greater levels of integration with social media and e-commerce, to name a few of the changes. In effect, the revolution that has completely disrupted the consumer payments industry over the last decade or so is finally coming to take into its fold the corporate payments industry, too.

I see three big trends that are likely to shake up this segment, which is at half a trillion dollars a year, and growing:

  1. Direct-to-consumer models: As firms across industries move to direct engagement with their customers, it is becoming increasingly necessary to deliver them the same level of digital experiences as the consumer payments industry does.
  2. Global payment flows: Cross-border payments now make up over 10 per cent of all corporate payments, and they are growing. These flows are almost always digital in nature, with the added complexity of regulatory compliance and risk management.
  3. Data monetization: Bank treasury services have had the advantage of managing and servicing fund flows between their corporate clients. And as these fund flows become increasingly digital, they have enabled banks to build a data goldmine. Banks are now actively looking to leverage this data to deepen their service offerings.

Banks and corporations have started responding to the call of digital, and the payments processing industry is currently going through a wave of infrastructure modernization. I see significant technology investments by CIOs across firms that are setting the stage for the next wave of digital transformation. The payments industry will look fundamentally different a few years from now. By adopting digital channels, embracing automation, adopting open standards and making smart bets in technology, banks and corporations can emerge as winners in the payments marketplace.

Making digital transformation happen

As digital transformation initiatives in payments pick up steam, there are four main areas of focus, each of which is important to ensure not just a solid foundation for a digital payments ecosystem, but also to ensure the groundwork for unlocking the revenue potential from treasury and payment operations. This is something yet to be tapped in most organizations:

  1. Enabling of digital channels: Buoyed by the consumer payments industry, there is a rapidly growing array of digital payment channels that need to be integrated into the digital payments service offerings.
  2. Process automation: Payment processes typically span across entities (bank-corporation-consumer), and integration across disparate systems will make for a critical foundation to enable scalable implementations.
  3. Payment analytics: Payment processes have always been data rich, and even more so when digital channels continue to grow. Effective use of the data to make better decisions (e.g., manage risk, prevent fraud) and, furthermore, explore data monetization opportunities, are becoming important.
  4. Adoption of standards: For any multi-entity ecosystem with entities across the globe to scale with technology, it is essential to establish standards. Adoption of open banking standards is essential for digital payments to succeed – and we are at an inflection point, given the increasing adoption of these standards.

Digital channels

The ubiquitous cheque has been the staple in corporate payments for years now. Despite being the most expensive payment instrument, the cheque has dominated the corporate payment world for decades. It is not just the processing cost of the paper cheque that makes it a burden for banks. It is also a security headache. It is well known that paper cheques are the largest vehicle of payment fraud.

All that is changing. And, as it usually happens, this started with the consumer payments business. As the direct-to-consumer models continue to evolve, the B2C payments business is growing rapidly (annual growth rate of 15 per cent led by digital e-commerce)6. And the digital payment technologies that are coming out of the consumer payments industry (Zelle, Paypal, digital wallets, et al) offer a rich choice for banks and corporates to offer digital experiences to their customers:

  • Disbursement of funds: Transfer volumes of Medicare/Medicaid funds by healthcare providers to their members continue to rise and should, by and large, be digital.
  • Refund management: In a direct-to-consumer world, corporations need to manage refunds to customers from excess payments and product returns. Customers used to instant digital payments from the e-commerce world are expecting a similar experience everywhere.
  • Loyalty/reward disbursements: As corporations build deep relationships with their customers, they continue to adopt customer engagement strategies from e-commerce retailers. These include cash-back payments and encashing of loyalty points, which need to be executed through digital channels. A similar revolution is around the corner in B2B payments, with the expansion of consumer-like payment rails, such as digital wallets, in addition to the existing ones like ACH, Wire, virtual cards, etc. We believe the convenience of digital payments is only a starting point. There is so much more to it by way of benefits:
  • Streamlining of payment processes has a direct impact on working capital management. Trade finance is key to enabling global supply chains, and fintechs are coming up with specific solutions.
  • Cross-border payments to suppliers and subsidiaries need to stand up to heavy regulatory requirements in addition to managing the risk of fraud. Digital payments are increasingly the safest alternative. In addition to enforcing compliance, digital channels can ensure transparency of global fund flows
  • Of late, acquiring a deeper understanding of the supplier ecosystem has become an important factor (see the section on payment analytics below).

Automation

70 per cent of corporate treasury and payments professionals list manual and inefficient processes among their top challenges. In addition to their high costs, manual processes are also error-prone, difficult to scale in response to variable volumes, and increasingly susceptible to fraud.

Process simplification and automation opportunities extend across the value chain – from establishing the payment exchange with suppliers (B2B) and consumers (B2C) to creating a variety of services around three-way (PO, invoice and receipt) matching, and all the way to the disbursement of funds through different digital payment channels. Several fintechs see this as a big area of opportunity and are building to this end auxiliary platforms that can integrate with corporate systems and automate the end-to-end processes.
Bank treasury services offer a slew of products and services – from cheque processing to ACH/Wire – to their corporate clients. For instance, a large bank helps one of the largest healthcare providers in the US process over 4 million transactions on an annual basis, covering their entire value chain, from providers – corporate hospitals (B2B) and individual doctors (B2C) – to pharmaceuticals (B2B).

  • There is a clear opportunity to digitally onboard these entities onto the payments network in a rapid, secure manner using DIY portals as well as create an ‘omni-channel’ like experience that minimizes onboarding friction.
  • Automating a payment network of this size and complexity is undoubtedly an integration challenge, given the multiple legacy systems at enterprises and, increasingly, ERP systems. With the increasing adoption of API (application programming interface)-based data exchanges, end-to-end automation with multiple payment rails is a necessary building block for digital payments.

Data and analytics

Payment processing through digital channels is data rich: strategies and execution led by analytics on the transaction data can help in a variety of ways: improving revenues, cutting operating costs, detect fraud and other anomalous behaviour.

Risk and fraud analytics: As payments migrate to digital platforms, it is almost inevitable that fraud becomes more sophisticated too. And, as the volume and complexity of payments grow, fraud is becoming just as hard to track, identify and prevent. Fraud prevention will have to move beyond transaction-centric assessment to leveraging AI for detection and prevention of emerging fraud. Broadly speaking, organizations need to think of payments fraud at two levels:

  • Account fraud: Digital identity theft is a leading cause of fraud. Methods like ATO (account takeover) and synthetic identity creation can be used to gain access into accounts and siphon funds. Tracking and preventing this requires going beyond the traditional knowledge-based authentication methods to monitor authentication journeys, looking for anomalous patterns.
  • Phantom payments: Businesses lose significant amounts to fraudulent payments, triggered both by employees (e.g., initiating a phantom payment) or payees (e.g., creating double invoices). Monitoring and flagging them requires a range of methods, starting from rule-based systems (e.g. proximity of transaction requests) to more sophisticated machine learning methods (e.g., payee behaviour risk-scoring and setting of guard-rails sensitive to each risk segment).

Data monetization: A bank managing the payment flows between its corporate clients and their network of suppliers and customers has the unique ability to understand the financial behaviour of all the firms in this network. This can be a powerful tool for the bank to develop targeted strategies to drive superior experience and value for its clients:

  • Working capital optimization: Banks can help their clients optimize their working capital by forecasting fund flows and using that to transfer the optimal funds into their payment accounts.
  • Service bundling: Using a combination of behavioural and transactional patterns, banks can help define optimal service bundles for their clients. For example, corporate payments that span multiple countries can be optimized with a combination of exchange-rate hedging and currency-float solutions.

Adoption of standards

Open Banking

Starting in 2015, when the European Parliament adopted open banking standards (PSD2), there has been a growing momentum in adoption of standards. And, as it happens with standards, this can catalyse innovation and efficiency across the world of payments once they reach a critical mass of adoption. Open banking regulations require banks to open up their systems and data to third-party providers through secure channels. This has the potential to accelerate:

  1. Seamless transfer of funds between banks, using standards as opposed to relying on the current custom of point-to-point software integrations.

    digital-payments-disrupt-or-get-disrupts

  2. The capability of corporations with multiple bank accounts across currencies to efficiently aggregate bank account data into a single accounting portal for automated reconciliation, mitigating issues in one of the most complex of payment transactions – crossborder payments.

Blockchain technology

Several financial services firms are increasingly looking to blockchain technology to mitigate the risk of fraud. The three fundamental underpinnings of the technology are distributed ledger, and immutable and permissioned access. Taken together to underpin a payment processing service, they make it possible to trace the entire sequence of wire transfers. Visa launched its B2B Connect Platform based on a private blockchain with the aim of enabling faster cross-border payments. Similarly, a host of banks, including HSBC, BNP Paribas and ING, launched Contour, a blockchaininspired platform designed to make the $18-trillion trade finance market more efficient and secure. I expect this space to see a lot more action in the coming years.

After decades of plodding along with archaic systems, the $2 trillion behemoth that is the global payments industry, is waking up, shaken up by the fintechs (a revolution of sorts that PayPal ignited).12 And, as it often happens, innovation in one sector rapidly spills over to adjacent areas; the dramatic change that started in consumer payments created the technology building blocks for digital disruption in corporate payments too. Combined with the adoption of standards and, most notably, the maturity of blockchain technologies, the corporate payments industry is primed for a burst of innovation.

A Complementary Partnership

“Data is the new currency.”— has gained immense popularity in recent years as data is now a highly valuable and sought-after resource. Overtime data continues to be accumulated and is becoming increasingly abundant.​​ The focus has now shifted from acquiring data to effectively managing and protecting it. As a result, the design and structure of data systems have become a crucial area of interest, and research into the most effective methods for unlocking its potential is ongoing.

While innovation and new ways keep coming to the fore, the best of the ideas currently consists of two distinct approaches in the form of data mesh and data fabric. Although both aim to address the challenge of managing data in a decentralized and scalable manner, they have different approaches and benefits, and they differ in their philosophy, implementation, and focus.

Data Mesh

The architectural pattern was introduced by Zhamak Dehghani for data management platforms that emphasize decentralized data ownership, discovery, and governance. It is designed to help organizations achieve data autonomy by empowering teams to take ownership of their data and provide them with the tools to manage it effectively. Data mesh enables organizations to create and discover data faster through data autonomy. This contrasts with the more prevalent monolith and centralized approach where data creation, discovery, and governance are the responsibility of just one or a few domain-agnostic team(s). The goal of data mesh is to promote data-driven decision-making and increase transparency, break down data silos, and create a more agile and efficient data landscape while reducing the risk of data duplication.

Building Blocks of Data Mesh

data-management-platforms

Data Mesh Architecture

Since data mesh involves a decentralized form of architecture and is heavily dependent on the various domains and stakeholders, the architecture is often customized and driven as per organizational needs. The technical design of a data mesh thus becomes specific to an organization’s team structure and its technology stack. The diagram below depicts a possible data mesh architecture.

It is crucial that every organization designs its own roadmap to data mesh with conscious and collective involvement of all the teams, departments, and line of Business (LoBs), with a clear understanding of their own set of responsibilities in maintaining the data mesh.

Data mesh is primarily an organizational approach, and that's why you can't buy a data mesh from a vendor.

Data Fabric

Data Fabric is not an application or software package; it’s an architectural pattern that brings together diverse data sources and systems, regardless of location, for enabling data discovery and consumption for a variety of purposes while enforcing data governance. A data fabric does not require a change to the ownership structure of the diverse data sets like in a data mesh. It strives to increase data velocity by overlaying an intelligent semantic fabric of discoverability, consumption, and governance on a diverse set of data sources. Data sources can include on-prem or cloud databases, warehouses, and data lakes. The common denominator in all data fabric applications is the use of a unified information architecture, which provides a holistic view of operational and analytical data for better decision-making. As a unifying management layer, data fabric provides a flexible, secure, and intelligent solution for integrating and managing disparate data sources. The goal of a data fabric is to establish a unified data layer that hides the technical intricacies and variety of the data sources it encompasses.  

Data Fabric Architecture

It is an architectural approach that simplifies data access in an organization and facilitates self-service data consumption. Ultimately, this architecture facilitates the automation of data discovery, governance, and consumption through integrated end-to-end data management capabilities. Irrespective of the target audience and mission statement, a data fabric delivers the data needed for better decision-making.

Principles of Data Fabric

Parameters Data Mesh Data Fabric
Data Ownership
Decentralized
Agnostic
Focus
High data quality and ownership based on expertise
Accessibility and integration of data sources
Architecture
Domain-centric and customized as per organizational needs and structure
Agnostic to internal design with an intelligent semantic layer on top of existing diverse data sources
Scalability
Designed to scale horizontally, with each team having their own scalable data product stack
Supports unified layer across an enterprise with the scalability of the managed semantic layer abstracted away in the implementation

Both data mesh and data fabric aim to address the challenge of managing data in a decentralized and scalable manner. The choice between the two will depend on the specific needs of the organization, such as the level of data ownership, the focus on governance or accessibility, and the desired architecture.

It is important to consider both data mesh and data fabric as potential solutions when looking to manage data in a decentralized and scalable manner.

Enhancing Data Management: The Synergy of Data Mesh and Data Fabric

A common prevailing misunderstanding is that data mesh and data fabric infrastructures are exclusive to each other i.e., only one of the two can exist. However, fortunately, that is not the case. Data mesh and data fabric can be architected to complement each other in a way that the perquisites of both technologies are brought to the fore to the advantage of the organization. 

Organizations can implement data fabric as a semantic overlay to access data from diverse data sources while using data mesh principles to manage and govern distributed data creation at a more granular level. Thus, data mesh can be the architecture for the development of data products and act as the data source while data fabric can be the architecture for the data platform that seamlessly integrates the different data products from data mesh and makes it easily accessible within the organization. The combination of a data mesh and a data fabric can provide a flexible and scalable data management solution that balances accessibility and governance, enabling organizations to unlock the full potential of their data.

Data mesh and data fabric can complement each other by addressing different aspects of data management and working together to provide a comprehensive and effective data management solution.

In conclusion, both data mesh and data fabric have their own strengths but are complementary and thus can coexist synergistically. The choice between the two depends on the specific needs and goals of the organization. It’s important to carefully evaluate the trade-offs and consider the impact on the culture and operations of the organization before making a decision.

What is Contract Pull Through?

The pharma sales team engages in contracts with brands, hospitals, clinics, infusion centers, doctor offices, IDNs, ONA, GPOs, and other networks. These networks are often referred to as pharma accounts, and contracts are lined up to improve overall sales, market share, and profitability. Contracts with these accounts are based on various factors such as rebate percentage, formulary tiers, and performance-based fees.

Now, the current size of pharma gross contracted sales is to the tune of 50B USD and projected to grow to 85B USD over the next 5 years[1], making this a big area for the pharma commercial team to have a close look and improve sales effectiveness while engaging with these accounts. Pharma companies are interested in the contracted sales, rebates, terms, and tiers data from the accounts to measure effectiveness. Mainly to figure out which of the existing accounts are underperforming, and which are performing above benchmark – and this aspect of pulling contract data across accounts to measure account effectiveness is the key objective of Contract Pull Through.

We may define Contact Pull Through, as the analysis of –

  1. How much an account has purchased (sales) by contract program and by brand;
  2. How much they’ve received in discounts (rebates, chargebacks);
  3. How they are doing against their baselines; and
  4. Where are the opportunities to buy more and save more?

Why does pharma need to focus on Contract Pull Through?

Large and mid-size pharma companies have Contract Pull Through from the accounts, as the top-of-mind problem, as even increasing effectiveness by 2-5% would mean savings to the tune of millions of dollars.

Based on our experiences working with the client’s market access team – we realized that the organization delegated the task of supporting field pull-through entirely to its payer account managers. These executives reported spending 75% of their time creating and pulling reports, time that could have been better spent with customers or in more strategic dialogue with the field members.

The key stakeholders for Contract Pull Through are the field team members i.e., Business Engagement Managers (BEMs) and Healthcare Market Directors (HDs), who need to focus on Contract Pull Through for –

  1. Generating contract awareness and pull through for major providers in their ecosystems
  2. Creating awareness around the contracts/terms offered by pharma firms for the products
  3. Engaging with customers to show their historical performance and current performance

Some specific Contract Pull Through use cases the pharma account team focuses on include –

  • A portfolio purchasing summary of the account, which enables the account to understand how much volume the account has bought, and the savings received from pharma products
  • Product contribution at an account level, which enables the understanding of how much volume is coming from each pharma product
  • Contract eligibility of an account, to understand which contracts are available at a certain account

Other business insights that Contract Pull Through data may help pharma companies are around –

  • How is the account performing as compared to others, in their ecosystem/region?
  • Which is the account’s dominant payer, and how does that payer work at a national level?
  • How much this account can purchase to reach the next tier?

Use case deep dive: Portfolio purchasing summary of an account

A portfolio purchasing summary of the account is one of the critical use cases handled through Contract Pull data – what the Contract Pull Through team is looking for is to understand a particular account within a regional ecosystem, across all periods of the contract or for a particular period –

  • What has been the number of gross sales? Has that gone up or down compared to its last quarter?
  • What part of the total gross sales is contracted vs non-contracted? What part of sales is attributed to specialty pharmacy? What percentage of account savings is attributed to contracting?

Also, another key insight to look at for portfolio purchasing summary for the account is to identify the product mix/segment mix (GPO, 340B, NCCN, etc.) across the portfolio, and double click on which product/ segment contribution has gone up, or down over the previous periods, and how does it fare against its anticipated baseline numbers.

Insights of this degree can immensely help the field team members understand how much volume the account has bought and the savings received from contracted pharma products, how are the accounts performing against their expectations, and where there are opportunities to buy more to save more.

How to generate the pull through business insights from data?

To arrive at these insights- the key data elements that must be looked into are contracts, chargebacks, 867 Sales, non-contracted sales, rebates, terms and tiers, account hierarchy, and zip to territory-mapping data.

These data sets coming from different source systems are ingested, assimilated, and presented as output for improved decision-making –

  • Firstly, it needs to be ingested into cloud-based or on-prem databases by using RPA tools like UIPath
  • The ingested data then goes through a set of data quality engine checks to ensure data is of expected quality.
  • The clean dataset then is transformed through the ETL process, where complex calculations through business-defined rules are applied, and
  • Presented through BI tools reports providing visualized/graphics and tabular data on gross sales, savings, performance, opportunities for an account, and other pull through insights.

Conclusion: CPT – Top of mind for strategic and financial objectives

BEMs/ HDs have Contract Pull Through insights generation as a huge part of their mind share. Thus, large and mid-pharma organizations are, or should invest in building a robust Contract Pull Through system to enable them to understand how the accounts are performing against their expectations, and in turn, identify the opportunities for them to sell more to optimize rebate payments. By doing that they have financial benefits to the tune of millions of dollars in terms of optimized rebates, and savings. As also the strategic incentive to understand the top accounts to concentrate on, and the key underperforming accounts to re-negotiate the contracting terms and tiers.

1 U.S. Pharmacy Market Size, Share & Growth | Forecast [2028] (fortunebusinessinsights.com)

Self-service AI refers to the intelligence that business users (analysts and executives) can acquire on their own from the data without the extensive involvement of data scientists and engineers. It means enabling them to acquire the actionable intelligence to serve their business needs by leveraging the low-code paradigm. This results in reduced dependency on other skills such as core IT and programming and makes faster iterations possible at the hands of business users.

As the data inside and outside of the organizations grows in size, frequency and variety, the classical challenges such as hard-shareability across BUs, lack of single-ownership and quality issues (missing data, stale data, etc.) increase. For IT teams owning the data sources, this becomes an additional task to ensure provisioning of the data in requisite format, quality, frequency and volume for ever-growing analytics needs of various BU teams, each having its own request as a priority request. 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 draw the exact insights that will help take direct actions based on critical insights 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 their business users to take their critical decisions faster every day leveraging the true power of data.

Enablers of self-service AI platform – Incedo LighthouseTM

Incedo LighthouseTM is a next-generation, AI-powered Decision Automation platform targeted to support business executives and decision-makers with actionable insights generation and their consumption in daily workflows. Key features of the platform include:

  • Specific workflow for each user role: Incedo LighthouseTM is able to cater to different sets of users, such as business executives, business analysts, data scientists and data engineers. The platform supports unique workflows for each of the roles thereby addressing specific needs:
    • Business Analysts: Define the KPIs as business logic formulations from the raw data, also define the inherent relationships present within various KPIs as a tree structure
    • Data Scientists: Develop, train, test, implement, monitor and retrain the ML models specific to the use cases on the platform in an end-to-end model management
    • Data Engineers: Identify the data quality issues and define-apply remediation across various dimensions of quality, 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 further on to 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 LighthouseTM using combined data and model pipelines that are configurable in the hands of the business users.
  • Integrable with external systems: Incedo LighthouseTM 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 LighthouseTM 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 LighthouseTM thereby making it a one-stop shop for insights and actions.

Incedo LighthouseTM as self-serve AI at a Pharmaceutical Clinical Research Organization (CRO)

In a recent deployment of Incedo LighthouseTM, the key user base is the Commercial and Business Development team of a Pharma CRO. The client, being a CRO, had drug manufacturers as its customers. The client’s 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 that have lower conversion propensity and/or have lower total lifetime value. This was mainly due to judgment-driven, ad-hoc and simplistic, static, rule-based identification of leads for the Business Development Associates (BDA) to work on.

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

  • The raw data related to the prospects – using which the features are to be developed for the predictive lead generation modeling – were lying in different silos inside the client’s tech infrastructure. This led to inertia to develop 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 LighthouseTM platform was deployed that allowed them to:

  • Combine all the data sources’ information into a Customer-360-degree view, enabling the BDAs to look at a bigger picture effortlessly. This was achieved by pointing the readily available connectors within the Incedo LighthouseTM platform to the right data sources, and establishing data ELT pipelines that are scheduled to run in tandem with the data refresh frequency (typically weekly). This allowed the client’s business analysts to efficiently stitch together various data elements, that were earlier lying in silos, in a self-serve model and include custom considerations that are region and product specific during the data engineering stage.
  • Develop and deploy AI/ML predictive models for conversion propensity using Data Science Workbench which is part of the Incedo LighthouseTM platform, after developing the data engineering pipelines that create ‘single-version-of-the-truth data’ every single time raw data is refreshed. This is done by leveraging the pre-built model accelerators for predictive modeling, 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 helped with the operationalization of various ML models built in the process, while connecting model outputs to various KPI Trees and powering other custom visualizations.
  • Deliver key insights in a targeted and attention-driving manner to enable BDAs to make most of the information in a short span of time. This is achieved through well-designed dashboards to rank-order the leads based on the model reported conversion propensity, time-based priority and various other custom filters (e.g. geographies, areas of expertise). The intuitive drill-downs were encoded using the region-specific KPI Trees to enable them to know the exact account portfolios of their business that were lagging behind. These KPI Trees were designed by the client’s business analysts within the platform’s self-serve KPI Tree Builder, saving multiple iterations with the IT teams. The KPI Trees allowed the BDAs to double click on their individual targets, understand the deviations from actuality, and review the comments from earlier BDAs who may have been involved, to decide the next best actions for each lead.

The deployment of Incedo LighthouseTM not only brought about real improvements in target conversions, but also helped transform the workflow for the BDAs by leveraging Data and AI.

The global financial services industry has seen disruption over the past few years with new fintech players and digital giants eating up into the market share of the legacy banking institutions. Whether it is digital credit lending platforms, payment tools or new credit card issuers, the common narrative to drive user adoption and build market share is enhanced customer experience through personalization.

The Value of Personalization for Retail Banking

There has been a sudden shift in the way some of the banks are leveraging personalization across the customer lifecycle. The key focus areas and objectives to enhance customer experience and deliver incremental impact for the bank are mentioned below :

  1. Building a Growth Engine to capture new markets and customer segments
    The next gen fintech players have been able to create data-enabled products that enable faster underwriting by using the prospect’s bureau scores, digital KPIs, social media data etc. to identify the creditworthiness of a prospect. The big banks need to move fast to enable similar, faster turnaround times for loan fulfillment to enable higher acquisitions from new customer segments.
  2. Maximizing customer lifetime value for existing customer base
    In order to maximize the share of customer’s wallets, banks have the following levers – improved cross sell, better servicing & higher retention rates. The best-in-class firms are leveraging AI-enabled, next best action recommendations to identify what products should be offered to a customer at what time and through what channel. The focus has also moved from reactive retention interventions to proactive, data-enabled retention strategies personalized to each customer.
  3. Improved risk assessment and mitigation controls
    Use of Personalization is not limited to marketing interventions, the digital world needs personalized controls when it comes to risk management, fraud detection, anti-money laundering and other control processes. Sophisticated AI models for risk, fraud & AML detection coupled with real time implementation is critical to build strong risk defense mechanisms against fraudsters.

The overall impact of personalization in Retail banking is dramatic with added opportunity to improve experiences across the customer touchpoints. Based on Incedo’s deployment of solutions for banking and fintech clients, given below is an illustrative example of use cases and potential opportunities.

value-potential-personalization

Building a personalization engagement engine requires integrated capabilities across Data, AI/ML and Digital Experiences

The reimagined customer engagement built on personalization requires a clear understanding of customer’s needs, behavior, requirements etc. and the ability to integrate these with customer front end platforms and channels (website, mobile app etc). This requires capabilities interwoven across the spectrum of Data, AI/ML and Digital Experiences.

  1. Data foundation and the strategy to enable a 360 degree view of the customer:
    Most of the banks struggle to stitch together a holistic profile of the customer in terms of their products, lifestyle behavior, transactional patterns, purchase history, offers used, preferred channels, digital engagement etc. This necessitates development of a clear data strategy right from capturing customer touchpoints to building 360 degree data lakes for the customer. Given the huge data storage requirements, this may also mean building modern digital cloud platforms that capture not only customer’s purchase history but also granular data points like digital clickstream data.
  2. AI, ML and Analytics-enabled decisioning layer to drive Next Best Action Recommendations:
    Identifying the right product/offer/service for the customer at the right time and through the right channel of engagement is important to ensure it converts into an optimal experience for the customer. This is done through a series of AI models, customer segmentation, optimizations etc. These AI/ML models are built on historical data and are tested, monitored and enhanced on an ongoing basis to ensure any new feedback is incorporated into the models.
    Building an AI/ML engine needs expert Data Scientists and Business Intelligence experts with a background in ML, statistics and contextual domain knowledge.
  3. Optimal Digital Experience to capture customer attention and maximize conversions:
    Data-enabled recommendations do not work if not supplemented with the right creatives and simplified communication that drive call to action for the customer. Digital Experiences that enable impactful omnichannel journeys whether through email, website, mobile app, ATMs, branches, service reps etc. are very important in this regard. The A/B testing of digital experiences that may include application forms, digital journeys, website interstitials, campaign banners etc. is critical to build best-in-class customer experiences.

While data, AI and digital experiences are the three key building blocks of the personalization enabled engagement layer, there is a need to orchestrate and integrate these capabilities to ensure that banks are able to deliver value from the personalization initiatives. Building these capabilities is no mean task and can take long time cycles to reap any tangible benefits, especially in cases where firms are building these capabilities from scratch.

How to turn the personalization opportunity into reality ?

While many traditional banking institutions have tried to build personalization enabled engagement, it has been observed that either the efforts fail or do not scale up over time, leading to non-optimal ROI on investments made.

Apart from building capabilities across data, AI/ML and digital experiences, it is critical to embed the personalization recommendations into enterprise users’ workflows whether it is a part of CRM, Salesforce, Credit risk decisioning systems etc. The end-to-end decision automation of workflow is critical to drive adoption and actual implementation of personalized experiences for customers.

Incedo’s LighthouseTM enabled CX personalization for the banks is an enterprise grade solution that enables shorter time to market for Data/AI enabled marketing personalization and accelerated realization of personalization opportunity.

Incedo’s LighthouseTM enabled CX personalization solution for the banks enables automated AI/ML enabled decisioning right from the data layer to customer reach out, to ensure personalized product, offer or service is delivered to the customer when it matters. The prebuilt library of Customer 360 degree data lakes and AI/ML models enable accelerated implementation of personalization initiatives. This is supplemented with digital command centers and on-a-click of a button operationalization of recommendations to deliver omni-channel engagement.

incedo-personalization-solution

No matter where the banking clients are in their personalization journey, the solution is implemented in a way that it helps realize the business impact within a matter of weeks and not years. The solution implementation is supplemented with a personalization roadmap for the organization, where Incedo’s team of experts work together with client teams to not only implement solutions but also help the firm build its in-house personalization capabilities over a period of time.

It is critical for banking institutions to acquire new customers, maximize customer value and retain their best customers at a greater speed and accuracy then ever before. Right from personalized account opening experience to hyper-personalized cross sell product and offer recommendations to trigger-based retention strategies, providing a “wow” experience to the customer needs personalization capabilities. Complementing the trust that traditional banks and credit unions have with these capabilities would ensure that they continue to maintain their competitive advantage over the new fintech players or digital giants.

In today’s world, where the evolution of digital technology, AI and machine learning algorithms has influenced human lives, the concept of AI-driven, personalized experiences across customer touchpoints with the business has been gaining traction for some time.

“85% of businesses say they are providing somewhat personalized experiences to customers, and 60% of consumers agree with that.” – Twilio Segment Report

“72% of customers rate personalization as ‘highly important’ in today’s financial services landscape” – Capco research report “Insights for Investments to Modernize Digital Banking”

The application of personalization is becoming ubiquitous now – from the kind of articles that search sites show us, to the posts and reels that come in front of us on social media, to the kind of products that get recommended to us everywhere on the Internet. Personalized recommendations have become the smart marketer’s greatest tool and weapon for reaching out to their customers and creating a differentiation from the competitors.

For early adopters of personalization in the banking sector, the focus today is on investing in increasingly better and faster ways of personalization. Personalization today combines features bank customers want and are willing to pay for as inspired by digital banking with the human touch that still remains vital for effective customer engagement. Some of the banks, however, are still new in the journey and trying to formulate the strategy of AI-driven personalization.

Below we discuss some of the avenues where the CMO’s office has been able to unleash the power of AI-driven personalization and reap huge benefits from it.

1. Personalized Product & Service Recommendations

Retailers have been using personalization extensively to sell to and engage with their customers better. While e-tailers pioneered this space, we are starting to see companies across Banking, Telecom, FMCG & Electronics sectors, etc. using the power of personalized recommendations to enable their cross-selling and up-selling campaigns.

Banks look at factors like demographics, income & employment, transactional activity levels, spending patterns, debt worthiness & repayment, etc. to build a 360-degree view of their customers. Using this intricate knowledge of their customers’ financials and spend behavior, they are able to create extremely personalized offers aimed to provide the customers with the right financial tools suiting their lifestyle and needs. The level of precision helps the banks not just attract customers better, but also trim down the costs of traditional mass-reach channels like call centers.

Wealth Management firms are using similar techniques as well. As part of a more services driven business, these firms are helping financial advisors cater to investors with more personally tailored advice. Based on the knowledge of the investor’s behavior and life goals, advisors get access to the recommendations around the next-best-action to take to their clients. Also, by understanding the advisors themselves, the WM firms are able to offer them a suite of services more aligned with the advisor’s personal style of research and portfolio building.

2. Personalized Marketing Communication

Not only are companies able to tailor their products and services, but they are also able to personalize the way they communicate these to their customers. Measuring the effectiveness of past campaigns on customers, marketers can tweak some of the below levers for marketing personalization:

Messaging: Depending on the buyer segment, the messaging for a specific product can be focused on offering discounts for the discount-diggers OR for providing detailed product features for the heavy-research users OR for product comparisons for the more flexible early-stage users, etc.

Channel Personalization: Using Channel Affinity models, marketers run focus campaigns targeting the right customers on the right channels. Banks, for example, target customers having high lifestyle spends with credit card display ads on shopping sites. At the same time, the HNI customers get offered a more personal touch with the relationship managers calling them with special customized offers.

Communication Time personalization: Knowing a customer’s travel search history, Telecom companies can offer international roaming plans at the perfect time. Banks also use this strategy to offer instant lines of short-term credit to customers with a low account balance. Also, in general, based on an understanding of when a customer is most active on social media or their smartphones, marketers can run social media campaigns or send notifications to the customer for maximum impressions.

3. Personalized Digital Experiences

Beyond trying to influence the buying behavior directly, the most effective form of personalization to be offered is to update the way customers engage with the firm on a regular basis. By knowing what customers are doing on the company websites, the marketers can get a far deeper understanding of the customers’ needs and expectations. This particular vision led the CMOs to realize the extremely inadequate digital behavior tracking that companies have on their own portals and applications. What followed was a surge in digital data collection platforms like Google Analytics and Adobe Analytics. A few companies have managed to deploy these solutions effectively to understand their customers better than ever. This led them to build models for Journey Personalization, which aimed at providing the customers with the fastest path to conversions based on their interests and preferences.

Businesses that have managed to leverage the power of personalization, have consistently been able to create a differentiated positioning from their competition. This has allowed them not only to attract customers away from competitors but also command a premium price at the same time. The clear business advantage has led them to invest heavily in more use cases and enhance their models from good to great.

Incedo LighthouseTM – A platform to natively support personalization use cases

With our proprietary platform Incedo LighthouseTM, we help clients successfully deploy multiple use cases for AI-driven customer personalization. The platform brings together Big Data (millions of customers, daily updated, across several dimensions), data engineering and data science in an efficient use-case centric manner in self-serve mode. The platform can serve multiple use cases for personalization together, e.g. cross-sell offers with the right channel for right customer cohorts at specific times of the year. This leads to faster and automated implementation of the journey:
– From data to critical insights: e.g. identification of cohorts of customers that would respond to deep discounts
– And, From insights-to-actions recommendations e.g. evaluating statistically the required level of deep discounting to optimize ROI

Significant success of AI-driven, personalized recommendations has not come without its fair share of speeding tickets. Couple of examples include:
– Compromising Personally Identifiable Information (PII) inside the machine learning lifecycle, thus jeopardizing customers’ privacy
– Inadvertently introducing biases into the recommendation algorithms, leading to discrimination and unfair business practices

Incedo LighthouseTM helps in protecting against these issues in a very direct manner – more on that in the next blog!

Wealth management industry is transforming rapidly as it pivots towards the fee based advisory model. The advisory model by nature requires a deeper level of relationship with the customers as compared to the commission based model which is more transactional in nature. At the same time, wealth managers are facing challenges from

  • Changing client mix and expectations,
  • Fee compression
  • Fintech disruption.

You can read details about how digital disruption is shaping the wealth management industry in our previous blog. Investment management has been commoditized and is no longer a differentiator. A robo advisor can perform portfolio allocation much better and at a much lower cost than a human advisor. If the advisors expect to charge more than robo advisory fee then they need to offer personalized financial advice based on holistic understanding of the client’s life stage, risk profile, investment objectives, preferences etc.

Issues with Traditional Segmentation Methods

The foundation of all personalization efforts is rooted in understanding the clients better and segmenting clients along the slices of client value, potential, demography, behavior etc. Client segmentation is informally practiced at wealth management firms but tends to suffer from some limitations

  1. Static segmentation – Client segmentation is not a one time product purchase, it is a continuous and dynamic process. Customers move from one segment to another over a period and their investment preference, risk profiles can change based on their own life events or market conditions. For example, in the current zero interest environment there has been more demand for the riskier instruments even from the conservative investor segments. Traditional or one time segmentation tools are not able to consider drifts over a period and can therefore provide stale results.
  2. Just focused on Value – Every practice or financial advisor at an informal level knows their most valuable clients as measured by assets under management or fees/ commissions earned. But value-based segmentation only provides you descriptive inputs about the advisory practice. It ignores other key parameters that can help in personalizing investment decisions, service models etc. Example: life stage has direct correlation with investment recommendation eg. 529 plans for mass affluents with young kids or IRA rollover recommendations for pre-retirees.
  3. Not scalable – Informal or semi automated segmentation methods have trouble in scaling when the number of clients increase and segmentation variables multiply. Traditional segmentation models can place clients in one segment or another but tend to provide mixed results when the number of variables and data points increase. On an average an advisor has about 80-100 clients. If we are talking about a large advisory practice with multiple advisors or ensembles with a shared servicing model, it is not possible to keep track of all clients and their changing variables without automating it.
  4. Limits personalization – The end objective of segmentation is not just to place clients in one bucket or another, it needs to inform the decision making process for personalized next best action. A static or non automated segmentation process stays mostly at macro level. To personalize client recommendations, micro segments need to be created and the manual segmentation methods struggle with that objective. Example: Within the retirees macro segment, the objectives, risk profiles and investment patterns of early retirees will be different from those of late retirees. In the accumulation stage, the investment objectives of investors with kids will be different from those with double household income and no kids. Unless we create micro segments, wealth managers will continue to provide advice which may be generic and at worst non contextual.
Segmentation needs to be dynamic, scalable, micro level and should inform the Next Best Action

Growing use of ML/ Data science in Client Segmentation

Although the use of data science and machine learning is growing in the wealth management space, the industry still lags various other consumer facing industries in using the full potential of Data and AI/ML. Today, there are multiple factors which are making it easier for wealth managers to use the power of machines to build segmentation engines.

  • The data sources and volumes have exploded and there is much more fine grained level of client data available than ever before.
  • There is large body of knowledge from the experience of other industries on how ML based segmentation enables data driven marketing
  • Lastly, cloud now allows for unlimited compute capacity by spinning concurrent workloads to perform complex processing and data analytics at minimal costs.

factors-driving-increase-datascience-machine-learning

Various wirehouses, BDs, RIAs, technology providers over the last few years have started using AI to drive their segmentation model and recommendation engines. Machine learning based client segmentation can create data driven clusters which may not be readily visible via manual segmentation. Machine learning algorithms can analyze multiple deterministic features and analyze their correlation to create unsupervised clusters sharing homogeneous characteristics and behavior patterns. Such clustering does not suffer from any unconscious bias stemming from informal segmentation.

A scalable machine learning based segmentation model relies on the following data types and is able to slice customer data along multiple dimensions. Some examples below:

Segmentation TypeBased onSegment ExampleData Required
Geographic & DemographicLocation, Age, Income, Profession, genderUrban vs Rural
Millennials vs Baby Boomers		Client & Account Data
Value/ Potential ValueGDC , AUM, Type of Revenue (Fee vs Commissions), NetworthUNHW, HNW, Mass Affluent, Masses
High Value Vs Low Value		Trades Date, Advisory Billing data, Positions Data
Risk ProfileGoals, Risk Profile, Return objectives, Time HorizonConservative vs Aggressive InvestorSuitability Data
BehavioralTrading Frequency,& PatternsPassive vs Active InvestorTrades Data, Positions Data, CRM
TechnographicEngagement with ApplicationsTechnologically challenged vs Tech Savvy ClientsPortal & App Analytics ( Number of logins, time spent)

This data can also be supplemented with external data to provide additional insights which may not be apparent from the first party data. For example, first party data such as client zip location when supplemented with external census data can provide valuable information about zip affluence, education level, demographic segment etc. Similarly, held away investments and accounts data can help paint a holistic financial picture of the client and determine the advisor’s wallet share.

Client Segmentation across Customer Journey

Let us see how client segmentation aids data driven decision making and helps in improving key metrics during the client’s journey:

Client Acquisition- RIAs can align their prospecting efforts with the client segment value proposition to ensure a larger prospect funnel and higher prospect to client conversion . As per the Schwab 2020 RIA benchmarking study, the firms that adopted an ideal client persona and client value proposition attracted 28% more new clients and 45% more new client assets in 2019 than other firms. Therefore the first step is to identify your target segment and align your messaging and marketing accordingly. example

  • A business development campaign aimed at Pre retirees and retirees needs to focus on themes of safety & capital preservation while one focussed on young professionals will focus on themes of growth and return. Segmentation engine can identify geographic areas which are likely to have prospects that match the firm’s target segments and where a particular campaign will find most resonance
  • In another example, the segmentation engine can classify the leads and prospects data into specific segments by matching the lead characteristics with existing client segments. Segmentation engines can predict if a lead is likely to become a high value customer and also suggest the kind of campaign that will appeal to them.
  • Wealth Managers are now combining client segmentation and advisor segmentation to predict and match which advisors will best serve a prospective client based on prospect’s preferences, life stage etc
Wealth managers are now using segmentation for matchmaking between clients and advisors

Client Growth- To capture the greatest wallet share of their clients, advisors should tie the investment recommendations to the client’s demographic, psychographic and risk segmentation. We talked earlier about recommendations for 529 plans for investors with young kids and rollover recommendations for pre retirees. Some more examples on how customer segmentation engines are feeding into next best action platforms to provide contextual recommendations for clients:

  • Growing popularity of ESG with the younger investors or the increased sales of life insurance to the urban middle age group during pandemic are good examples of how advisors and product companies align product recommendations with client segment’s preferences.
  • Similarly, if the clients are more focused on increasing their retirement savings, then recommendation around how they can contribute more than defined limits using backdoor roths will be appreciated by client
  • If the client is in a high tax bracket currently, then the advisor needs to recommend tax deductible IRAs while if they are going to be in higher tax brackets during their retirement, then Roth IRAs may be a better investment vehicle.
  • When segmenting based on the client’s browsing behavior, wealth managers can also send research reports and/or articles pertaining to sectors/ investment products that the client searches for in the portal. In addition, the portal can provide these inputs to advisors on the client dashboards for their next conversation.

As technology such as direct indexing mature further, clients will increasingly ask for customization based on their values, preference, beliefs and the wealth managers will have to offer customized portfolios at scale.

Client Servicing- Effective segmentation also helps in building a tiered service model with differentiated services to the most valuable clients and repeatable services to all other clients. Some wealth management firms craft personalized experiences for their top clients based on their hobbies and interests. Psychographic and technographic segmentation also help in devising the service channel for the clients. For example,

  • Clients that delegate all their investment responsibilities to their advisors and give them discretion on their accounts tend to prefer a light touch servicing model.
  • Clients who want high touch services and want to validate investment decisions would be more impressed by detailed research and analysis.
  • While a third category technically savvy clients want to have all the information , portfolio and plan details available anytime anywhere and prefer online service channels

Client Retention- A client mix skewed towards low value and unprofitable clients can encumber and advisory practice’s service levels & profitability and can put the most valuable clients at attrition risk. Many wealth managers have their bottom 50-60% of clients contributing only about 5% of their revenues and the top 20% accounting for more than 80% of the revenue. Therefore, advisors should periodically shrink to grow better. Client segmentation can help wealth managers prioritize clients for retention and for letting go based on current value, potential future value, influence potential. Ageing baby boomer population has brought on another kind of client attrition risk for advisors. As per industry studies, the financial advisors are not retained 70% to 90% of the time when the wealth transfers to the next generation. The client retention efforts in such cases therefore not only need to focus on the immediate clients but also on the next generation.

Segmentation is a growth as well as a Defensive Imperative

Thus, ML based segmentation can greatly aid data driven marketing efforts for wealth managers and lead to a higher return on the marketing dollars. It leads to measurable efficiencies in client servicing, attracting more clients and retaining high value clients. Lastly, it lays the foundation for a personalization engine for targeted recommendation, communication and servicing. While we have focussed above on how client segmentation can turbocharge an advisory practice’s growth, it is also a defensive imperative for the wealth management industry. FAANGs have perfected customer segmentation and personalization to an art form and are eagerly eyeing trillions of dollars of the wealth management industry. Wealth managers would do well to weaponize their data by using the power of machines and insulate themselves against the looming threat of big pocketed disruptors.

FAANGs have perfected customer segmentation and personalization to an art form and are eagerly eyeing trillions of dollars of the wealth management industry

To achieve the full promise of ML based segmentation, data infrastructure needs to support running of segmentation models at scale. To paint a holistic client picture, wealth management firms also need to break data silos and ensure availability of high quality, harmonized and consumable client data. Our next blog will discuss the data challenges in the wealth management industry and how the modern data management techniques can help overcome these challenges. Till then Happy Segmenting.

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