âWe donât have enough historical transaction data.â
âWe donât have big enough sales volumes.â
âWe donât want to be the cheapest.â
âWe canât change prices often enough.â
Many e-commerce and retail companies (falsely) assume that dynamic price optimization is not relevant for them â and these are just some of the common misconceptions that we keep hearing.
The truth is, AI-powered dynamic pricing itâs not just for extremely time-sensitive businesses like hotel bookings and flight tickets.
Dynamic price optimization works for many types of businesses and products.Â
That said, if youâre not optimizing your prices proactively yet, youâre most likely leaving easy money on the table. (Itâs not just a myth that a 1% change in price can translate into a 10-20% increase in profits.)
A quick disclaimer before jumping into the topic: sometimes you hear people referring to rule-based pricing as âdynamic pricing,â but in this article, we talk about automatic price optimization based on machine learning â and more specifically, reinforcement learning.
5 types of products that almost all retailers carry with a perfect use case for dynamic price optimization
So, how do you know if price and profit optimization works for your business?Â
For the sake of simplicity, letâs take a hardware store as an example, and see what are the 5 different types of products they could use dynamic price optimization‘ for:
- Premium-priced products with added value, e.g., Weber barbeque grills
- Products with no direct competition, e.g., Torx screw drive vs. slot head
- Products with high volumes and low prices, e.g., screws, wall plugs, and suchÂ
- Category B products, e.g., two-by-fours (category A) vs. nails (category B)
- âForgotten products,â e.g., single screwdrivers for professionals vs. screwdriver sets for laypeople
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1. Premium-priced products with added value
Suppose youâre selling products that carry a tangible (or sometimes imaginary) surplus value (anything from a sustainable and transparent production chain to higher-quality materials or a brand that reflects a particular lifestyle).Â
The odds are that youâre using price signalling and positioning yourself in the market with a premium pricing strategy.
However, the truth is that most premium pricing is not done as systematically as it should: most brands simply take costs, add a needed margin and some extra on top of it, align the price point with their direct competitors, and then simply stick with that price.
This is where dynamic price optimization comes in.Â
With dynamic price optimization, you can subtly test your price point in the market without damaging your brand image (or alternatively in some cases, use data about the relation of price and demand to inform your pricing strategy at a higher level).
What often happens with premium-priced products is that you find out that you could increase your prices quite a bit before it affects your sales volumes at all.
Hardware store equivalent: High-end barbeque grills like Weber. Some people are willing to pay extra just to get a Weber in their backyard. They wonât even consider a cheaper option because theyâre so convinced of Weberâs betterness â regardless of if itâs true and if a cheaper option can get the same job done.
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2. Products with no direct competition
Some products have no direct competition in the market, which means the single thing that moves the needle is the price you sell that specific product at.
In a very competitive market (read: if all your competitors sold exactly the same product), you wouldnât necessarily have much room for even the smallest price changes.Â
In a situation where there is no direct competition, people are not going to go elsewhere â simply because they canât. This is why a controlled increase in product price can make all the difference in the world in your profit margins.Â
Hardware store equivalent: Torx screw drive vs. slot head. If you need to use durable Torx head screws that resist cam-out better than slot heads, your only option is to buy a Torx head screw drive.
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3. Products with high volumes and low prices
Low prices, low margins, right? Not necessarily, if you can outsource your pricing to artificial intelligence.
If youâre selling a lot of products for a low price, itâs tempting to think it doesnât pay off to test several price points.Â
No one could afford to do that manually, but with a dynamic pricing tool, you can automate this process.
By doing so, you can get better profit margins from low-priced products with high sales volumes by adjusting the price based on the real willingness to pay and in real-time. And with high sales volumes, that 20-cent difference in price can make a huge difference.
Hardware store equivalent: Screws, wall plugs, and such.Â
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4. Category B products
Youâre surely pricing your loss leader products and bestsellers actively but might not be optimizing prices for other products that bring in the majority of profits.
Category B products might not bring you as much money as your precious As. With proper automated optimization, they could help you increase your overall profit margins by heaps.
If you need a few two-by-fours (category A), youâre going to need some nails (category B), too. But youâre not going to go that extra mile and visit another hardware store for cheaper nails. In fact, youâre most likely no to even know which store sells them cheaper.Â
The same goes for more expensive safety shoes and cheaper protective gloves, or paint and mineral turpentine.
Hardware store equivalent: Two-by-fours (category A) vs. nails (category B)
5. âForgotten productsâ
Some products are selling steadily year over year, no matter what.Â
Take classic lampshades, for instance. These are not necessarily the cash cows of your business but theyâre still doing relatively well, which is why many retailers end up not prioritizing finding the highest possible price point that maximizes their profits.
A very similar situation occurs when a new mobile phone model is released to the market.Â
To avoid having to put old models on sale (which typically means losing money), you might want to consider lowering the price step-by-step and getting an estimate of when that product will run out of stock at a particular price point.Â
Getting back to our hardware store example: if youâre a construction professional, youâre more likely to buy a single screwdriver for specific use (the pricing of which retailers often tend to ignore), while the rest of us just want to get the cheapest screwdriver set (the pricing of which is retailers definitely donât ignore).
Hardware store equivalent: Single screwdrivers for professionals vs. screwdriver sets for laypeople
Why machine learning and AI are shaping the future of product pricing
When dynamic price optimization is not the best choice?
All that being said, we want to be completely honest: there are a few scenarios when dynamic price optimization with reinforcement learning is not the optimal choice.
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Scenario 1: You canât change your prices at all
You donât need to change your prices every day or even every week to benefit from dynamic price optimization.
But if you canât change your prices at all for a reason or another, you wonât get any data on how price changes affect your conversions and demand. In that case, our algorithm wonât be able to give you any pricing recommendations.
This can be a problem:Â
- in heavily regulated industries,
- for retailers with fixed resale prices, and
- for some omnichannel retailers with no electronic shelf labels and a pricing strategy that prevents from having different prices online and offline.
However, in omnichannel, you can still benefit from price recommendations. Instead of automatic price optimization, you can use the algorithm to inform your overall pricing strategy at a high level and to help you understand market changes and how your pricing affects the level of demand.
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Scenario 2: Low sales volumes and short sales lifecycle
Slow sales cycles or small sales volumes alone are not problems for our algorithm: it will give you just as reliable recommendations; itâs just learning a bit slower than it otherwise would, but eventually, you will get the same benefits.
But if your sales volumes are low AND your sales lifecycle short (e.g., just 30 days in total), our algorithm wonât have enough opportunities to learn from your transaction data.Â
In some cases, itâs possible to get around this by optimizing a group of similar products instead of single SKUs (e.g. if youâre selling the same print in 50 different colors).
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Scenario 3: Unique items that are sold just once and come with a high price tag
Letâs say you want to sell your house. In this case, thereâs just one house for sale and you sell it just once, so the reinforcement learning algorithm has no transaction data to learn from.
In this case, it makes more sense to use a neural network thatâs provided with the right data set in advance (which is obviously more expensive, but the high price of the house makes up for it). The same goes for any unique, premium-priced items like antique jewelry or expensive art.
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Scenario 4: Youâve already found your optimal price point (for now)
Disclaimer: This is very unlikely to happen. This is simply because most businesses carry so many SKUs in their inventory that itâs simply impossible to find out the optimal price point for even your key items â let alone for all of them.
Just for the sake of it, letâs imagine you have already found your optimal price point for your bestselling product.
In this case, our algorithm would make small price changes â just to find out after a while that youâve already found the point where you can reach your maximum profit margins.
Whatâs good to keep in mind, though, is that your optimal price point also changes over time.
So even if you knew your optimal price point right now, youâd still need to continue testing how it changes over time to avoid missing any opportunities.
Boost customer conversions with intelligent dynamic pricing.
Conclusion
As we saw above, the beauty of a machine learning based price optimization tool is that itâs able to increase profit margins equally well for high-end e-commerce brands, cut-price retailers with high sales volumes, and anything and everything in between. You also donât need a large product portfolio or massive amounts of historical data to benefit from dynamic price optimization.Â
But the best part?Â
Our dynamic price optimization tool can produce 10% better profit margins than traditional methods of calculating price elasticity (and a LOT more compared to not optimizing your prices at all).Â
Actually, weâre so confident it works for just about everyone that we even promise a 5% increase in profit margins during the first 90 days of use only (T&Cs apply).
