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KPMG Digital Lighthouse: Powering Life Sciences with advanced analytics

© 2021 KPMG LLP, a Delaware limited liability partnership and a member firm of the KPMG global organization of independent member firms affiliated with KPMG International Limited, a private English company limited by guarantee. All rights reserved. The KPMG name and logo are trademarks used under license by the independent member firms of the KPMG global organization. NDP058692-1A

ContentsIntroduction 2

KPMG Digital Lighthouse serves the Life Sciences industry 3

What sets us apart 4

Life Sciences lifecycle capabilities in the context of D&A: maximize revenue, contain cost, reduce risk 6

The analytics value wheel: revenue, cost, risk 9

KPMG accelerates solutions on the analytics value wheel by enabling action on a rich array of Signals 11

The KPMG Signals Repository: An advanced decision platform 12

The value of a Signal is the context it provides 13

Case studies 14

Thought leadership 22

1KPMG Digital Lighthouse:

Powering Life Sciences with Advanced Analytics


Introduction

The Life Sciences industry is moving toward a dramatically different future, where more diseases are cured, treatments are personalized, and patient/consumer preferences are at the center of decision-making. This future will be powered by deploying data science – and richer data from the external world – to drive the earnings growth that shareholders and markets will continue to expect.

In this context, there are a number of data trends that have significant potential to accelerate opportunities for the Life Sciences industry. The industry as a whole is seeing a wide spectrum of innovation—from interoperability of useful health data to next-generation therapies and personalized medicine.

Both regulatory requirements and market realities are driving movement toward value-based care. This movement will be achieved through the use of advanced analytics to drive a greater focus on outcomes and the patient experience. As the move toward value goes hand in hand with closer scrutiny of healthcare costs, many organizations in healthcare and life sciences are adopting new alternative payment and reimbursement models.

After significant government- and growth-driven technology investments over the past decade, there is a need for organizations to increase their focus on performance optimization. These considerations are particularly applicable for organizations in subsectors that are more likely to pursue roll-ups to gain better market penetration and achieve significant economies of scale.

Finally, the evolving view of the patient as connected consumer is driving vertical integration in subsectors as varied as pharma and biotech, medical devices, physician practices, and home health. Designed to create entities that meet the needs of the whole patient, we believe that their success in the coming years will be underpinned by their ability to continuously listen to and decision on signals from the world around them.

Increasingly, market leading organizations wanting to take advantage of these trends will require enhanced capabilities in data science, Machine Learning, and harvesting complex, varied, rapidly evolving internal and external data.


The KPMG Digital Lighthouse is a center of excellence for data & analytics. It works within the broader KPMG Life Sciences ecosystem to build client-leading advanced analytical solutions. Our goal is to accelerate our clients’ ability to use data to lead their marketplace, to adapt rapidly to disruption and, ultimately, to help achieve better worldwide health outcomes.

KPMG Digital Lighthouse serves the Life Sciences industry

KPMG is one of the largest providers of professional services to Life Sciences organizations globally, serving 100% of the top 25 global life sciences companies, 90% of the top health insurers, and half of the top healthcare centers. Within this ecosystem, the Lighthouse has dedicated Life Sciences professionals that bring data-driven solutions and capabilities to accelerate digital transformation at global scale for the clients we serve.

The Digital Lighthouse fields a team of professionals dedicated to the Life Sciences sector.

Data Scientists & Modelers

Software & Data Engineers

Advanced Analytics Consultants

Experience in analytics, statistics, data mining, machine learning, natural language processing, mathematics and optimization.

Problem-solving algorithms, models and testing

Experience in large scale and distributed processing methodologies.

Rapidly ingest, transform and mine data.

Evaluate, design, build, test and manage ‘big data’ architectures.

Client Solutioning ExpertiseBusiness consulting and statistical background, combined with real-world experience in applying analytics to solve business issues.




What sets us apart

We combine AI with industry expertise and a business-first perspectiveSolve complex issues through the Life Sciences value chain with analytics and automation supported by technology

Build D&A solutions and end-to-end services integrated with the business

At every opportunity, accelerate innovationDeploy KPMG’s proprietary “built for purpose” AI acceleration platforms to allow our clients to transform with speed and scale

Drive innovation through cutting edge technologies such as IoT and Blockchain

KPMG has been recognized by analysts as a leader in data, analytics, and digital transformation, including:Data Science and Artificial Intelligence/Machine Learning implementation

Business Transformation Consulting Services

Worldwide Digital Transformation Consulting & Systems Integration Services

Provide context to decision makersEnhance our clients’ decisions with complex, varied, and fast-moving Signals that provide context from the broader world

Engineer and deploy Software-as-a-Service (SaaS) solutionsIncluding on the Cloud—to create ongoing value


Data-driven capabilitiesLighthouse professionals are proficient in the latest data analytics skills required to unlock value from data science

Statistics & modeling

Big data architecture

Supervised techniques

Information extraction & retrieval

Decision science/ops research

Natural language processing (NLP)

Unsupervised learning & clustering

Data mining & machine learning

— Bayesian data analysis — Discrete choice models — Exploratory data analysis — Linear regression — Monte Carlo methods — Panel data/longitudinal data

— Statistical estimation adaptive filtering

— Survival analysis — Time series analysis — Advanced regression

— Cloud computing — MapReduce — Stream processing

— Decision trees — Ensemble methods, random forests

— Logistic regression — Neural networks — SVM

— Document clustering — Fuzzy matching and term weighting

— Link analysis — Web crawling — Web scraping

— Decision theory — LP/mixed integer programming

— Stochastic processes & queuing theory

— Optimization & simulation

— Machine translation — Named entity recognition (NER)

— Part-of-speech tagging — Sentence parsing and chunking

— Sentiment analysis & text classification

— Topic modeling & keyphrase extraction

— Word sense disambiguation

— Unsupervised learning & clustering

— K-means — LDA

— Dimension reduction — Feature selection — Large-scale machine learning & algorithms

— Social network analysis and mining

— Ensemble methods and random forests

— Supervised techniques: SVM

— Supervised techniques: neural networks

— Supervised techniques: decision trees

— Supervised techniques: logistic regression

— Unsupervised learning & clustering: (k-means, LDA)




Life Sciences lifecycle capabilities in the context of D&A: maximize revenue, contain cost, reduce risk

Life Sciences lifecycle capabilities from a D&A perspectiveKPMG has identified dozens of key capabilities across the product lifecycle of Life Sciences companies – pharma and med device – where our Data & Analytics offerings can bring substantial value to the business by driving combinations of one, two, or all of the following outcomes:

— Revenue maximization

— Cost containment

— Risk reduction

These focus areas can be viewed on the Analytics Value Wheel (page 9).

Revenue maximizationRevenue maximization is achieved through Artificial Intelligence (AI) and Intelligent Automation (IA) primarily in the Product Launch

and Commercialization phases of the product lifecycle. At the core, the focus is on optimizing customer, product, and channel; analytics can help answer questions such as: where is the market/what product decisions drive value realization (micro-segmentation, demand forecasting), how do we best reach each customer (sales force optimization, customer activation, channel selection, discounts/incentives/promotions mix), and how do we maximize value for customers (predicting drug adherence, discounts and rebates optimization).

Cost containmentCost containment is achieved throughout the product lifecycle, with analytics focused on predicting drivers of “mismatches” between

what is required to achieve a goal vs. what is currently done that can be corrected. The questions analytics can help answer include where can we do intelligent G&A optimization, where can we cut sales and marketing spend while increasing impact (sales force allocation, sales territory optimization, channel mix selection), where can we remove excess operational overhead (manufacturing optimization, supply chain forecasting, call center optimization), and where is there preventable commercial

leakage or waste (commercial terms enforcement, commercial terms optimization with payers, product spoilage/diversion, fraud/waste/abuse).

Risk reductionRisk reduction is achieved throughout the product lifecycle, with a particular focus post-launch on detecting fraud, waste, and abuse. This is accomplished by weaving a Big Data

fabric—rich contextual data from multiple internal and external sources—around the value transfer ecosystem and deploying machine learning to score risk. This helps decrease false positives and detect previously unobservable signs of risk by tracking complex behavioral patterns. So, analytics can answer questions such as: which discounts/free goods may be fraudulent, which sales people may be engaged in T&E abuse, which HCP spend (e.g., on speaker events) may be fraudulent, which pharmacies are taking unfair advantage of a copay card program, and which doctors may be abusing a free drugs program?

In addition to compliance, predictive analytics or “advanced listening” can be used to decrease risks within operations. For example, in R&D, a predictive model can help identify which sites/PIs may not enroll sufficiently quickly/have patient drop-out; in manufacturing, a question analytics can help proactively address which product batches are at increased risk of rejected and why.


Area Capability Revenue Cost Risk

R&D Business planning (project and resource management)

Operational excellence (KPIs, R&D study and process metrics)

Performance improvement and cost optimization

Site selection

Clinical development Patient engagement

Mobile health and wearables

Data warehousing and analytics

Early payer engagement & RWE generation

Quality assurance

Product launch Global market access

US payer market dynamics

Commercial resourcing & ROI optimization

Patient & HCP experience

Government pricing

Multichannel marketing

Patient journey

Patient service program design

Commercialization Resource allocation

Strategic brand planning

Positioning & messaging strategy

Micro-segmentation & demand planning

Inventory management

Competition Social listening

Market share & predictive analysis

Generic Supply chain transparency

Predicting inventory fluctuations

Lifecycle capabilities for Pharmaceuticals/Medical Devices in the context of D&A: maximize revenue, contain cost, reduce risk

GenericCommercialization CompetitionProduct launchClinical developmentR&D

Throughout a Life Sciences product lifecycle, KPMG has identified opportunities to accelerate revenue maximization, cost containment, and risk reduction using data & analytics.

*Checkmarks denote which Focus Areas – revenue, cost, or risk – would likely be most affected with this Lighthouse capability.




The analytics value wheel: revenue, cost, risk

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RISK REDUCTION

KPMG has observed that leading Life Sciences companies find substantial value in focusing on the areas in the analytics value wheel. Large revenue maximization opportunities are often found in identifying and winning valuable customers, enhancing product considerations, optimizing channel effectiveness (including the sales force) to serve specific segments, and optimally expanding geographic footprint. Similarly, cost reduction efforts often fruitfully focus on stemming commercial leakage, aligning the operating model with profitable segment drivers, reducing overhead, and optimizing commercial capabilities. And, as companies consider risks associated with, e.g., HCP interactions, discounts, and third party distributors, KPMG has discovered a data lens to further drive insights into cost containment and revenue growth.

Advanced D&A techniques like Machine Learning and AI, powered by a growing set of external signals in addition to internal data, allow companies to reconsider all these areas in non-traditional ways to derive even more value.

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Revenue MaximizationIdentifying most valuable customers and how to influence their behavior

Product decisions that drive value realization

Mapping product mix to most efficient channel to serve specific needs/wants of customer segment

Further penetration in profitable geographies with corresponding investments

Business strategyOperational and performance alignment with business strategy

— Micro-segmentation — Customer value simulation

— Customized sales & support programs

— Demand forecasting — Pricing optimization — Discounts/incentives/promotions optimization

— Product support (e.g., information programs, speaker programs)

— Personalized channel selection (including digital)

— Salesforce optimization — Sales territory optimization

— Marketing mix optimization

— Returns processing — Customer activation — Demand planning & forecasting

— Hyperlocal demand planning

— Market selection — Sales territory optimization

— Population health

Cost Containment

— Contract management

— Commercial optimization (payors)

— Commercial terms enforcement

— Price simulation

— Manufacturing optimization

— Inventory management — Call center optimization & resolution

— Order management — Clinical trial site selection

— Predictive quality

— Intelligent G&A optimization

— Workforce shaping — Talent investment optimization

— Insourced/outsourced functions analysis

— Real estate rationalization

— Channel migration

— Salesforce & marketing optimization

— Sales territory optimization

— Channel optimization — Cost to serve/complexity management

Identifying opportunities for cost containment and revenue optimization through commercial terms

Aligning operating model with profitable segment drivers

Efficiency and cost reduction

Optimizing commercial capabilities and revenue growth

Risk Reduction

— HCP interactions — Anti-Bribery & Corruption

— Third parties — Travel & expense — Patient services

— Retaliation — Low performers — Exit & churn — Ethical culture — Talent retention

— Channel stuffing — Returns — Invoicing — Kickbacks — Contract compliance

— Donations & free goods — Samples — Price compliance — Diversion — Coupon programs — Copay cards — Discounts & rebates

Accelerating key compliance and internal audit areas

Reducing human resources related risk

Mitigating procurement and supply chain risk

Identifying and responding to risk in commercial activities


Being the first to discover an emerging opportunity or risk is a real market advantage and offers material value. To realize that value, companies need to be able to “listen” to all available data, and transform these data into “Signals”. Signals are sophisticated expressions of data that can be combined to uncover relationships and understand patterns.

Equally important, as market leading organizations seek to take advantage of the mega trends in Life Sciences, they will need to enhance their capacity to rapidly act on the harvested Signals. Advanced capabilities for action – even as Signals become faster, more complex, and diversified – are provided by carefully weaving Machine Learning into business processes.

Many Life Sciences organizations are deploying data science in parts of the life cycle. KPMG helps accelerate solutions by bringing advanced capabilities that listen to context in the broader world and allow rapid action on it.

KPMG accelerates solutions on the analytics value wheel by enabling action on a rich array of Signals

Other Enterprises, Customers, Regulatory Agencies, Economies—all intersect and interact in complex ways.

Organizations operate within a complex network of entities and institutions.

It’s no longer “sufficient” to simply understand Life Sciences market drivers. Companies need to “listen” to everything, to cast a wider net.

Markets are unbounded.

Relying on traditional operating principles, rules-of-thumb, and KPIs alone will not tell the whole story.

The old way of doing business, doesn’t fit the current state.

Keeping up with the change, the evolution, is a matter of survival.

A continuous, ever-changing, ever-evolving environment.

There’s an explosion of data, not only in the amount available, but also in terms of the type and complexity: (e.g., social, geospatial, demographic, unstructured)

A rapidly growing data-rich universe.

The traditional indicators or “decision drivers” do not and will not provide enough to making winning decisions for the future—a new generation of insights must be discovered.

Limited data availability




The KPMG Signals Repository: An advanced decision platform

The data organizations rely on – their lifeblood – are fleeting and perishable. New data and data sources are available 24 hours a day, 7 days a week, 365 days a year. These sources are increasingly high-dimensional and complex: they capture thousands or millions of variables about entities; they are non-linear; they combine geospatial and temporal characteristics. How do organizations know they are listening to the right signals? How can traditional KPI-led and human-led measures evolve to the speed and scale of modern decision-making?

The KPMG Signals Repository helps organizations pay attention to the right signals, 24 hours a day, 7 days a week, 365 days a year—even as the data rapidly grows and evolves.

Organizations know all of this change is out there, and the opportunity to interpret and capitalize on data and signals is great.

They understand this is critical to stay relevant in the marketplace and remain competitive.

They are already on a journey of discovery, but they want to accelerate that journey.

KPMG can help harvest new data, and different types of data, and interpret more signals, and uncover more complex patterns to help organizations reach the next level today, not tomorrow.

KPMG Signals Repository provides access to over 60,000 (and counting) external temporal and geospatial signals across multiple countries. This data is collected continuously from thousands of open sources around the world.

Structured and unstructured data is transformed into complex expressions, creating tens of thousands of signals. When used by machine learning and other AI systems, it helps our clients significantly improve their accuracy in predictions.

These Signals enrich the information our clients currently use and allow them to better ‘see’ how the future may look.

The Signals Repository also contains data science assets that act as accelerators for functional areas. One example is a Risk Library: an acceleration platform to rapidly identify fraud, waste, and abuse across multiple Life Sciences risk domains such as HCP interactions, T&E, third parties, and free goods through a unified framework of rules. These accelerators are powered by core external signals from the Signals Repository.


The value of a Signal is the context it provides

Within the KPMG Signals Repository, data are transformed into signals, then subsequently engineered into features within predictive analytical models. These features provide context.

They allow machines or algorithms to gain the same contextual understanding that we are able to observe as humans in day-to-day operations. These Signals lead to understand hyperlocal population health, behavior, and dynamics, and enable many of the capabilities on the Analytics Value Wheel.

Data Signal Context

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Rate

GPS

Time

Spend

Businesspartners

Demographic

Weather

School Calendar

RealEstate

Portal Login

Population Migration

Bank Branches/ATMs

CrimeStatistics

Internet of Things

Social

Examples

Physician specialties Density of specialties by neighborhood and practice area

Healthcare vibrancy of the neighborhood

Location of hospitals, clinics, and pharmacies

Relative density by neighborhood Access to advanced healthcare

Population migration. Net growth in population in a neighborhood over time.

Erosion/expansion of specific patient segments

Scheduled drug sales value. Moving average by drug by zip by year.

Comparative demand growth in a region

Rate of smoking, obesity, and health behaviors

Ratio of negative behaviors by age group over time within neighborhood

Likelihood of current and future medical conditions




Case StudiesD&A case studiesKPMG has developed and deployed D&A solutions across the Life Sciences value chain. Additionally, we are able to reach across industries – including consumer & retail, professional services, and telecom – for leading practices and learnings that can be fruitfully applied to the challenges faced by Life Sciences organizations.An asterisk (*) denotes case studies from adjacent industries– where learnings can be fruitfully applied to Life Sciences.A double asterisk (**) denotes areas where an approach has been developed but not yet deployed.A check mark shows the primary Focus Areas affected by the solution.The case studies below describe solutions across the analytics value wheel – from hyperlocal demand planning to detection of drug diversion. The unifying theme is that new D&A approaches to traditionally ‘solved’ problems were able to drive additional value. These D&A solutions were powered by large sets of signals – many external signals from the KPMG Signals Repository – to augment client data. We believe that data science enables companies to reconsider all the areas on the analytics value wheel in a new light to enjoy high ROI and create a healthier world.


DetailsReference Number

Focus Area

Value Area Capability Case Stu. Revenue Cost Risk

Contract Negotiations

Machine learning driven contract content extraction* 1

Operational Efficiency

Preventive maintenance of medical equipment 2

Supply chain transparency for enhanced profitability 3

Predictive Supply Chain Risk Management 4

Predictive quality/maintenance* 5

Working capital scenario planning 6

Lean Overhead Design

Optimizing workforce behavior & predicting high performance and churn* 7

G&A cost reduction through optimization assessment* 8

Go-to-Market Efficiency

Sales force deployment optimization **

Customer Value Propositions

Omni-channel analytics for patient outreach & adherence 9

Customer loyalty, engagement, attrition, and promotional effectiveness* 10

Enterprise AI strategy and opportunity prioritization* 11

Product Considerations

Signals-driven sales forecasting 12

Channel Efficiency Sales force effectiveness for customer behavior **

Geographic Footprint

Site selection/geographic footprint rationalization* 13

Hyperlocal market demand monitoring & demand shift prediction* 14

Risk & Compliance

Prescriber & prescription FW&A detection in a free drugs program 15

Copay card fraud detection 16

Pharmacy claims fraud detection 17

Detection of drug diversion 18

Early Warning Risk-detection for Post-Market Surveillance using NLP and AI 19

Post-Market Surveillance: Managing Complaints through Intelligent Risk Prediction and Prioritization 20




1 |Machine learning driven contract content extraction*

A large wireless telecommunications company was looking to automate its data abstraction processes and reduce the number of programs used for work management and data storage in order to improve business planning and contract consistency. Employees spent time on document handling and manually-intensive tasks. KPMG developed a single Cognitive Contract Management Solution that: (a) Automated consistent and efficient extraction of a high volume of contracts and contract-related documents, (b) Uploaded the metadata back into the source system, (c) Allowed for a quick and efficient review of the entire contract portfolio (d) Facilitated full oversight of all extensions and terminations of contracts

This Saved hundreds of hours of staff time originally dedicated to contract review. It also Increased the speed of document data abstraction, Informed commercial planning and decision making, Reduced labor costs, allowing staff to focus on higher value generating activities, Consistency across contract types, Improved risk and compliance

Combined with metadata extraction and analysis, this solution could be used to determine commercial leakage and reduce costs.

2 |Preventive maintenance of medical equipment

This predictive analytics solution proactively identifies devices that are in danger of failing multiple days before the failure occurs. Using machine learning, it automatically identifies the relevant features and patterns that indicate an imminent problem and automatically connects to the assigned technicians as well as the customer to notify about the high risk devices and components. It is also able to identify the potential causes for a failure. This solution can be modified and deployed in a broader transformation of the Quality function.

3 |Supply chain transparency for enhanced profitability

Using a holistic data and analytics tool, one of the world’s leading providers and manufacturers of healthcare solutions was able to enhance its supply chain efficiency.

KPMG used a state-of-the-art data and analytics tool enabled the current situation to be analyzed, providing full transparency throughout the supply chain.

The combination of customer, order, inventory, sales, and cost data facilitated full transparency on customer and product profitability. Based on this new level of data transparency, more than 60 levers along the supply chain for one of the analyzed markets were identified, for a total potential margin improvement of 3.5%. Improvements will be mainly achieved in the following areas: Customer (lifecycle) management, revenues, inventory, and cost to serve.

After showcasing the initial results, defined measures were implemented quickly to fully realize the identified potential.

D&A case studies—Descriptions


4 |Predictive Supply Chain Risk Management

The client, a major medical device company, was reactive to supply chain risk management and needed to improve visibility and reduce contingency costs. KPMG designed a digital solution which uses smart internal and external data points to predict and forecast events based on live data through advanced analytics. Results were visualized in an interactive dashboard. The client was able to find significant reduction of contingency costs. This presented a significant improvement in the client’s visibility of overall supply chain performance and risk management requirements.

5 |Predictive quality/ maintenance*

Similarly to pharma and med device companies, a leading manufacturer of consumer and commercial automobiles has major costs repairs related to warranty and recalls. Even a small percentage of reduction in repair and recall cost adds hundreds of millions of dollars to the bottom line. Hundreds of engineers were involved in the detection and investigation of safety and quality issues related to its millions of vehicles of all makes and models and was looking for an automated solution to predict potential issues as early in the problem lifecycle as possible. A dynamic big data search application to help the engineers investigate the issues was also required.

KPMG used the client’s on-premise platform to design and develop an end-to-end automated solution that included:

Data Ingestion: a program to incrementally ingest new data sources to the platform

Data Profiling: a distributed data profiling framework that provides quality reports, advanced analytics, and visualizations that generate insights for business users

ML Prediction Models: multiple data models to predict vehicle issues based on all available structured and unstructured data

Severity and Prioritization Engine: specific engines to prioritize issues and assign severity

Search Application: a search application to search the large number of data sources in the big data platform, enabling further investigation issues

UI and Industrialization: built custom UI and dashboards to streamline the end user experience

This solution replaces the manual and time-consuming processes associated with issue detection, investigation, and prediction. As a result, the client is able to detect the issues much earlier than previously possible which in turn reduces costs for repairs and potential for recalls.In addition, the search application gives comprehensive access to the big data environment tostreamline the investigation process.




6 |Working Capital Scenario Planning

A global medical supplies and pharmaceutical distributor needed to refine its working capital scenario planning processes. The client previously used an Excel-based model to forecast aggregated long-term working capital requirements. This approached offered little visibility into the data or ability to diagnose underlying product- and segment-level issues and identify root causes. The client had significant data limitations and forecast scenario planning was based on institutional memory instead of verified, detailed data on accounts payable/receivable, inventory, net sales, and margin. KPMG developed a three-phased approach that included: scoping the forecasting process, prototyped a data model/dashboards to illustrate future state functionality, and creating an industrialized production-ready model and dashboards for long term usage and iterative development.

The outcome was a documented, enterprise-wide view of working capital drivers and measures. This was enabled by automated, efficient, and scalable procurement and transformation of forecasting data for FP&A stakeholders. The outcome led to enhanced visibility into events and trends to better understand working capital needs and root-cause drivers, and to enable early identification of irregularities. Ultimately, this improved the client’s ability to predict working capital needs, through diagnostic variance analysis, driver- based forecasting, and enhanced scenario planning, driving effective capital deployment and increased value.

7 |Optimizing workforce behavior & predicting high performance and churn*

A global professional services firm wanted to optimize its workforce performance and reduce recruitment and on-boarding costs by identifying characteristics of high performers and employees at risk for voluntary separation.

KPMG developed an “always-on” model that combines advanced analytics and almost 10k internal and external signals, along with machine learning, to identify characteristics of high performers, future leavers, and performance drivers and recommend management interventions to reduce voluntary turnover.

The model examined a wide range of data signals including client engagement, phone and e-mail communication patterns, commissions earned, meeting participation, and other activity-based signals of effective workforce performance or employee satisfaction.

KPMG’s model is designed to pull data from relevant sources automatically and provide continuous results back to the client, enabling the client to make real-time decisions with current information.

The client was able to achieve significant improvements in workforce performance and satisfaction while reducing recruitment and replacement expenses. The client was able to determine that 65 percent of employee attrition is identifiable, 60 percent of high performers are identifiable within the first months of employment, and 75 percent of performance variance can be identified and managed.

Performance managers and HR teams are now able to access dashboards displaying weekly or daily scoring of employees for risk, future performance, and micro-behavioral KPIs, as well as specific remediation recommendations for employees.

The client can apply these signals and characteristics to its employment base to improve the satisfaction of high performers and to reduce turnover by intervening with employees at risk of voluntary separation.


8 |G&A cost reduction through optimization assessment*

A Professional Services firm had experienced double-digit YoY growth. The business complexity had risen but the back-office had not kept pace: G&A cost had continued to grow in parallel to revenue growth. The client was not achieving scale economies needed to identify saving opportunities to reduce G&A cost. The client needed a rapid assessment across multiple back office functions including Finance, HR, Facilities, Legal, Procurement, and IT, to identify cost optimization opportunities that support sustainable growth in preparation for budget review and strategic planning. KPMG leveraged proprietary and fee-based benchmarking databases to provide the client with meaningful analytics-driven quantitative, qualitative, and sector insights using a hypothesis-driven approach to rapidly identify high-value cost levers. This was combined with operational analysis for each down-selected opportunity to refine indicative savings, benefits, and timing of delivery. Opportunities were translated into implementation plans to rapidly capture benefits.

As an outcome of the analysis, KPMG discovered $3.7 – 6.8M of indicative savings through indirect spend analysis, $1.2M worth of savings in recruiting cost, and $7.3M in personnel cost savings by establishing a shared service center in a brown field offshore location. This led to a potential 2-3% reduction in G&A cost as a percent of revenue.

Further analysis was performed on AR aging report and led to identifying opportunities to improve working capital, prioritize collection target accounts based on value, and recommendations for collection improvement and monitoring. Further, KPMG conducted sessions around Procurement Optimization, Working Capital, Quality Close, and Cloud Optimization to help the client drive identified opportunities to benefits realization.

9 |Omni-channel analytics for patient outreach and adherence

A global pharma company launched a Connected Treatment system in which patient dosing and administration was directly captured (auto-injector) and transmitted (mobile app) to the HCP, enabling real-time monitoring of adherence. Despite paid outreach, adoption was low.

KPMG performed a detailed journey analysis to understand the challenges that patients undergo and how using the Connected Treatment system would fit within their lives. KPMG consumed a range of data including: mobile app downloads/registrations, Connected Treatment system usage, nursing calls and interventions, path analysis across the web, and prescribing behavior to identify opportunities across the pathway to collect mobile data and aggregate analytics into a real-time feed of actionable adherence measures.

This effort led to direct increase in revenue through improved adherence. It also indirectly increased revenue by using the improved compliance rates with patients as messaging tool to HCPs (to write more prescriptions of the underlying drug as well as to encourage adoption of the Connected Treatment system). And, this enabled the company to pursue more innovative social outreach.




10 |Customer loyalty, engagement, attrition, and promotional effectiveness*

Evolving technology and increasing customer expectations are creating business pressures more than ever in the Entertainment industry. With the rapid growth of other entertainment options and an aging customer demography, business had—for this land-based wagering company—been declining in recent years. In order to better compete with online gaming and other forms of gaming entertainment, this company was looking for a way to leverage its loyalty program data to discover new business strategies and individualized customer tactics.

KPMG identified 3 key areas to explore: (a) Individual drivers of customer engagement, (b) Customer-level sensitivity to promotions, points, and incentives, and (c) Imputed wallet size and share.

To start, KPMG determined and codified each individual’s normative pattern of engagement with the company for all spend and non-spend interactions. Using these time series Signals, KPMG developed behavioral clusters of individuals, then compared all members within each cohort in order to determine wallet size and share. Individuals were also examined within and independent from their respective cohorts in order to determine their sensitivities to incentives and promotions. Ultimately, individualized and segment-level marketing plans were generated that would maximize engagement.

The exercise revealed a number of customers that the company believed were healthy and fully engaged who were in fact slowly attriting; the company was able develop marketing tactics to reinvigorate many of them. More importantly, the solution enabled more effective use of marketing dollars, and increased wallet share and total spend from targeted customers and segments.

11 |Enterprise AI strategy and opportunity prioritization*

A major pharmaceutical retailer’s previous efforts to build analytics capabilities were decentralized and stunted by the inability to gather the correct people and technology. The client was challenged by shrinking margins and needed to build internal capabilities to be able to act smarter and faster with data driven decisions. The organization sought an overall strategy focused around three strategic imperatives: (1) maintaining competitive advantage in a customer experience-centric environment, (2) creating a business need for expanding the advanced skillset headcount, (3) remediating a decentralized data management culture by creating a holistic data foundation strategy focused on developing AI capabilities.

KPMG developed an AI Point of View, mapping the journey to building artificial intelligence capabilities and how to approach investing in AI, and showing how competitors are taking advantage of AI and Machine Learning. KPMG also assessed the client’s existing data landscape, evaluated data sharing partnerships and confirmed current imperatives to identify initiatives that could improve the client’s access to data and insights and understand existing capabilities.

KPMG prioritized initiatives amounting to $10M gross revenue equivalent and developed a short term timeline to build foundational requirements and execute prioritized initiatives. This work prepares the client to: gain internal support for AI investments, inform stakeholders on the AI priorities identified by the executive team, start executing against prioritized opportunities, and build a sustainable data foundation to support the advanced analytics infrastructure.


12 |Signals-driven sales forecasting

This predictive solution, enabled by the KPMG Signals Repository, considers internal and external signals to generate more accurate forecasts of sales and revenue both short-term (quarters) and long-term (years). It evaluates a suite of dozens of predictive algorithms and thousands of external and internal signals to achieve greater accuracy than traditional forecasting methods. It also adds a layer of automation to a traditionally manual process and can deployed as part of a broader Finance transformation.

13 |Site selection/geographic footprint rationalization*

Similarly to Pharma, retailers are constantly looking to select new sites related to ambitious expansion programs (locally, or in entirely new geographies), or contemplating modification to existing sites through reinvestment or relocation. Others want to monitor current locations to assess markets suitable for expansion. Aggressive timelines, limited budgets, limited knowledge of new locations and an overwhelming number of other considerations when making these decisions make the need for intelligent site selection critical.

KPMG brought a library of thousands of “signals” deemed relevant in providing initial data needed to determine the viability of potential and existing store sites. KPMG developed

a highly accurate predictive model for sales at each location under consideration. The customized D&A models created for the client contained thousands of relevant signals, or drivers of demand, for any particular store site and the insights available provide a clear picture of how any potential location will perform over time.

If predicted sales at a potential location do not meet given metrics for success, the client can abandon the potential site and explore more suitable locations with the help of KPMG’s predictive capabilities, thus eliminating the typical guesswork in avoiding unprofitable locations—particularly as it ventures into unfamiliar new geographies.

14 |Hyperlocal market demand monitoring & demand shift prediction*

A global wireless service provider wanted to increase acquisition and reduce churn by understanding the effect of different marketing campaigns and strategies on local market subscriber movement; the carrier also wanted to deploy an automated watch-dog to quickly spot unexpected shifts in local market share.

KPMG leveraged advanced variable selection algorithms to identify the drivers of market subscriber movement from more than 31K internal and external signals, covering promotion, retail store footprint, consumer surveys & other primary research, plan pricing, housing market & real estate performance, wireless/broadband network quality & coverage, ethno-graphic, tax return, and population migration Signals.

Based on identified drivers, micro-geographies were clustered into similar markets, which were analyzed separately by tree-based gradient boosting models.

The model was then and is currently deployed into KPMG’s Always-On decision platform, which enables the client to continuously monitor subscriber health and be alerted when local market dynamics—often driven by their own and competitor actions—change unexpectedly.

The Always-On platform predicts the porting activity (subscriber movement both in and out of the network) for all markets in the entire country. With continuous monitoring of the daily activity in each market, alerts are triggered when anomalous activity is detected. Each alert is supported by causal analysis which can “point the way” towards intervention and remediation.

In addition to understanding what has already happened, the solution also allows the client to generate and evaluate – simulate – how each market will respond to a certain types of marketing actions, such as store openings/closings, advertising and promotions.




15 |Prescriber & prescription FW&A detection in a free drugs program

This proactive monitoring solution weaves a Big Data fabric around prescribers, patients, pharmacies, and prescriptions for scoring FW&A risk in the context of a free drugs program. It uses rules-based analytics and machine learning to create risk scores for prescriptions and physicians, as well as context for these scores. Results are delivered through an interactive visualization that facilitates executive level analysis of the overall program (e.g., risk types by geography, prescription trends) and investigative drill-down to the individual/activity level to create a jumping-off point for investigations. The solution is a foundational part of a broader Compliance transformation framework.

16 |Copay card fraud detection

This risk monitoring solution uses a proprietary library of rules to identify pharmacies which have a higher likelihood of being engaged in copay fraud. Tying together numerous data sets and activating the rule-set, it surfaces prescriptions that are likely to be fraudulent and allows a rapid overall overview of the pharmacy. Results are shared in KPMG hosted investigative dashboards that enable clients to make an informed decision regarding appropriate remediation actions regarding the activity and party. The solution is a foundational part of a broader Compliance transformation framework.

17 |Pharmacy claims fraud detection

This analytics-driven monitoring solution seeks to identify pharmacies likely to have purchased diverted product or made claims under programs for which they are ineligible. It ingests files from over 50 data sources, harmonizes the data, and uses a set of rules to identify red flags and pinpoint potential diversion locations. This solution can be deployed as part of a broader Compliance transformation.

18 |Detection of drug diversion

This continuous monitoring and detection solution leverages predictive analytics using unsupervised learning techniques to identify potential drug diversion (e.g., theft for personal use). It consumes thousands of Signals from multiple administrative systems such as electronic dispensing machines, physicians’ orders, time & attendance systems, patient admissions systems, etc., and generates a series of scores, including an overall risk score for each caregiver every month.

19 |Early Warning Risk-detection for Post-Market Surveillance using NLP and AI

Inadequate detection and delayed reporting of adverse effects in post-market surveillance can have profound financial, legal and reputational consequences for Pharma and Device manufacturers. There is a rich source of unstructured risk information in voice, chat and email interactions between manufacturer sales and call-center support teams and health providers and patients that can support earlier detection potential risks before they mushroom into larger issues. By use of advanced natural language processing (NLP) and AI techniques, KPMG has helped a major device manufacturer automatically flag interactions indicating potential risk to be reviewed and potentially escalated earlier in the post-market lifecycle.

20 |Post-Market Surveillance: Managing Complaints through Intelligent Risk Prediction and Prioritization

Device and Pharma manufacturers face intense pressure to manage, respond, and report an ever-increasing influx of device and drug complaints. Besides potential fines and reputational damage from regulatory non-compliance, keeping up with demand is leading to increasing quality and cost pressures by the complaint management and medical affairs teams. Complaints now arriving via a variety of different channels, including email, mobile apps, call center voice interactions and free-text chats. KMPG is working with a leading medical device manufacturer and distributor to build an AI/ML based model that uses past complaint history to actively predict and update the predicted risk of complaints in the system and help the teams achieve intelligent prioritization and staff load balancing to help improve patient safety, reduce turnaround time, respond proportionally to predicted risk and more efficiently allocate team resources for improved cost efficiency.


KPMG has recently released a number publications that represent a global point of view. Visit KPMG’s Advisory Institute for these publications and additional articles, webcasts and podcasts: http://www.kpmg-institutes.com/institutes/advisory-institute.html

Thought leadership

Pharma outlook 2030: From evolution to revolution

Two seismic shifts are impacting the industry: the need to demonstrate value from therapies; and the move from treatment to prevention, diagnostics and cure, all of which is bringing in a host of new competitors. Those pharmaceutical companies that manage to embrace the most appropriate archetypes, and master disruption, have the greatest chance to deliver real value to patients and be successful in the new, disrupted world.

Opportunities and Challenges in an Evolving Market: 2020 Healthcare and Life Sciences Investment Outlook

Our analysis of the 2020 KPMG Healthcare and Life Sciences Investment Outlook survey results sheds light on how the investment landscape will be impacted by significant changes in the healthcare and life sciences industry.

Pricing for Survival: As demand for pharmaceutical cost containment intensifies, novel drug-pricing approaches are critical

Specialty drug costs are so high that the entire pharmaceutical industry is now under scrutiny for its pricing practices. Although payers and regulators see the need for innovation, there is a much higher onus on drug companies seeking market access to demonstrate the value of their products. In this piece, we explore the practical approaches to pricing for value that pharma manufacturers can leverage today.




Using Analytics Successfully to Detect Fraud

In this article we examine factors behind the low detection rate of fraud using analytics, and ways that companies can build greater confidence in the effective use of analytics to combat fraud. In looking at the analytics-related aspects explored in KPMG’s Global profiles of the fraudster, the article offers perspective on the positive implications for businesses when trust is carefully managed in an anti-fraud analytics program.

Digitalization in Life Sciences: Integrating the patient pathway into the technology ecosystem

The effects of digital transformation and digitalization will be far-reaching. The industry will be migrating from a business model built around developing blockbuster drugs to one cultivated around a technology ecosystem which connects all stakeholders and, most importantly, deeply integrates the (prospective) patient.

Life sciences innovation and cyber security: Inseparable. Breakthrough drugs and devices present greater opportunities…and risks

Digital innovations are poised to take the life sciences industry into the future. However, it is crucial for organizations to remember that, for all the opportunities these technologies offer, they also come with significant cyber security and privacy risks.

Life sciences innovation and cyber security: InseparableBreakthrough drugs and devices present greater opportunities…and risks

kpmg.com


Bill NowackiManaging Director, Life SciencesT: 224-848-9237 E: [email protected]

Contact usMarcos SalganicoffDirector, Life SciencesT: 484-362-8109 E: [email protected]

Jordan SeiferasDirector, Life SciencesT: 201-320-2536 E: [email protected]

Some or all of the services described herein may not be permissible for KPMG audit clients and their affiliates or related entities.

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