TWICE 2.0 — Coming soon.
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In the rapidly evolving landscape of recommerce, the ability to access, organize, and analyze raw data is a critical factor for scaling operations. With the introduction of Twice 2.0, the focus shifts heavily toward structured data presentation through tables. This approach is not just about viewing inventory; it is about creating a foundation for deep analytical work.
Data tables serve as the most efficient method for aggregating and grouping complex information within a rental or resale business. By structuring data in this format, it becomes significantly easier to:
Perhaps the most transformative application of this data structure is its compatibility with Artificial Intelligence. Clean, tabulated data is the ideal fuel for AI agents. By feeding this raw data into an AI model, businesses can move beyond basic reporting to predictive analytics.
Instead of manually calculating margins, operators can ask complex questions such as, "What is the most profitable product currently in rotation?" or "Which inventory segments are underperforming?" This transition from manual review to AI-assisted insights allows for faster, data-driven decision-making that directly impacts the bottom line.
Improving operations going forward requires a deep dive into raw data. Whether through human expertise or AI assistance, the data tables in Twice 2.0 provide the essential source material needed to uncover hidden opportunities and optimize circular business models.
Speaker: If you look into the Twice 2.0, you will see a lot of tables, because those tables are kind of an easy way of analyzing your data and aggregating data, grouping data, taking it to another system and then taking the analysis further.
Or even passing that data for an AI and asking AI questions about it, like, "Hey, what would you see to be the most profitable product that I have based on this data available?"
So to sum up, I would say that in order to improve your operations going forward, you'll be looking into many of the data tables that we have as the source of raw data to find the insights needed, whether it's by your own expertise or maybe utilizing an AI agent to do that.