Margo is a rising sophomore at Duke University, planning to major in Sociology and Psychology, with a certificate in Markets and Management (MMS).

She thrives in innovative, fast paced environments and is curious about opportunities connecting business and innovation to drive social good.

Franklin is a rising sophomore at Duke University, studying Economics with an Innovation and Entrepreneurship Certificate. He is interested in creating connections and driving change for social good.

Cindy Syren is currently a junior at the University of Southern California pursuing a B.S. in Business Administration with a concentration in Business Analytics, Data Science, and Statistics and a minor in Political Science.

Cindy currently works as a Data Science, Management, and Technical Writing Intern at Silicon Valley Consulting and as a Model signed with Newmark Models. Cindy’s research focuses on Data Science, Political Science, and Sociology.

Diana Gaspar is a senior at the University of Michigan, Ross School of Business studying Marketing and Strategy.

She hopes to gain knowledge of sales, strategy and technology, during her summer with Silicon Valley Consulting.

In her free time, she enjoys watching rom-coms, volunteering in her community, and listening to global artist BTS

Lacey is a rising senior at the University of Michigan, studying English with a minor in gender and health.

She spends most of her time reading, both listening to & playing music, hanging out with her friends, and writing.

Problem:
A digital consumer platform with nearly 2 billion users recently expanded into the eCommerce space. When they launched their eCommerce integration into their platform, they started to receive a lot of data on their customers. The challenge they were facing was figuring out how to best interpret that large amount of data to better understand how users are using the new feature.


Solution:
We built a custom dashboard that updates in real-time, and visualize the whole process on how buyers and sellers are using the new feature. The following are some of the dashboard visualizations: What ad brought them to the store How many times did they view an ad or similar ad from the seller before they made a purchase Number of chargebacks per user 4. What products user have engage with most

Problem:
Several eCommerce stores did not know if certain posts on social media were actually bringing in sales. They wanted to track the ROI of social media posts.


Solution:
We created an AI/ML system that calculates the Return-On-Investment (ROI) of social media posts. When a company shares a post on their social media, this system is able to attribute how many sales occurred due to this social media post. This system is being used by several E-Commerce companies.


Extra Info:
Additionally, due to a recent partnership, this system has been adapted to work with an influencer marketplace website.
The aforementioned website utilizes our technology in the background to track the ROI of Social Media Influencer Marketing paid for by enterprise brands. We then helped this online influencer marketplace platform build out their content bidding system.

Problem:
This online reviews platform has nearly 200 Million active users. They were interested in improve ad engagement from users.
Their hypothesis was to improve the user testimonial shown on the ad (for the business) automatically using Machine Learning.


Solution:
We helped this user platform build an automated system that would' optimize the user review shown with each ad on their platform.

This system used AI/ML, specifically Natural Language Processing, in order to analyze which part of any user review would be best performing for the ad for that business that is advertising. This affected over 29 million users and businesses. This resulted in an 8% increase in user engagement with ads.

Problem:
This online platform has a large network of users constantly wanting to learn extremely niche topics. Their hypothesis was that users would pay to have experts answer their questions.


Solution:
We helped this online user platform create a "pay for expert advice" service in order to monetize off their users. Our task was to created an automated AI/ML service that can identify and rank experts on any internet subject. This service affected millions of users that either used the platform directly or used a secondary-platform connected to that platform.

Problem:
This online platform has a large network of users constantly wanting to learn extremely niche topics. Their hypothesis was that users wanted to only buy products recommended by experts and other users.


Solution:
We helped this online user platform create an automated system that would recommend products to users based off of posts from other users on the platform.

This system would allow the user platform to passively monetize their users by automatically recognizing a potential product being talked about, determining if other users are speak highly of the product.

Then automatically creating an affiliate link with Amazon, then automatically recommend that product to the right users.
This system utilized a lot of AI/ML, specifically Natural Language Processing, in order to determine positive sentiments or negative sentiments on a potential product identified by our other AI/ML systems.

This service affected millions of users that either used the platform directly or used a secondary-platform connected to that platform.

Problem:
This online user platform has around 400 million users. They wanted to personalize ads in order to increase user engagement with ads, therefore also increase average ad CTR and company revenue.


Solution:
We helped this online user platform create an initial version of the "Look-Alike" feature on their ads platform.
This feature allows advertisers to upload a list user identifiers (e.g. user's email, user's phone number, user's forum id) in order to do targeted ads towards similar users.
This feature showed a potential increase in ad engagement by 30%.