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Pricing strategies should factor in the client’s objectives, its cost structure, the competitive landscape, and the market elasticity of demand (sometimes referred to as economic value). Pricing optimization techniques are closely connected to product-line development because different customer segments generally need different products/services that in turn correspond to different price points. Under a predetermined set of assumptions, complex mathematical models are routinely developed and lead to adjusting prices dynamically to factors such as inventory issues, seasonal demand, major players innovation forecast (e.g. software upgrades), etc.
Given the precise nature of these models’ outputs but the highly volatile and often inaccurate nature of their inputs, pricing strategies are often developed in conjunction with scenario planning. The outcome can for instance be a continuous price curve on which the client may choose to position itself based on its own beliefs and preferred assumptions.
- Articulate the client’s objectives (short term profit, market share, or other strategies)
- Select the preferred optimization model and determine required inputs/outputs
- Collect data needed to run the model: client’s cost structure, competitors’ prices/cost structures, customer surveys/interviews, impact of different factors such as seasonal balance, competitive response, new entrants, potential levers (economy of scale in procurement, introduction of new technology in operation, etc), marketing campaigns.
- Load, run, revise the model
- If time and cost permit, implement pilots to gain more confidence in the model’s predictions
- Describe decision making processes that will permit to implement the proposed pricing strategy
- Develop a framework for the client to be able to use future data to upgrade the pricing model