A major advantage of selling online is the wealth of data you can collect about shoppers, their preferences and their buying paths. While this data can be used to inform many strategic business decisions, another impactful way you can use the data available to you is to optimize your prices in real-time.
Dynamic pricing, already widely used by airlines, hotels and the entertainment industry, automatically adjusts prices on ecommerce sites with algorithms that gauge the volume of demand. The higher the demand, the higher the price.
The value of this comes from matching your price with the value customers perceive your product is worth (and what they are willing to pay) so you can maximize your revenue. For example, on any given flight, there’s a good chance that no two bookings were the exact same price. Understandably, higher demand for later departure times or around holidays can dramatically affect how much you pay.
Ideal Time for Other Industries to Adopt
As ecommerce data and the algorithm economy continue to flourish, so too does the opportunity for other industries and businesses to start leveraging real-time dynamic pricing. Major e-tailers and box stores like Walmart, Best Buy and eBay have already developed algorithms of their own and Amazon now updates product prices millions of times every day based on a complex algorithm to assess the demand for products.
For small and medium-sized businesses, the best time to adopt a new technology is usually after it has been tested and the best models have been identified. With many of the big players already working out many of the bugs, Gartner is predicting this may be an ideal time for companies that haven’t already to implement dynamic pricing. Leveraging it sooner and more effectively than competitors offers a huge opportunity to increase revenue.
B2B Adoption
There are many new applications that automate pricing optimization for B2B companies. While some assume they don’t have the price elasticity to take advantage of dynamic pricing, this is often not true. Hindsight analysis and pressure from business cycles (ever wonder why the best deals come at end of a month or quarter?) often influence prices in a B2B setting far more than any analysis of demand.
If your organization can move to predictive pricing based on the personas and buying path of your audience rather than recent history and internal pressure, then there is a huge opportunity to improve revenue.
Increase Social Engagement
If increasing social engagement is important to your business, consider using dynamic pricing to reward those that actively engage with your organization online. This can be done overtly with an offer or subtlety by consistently rewarding positive consumer behavior. This technique increases user generated social growth and has been used for years by the gaming industry to increase active user counts and revenue.
Maximize Recurring Revenue
If your business includes a subscription component, then adopting some sort of price optimization can drive years of increased revenue. Since the lifetime value of a client for a subscription business is so dependent upon the starting point, a small increase in the initial price can lead to a large revenue gains over a customer’s lifecycle.
Not Without Risk
Along with the opportunities are risks that need to be understood and mitigated as part of any price optimization adoption decision. Some to consider include:
- Perception of price gouging. There is no getting around it – most know at some level that others might be getting a better deal. We also understand that the cost of a single SaaS subscription will vary if we are looking for 10,000 instead of 10 licenses. However, with online pricing available to consumers, great care must be given to how any pricing model is communicated.
Consider basing your price differentiation on your personas and vary the value in terms of the product mix in your offer or the price based on size, location or the key KPIs that drive your market.
- Automating Mistakes. A problem with the implementation of anything involving machine learning and automation is that if something is wrong, it is wrong very efficiently. A classic example occurred in 2011 when a text book on the genetics of a fly ended up with an online price of over $23 million on Amazon because of competing algorithms (plus shipping and handling, which makes one wonder what the threshold for free shipping is!). Unfortunately for the author, none were sold at that price. But the example provides a good reminder that you need to remain vigilant when reviewing or implementing any pricing automation.
- Not knowing costs. This is often overlooked but you need to know your marginal costs. If pricing is automated based on variable conditions, then any change in costs must be accounted for.
So along with any opportunity that you see for price optimization with your business be sure to consider the associated risks. Again, with a clear understanding of your market and buyers you can mitigate some of these risks and turn them into advantages.
Is Your Ecommerce Store Ready for Dynamic Pricing?
While the opportunity for more revenue is huge with dynamic pricing, it is not without risk. But, I believe most ecommerce stores would benefit from having dynamic pricing algorithms added to their ecommerce solution.
