Stop leaving money on the table - find the perfect balance of profit, revenue and customer demand with pricing optimization 

Download the case study to see how pricing optimization delivered 23.9% margin increase for European DIY retailer

How to set prices to address local demand and tackle competition


Pricing is one of the central decisions and a key to your business's profitability. It should correlate with local customer preferences and your competition.

That’s the simple and straightforward part. 

What makes things a bit more complicated nowadays is customers.

Consumer behavior is fragmented – and it varies over time and place. The optimal product-price combination for one is not optimal for another.

What’s more, the product range has exploded and the frequency of new launches has accelerated. That’s lead to more intensive competition.

When everything’s transparent, thanks to the internet, there’s a lot to consider when you’re setting the prices.


Case example - discount retailer
Discount retailer with some 150 stores wanted to improve their price-setting process. They assumed that they are selling certain products at too low prices whereas some others might be too expensive compared to the competition.

With price optimization the retailer gained an understanding of individual products’ price elasticities as well as competitor pricing.

As a result, the retailer could see which products had to be sold with reduced prices and which products’ pricing could be increased – despite the competitors’ actions.

How do you use price optimization?


Typically price optimization is used to optimize assortment pricing – that is setting optimal prices for a total assortment portfolio – or optimizing campaign pricing.

In both cases you want to find answers to two questions: what categories are most price sensitive and what SKUs have the strongest price elasticity.

Assortment price potential is based on identification of non elastic products, Gross sales of non elastic products and profit increase based on increased price.

Campaign price potential is based on identification of price elastic products, campaign sales and average lift in the optimal price levels of elastic products and profit increase is based on increased sales.

How price elasticity helps in pricing optimization - Houston Analytics.001


Price elasticity determines the optimal price


Price elasticity tells how the sales of a product change as the price changes.

When price elasticity and price images are known, product and service prices can be set on an optimal level to maximize both customer traffic and campaign profit.

Targeted and profitable pricing closely involves monitoring competitor prices that together with optimization provide improved profitability even in the short term.  

Benefits of price optimization

Generate effective promotional strategies, price points, and tactics by category down to individual SKUs at scale

Compare pricing scenarios and enhance understanding between financial impacts, competition, customer perception and reactions

Apply pricing effects to improve forecasts, planning, execution and measurement of campaigns & promotions

Webinar: Pricing optimization driving improved profitability

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Frequently Asked Questions


What are the benefits of the pricing optimization tool?

Pricing optimization tool guides you to generate effective promotional strategies, price points, and tactics by category down to individual SKUs at scale.

In the retail sector, Pricing Optimization has generated over 20% margin increases with base assortment and over 14% sales increase with campaign assortment. 


How are my pricing rules, policies and price positioning related to the pricing optimization?

Houston Analytics’ Price Optimization tool takes your pricing rules, pricing policies and price position into account during the optimization phase and the results are compliant with them. 


What data is typically required for the pricing optimization tool? 

The minimum data set includes receipt row data containing all purchase information, product master data with all product information and product hierarchy levels with product hierarchy information plus competitor pricing data. 

It's also noteworthy that in order for the price optimization model to work, individual products have to have multiple price points in their historical data.