The Difference Between Data and Insight
RepairCoreX automatically collects data on every ticket, every invoice, every customer interaction, every inventory movement. After 60 days, you have a substantial dataset.
But data sitting in a dashboard isn't useful. Insight is useful — understanding what the data means and what action it suggests.
This guide walks you through the key metrics in your RepairCoreX analytics and what each one should tell you.
Metric 1: Ticket Velocity
What it is: The number of tickets created per day, week, or month. What it tells you:A rising ticket velocity that doesn't translate to proportional revenue increase has the same implication.
Rising ticket velocity with proportional revenue increase means genuine growth.
Action signals:Metric 2: Average Ticket Value (ATV)
What it is: Total revenue ÷ total tickets in a period. What it tells you: Whether you're capturing the full value of each repair. How to read it: Your ATV should be consistent with your pricing. If your most common repair (screen replacement) should average ৳3,500 but your ATV is ৳2,400, there's a gap. Either you're doing many low-value repairs that pull the average down, or you're losing revenue through uncaptured parts, labor, or informal discounts. Action signal: When ATV drops, investigate at the repair type level. Which service categories have declining average values? That narrows the investigation.Metric 3: Customer Return Rate
What it is: Percentage of this period's revenue from customers who have repaired with you before. What it tells you: How effectively you're retaining the customers you earn. Healthy range for an established shop: 50–65% return rate. Lower means acquisition-dependent (expensive, unsustainable). Higher means you may be overly dependent on a small loyal base without growing your customer pool. Action signals: Low return rate → Focus on follow-up, loyalty incentives, consistent quality that makes people want to come back.Unusually high return rate → Your loyal customers are very loyal, but you're not converting enough new customers into repeat visitors.
Metric 4: Technician Throughput
What it is: Tickets completed per technician per day/week, and average completion time per repair type. What it tells you: Which technicians are most efficient and where the bottlenecks are. How to read it: If Technician A completes 12 standard repairs per day and Technician B completes 7, the gap needs explanation. Is B handling more complex jobs? Is B in training? Or is B the same experience level with a significantly different output — indicating a skill or focus issue?Compare within the same repair types for a fair picture. A technician who handles complex board repairs shouldn't be compared directly to one doing screen replacements.
Action signal: Persistent throughput gap without explanation → Observation and conversation. Use the data as the starting point, not the conclusion.Metric 5: Parts Cost as Percentage of Revenue
What it is: Total parts cost ÷ total repair revenue in a period. What it tells you: Whether your parts pricing is maintaining your target margins. Healthy range: Parts should represent 30–45% of total repair revenue for most repair shops. If parts cost is 60%+ of revenue, your margin is being compressed — either your supplier prices have risen and you haven't adjusted customer pricing, or you're not capturing all parts charges on invoices. Action signal: Rising parts % → Time for a price review. When did you last update your service prices relative to supplier cost changes?Metric 6: Warranty Claim Rate
What it is: Warranty claims as a percentage of completed repairs. What it tells you: The quality level of your repair output. Healthy target: Below 3–5%. Above 10% for any repair type indicates a quality issue. Action signal: High warranty rate on a specific repair type → Investigate the procedure. Wrong parts? Incomplete process? Insufficient testing before delivery?Building Your Weekly Analytics Habit
Pick a day and time — Friday 10am, for example — and spend 10 minutes reviewing these six metrics.
You're not looking for perfect numbers. You're looking for changes from the previous week or month. A number that moves significantly is a question worth asking. Most weeks, nothing alarming. Occasionally, you'll catch something early enough to fix it before it becomes a problem.
This is what data literacy looks like in practice: not big analytics projects, just consistent, focused attention to the numbers that reflect what your business is actually doing.