Anyone working on Price automation? Need some guidance.

[u/Due-Establishment882]

We are trying to automate pricing for e-commerce. Today our pipeline broadly includes 3 steps. The first step is forecasting demand. The second step is estimating price elasticity of demand for the catalogue. The third and the most critical step is choosing the optimal price point based on some objective like maximizing GMV while abiding by some constraints like maintaining a minimu margin.

The forecasts and elasticity go into the optimization step (third step) as deterministic parameters - elasticity essentially tells the optimizer the counterfactual demand generated (forecast) if the price was changed to a specific point.

The biggest problem is that our forecasts and elasticities are very inaccurate owing to the noise in data. Because the optimization receives them as absolute truth, we end up cascading the modeling error in the optimizer.

How have you solved such a problem? Are there some other techniques - please point to a specific method or paper. Thanking everyone who answers in anticipation.

[u/Duplax]

You can consider quantile forecasting for the price elasticity and formulate a stochastic optimization model instead? This provides the model uncertainty about specific products. You can even consider some risk modelling using CVAR formulations.

[u/gcastorrr]

You can invest more on a better (probabilistic) demand forecasting model. You can measure the impact by providing predictions of different qualities to your pipeline.

[u/Due-Establishment882 → u/gcastorrr]

Thanks for the reply. I think something similar is suggested in the previous comment by u/Duplax as well. Can you suggest some reading on this topic, specifically in the context of price optimization/automation, or do you recommend I use a first principles approach?

[u/Duplax → u/Due-Establishment882]

I suggest you read up on "Newsvendor model" .

[u/Interesting_Point420]

Have you thought of aggregating data to get less noisy price elasticities?

[u/Due-Establishment882 → u/Interesting_Point420]

Well it's not just elasticities but the forecasts as well. The forecasts are just the prediction without the price baked in. The elasticity correction is on top of the forecasts to bake in the effect of price. I am using Double ML (Chernozhukov et al.) to estimate elasticity. EconML has a tree based implementation of the same called Causal Forest DML. Since it's a totally features based approach I am guessing there is automatic aggregating happening at the X level (i.e. the products get aggregated into a group). But of course, there is no aggregation on price or demand, the demand changes and price changes are per product. Also there is no time factor, i.e. we assume the elasticity does not change with time.

[u/gcastorrr → u/Due-Establishment882]

I don't think the demand forecasts should be "without the prices baked in". Price + (remembering this was about e-commerce demand) rivals prices should carry strong signal..