case STUDY: pRICE OPTIMIZER
Explore the alternatives and find your optimal pricing strategy.
Client
Our client, an established and well-known European manufacturer, with more than 100 products, was interested in figuring out how product-loyal their customers are.
“Are any slight changes in the price going to affect the performance of a product?”. Their business goal was to get for each product an indication of whether a product is price sensitive or not. They wanted to know how a price strategy would affect product-loyal customers and the whole market in general.
Challenge
The price elasticity of a product is a measure of consumers’ responsiveness to the change in the price of a product. This is exactly what RetailZoom’s client was interested in finding out about their products.
Price elasticity of demand (PED) shows the relationship between price and quantity demanded. PED provides a precise calculation of the effect of the percentage change in quantity demanded by the percentage change in price.
Our Data Science team had to impose some constraints to ensure the validity and accuracy of the model. To eliminate instances of products with very volatile prices, an aggregation of the transactional data on a weekly level was applied. Products with no continuous appearance for a predefined period were excluded from the model (e.g., seasonal products).
Solution
RetailZoom already had 2 years’ worth of transactional data for the region the client was interested in. The solution consisted of an algorithm developed internally by RetailZoom, based on a history of prices, sales, and stores:
- Multivariate regression modeling adjusted for all non-price related factors
- Base price and promo price estimation to appropriately define the elasticity of each product
- Share of each product sales
- Seasonality decomposition/adjustment
Additionally, all other factors that affected sales were taken into consideration, such as:
- Category Seasonality
- Expected Sales
- Holidays
- Trends
So, all these factors were incorporated into our models to decompose the sales of the product and extract the real effect of price, through a price elasticity indicator.
The user could also define his/her own cost parameters for each product and the promotion periodicity for a specific price, as well as different price scenarios. The goal was optimality, which refers to the maximum profit. Based on a product’s cost, current base price, and elasticity, the algorithm suggests an optimal price. This optimal price is then used to output an estimation for the expected Quantity, Volume, Revenue, and Profit.
Results
RetailZoom delivered a tool that can provide the price elasticity indicator for each and every one of your products. Α section where you can apply different scenarios based on your strategy to see how this is going to affect the whole market is provided.
The marketeer is in a position where he/she can create prices, taking into consideration the position of each product in the marketplace.
- Estimate the sales and profit impact of retail price changes for all stock-keeping units (SKUs)
- View optimal price rises that will not affect your performance
- Understand the distribution of retail prices in the market
- Test an unlimited number of price levels scenarios and explore the benefits of each one
- Create prices that have a unique and sustainable value
What you will get from us:
Mid Year/Yearly
Manually import your production costs without sharing
Remove uncertainty: Multiple pricing scenarios can be tested
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