The traditional understanding of Price Elasticity focuses on the influence of product price on sales of a particular product. But price also strongly impacts Google Shopping performance – which is used by most online retailers to acquire new customers and maximize Customer Lifetime Value through repeat purchases. Because of this connection, price decision makers and marketing campaign managers need to start looking at an extended model of Price Elasticity that also accounts for the impact of price changes on advertising cost and performance.
“Pricing is the most effective profit lever.”
This statement from Robert J. Dolan made in 1996, holds true now more than ever. Price’s impact on business is much bigger than just on sales volumes and margins. Prices have become a fundamental part of online advertising, not just on Google, but also on other PLA based ad networks.
In terms of Product Advertising success, price plays a huge role in two places:
- Frontend: Prices (and price changes) have become a major visual element in online ads.
- Backend: deep within the auction algorithms, prices and their competitiveness are important criteria for Google and other PLA based networks to select which products to show – and how often.
Let’s take a look at each of these in depth to see how exactly price changes affect Product Advertising success.
Visualizing prices in ads is just the tip of the iceberg
The rise of feed-based advertising has made it easier for customers to compare prices than ever before. Google constantly experiments with how to visualize prices, price slashes, and promotions:
Example: Price slashes, price drops, special offers. Image courtesy of Merkle.
These experiments show just how important price has become as a visual element to Google and those who shop there.
Algorithms decide which products to show based on price
It should come as no surprise then that Google takes price into account when deciding which products to showcase in the paid search results (Google Shopping) and in what order. Crealytics has conducted research on the role of price in Google Shopping, and the results show that competitively priced products perform significantly better in terms of traffic acquisition than those that are more expensive:
This information leaves retailers with two important levers to operate when trying to increase sales through Product Advertising: Product Price and Product Bid (MAX CPC). In order to drive more sales, a retailer can either increase advertising budgets, decrease product prices, or a combination of the two.
The example below shows two scenarios that explain when pulling each of these levers independently.
While increasing advertising spend can significantly increase the number of impressions and sales, it pales in comparison to the performance uplift we get from lowering the price. Ideally, both levers would be used in combination.
Should next generation pricing decisions consider sacrificing product margin in return for more and cheaper advertising clicks and subsequent conversions?
In order to make sense of both effects, we propose an extension to the current understanding of Price Elasticity of Demand.
Elasticity of Demand
Traditionally, Price Elasticity of Demand researches how the sales of a product are affected when raising or lowering its price. Elastic basically means ‘responsive’:
Lower / higher price refer to market average price. More sales / fewer sales refer to sales make with market average prices.
But, the traditional model of price elasticity does not measure the impact of a price change on advertising performance for online retailers, e.g. the advertising costs incurred to acquire a new customer and to sell products.
Elasticity of Product Ad performance
When managing Google Shopping campaigns, we observed the following phenomenon:
We, therefore, propose to extend the model to include the secondary effects of advertising. It is similar in that it helps to explain the impact of price change on advertising performance.
Benefits of the new Price Elasticity model
Online advertising is a decisive instrument for new customer acquisition and for customer lifetime value optimization through repeated purchases. As Google Shopping and other PLA based ad networks use product prices as an algorithmic signal, prices do have a strong influence on product ad performance in terms of total reach, cost and conversion. Alas, the traditional Price Elasticity model is blind to this kind of impact.
We believe that better price making decisions can be made by breaking down data silos to understand and include the impact of price changes on advertising performance. Armed with that knowledge, companies can stop focusing on departmental efficiency metrics and start to optimize for enterprise level profitability.
