Marty Afkhami helped optimize digital marketing spend for a client. First, he consolitdated and analyzed data from various platforms like Facebook, Google, and TikTok in real-time to determine which campaigns were producing the most traffic. This allowed them to shift marketing budgets within days instead of traditionally waiting for several months to optimize spending to generate more traffic. Next, he tracked leads generated from these campaigns and performed another budget adjustment based on most leads generated.  He then evaluated the lead conversion into customers by blending the marketing data with CRM and customer data, and was able to identify the most effective marketing campaigns, which led to another marketing budget adjustment. Finally, he analyzed the lifetime value of customers to further optimize their marketing budget, resulting in a significant reduction in spending while almost doubling the number of customers gained.

Marty Afkhami turned his focus on AP, which uncovered over 10 million dollars in duplicate invoices and unapplied credit memos. Reviewing and analyzing the AP payment discounts data, he prescribed the transition to a twice-weekly payment cycle, which increased the utilization of payment discounts. Previously, the annual budget was withdrawen once a year from the endowment investments. With the addition of HR data–salaries, to the data warehouse he was able to estimate funds required in the bank accurately. This allowed for quarterly withdrawals from the investment account, enabling continued growth of the endowment for three additional quarters.

Marty Afkhami successfully planned and implemented a global Tableau data visualization environment at Laureate Education, which contained over 10,000 visualizations and 5,000 users and facilitated data democratization across the organization.

Marty Afkhami was key in the development of AI and model-building initiatives at Laureate Education, that identified potential leads based on current and previous students for an online university. He significantly reduced the cost of student acquisition and increased lead conversions for an online university by creating a profile of 80 different parameters for their students.

Customer acquisition is costly; retaining your current customers is a good idea. Having built a comprehensive profile of students for lead generation we implemented and trained an AI model to identify those at risk of leaving and implemented measures to prevent students from dropping out, leading to a win-win situation for all parties.

In another project at the Metropolitan Police Department, Mr. Afkhami architected and lead the team that consolidated data from over 40 different systems and built the data governance to identify system of record for each data element and cleanse the entire system. This resulted in good, clean data that everyone knew they could rely on.

Computer vision is a branch of AI that examines images and videos and identifies and describes what they contain. It has many practical applications, such as counting cattle from an aerial photo or a stack of 2x4s.  MPD produces more video from stationary and body-worn cameras in a single day than all of Hollywood in a year.  We built a prototype AI vision system to examine hours of video and identify a car with certain characteristics heading away from a crime scene.

Marty Afkhami also helped a Texas public construction company by analyzing their previous and current contracts with the Texas Department of Transportation along those awarded to their competitors. This led to a sweet spot for the client, helping them increase revenue by maximizing the bidding value for each RFP.  He did this, by evaluating every single part of the previous contract awards and determining if the customer should bid on the RFP.  Then determine the maximum amount of the bid that ensures they are awarded the contract.

He implemented the collection, cleansing and normalization of financial data from various systems for 80 universities across the globe for consolidated financial reporting.