Centralize Customer Data from Shopify, Amazon, and Etsy with a Unified AI System

Most multi-channel ecommerce stores operate with siloed data. Your Shopify analytics cannot see a customer's purchase history on Amazon. This means you cannot identify your most valuable customers across all channels, leading to wasted marketing spend and missed opportunities to build loyalty.

Data connector tools can pull data into a spreadsheet or BI dashboard, but they do not solve the core problem. You get a sheet of Shopify orders and another of Amazon orders, but the tool cannot tell you that `jane.doe@gmail.com` from Shopify is the same person as Amazon buyer ID `AMZ987`. An analyst must still spend hours manually merging rows and cleaning data just to calculate a simple cross-channel lifetime value. These connectors move data, but they do not unify it.

Consider a 15-person company selling on Shopify, Amazon, and a B2B wholesale portal. Their best customer is a cafe owner who buys 50 lbs of coffee every month from the wholesale portal. But their marketing team targets a different customer who bought one bag on Shopify three times. The marketing team has no visibility into the B2B data, so their retention campaigns are aimed at the wrong people. This happens every day because their tools cannot create a single, authoritative customer record.

The structural problem is that off-the-shelf tools are built for either single-channel analysis or enterprise-scale data warehousing. There is a gap for businesses that need a unified view but do not have a full-time data engineering team. They are stuck between simple tools that maintain silos and expensive Customer Data Platforms that require a $20,000 annual contract and a dedicated team to manage.

The project would begin with a data audit of each sales channel: Shopify, Amazon Seller Central, and any custom platforms. We'd map every data object related to customers, orders, and products. This process identifies which fields, such as email or shipping address, can reliably link identities across platforms. You receive a data map and a proposed unified schema for your approval before any code is written.

The core system would be a set of AWS Lambda functions written in Python, each responsible for polling one platform's API for new data every 5 minutes. These functions push raw data into a Supabase Postgres database. A separate FastAPI service then runs a scheduled process to clean, de-duplicate, and merge this data into a unified `customers` table. For complex data cleanup, like extracting structured details from unstructured note fields, we use the Claude API. This entire architecture costs less than $50 per month to operate for up to 10,000 monthly orders.

The final deliverable is a clean, centralized Postgres database that you own and control completely. This database can be connected directly to any BI tool, like Metabase or Looker, for real-time reporting. You also receive the full Python source code in your GitHub repository and a runbook detailing how to monitor and maintain the system.