Price-elasticity models do not fit here (since they need previous/historic data with price changes to 'learn' the effect).
We have sales-data for different items (with no price-changes till now). Business owners are thinking of changing the prices for the first time, and would like to estimate the customer reaction. (Customers are not tracked, anonymous and opportunistic).
Any pointers on the right approach (R package) ?
without any further information, there is no way to accurately forecast how the volume of sales will change when the price changes.
So, there are two things you can do:
1. if you have substitute products for which you know their elasticity, you can extrapolate the sales volume of your product in question based on the computed elasticity of the other substitute product.
2. If you don't have data for substitute products (or, you don't think good substitutes exist in your market), then you can perform a small market-research, i.e. ask a number of customers if they would be willing to buy the product in question at a smaller or higher price, and compute demand elasticity from this market research.
3. Or, you may try limited-time only incremental discounts, and see how the market moves, and based on this information, compute the elasticity of your product.
4. In general, if your sales seem to be declining as time goes by, it's a good indication that your price is too high for the current market segment you have targeted, and you need to lower prices. On the other hand, discounts, if done carelessly, will only "buy sales", but may very well cause you to actually lose money -maybe due to logistics and/or supply chain management issues that your sales campaign will bring forth, and so on... there are several studies that have documented this effect.
None of the above is related to R, or MATLAB, or Java, or FORTRAN, as they are simply methods you have to resort to, to compute an approximation of the product's elasticity. Also you need to remember that elasticity is not a static concept, but varies with time, and many other parameters. What may seem as a good bargain price to your customers today, may seem way too much to them tomorrow, so this information needs to be constantly updated.
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