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.
Price comparison and The Shopper’s journey
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.
The relationship between pricing and demand
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:
- Demand is affected not only by pricing but a number of factors, including competing offers and substitute products, diluting converting traffic to your website.
- For many products and industries, demand is volatile, generally changing with the seasons and (particularly in fashion) short-lived trends.
- We can only estimate pricing as the demand-price relationship typically isn’t known, so it’s difficult to operate with absolute accuracy.
How to research demand forecasts
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.
