The most direct way to use AI for data analysis is to have it interpret the data you already have. You don’t need to build a complex predictive model to get started. As Charles Camisasca explained on Ecommerce Coffee Break, you can simply export your sales, marketing, and customer data into a spreadsheet, upload it to a large language model, and ask it to find insights. Joanna Lambadjieva made a similar point on Seller Sessions, noting you don't need coding skills to have an AI analyze your performance data and give you actionable next steps. It’s about turning raw data into a summary of key metrics and concrete recommendations.
However, the quality of your AI's output depends entirely on the quality of your input. On The eCom Ops Podcast, Steve Zisk emphasized that having clean, structured data is the most critical component of a real AI strategy. Once your data is in order, you can move on to more powerful applications. For instance, Richard Harris discussed using AI for predictive analytics, which can forecast the future value of a customer before they even make their first purchase. This lets you focus your marketing spend on acquiring the right kind of customers from the start.
Your first step should be to export a CSV of your sales data from the last six months and ask an AI tool to identify your five most important trends and suggest one action to take for each.