The POS sitting in your store has, almost certainly, every answer you need to run a sharper business — and almost certainly, you have never looked at it past the daily sales summary. Below are four queries that take a combined 30 minutes a month, require no engineer, and answer the four most important strategic questions in jewelry retail.
Query 1: Repeat Rate by First-Purchase Category
The question: of the customers who bought a fashion piece as their first purchase, what percentage came back within 12 months? Same question for fine jewelry, for bridal, for repair work.
How to run it: pull every customer whose first transaction was 13–24 months ago. Filter their first-transaction category. Count what fraction made any second purchase within 12 months of the first.
What you'll likely find: repair-customers and fine-jewelry customers come back at 35–55% rates. Fashion customers come back at 20–35%. Bridal customers come back at 50–70% — but their second purchase is often a year or more later (anniversary).
Why it matters: it tells you which "first purchase" types are worth acquiring at higher CAC, because they monetize over multiple years. A repair customer acquired at $80 CAC who comes back four times over five years has a totally different LTV than a fashion-jewelry customer acquired at $80 CAC who never returns.
Query 2: Top 10% of Customers by Lifetime Spend
The question: who are your top 10% of customers by total spend, and what percentage of total revenue do they represent?
How to run it: group all transactions by customer. Sum total spend per customer. Sort descending. Calculate the cumulative revenue contribution of the top 10%.
What you'll likely find: in healthy fine-jewelry brands, the top 10% of customers generate 35–55% of revenue. In some brands we've seen, the top 5% generate 40%+ of revenue.
Why it matters: most marketing spend gets directed at acquiring new customers. The math says the highest-leverage spend is often retaining and re-engaging the top 10%. If you don't have a personal-outreach motion for those customers — a quarterly handwritten note, a phone call when new collections drop, an early-access invite — you are leaving the most lucrative growth lever on the table.
Query 3: Average Days Between First and Second Purchase
The question: among customers who eventually made a second purchase, how many days passed between purchase 1 and purchase 2?
How to run it: for every customer with at least 2 purchases, calculate (date of purchase 2) − (date of purchase 1). Take the median.
What you'll likely find: typical median is 90–180 days. Knowing your specific number is what's useful.
Why it matters: it sets the timing of your retention campaign. If your median is 120 days, your "we miss you" automated email should fire at day 100 — slightly before the natural reorder window — not at day 60 (annoyingly early) or day 200 (after they've drifted to a competitor). Most brands set arbitrary timing. Setting it to your actual data shifts open and conversion rates by 30–50%.
Query 4: Sales by Day-of-Week and Hour
The question: when does revenue actually come in?
How to run it: group all in-store transactions by day-of-week and hour. Plot on a heatmap. (Most modern POS systems can output a CSV; pivot in Excel.)
What you'll likely find: most indie jewelry stores see 50–70% of weekly revenue between Thursday afternoon and Saturday afternoon. Many stores are open 9 hours a day, 6 days a week, and would lose less than 10% of revenue by closing on the slowest day.
Why it matters: staffing. If you're staffing equally across the week, you're overstaffed Mon–Wed mornings and understaffed Saturday afternoons. Restructuring schedules to match the revenue heatmap is the highest-leverage operational change most stores can make. Real example: one brand we worked with cut Tuesday hours, added a second person on Saturday, and net revenue grew 8% the same quarter — same total payroll.
What To Do This Week
Pick one of the four queries. Spend 30 minutes pulling the data. Look at the answer. Most readers will be surprised by at least one finding. The next month, run the second query. By month four, you'll have a quarterly cycle of looking at your business through real data instead of intuition.
Intuition is good. Intuition married to four monthly data queries is dramatically better.