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Case Study

Driving Revenue Growth through Data SSOT (Single Source of Truth)

The client is leading global chemical producer. They are headquartered in New York
City with creative, sales, and manufacturing facilities in 44 different countries. They supply
the food and beverage, fragrance, home and personal care, and health and wellness end
markets with innovative solutions that allow them to create the products consumers know
and love.
The business goal was to drive additional revenue from their top 25 customers. After
analyzing their processes, our practitioners discovered that account/client managers had
a relationship driven sales process, and were not equipped with knowledge across
business units and lacked understanding of products. They also had multiple constraints
such as restrictions to sell specific products in specific geographies. Besides some
products were customized to specific customers and could not be sold to others.

Altimetrik created a Single Source of Truth (SSOT) and enabled insights for cross sell and
upsell using machine learning algorithms considering product attributes, market data,
sales, constraints of geographies as well as customer spend capacity. These actionable
insights were validated and made easy to consume through a user-friendly dashboard.

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Digital Business Methodology

The Growth Code

We give you the DBM Growth Code: A step-by-step guide written by practitioners to help you accelerate digital business.

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Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs.Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify.
Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indicationsUtilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes