Picture this: you upload a file of orders and a few minutes later you have a clear report on what is growing, what is falling, who your best customers are and what to do next. No spreadsheets, no formulas, no analyst. That is not science fiction — it is what AI does today.
Why you need it
As a vibe coder you are constantly looking for ways to streamline processes and make data-driven decisions without drowning in busywork. Sales analytics from a CSV is exactly that kind of tool. It lets you:
- Take the pulse of the business fast: you immediately see revenue, average order value and month-over-month dynamics. It is the heartbeat of your project, always in view.
- Spot growth drivers and dead weight: the AI shows you the top products and categories that bring in money and the ones just taking up space. That is critical for tuning your assortment and marketing effort.
- Understand your customers: you learn who buys more often and spends more, and what share of purchases are repeats. That is the foundation for effective retention and acquisition strategies.
- Make decisions you can defend: instead of intuition, concrete recommendations — what to double down on, what to check, which three actions to take first. That saves time and money.
How to use it
It all starts with your export. Follow these steps to get the most out of the AI:
Prepare the export. Export orders from your CRM, store or marketplace as CSV or Excel. Make sure the file has columns for date, product, category, amount, customer and status. If you plan to use cloud AI (ChatGPT/Claude), you must anonymise personal data (customer names, phone numbers, addresses) by deleting or replacing those columns. That is a requirement of Russia's Federal Law 152-FZ on personal data, and of common sense. If the data is not anonymised, use local models, for example via Ollama.
Describe the data to the AI. Upload the prepared file into your tool of choice (ChatGPT/Claude with file upload, or Ollama + Filesystem MCP for local work). Then give the AI context:
Here is an order export (CSV). Columns: <list the column names and briefly say what they mean, e.g. 'date - order date, product - product name, category - product category, amount - order total, client_id - unique customer identifier, status - order status'>. Business: "<briefly describe your business, e.g. 'an online store selling craft coffee'>".
The AI should confirm that it understood the data structure, or ask clarifying questions.
- Baseline breakdown. Use this prompt to get the key metrics and trends:
Analyse this data. Calculate total revenue, order count and average order value. Show revenue and order count by month. List the top 10 products by revenue and by units sold, plus the top 5 categories. Build revenue trend charts and an ABC analysis of products. Give a short conclusion: what is working, what needs fixing, and propose 3 first actions to improve sales.
- Customer segmentation. To understand your audience more deeply, use this prompt to build customer profiles:
Using the customer data (client_id, order count, total spend), identify 3-5 customer segments. For each segment describe its characteristics (for example 'new', 'loyal', 'big spenders', 'churning'). State each segment's share of total revenue and order count. Build a Customer Persona or ICP (Ideal Customer Profile) for the most valuable segment.
Tricks nobody writes about
- Iterative analysis: do not try to get everything at once. Start with the baseline breakdown, then go deeper. If the AI produces something opaque, ask it to explain or rephrase. This is a dialogue, not a one-shot request.
- "What if...?" scenarios: once you have the baseline report, ask the AI things like: "What if we focus promotion on products in category X? How would that affect revenue?" The AI can propose hypotheses and even help test them if you have additional data.
- Comparing periods: load data for two different periods (last year and this one, say) and ask the AI to compare them. "Compare these two files by revenue, average order value and top products. What changed?" That gives you a read on trends and on whether your actions worked.
Pairing it with the qvib.pro engine
So you do not have to assemble these prompts and scenarios by hand every time, the qvib.pro engines ship ready-made rules and roles. Combine "Sales analytics from a CSV export" with Filesystem MCP and the prompts "Data/dashboard breakdown: what matters and what to do" and "Customer persona / ICP from data" and you have a finished solution. It makes a successful run easy to repeat and lets you focus on interpreting results instead of phrasing requests.
Full card in the arsenal: https://qvib.pro/arsenal/combos/sales-analytics-csv/