Pricing your products correctly is one of the most difficult aspects of doing business. Charge too little and you are left with no profit. Charge too much and no one will buy your product.
How will you price your product optimally? How will you account for cost of inventory? How will you plan for changes in the market? How will you react to competitors' price changes?
Pricing is a difficult science and art, and advanced algorithmic methods can help. We create pricing models for companies using a judicious blend of techniques from economics, finance, management theory, algorithmic computing, A.I. and data science.
Newer A.I. algorithms tend to be “black box” models. They attempt to emulate the data they are trained on, but do not generate any explicit reasoning for the results they provide. This is why, human understanding of models should be “baked in” into the algorithm, and A.I. should be used judiciously to fill in the gaps. This provides better results in almost all cases, definitely so in pricing.
The pricing algorithms we create do use A.I., but in conjunction with other models from economics, finance, game theory, and statistics. This achieves the following:
* Using an appropriate blend of algorithms, more accurate conclusions can be drawn regarding market behavior from lesser data. And data, however “big” it may be, is always sparse — considering the complexity of price interactions and the ever-changing nature of the market.
* The models we create will not only suggest a price, but also explain its reasoning in human relatable terms: demand models, elasticity of demand with changing price, future predictions, and the reason today's price action is based on all the above.
* Because the algorithms are created in human-relatable terms, they can be embedded in software which works alongside humans instead of replacing them. This allows the best of human creativity and empathy to work alongside hard, number crunching, statistical and predictive algorithms: giving better results than either could alone.