Updated on July 14, 2023 | 3 minute read
There are plenty of factors affecting purchase behaviour and product conversion rate including price and other price-related factors. While it’s hardly a groundbreaking discovery, pricing is a strong predictor of conversion rate for each of your products.
From a marketing perspective, pricing helps to position the product in the market, and can affect how that product is perceived by consumers. While shopping online, purchase decisions are often strongly influenced by a trade-off between product price, and how much the consumer is willing to pay.
Over the past decade, many retailers have created and optimised a platform to capture and cultivate online traffic. Following the global pandemic, other consumers have even shifted their presence to an entirely digital approach.
The popularity and pervasive nature of the internet now means price comparison is easier and more accessible than ever, enabling consumers to make faster and more informed decisions, whether shopping in-store or online.
The Google Shopping carousel is a good example of being able to distinguish pricing between different retailers. However, shoppers should take into account that Google does not rank product listing ads by best value, cheapest price, or greatest quality.
The ability to quickly compare prices across a number of retailers drives consumers’ price sensitivity: even making slight adjustments can affect the demand for the product. As such, it’s crucial to research competitor’s pricing on matching products in your catalogue.
This relationship is referred to as the price-demand function, expressing profit or revenue as a function of product price.
There’s also a way to measure how much demand changes as price changes, known as price elasticity, expressed in the following equation:
Price elasticity = % change in demand / % change in price
Using the demand-price function, we can calculate the optimal price of a product. This function is systematically computed for all available price levels. However, there remain extraneous, real world variables to take into account:
However, machine learning and historical sales data can be leveraged to estimate advanced demand functions, by considering a variety of factors, including product attributes like brand,
product type and price, competitor offers, their marketing activities and promotions, macroeconomic indicators, and even seasonal or weekly weather data.
While the latter is particularly important for fashion retailers and suppliers of outdoor furniture, it shouldn’t be overlooked by retailers in other industries, such as health and beauty, sports and leisure, and many more.
By regularly calculating data models with a wealth of information like the above, you can glean greater insight into the impact of pricing in conjunction with other variables.
Furthermore, accurate data models require a vast amount of eligible and up-to-date information. We recommend expanding your data to include external sources with diverse data sets to increase the reliability of your demand estimates.
Researching your competitors’ pricing is important in making your own pricing decisions. If you use a dynamic pricing tool, you should use research into competitors’ prices to implement pricing strategies. For instance, it can be used to undercut your competitors, or even simply to track your industry’s price-makers.
Better yet, it may even be worth investing in the adoption of AI to drive your pricing management. However you choose to manage pricing, it’s always worth rigorous A/B testing.
Hopefully we’ve been able to share some useful advice on how and why to conduct demand-price research and making well-informed decisions when creating or adjusting your product pricing. With the help of competitor research and (vast) data-driven insights, you can boost the accuracy of your pricing estimates, increasing your conversion rates and ROAS.
For more information on managing Shopping campaigns and product listing ads, or for best practices, check out our resources.
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Olivia is a Google Shopping specialist within the marketing team. She creates content to simplify the Google Shopping experience, and help our clients discover if Google Shopping is the right channel for them.