To be able to set up a dynamic model in order to compute optimized prices, we estimated sales probabilities. We used a logistic regression approach to quantify how offer prices and specific market situations affect sales. We consider up to 10 offer dimensions (e.g., price, quality, ratings, feedback count, shipping time) per competitor for a particular market situation.
The data set that we used for the regression analysis contains both the requested market situations from Amazon as well as our seller's own data (offers, sales, and inventory). Our partner requests market situations for each offered book roughly every two hours (i.e., >20 M market situations per month which result in >140 M single competitor observations per month). We used thirty customized features, e.g., the price rank of our offer price within the competitors' prices. As a result, we were able to predict sales probabilities for any offer price and for any market situation. Using efficient solution techniques, we are able to compute optimized prices for current market situations.
The application of our dynamic pricing strategy works as follows: First, we observe current market situations for our products, then we calculate optimized prices according to the model, and finally adjust prices on the market platform. This procedure is repeated every two hours or in case of changing market situations. This way, our strategy is able to respond immediately to new situations as prices can be adjusted in milliseconds. Moreover, the new incoming sales observations are used to further improve the strategy by estimating demand more accurately.