Sure thing, let’s break it down into a quick workflow you can test. First, grab a list of past releases—include the foil type, embossing pattern, price, marketing spend, and the sales volume. Treat each release as a sample. Next, encode the categorical features (like “gold foil,” “black foil,” “raised emboss,” “etched emboss”) into numeric values using one‑hot encoding or ordinal encoding if you have an inherent order. Run a logistic regression or a random forest to predict the probability that a release will hit your target volume. The forest will also give you feature importance scores—those tell you which texture or embossing pulls the weight. If you want more nuance, try a gradient boosting model (XGBoost or LightGBM) and perform 5‑fold cross‑validation to guard against overfitting. Once you have a model, simulate a few scenarios: pick a set of design options, feed them into the model, and rank the predicted sales probability. Pick the top three or four and run a small test release to confirm the model’s accuracy. Keep track of every iteration in a dashboard so you can see the learning curve. And hey, if you’re going to write the code for this, set a reminder in the app you built to ping you every two hours to grab a snack—data isn’t going to compile itself.