You know the stats.
You’ve read that for 83% of customers, price is the #1 priority.
You’ve seen e-commerce take a larger and larger share of retail sales. Even the last tribes untouched by the modern world have heard about how, twenty-five years ago at the time of writing, one business changed the way that goods were purchased forever. You also know it’s not simply about price, unless you somehow operate your business in a vacuum, without competitors or substitutes.
Whether you are strictly e-commerce, brick and mortar, or a crafty combination of the two, the very fact you have found this article tells me you are looking for new ways to squeeze greater value out of your pricing strategy. This article, part one of our A.I in Dynamic Pricing Optimisation series, will explore the Price Elasticity of Demand (or PED) as a concept, and then how Artificial Intelligence can use this notion, as part of a broader suite of tools, to add value to your business (improve your pricing strategy and increase revenue). As briefly mentioned above, PED is not the only factor at play in your pricing strategy. Competitors, substitutes, brand loyalty, your own suppliers are all pulling different levers, and later articles will discuss how A.I can be implemented to take these into account.
But for now, it would serve us well to go through the economic concept behind PED (feel free to skip the next paragraph if such a sentence triggers an automatic yawn in you).
The price elasticity of demand is, as its name suggests, a measurement of the effect price has on demand (or goods sold). A good or service is deemed to be elastic if a relatively small change in price results in a large change in demand. By contrast, a good or service is said to be inelastic if significant changes in price only result in small changes in demand.
Assuming that the majority of goods and services sold have a degree of elasticity, we can plot the demand curve. This gives us a simple indication of, all other things being equal, how many units we can expect to sell if we simply toggle the price lever.
With an understanding of PED, it’s now time to explore how Artificial Intelligence can be used to find the balance between quantity sold and the acceptable price at any given time. While it would be possible to use a rules-based approach, the most intelligent solution is one that uses Reinforcement Learning. Within the domain of Dynamic Pricing, an RL agent is given the freedom to change price and is rewarded for its success (and punished for its failings).
The result of this is a tool that is rewarded for increasing your business’s revenue over time.
What does this mean for you, in layman’s terms?
A.I Dynamic Price Optimisation is continually calculating PED, trying to find the correct balance to generate the greatest total revenue. When we start clients off with a new price optimisation implementation, we typically test three to four strategies around PED to see which generates the most revenue. These sort of strategies can deliver upwards of 10% increase in total revenue. As such, price optimisation, with Artificial Intelligence at the helm, is allowing e-commerce businesses to intelligently price their entire product range, to take advantage of demand at an SKU level.
If I was to give you one piece of information to convey the efficacy of this pricing strategy, This has seen increases in revenue of up to 20% when it was famously rolled out at Amazon.
Please note, PED as part of a broader A.I-Powered Dynamic Pricing strategy is not about loss leadership or a race to the bottom. The strategy is focused on intelligently charging higher prices when there is greater traffic through you site, and lowering it during quieter times. The A.I takes into account your profit margin to ensure you are not taking a loss.
Next steps:
We use PED as a critical tool in understanding how beneficial A.I could be. If you chose to undertake a pilot with us, we would go through your historic data to understand the price elasticity demand of your products. We would like to go through any competitor pricing data you have access to (to better understand the space within which you operate). This proof-of-concept usually takes one week. Upon completion, if there is a degree of predictability in the data, we like to progress with a trial. For this, we select five to ten of your range and grant control of pricing to the A.I. This requires a degree of customisation, depending on which platforms you utilise, but from this point onwards, the A.I is hungry to generate the most revenue for you at any given time.
This article will have raised questions in some of you, primarily around the other factors at play when it comes to demand. An intelligent A.I PED model will take these elements into account, elements such as:
- What competitors are doing
- Supply chain factors
- Brand Loyalty
In conclusion, A.I offers a great opportunity to generate greater total revenue for your business. If you would like to have a chat about what Remi A.I can do for your business’s pricing strategy, reach out. We are proud to offer a free consultation and a review of your existing pricing strategy.
Stay tuned on the Remi AI blog as we build out the complete supply chain offering!
Or, if you're ready to start seeing the benefits of A.I-powered inventory management, start the journey here.
Who are we?
Remi AI is an Artificial Intelligence Research Firm with offices in Sydney and San Francisco. We have delivered inventory and supply chain projects across FMCG, automotive, industrial and corporate supply and more.
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