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Our Thought Provoking Insights

Price optimisation: eliminating guesswork

With the surge in inflation across supply chains everywhere, price has become the major challenge for every leadership team. How much of their own higher costs should they pass on? What will their immediate competitors do and therefore, what might be the impact on market share? And of course, what will the impact be on their margins?


The cost of living crisis is impacting consumer spend, footfall and sales. Retailers must understand better how their pricing strategy impacts margin and few have the demand forecasting ability to model in enough detail and fast enough.


For many retailers, creating a pricing strategy and pricing of goods still relies heavily on guesswork and gut feel.

This approach risks leaving considerable money on the table. Price optimisation mitigates this risk - here are three key areas well worth exploring.


Understanding price elasticity


A large part of price optimisation is driven by the theory of price elasticity of demand which measures the responsiveness of demand to a change in price. If demand fluctuates quite dramatically to a small change in price, a product is known as price elastic (e.g. luxury or non-essential goods). Conversely, if price changes result in little impact on demand, a product is known as price inelastic (e.g. necessities like bottled drinking water).

Utilising machine learning to eliminate guesswork


The most advanced price optimisation solutions use machine learning models to determine the effect of price on demand. The constant addition of new data helps to refine and improve the model’s understanding and prediction of customer demand, and output the most accurate optimal prices, be this initial, dynamic, or clearance pricing. These solutions give merchandising teams the power to test out different pricing scenarios and their impact on revenue before committing to a plan.

Leveraging dynamic pricing


Dynamic pricing can be utilised alongside optimisation, where the best prices are considered at a specific moment in time and are kept fluid as new data becomes available. These are factors that influence a customer’s propensity to purchase an item at any point such as changing competitor pricing (or promotions), inventory levels, or other external market pressures.

Today, consumers are increasingly sensitive to constraints on their spending and the price they pay. For retailers, getting the price right is ever more important.

Price optimisation gives retailers a clearer insight into how customers will react to changes in pricing. They can then forecast the overall effect on the bottom line, enabling the best-informed decisions to be made. These are essential prerequisites of an effective pricing strategy.


At TPC, we work with retailers on optimising their prices using data science techniques to ensure they are making data-informed pricing decisions that drive profits. Have any questions? We’d like to hear from you! Find us on LinkedIn or contact us via our website to learn more.

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