VJ_analytics

Victoria Johnson · Remote · US and UK hours

A sales and RevOps professional who uses data to solve business problems.

Five years of US remote B2B inside sales in building materials and manufacturing, now applied to analytics. I work in SQL, Power BI, Tableau, and Excel, and every number I publish is traceable to a specific artifact. The green chips on this site name the source of each claim.

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Victoria Johnson, Business and Data Analyst
Victoria Johnson · Business and Data Analyst
97% of 93,358 Olist customers purchased exactly once, isolated across six SQL queries olist_analysis.sql
81.2 days average gap before a second purchase, the intervention window behind a day 55 to 65 reactivation recommendation Olist case study
904,790 reported crimes analyzed across 15 fiscal years of London transport data in Tableau TfL exec summary

What I do

Analytics with a sales floor behind it

I spent five years selling windows, doors, and cabinets to US contractors, working CRM pipelines, quotes, and follow-up cadences. That means I read a retention query the way an operator does: not "97% churn" but "which customers do we call, on which day, with what offer." My analysis ends in a recommendation someone can act on, or it is not finished.

My toolkit covers SQL (MySQL), Power BI with DAX and star schema modeling, Tableau, and Excel through Power Query and Power Pivot. Each project below is packaged the way a stakeholder would receive it: case study, executive summary, presentation deck, and documented code. Where the data cannot support a conclusion, the case study says so.

Selected work

Featured case studies

SQL · MySQL · Power BI

Olist: why 97% of customers never come back

A six-query retention investigation across a Brazilian e-commerce marketplace. Ruled out product and service as causes, found the timing window that matters, and surfaced a seller-level lever retaining customers at 4 to 8 times the platform average.

Read the case SQL · Excel

Vrinda Store: diagnosing a revenue decline

A 31,047-row order-level investigation into where and why revenue fell, ending in a quantified recovery recommendation documented in a ten-page case study.

Read the case Power BI · DAX

Global Superstore: a profit brief for the CEO

A four-page Power BI drill-down built as an executive briefing, isolating where discounting and delivery performance erode profit across a 51,000-row retail dataset.

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All five projects