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Case Studies: Don’t take our word for it.
In the rapidly evolving landscape of analytics and artificial intelligence (AI), Deloitte LLP has taken a pioneering step to overcome the challenges of the tech talent shortage, particularly in the specialized fields of analytics and AI. This case study explores Deloitte’s strategic alliance with Experfy, a move that signifies a major shift towards an innovative future of work model by embedding Experfy’s on-demand freelance talent into Deloitte’s leading analytics and AI offerings.
The Problem
Vistaprint, a global leader in mass customization and web-to-print marketing materials, sought to enhance their email marketing strategy by introducing a recommendation system. The system aimed to not only predict products that customers are likely to purchase but also influence customer behavior and self-improve over time, incorporating new products seamlessly. Vistaprint’s ambition was to go beyond traditional recommendation algorithms, requiring a solution that could dynamically adjust to customer data and generate incremental responses.
Problem
Macy’s faced a challenge in their fulfillment operations – optimizing the utilization of box capacity to minimize the number of boxes and bags required for customer orders. This optimization was crucial for reducing overall shipping costs and enhancing the customer experience by ensuring timely and efficient delivery.
The Problem
The client, Keurig Green Mountain is a consumer product company, and their innovation team wanted to provide a constant supply of their product to consumers via anticipated direct-to-consumer delivery subscription service. Using data from a 6-month consumer research panel, they wanted to know how to predict long-term user consumption rates with 90+% confidence in addition to the minimum number of days of data required to make that prediction. This prediction would be used to determine when deliveries should be made.
Problem
Amway, a global leader in direct sales, faced challenges in understanding and leveraging the vast amounts of unstructured customer review and survey data collected across various countries. This data, crucial for enhancing customer satisfaction and tailoring product offerings, was not being utilized to its full potential. Amway sought to employ Natural Language Processing (NLP) and sentiment analysis to analyze this data, initially focusing on the Chinese market, with plans to expand to other languages based on the success of the initial phase.
The Problem
Over the last year, our client had been spending a substantial amount on recruiting patients for clinical trials. The pathway from when a patient inquires about the trial, is screened, has bloodwork done, and is then enrolled in the trial is a complex one. Our client wanted to develop a sophisticated approach to determine what is and what isn’t working on a city by city basis. Next, the client wanted to optimize spend across different advertising mediums.
The Problem
Taboola, recognized globally as the premier content discovery platform, faced the challenge of advancing its predictive analytics to enhance recommendation accuracy, natural language processing, and computer vision. Operating on a grand scale with a reach of 1B unique visitors and delivering over 360 billion recommendations monthly, Taboola aimed to employ deep neural networks (DNNs) to elevate its algorithm’s performance. This endeavor required innovative deep learning (DL) expertise, particularly in areas where established truth sets were absent, necessitating a creative and scientifically rigorous approach.
The Problem
Background: Total Administrative Services Corporation (TASC), the nation’s largest privately-held third-party administrator (TPA), has long been at the forefront of offering innovative services and solutions to businesses across all 50 states. With a significant footprint in the industry, evidenced by over 8,000 field representatives and more than 900 associates, TASC has successfully served tens of thousands of businesses, generating annual revenues exceeding $100 million. Despite its impressive scale and array of over 21 products and services, TASC identified a crucial area for enhancement: the predictive accuracy of customer behavior.
The Problem
Client Background:
ModusOne Health, a distinguished healthcare consulting and clinical documentation improvement company, sought to enhance its operational efficiency by consolidating diverse data sources and documentation processes into a unified, user-friendly software solution. Dealing with a multitude of platforms such as sFTP, Excel, Google Docs, and more, the organization faced the challenge of streamlining data management while ensuring stringent compliance with HIPAA regulations.
The Problem
Background:
Fresenius Medical Care – North America, a leading healthcare organization, sought to leverage advanced data analytics to improve patient care and operational efficiency. Recognizing the potential of data science in transforming healthcare services, the organization embarked on an ambitious project to analyze vast amounts of health data. The goal was to uncover insights that could lead to better patient outcomes, more efficient service delivery, and enhanced predictive capabilities in patient care management.
The Problem
Johnson & Johnson Innovative Medicine (formerly Janssen), a global leader in pharmaceuticals, aimed to harness the power of digital opinion leaders in influencing therapeutic areas, specifically within the Nordic region of Europe. Understanding the pivotal role of digital influencers in shaping healthcare discussions, the company sought to develop a comprehensive analysis framework to identify and evaluate these key individuals across Denmark, Sweden, Finland, and Norway.
The Problem
Background:
Ananda Development Public Company Limited, a leading real estate developer in Thailand, specializes in creating high-quality living spaces with a focus on connectivity to mass transit stations. The company’s portfolio includes acclaimed condominium brands such as “Ashton”, “Ideo Q”, and “Ideo Mobi”, as well as innovative housing projects under “Artale” and “Urbanio” among others. With a robust presence in Bangkok and its vicinity, Ananda Development aims to enhance the urban living experience through its unique architectural designs and strategic locations.
The Problem
Client Background:
Sourcewater, a leading data source for oilfield water data, recognized the need to innovate in analyzing satellite imagery for oilfield water-related features. Their objective was to internalize this capability, reducing reliance on external services, cutting operating costs, and enhancing the frequency and capabilities of satellite scans, thereby retaining intellectual property from investments in advancing these methods.
The Problem
Client Background:
Big Spaceship, an award-winning creative agency celebrated for leveraging cultural intelligence to solve significant business challenges, operates with a dynamic team of 115 employees from a central office in Brooklyn. Renowned for its work with industry giants like JetBlue, Starbucks, Google, and Hasbro, the agency aims to identify and capitalize on emerging trends before they become mainstream.
Problem
Push Observer has a growing business and a dedicated workforce. The company provides a wide variety of content based media services to its clients. It had dozens of employees sifting through newspaper clipping, television ads, and social media to identify places where their clients’ brand had been mentioned. Even though they had put in place systems such as Volicon, which provides video content logging, there were still many gaps that required Push Observer to work manually. Once the media mentions had been identified, Push Observer staff manually faxed its reports to the client. Push Observer, based in Tanzania, wanted to expand to ten additional African countries, but the present approach requiring manual work was not scalable.
The Problem
Our client is a leading mailing optimization company that helps large brands analyze their opportunities to mail more efficiently at a lower cost. While successful, the client felt that their internal sales organization could be more targeted in communications with both existing and prospective clients by better understanding the timing and type of interactions possible throughout the customer lifecycle.
The Problem
DePaul approached Experfy as they were facing a challenge common to a lot of organizations: they have a great product, engaged users and collect tons of data about it, but they don’t benefit from it’s full potential. The solution envisioned was to use Tableau to get the most value from this data.
The Problem
As a start-up advertising agency, we had some theories on the evolution of the ad industry. We wanted to explore this evolution in order to create a new agency crafted to service clients today and into the future.
The Problem
I host a lot of CEO-level events, for XPRIZE, Singularity University & Abundance 360.
One of the benefits of attending these events is meeting people that could be important to you – future business partners, customers, investors, etc.
The Problem
The client had a peculiar problem — loads of data from multiple sources, but was unable to manage a load into his database on cloud for analysis and hypothesis generation. Adding to it, the messy data structures were not helping the cause.
The Problem
I host a lot of CEO-level events, for XPRIZE, Singularity University & Abundance 360.
One of the benefits of attending these events is meeting people that could be important to you – future business partners, customers, investors, etc.
The Problem
The client had a peculiar problem — loads of data from multiple sources, but they were unable to manage a load into their database on cloud for analysis and hypothesis generation. Adding to it, the messy data structures were not helping the cause.
The Problem
The client was a European energy service firm interested in researching customer churn. Competitive energy markets for residential customers have meant that establishing customer retention through pricing driven contracts as well as quality of service are key. Finding leading indicators of a likelihood to terminate a relationship, within a specific time range, with a provider would empower the firm to move proactively to engage with the customer in an attempt to continue to provide energy for the customer.
The Problem
The client was a company that had invented a unique system to scan houses for possible energy remediation. It created a device that was mounted on cars and took thousands of pictures of city streets, much like Google Streetview. Unlike StreetView, though, this device took near-infrared (NIR) and LiDAR (a pulsed laser technology) images. The purpose was to scan houses to determine which would benefit the most from energy remediation services. The client was positioning itself to sell these images to local energy companies, which would then be able to optimize their remediation efforts to the houses that would benefit the most.
The Problem
W2O Group is a top-ranked independent network of complementary technology-enabled marketing and communications firms. They were performing an in-depth summary of online conversations surrounding a pharmaceutical brand and its competitors. The purpose of the analysis was to develop an understanding of the relevant online conversation, both pre- and post-FDA approval of a brand, to identify stakeholders and discover discussion patterns within the set of conversations. This in-depth competitive analysis focused on conversations from patients and caregivers.
The Problem
Our client is an active regional property developer with an interest in serving the booming senior living marketplace. While there are some large players in the industry, his firm felt that opportunities still exist for facilities with quality offerings in the right location. Without facilities currently open, the client can easily see where existing facilities are located but not necessarily where gaps in the market might exist.
Problem
Measuring the success of our financial literacy program was not going to be an easy task for us when we considered all of the variables. We had collected a lot of data from our students over the past 4 years, and we wanted to use all of that data to determine how impactful our program had been for our students.
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