Five projects: corporate analytics work at Audi and Samsung, and three full-funnel and consulting builds from my MS in Marketing Analytics. Each one ran the same loop. Segment the audience, model the economics, allocate the budget, then tie every dollar to a KPI.
As a Regional Operations intern, I built an end-to-end funnel analysis spanning Awareness → Consideration → Purchase → Retention, synthesizing paid-media, web-analytics, lead, and CRM data across thousands of monthly leads and 5 channels, giving regional marketing leadership a single view of where the funnel leaked.
Specific figures are generalized to respect Audi’s internal data.
Procurement at Samsung runs a funnel of its own: 500+ vendor onboarding requests a year across 32,000+ SAP vendor accounts, each one a journey from request to compliant, transacting vendor. As the team's Process Innovation analyst, I treated it the way a marketer treats a conversion funnel: instrument it, find the leak, fix it, then automate the reporting.
Figures reflect my own measured process improvements; no Samsung business data is disclosed.
Bethesda Bagels is an established DMV bagel shop (est. 1982) with strong walk-in demand but no structured digital program. I built an end-to-end plan (audit → paid search → social → B2B → measurement), treating each channel as a job to be done rather than a box to check.
Each platform was matched to a distinct buyer and intent level:
The audit surfaced the highest-leverage fix: mobile PageSpeed of 73 vs 94 desktop and a 4.7s mobile load, on a site taking almost all its paid traffic from phones. Paired with 32 broken internal links and a clunky loyalty flow, that's revenue leaking before the bagel's in the cart.
PageSpeed + Semrush: found the mobile-speed and broken-link drag.
Google Search on high-intent keywords: 8.2% CTR, $0.28 CPC.
Meta, Snapchat & LinkedIn by audience and daypart.
A/B tests + GA4 KPIs on loyalty sign-ups, followers, online orders.
The real question was who was most winnable. Working from MRI-Simmons brand-preference data in Tableau (index 100 = category average), I found AWAY's sharpest opening: young Asian millennial men, 30–34. The read was counterintuitive. Among Asian consumers, women already favor AWAY over Rimowa (115 vs 90), but men lean the other way (84 vs 111). That gap is the strategy: hold the female base, convert the men.
Three data-driven recommendations followed:
Every funnel stage was then judged on the metric that fit it instead of one blended number: CPM and reach at the top, CTR and CPC through the middle, and ROAS at the bottom.
YouTube and Instagram reach, measured on CPM and reach.
Instagram and LinkedIn engagement, measured on CTR and CPC.
Retargeting to purchase, measured on ROAS.
A semester-long consulting project for the Smithsonian National Museum of Natural History. Four teams were each assigned a slice of the 18–24 undergraduate audience: public universities, private universities, community colleges, and HBCUs / minority-serving institutions. Ours owned community colleges: the segment with the least museum-going habit and the most to gain.
I ran the environmental analysis and primary research, then analyzed it in SPSS and Tableau to move from a broad segment to a defensible target. What surfaced shaped the recommendations:
The strategy hinged on the pillar the target audience wasn't feeling: accessible.