Date of Award

7-2026

Document Type

Thesis

Degree Name

Master Science in Social Media Marketing

Department

Marketing

Second Advisor

Elizabeth Kresock

Third Advisor

Kyle Coble

Abstract

Existing social media marketing literature consistently fails micro-small businesses with five or fewer staff. These sources default to models built for brands with dedicated marketing teams and large budgets. This project addresses that gap using a mixed-methods, practitioner-based action research design. It combines quantitative platform analytics with qualitative process documentation. The research covers four businesses at distinct stages of digital development: Torched Motorsport Supply, a new automotive parts retailer built from zero digital infrastructure; Byrne Diesel Performance, an established diesel shop with an unoptimized presence; Texas Best Compost, a startup developing its brand identity and digital launch disrupted by supply chain failure; and 73Dipstick, a niche manufacturer with a strong web presence but limited YouTube visibility.

Findings demonstrated that algorithmic architecture is the primary determinant of platform reach, with identical content producing different results across Facebook, Instagram, and TikTok. A Search Engine Optimization intervention, using free tools and AI-assisted copy, improved a business website's SEO and produced a verified customer acquisition within seven days. The content repurposing model, generating short-form clips from long-form YouTube source material, proved sustainable for solo operators and generated more content interactions than other short-form and still-image content.

These findings are synthesized into a practical Quick Start Framework for micro-small business owners with no formal marketing background. This framework covers audience research, SEO optimization, platform alignment, content strategy, and AI tool usage. It is grounded in peer-reviewed scholarship and first-person field testing. Unlike existing literature, it offers an accessible approach for micro small business owners to start their social media marketing plan.

Research Highlights

  • The Problem: Existing social media marketing literature consistently fails micro-small businesses with five or fewer staff members by defaulting to resource-heavy models built for large brands with dedicated marketing teams and substantial budgets.

  • The Method: A mixed-methods, practitioner-based action research design was conducted across four active small businesses at distinct digital development stages, combining quantitative platform analytics with qualitative process documentation and AI-assisted search optimizations.

  • Quantitative Finding: The FRAM vs. AC Delco short-form video clip produced highly variable algorithmic reach across platforms within the same 24-hour window, generating 102,000 views on Instagram, 33,000 views on TikTok, and 792 views on Facebook; a targeted six-day SEO intervention using free tools and Claude AI improved a business website's audit grade from a C- to a B; a one-day, $8.00 TikTok Promote campaign generated 2,607 views and 16 followers at a cost of $0.50 per follower; the 73Dipstick YouTube channel achieved 9,262 views in April 2026 following description optimization that increased average word counts from 16.5 to 42.0 words.

  • Qualitative Finding: Algorithmic architecture is the primary determinant of platform reach for identical content; short-form content repurposed from long-form YouTube source material provides a sustainable strategy for solo operators; niche technical content in specialized trades acts as a self-selecting audience filter regardless of the platform used; comment sections on TikTok exhibit a higher frequency of aggressive, hostile, and inflammatory behaviors compared to the more civil engagement observed on Facebook and Instagram.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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