Choosing what price to sell a product for, to whom and at what time is a complex process.  Price too high and you will lose customers to competitors. Price too low and you will lose revenue and margin.

There are many opportunities to maximise revenue by tailoring prices to individual products and customers. There are also opportunities to cross-sell and upsell for correlated products. Research has shown [1] that price management can increase company margin by 2% to 7%! However, many companies do not achieve this level of success due to the incorrect application of analytics, poor data integration and lack of visibility. Problems in applying pricing analytics tend to lead to a reversion to gut instincts and simple rules, which can result in missed opportunities and falling margins.



Peak integrates your business data on sales, pricing, costs and customers. It then combines this with data on market conditions and competitor prices in our cloud-based data platform to create a single pricing data model for your business.




We achieve this by modelling price elasticities (using Bayesian hierarchical modelling approaches).  Demand is modelled using forecasts from the price model combined with our proprietary algorithms (typically Kalman filter based self-correction algorithms). Models are combined and optimum prices for a range of products are set using cutting edge optimisation techniques (such as augmented Lagrangian nonlinear optimisation).


Decision support and real-time price suggestions are made using Peak’s proprietary platform. Insight and suggested actions are delivered to your business teams and the output can be integrated seamlessly into your IT systems to ensure optimal pricing decisions at all times. Through Peak’s service, our technologies will maximise revenue and ensure that impacts of pricing decisions can be monitored in real time, giving you the ability to win in the Data Economy.

[1]. Source: Getting pricing right. The value of a multifaceted approach. Larry Montan, Terry Kuester, and Julie Meehan.

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