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Who We Work With​

Visionaries and Innovators Choose EA

Mid-market executives who envision a bigger, bolder future turn to EA to realize their vision. Because even the most accomplished analytics leaders face blockers. Our skilled data, analytics and AI consultants help customers get unstuck and envision a path forward.

We align with industry-leading data and analytics technology vendors to deliver success. Working with proven partners is a winning combination that empowers us to help growth-mode businesses at any stage of their journey.

We Help Customers Become Data-Driven

We’ve collaborated with hundreds of CDOs, CIOs, VPs of Business Intelligence and other business/IT decision-makers. We build upon their past investments while simultaneously developing a roadmap toward a stronger future – one of tangible business value and growth.


Working together, we imagine the transformative possibilities for businesses with improved data and analytics as a foundation. Our consulting engagements drive new insights, build confidence, and deliver measurable outcomes.

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What Customers Say

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Industries & Domain Expertise

It’s important to work with people who understand your business. We’ve worked in many industries; our expertise is broad and deep. Below is a sampling of the industries and KPIs we’ve developed for them.

Financial Services
& Insurance

Data, analytics and AI offer proven transformational capabilities to financial services and insurance markets. Fraud detection and prevention, investment opportunity evaluation, credit risk assessment, and streamlined claims processing are common examples of how vast amounts of data can be used to gain a competitive edge, improve operational efficiencies, and better serve customers.  

Sample analytics projects:

  • Credit Analysis
  • Loan Exposure & Monitoring Insights
  • Portfolio Forecasting
  • Investment Insights & Optimization
  • Fraud Analysis

Healthcare &
Life Sciences

In a rapidly evolving industry, healthcare payers, providers and other organizations need to manage and make sense of large amounts of complex data from a variety of sources, including electronic health records, clinical trials, health insurance claims, wearables and more. To stay competitive, healthcare companies must invest in advanced data, analytics and AI to support the Quadruple Aim, stay compliant, and meet increased expectations for consumerized healthcare services. 

Sample analytics projects:

  • Claims & Revenue Cycle Analysis
  • Income Analysis – Payments, Write-offs, Adjustments
  • Value Based Care Insights & Recommendations
  • Provider Behavior and Patterns

Higher
Education

Data, analytics and AI are essential tools for enhanced teaching, student learning and institutional operations. By analyzing student data, educators can identify areas where students may be struggling and provide targeted support to improve academic performance. Data can also be used to predict student outcomes, such as graduation rates or future career paths, which can inform institutional strategies and resource allocation. With AI, higher education institutions can automate routine tasks and personalize learning experiences, freeing up faculty time and improving student engagement.

Sample analytics projects:

  • Student Analytics
  • Student Longitudinal Tracking
  • Research
  • Institutional Growth & Advancement

Manufacturing
& Logistics

With the rise of Industry 4.0, the Internet of Things (IoT), and other technologies, manufacturing and logistics companies are amassing a wealth of data to help optimize their operations, increase efficiency, and reduce costs. By leveraging advanced analytics and AI, companies can uncover new insights for more informed decisions and competitive advantage. Whether it’s predicting demand, optimizing inventory, or identifying bottlenecks in the supply chain, powerful data and analytics are critical to thrive in a hyper-competitive environment.   

Sample analytics projects:

  • Inventory Optimization 
  • Demand/Forecast Planning Analysis
  • Supply Planning Analysis
  • Shipping Lane Optimization
  • Control Tower

Sales &
Marketing

To fuel strategic growth, mid-market businesses must collect and analyze data from various sources to gain a better understanding of their customers’ needs, preferences, and behaviors. That’s why data quality and analytics are key for developing effective marketing and sales strategies, tailored product offerings, and optimized pricing and inventory management. In addition, AI-powered technologies such as chatbots, recommendation engines, and predictive analytics can help automate and streamline routine tasks, freeing up valuable time for employees to focus on more high-level, strategic work.  

Sample analytics projects:

  • Pricing Optimization
  • Sales Optimization Analytics 
  • Campaign Analysis
  • Customer Segmentation
  • Cross-Selling Analysis

Finance &
Operations

Finance and operations executives rely on data, analytics and AI to gain valuable insights into their financial performance, identify trends, and make informed decisions about their businesses in present and future states. Data analytics enable efficient monitoring of key metrics, optimize resource allocation, and identify cost-saving opportunities. AI technologies enhance forecasting accuracy, automate repetitive tasks, and enable real-time analysis, boosting operational efficiency. Together, data analytics and AI empower businesses to drive strategic decision-making, improve financial performance, and streamline operations in a data-driven world.

Sample analytics projects:

  • Revenue & Cash Flow Forecasting
  • Customer Profitability
  • Preventive / Predictive Maintenance
  • Customer Support Optimization

Technologies

Evolution Analytics is technology-agnostic. Our unbiased, vendor-neutral approach means that you get the best solution for your unique needs. While this list is a strong representation of the analytics tools and technologies we use, we continuously identify and vet new products and follow market trends to ensure our clients build their future with solutions that will be part of today and tomorrow’s modern data architectures.

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