Almost Timely News: 🗞️ How to Use Generative AI For Retail Analytics (2026-03-01)

Almost Timely News: 🗞️ How to Use Generative AI For Retail Analytics (2026-03-01) :: View in Browser

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What’s On My Mind: How to Use Generative AI For Retail Analytics

In this week’s newsletter, let’s use AI to build some advanced retail analytics, specifically for my video game character in World of Warcraft. By way of background, the video game World of Warcraft has an in-game marketplace called the Auction House. I’ve done lots of articles in the past on it and why I play the game, and the new expansion, Midnight, just dropped.

It’s an amusing side hobby of mine to try and run a profitable resale business, reselling in-game items to other players. However, I’ve not sat down with my data recently to see how to tune my in-game business, nor have I done an inventory to see what sells and what’s a waste of time. As we move into a new expansion, I have over 1,100 items up for sale and not all of them sell well - or at all. It’d be good to know what to keep and what to throw overboard.

Prior to generative AI, we would have had to do this analysis with classical data science tools like the programming language R or statistical environments like SPSS, which seems like vast overkill and a ton of work for a video game. And to be clear, this is all fun - you can trade in-game currency for real world currency, but the exchange rate is basically 15000:1; 15000 units of in-game currency convert to one unit of real world currency, and on a daily basis I earn around 3000 units, so… this isn’t a money maker. It’s just for fun.

So today, let’s build a retail analytics system for my in-game auctions business. There’s obviously some applicability to the real world and real world retail businesses, but that’s a topic for a different time.

Part 1: The 5P Framework by Trust Insights

Before we begin, we should define clearly what we’re doing. If we have a great project plan, then generative AI will be able to put this all together for us fairly easily. If we don’t have a great plan, then we’re going to spin our wheels a ton. In the age of agentic AI, there is no framework more powerful or useful for agentic AI prompting than the 5P Framework by Trust Insights.

The 5P Framework by Trust Insights is:

• Purpose - What is the question we’re trying to answer? What is the problem we’re trying to solve? Why are we doing this?

• People - Who should you involve? Who needs to know about this? Who are the stakeholders, the customers, the people impacted?

• Process - How will we answer this question, solve this problem? What are the specific methods, processes, techniques, and tactics involved?

• Platform - What tools will we use, digital and analog? What data will we use? Where does that data live?

• Performance - How will we measure success? How do we know we solved the problem? How do we quantify success? What KPIs and outcomes do we need to achieve?

Let’s step through mine.

• Purpose: I want to eliminate products I’m selling in the World of Warcraft auction house that buyers aren’t buying. I want to increase sales of the ones that are selling. I want to know what product categories perform best.

• People: I’m the primary seller, but I have multiple characters. Some are gathering characters that gather things like herbs and ores. Others are crafting characters that are blacksmiths or leather workers. And of course, we have the customers themselves. The World of Warcraft Auction House is a region-wide auction house, so everything is a commodity and everyone is a commodity buyer. A mana potion or storm silver ore I sell is identical to every other seller’s so the only differentiations are price and timing. Where there’s an opportunity is that there are products other players either don’t know where to get - arbitrage of knowledge - or are laborious and boring to get - arbitrage of effort. For example, if you want an Exodar doublet (a shirt), you have to go to the Argent Tournament stand in Northrend, which is quite a hike.

• Process: This is part of the meat and potatoes of this assignment. We’ll use generative AI to gather retail research data, then write the code to ingest, process, and analyze the data, then tell me what to do. What should I sell more of? What should I sell less of? What items have never sold that I should just disenchant into their components?

• Platform: Generative AI is part of the platform, but only a small part. We really need statistical and mathematical methods, probably in a programming language like Python, to process and analyze our data. We need to get my specific resale data. My own data comes from the Trade Skill Master addon, which means I have to export it from the addon and convert it into a usable format.

• Performance: My goal is to eliminate all products from my inventory that haven’t sold in a year. If it hasn’t sold in a year, it’s probably not going to sell. Those products I should throw overboard. If there are products that are selling fast, I need to know how fast, so I can plan restocking times. My ultimate goal is to go from about 3000 units of currency in sales per day to 5000 without any additional time commitments - I want to optimize what I’m already doing, not do more work. The time I save not reselling items that won’t sell is time I can allocate to getting more of the items that do sell.

Now, this is a good start. My next step is to feed the 5P Framework by Trust Insights into generative AI to help me fill in the gaps. I’ll literally take this entire section, put it into AI, and have it ask me questions about things I’ve overlooked or forgotten.

Part 2: Finding All The Data

Once I’ve got my fully baked project plan, it’s time to do the research and get the data. For any project like this, I always start with Deep Research. In this case, I want deep research on what I don’t know about retail analytics. It’s been a hot minute since I’ve had to do retail analytics and most of my knowledge is foundational, things like RFM analysis. I don’t know what’s happened in the last few years in the space, nor do I know what corresponding code and tools exist that we could build with.

There are a ton of companies that offer retail analyti