Capstone – Project Charter

Using Neural Networks to Unearth Patterns of Consumer Behavior

A Collaboration Between

Engagement Synopsis

Consumer Orbit, is a Consumer Science company that utilizes Big Data to help clients answer marketing and business questions about their customers. We work with clients to find data-driven answers to challenging business questions. Through our patented approach to data integration, we turn big data into action. We have combined the best available consumer behavior, attitude and media preference data with technology that allows us to create data-driven solutions designed to solve each client’s unique business challenges. The results are strategic, actionable and measurable, allowing clients to make informed decisions about things like media budget allocation, sales team alignment and product assortments at the market or store level. For this project Consumer Orbit is offering a non-statistical sample of our time-series online search data looking for more insight into which statistical tools are best at discovering consumption patterns for the American consumer and using those consumption patterns to predict behavior. Key project milestones: 1. Get Up-To-Speed on Consumer Orbit's Data & Design the Input and Output of the Neural Network 2. Conduct and carry out initial predictive modeling/classification/regression through the use of LSTM (Long-Short Term Memory) neural network architecture of online search data 3. Reexamine online search data with DNC (Differentiable Neural Computer) architecture to discover and predict consumer behavior patterns 4. Perform a comparative study and theoretical analysis of the two different results 5. Report the results to the company. A special attention will be given to potential bias and application research.

Company Information

CompanyConsumer Orbit LLC
HQMissouri
RevenueUnlisted
Employees10-25
StagePre-Revenue Startup
Hiring Potential
Websitewww.consumerorbit.com

Company Overview

Consumer Orbit, headquartered in Kansas City, with offices in Ft. Lauderdale, New York and Las Vegas, uses Big Data to help clients answer marketing and business questions about their customers. Founded by a team of experts in data modeling, statistics, analytics, data and media, we work with clients to find data-driven answers to challenging business questions. Through our patented approach to data integration, we turn big data into action. We have combined the best available consumer behavior, attitude and media preference data with technology that allows us to create data-driven segmentations and analytics designed to solve each client’s unique business challenges. The results are strategic, actionable and measurable, allowing clients to make informed decisions about things like media budget allocation, sales team alignment and product assortments at the market or store level. We integrate our client’s customer data, transactions, supply-chain information, and market research intelligence with our proprietary TotalViewTM database of the American Consumer with thousands of attributes on over 120 million households (and 63 trillion individual pieces of data). While the process is patented, the results are pure magic.

Company Supervising Team

Company Admin

Kristi

[email protected]

Course Info & Engagement Details

SchoolUniversity of the District of Columbia (UDC)
Engagement FormatCapstone - Small Team Consulting Project - Students work in small groups of 2-6 directly with faculty and host company project champions on developing real solutions to real-world challenges.
ProgramComputer Science and Information Technology
CourseThe Use of Conscious AI to Improve Business Performance
LevelGraduate
Students Enrolled10
Meeting Day & TimeMonday and Wednesday 4:30pm-5:50pm
Student Time Commitment4-7 Hours Per Week
Company Time Commitment1 Hour
Duration9 weeks (02/29/2020 - 04/30/2020)
Departments Involved
File Attachments

School Supervisors

Instructor

Byunggu

[email protected]

Students

There are currently no students assigned.

Collaboration Timeline

  • December 20, 2019

    Collaboration request published. Companies may express interest in participating.

  • December 29, 2019

    School faculty will begin interviewing interested companies and discuss project ideas.

  • February 21, 2020

    Final date for companies to express interest in participating.

  • February 21, 2020

    OFFICIAL PROJECT LAUNCH: We’ll find a time on this day to web conference you into our class to kickoff the project.

  • February 28, 2020

    School faculty and project champion finalize project charter, legal documents, and background materials.

  • April 30, 2020

    OFFICIAL PROJECT END: We’ll find a time on this day to web conference you into our class to close the project.

Key Milestones & Project Process

  • February 24, 2020 - KICKOFF MEETING @ 5:00 PM ET

    Kristi, the Consumer Orbit’s SVP of Analytics / CIO will web conference into class to discuss her product, vision, and the project scope


    Suggested Deliverable:

    Prepare for kickoff meeting by familiarizing yourself with Consumer Orbit’s website: https://www.consumerorbit.com 

  • March 6, 2020 - Get Up-To-Speed on Consumer Orbit's Data

    Online search data sample and schema from across 200 different retailer and shopping comparison websites:

    • How is the data generated?
    • How large is the data set?
    • What is the data schema and structure?

    Suggested Deliverable:

    • Design the input and output of the neural network
  • March 27, 2020 - Conduct and carry out initial predictive modeling/classification/regression through the use of LSTM (Long-Short Term Memory) neural network architecture of online search data

    • Conduct and carry out initial predictive modeling/classification/regression

    Suggested Deliverable:

    • Complete LSTM Portion of the project
  • April 17, 2020 - Reexamine online search data with DNC (Differentiable Neural Computer) architecture to discover and predict consumer behavior patterns


  • May 6, 2020 - Perform a comparative study and theoretical analysis of the two different results

    Pay special attention will be given to potential bias and application research.


    Suggested Deliverable:

    Report the results to the company