Answering your business questions

Pick the question closest to yours. Don't see it? Get in touch and I'll tailor an analysis to your business.

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01 Sales & revenueWhere is my revenue actually coming from? 02 Demand & inventoryAm I ordering the right amount of each product? 03 Customer retentionWhy aren't new customers coming back?

Sample 01 · Power BI · Excel · Revenue breakdowns

Where is my revenue actually coming from?

For owners who want one clear view of the business instead of a weekly spreadsheet.

what I found

Coffee is the main driver of revenue, bringing in $271.5K in sales, with Barista Espresso leading at $92.1K. Sales stay fairly steady throughout the week, while the busiest sales hours across locations are between 9 AM and 11 AM.

Methodology

I combined six months of register data into one dataset and used Excel and Power BI to break down revenue and profit margins by product, store, time of day, and weekday. This helped identify top-performing products, locations, and sales trends.

questions it answers → where to find each one in the report

  • Which products and categories bring in the most money?Revenue by Product Category
  • Which items earn the best margin, not just the most sales?Most Popular Products and their GM %
  • Which location and time of day is busiest?Revenue by Time of Sale
  • Which days of the week are strongest?Revenue by Day of the Week
  • Is revenue growing, and are costs keeping pace?Revenue x COGS Trend
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Sample 02 · Power BI · R Studio · Statistical forecasting

Am I ordering the right amount of each product?

For businesses that make or stock perishable goods and order by feel.

what I found

With the strategy from the report, the business could see 28 fewer out-of-stock days, 10,265 fewer wasted units, and an additional $20K in revenue each year.

Methodology

When a product sells out, the sales data doesn't show how much demand was actually missed. I estimated that lost demand first, then tested several seasonal forecasting models for each product and selected the ones that performed best on unseen months. I used those forecasts to set reorder points and build a daily ordering plan. Built in R and Power BI.

questions it answers → where to find each one in the report

  • How much revenue am I losing to sell-outs and waste?Identifying Revenue Leakage
  • Which products cause most of it?Identifying Revenue Leakage
  • How much of each product sells on each day of the week?Modeling Product Demand
  • When should I reorder, and how much?Inventory Management Table
  • What is it worth to change how I order?Results Summary
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Sample 03 · Power BI · Python · Survival analysis

Why aren't new customers coming back?

For online stores growing on ads and discount codes.

what I found

57% of customers placed a second order within 60 days of their first. After 90 days without a purchase, only 13% came back. Based on the analysis, five changes could generate an estimated $230K in additional annual profit.

Methodology

I grouped customers by the month of their first purchase and tracked how long it took them to order again. I used a survival model to see how repeat purchase rates changed over time, then a regression model to identify the factors that influenced them, including ad channel, discounts, first product purchased, returns, and late deliveries. I also built a customer lifetime value model to identify which customers were most worth targeting, then tested its accuracy on a year of data it hadn't seen before. Built in Python and Power BI.

questions it answers → where to find each one in the report

  • Is my growth coming from new customers or repeat ones?Growth vs. Retention
  • Are this year's customers worth as much as last year's?Cohort Retention
  • When do customers come back, and when is it too late?The Second-Order Window
  • What makes a first-time buyer come back?What Drives Repeat
  • Which ad channels and discount codes pay for themselves?Acquisition Quality
  • Who is worth a win-back email right now?Segments & Win-Back
Live report on sample data. Hover any chart for exact numbers. Switch pages with the tabs along the bottom of the report. Open full screen
Claron is a high achieving young man with many talents and skills. He is an amazing mathematician, but possesses the soft skills to also communicate his findings effectively. When he did a project for my small business he was articulate and understood the implications of the findings on the sales aspect of the business. I highly recommend Claron for technical roles, math related projects and as a collaborative team member.
Nancy Aichholz Founder, That's So Sweet! · Fractional CMO & Brand Strategist