Case Study: Dynamic Pricing

Dynamic Pricing for Homewares Retailer

The Remi AI Price Optimisation module was able to drive higher revenue and margin, while still remaining within the client’s pricing strategy.

Client: One of Australia’s largest homewares retailers

Challenge: Traditional approaches to pricing strategy and no strong demand forecasting methods in place

Solution: Remi AI Price Optimisation platform

 

 
The Challenge:

As a household name in homewares retailing in Australia, the client had systems that had been in use for many years. On top of this, there was no demand forecasting in place beyond the gut feeling of the category managers.

 

The pricing strategy was relatively static, and one of following the market and competitors rather than leading. As such, there was quite often margin and revenue left on the table.

 

The Solution:

The Remi AI platform allowed the client to utilise some of the most advanced forecasting methods available today, coupled with decision-making A.I to bring a data-driven layer to pricing decisions.

 

The platform’s Price Optimisation Module is able to automate millions of pricing decisions daily, using A.I that is trained to make intelligent pricing decisions within the bounds of the client’s business constraints and desired pricing strategy.

 

The Outcomes:

The Remi AI Price Optimisation module was able to drive higher revenue and margin, while still remaining within the client’s pricing strategy.

 

Starting with 3 pilot categories, the platform was tested and shown to increase revenue between 5% and 20% in any given week - a fantastic result.

The Platform:

Each client is different, producing different optimisation opportunities and business constraints. On average we have seen between 1% and 20% increases in revenue against comparable periods using Price Optimisation in Retail - an exciting opportunity for anyone yet to adopt it.