I started using Repricers many moons ago, using dirt and a stick. (Ok, it was Excel, but it all feels so prehistoric now.)
My system was certainly not perfect, but it worked.
Years later, Repricing software packages came out. There were oodles of free ones and some paid ones, too. Most were awful – even worse than my scrappy spreadsheets – but a few were markedly better than what I was using. So, I shelved my duct tape-and-spit system in favor of them and didn’t look back.
Till I started working with Artificial Intelligence and Machine Learning.
At their core, standard Repricers are just predetermined pricing strategies. You set floor and ceiling prices for each product, and the software raises or lowers your pricing according to your strategy/rules/business goals. If you’re using the software to its fullest, you’ll factor in inventory levels, demand, seasonality, and such. Still, in my experience, far too many marketers focus on the prices themselves, unfortunately.
In the ole’ days, marketers would have strong reactions to Repricers. They either loved them or hated them. Truthfully, it was mostly hate. Folks couldn’t get past the fact that they might lose margin dollars. They didn’t see any upsides. They assumed it was another area where they’d have to give up their cold, hard cash. The wrong assumption, but alas…
Amazon changed a lot of that. People who hate Repricers tend to be maniacally obsessed with The Buy Box. (Insert angels singing here.) So, when the Repricer-haters started selling their wares as third-party merchants on Amazon, Walmart, eBay, etc., they’d often implement some repricing tool/strategy. Sadly, to this day, most marketers still mostly use Repricers to lower prices in response to the market. (In other words, they’re reactive instead of proactive.)
What should YOU know about Repricers?
First, Repricers are now mostly referred to with more bougie terms like Price Intelligence and Price Optimization.
Price Optimization enables you to track, analyze and change pricing data 24/7/365. Some companies use it to ensure they have the lowest market price. Others use it to track competitors, test, monitor distribution and affiliates, identify the best prices for new product introductions, and evaluate competitors.
ALLOWS YOU TO DYNAMICALLY SET “THE PERFECT PRICE” FOR EACH AND EVERY USER
Price Optimization can be done in many different ways, but the two major types are Rules-Based Pricing (think of me with my stick and dirt, er, Excel) and Dynamic Pricing (aka Algorithmic Pricing.)
Rules-Based Pricing/Repricing (RBP) is semi-automatic, human-powered, and easy to execute. You assign the rules (ex: beat all competitor’s prices by 1% or increase the price by 10% when the in-house quantity drops to 50), and the system implements them. With RBP, you have the most influence on the outcome because you are the Captain of the Ship.
Dynamic Pricing/Repricing (DPP), on the other hand, is AI/ML powered, fully automated, and far, far, FAR more complex. The Machine customizes the best/optimal prices based on the data per product, customer, or combination. There’s no limit to the amount of data you can use in your calculations: competitor data, behavioral data from your prospects/customers, inventory levels and availability, warehousing and shipping considerations, profitability, seasonality, sentiment, and so on. Best of all? The Machine learns as it gains more data which, in turn, helps influence its process and your outcomes.
The Travel Industry has used Dynamic Pricing for years. Many travel companies use what they know about what you want and you (as a buyer) to determine your price. That’s why you and I might be leaving from the same airport, on the same airline, at the same time and get wildly different prices. Yes, sometimes it’s because they truly did blow out the first x% of tickets and are now selling to the next tier buying group, but far more often, it’s because they’ve identified that the amount you’ll pay is different from what I’m willing to pay. The same goes for hotel rooms, rental cars, and many experiences.
And before you say, Travel is a different industry; retailers, catalogers, and other eCommerce companies use Dynamic Pricing, too. It’s more blatant in Travel, but you’ll easily spot it in electronics, small appliances, fitness, phones, and gaming. Some of the most effective implementations of Dynamic Pricing are in health/beauty, apparel, and flowers, but they’re usually harder to spot because the product prices are standardly smaller. Plus, there’s often more geotargeting and event targeting at play.
Remember: the goal of Dynamic Pricing isn’t to find the lowest price at which a user will buy your product(s). It’s to find the optimal price. Mileage may vary on what companies think “optimal” is. It could be the maximum price, the best price to get rid of the largest amount of inventory, the most sustainable long-term, the most effective price for maximizing LTV (Lifetime Value), the best price for a particular segment or channel, etc. Choose what works for your business.
SETTING THE “PERFECT PRICE” FOR EACH INDIVIDUAL PRODUCT
Some marketers like to set their prices in relation to what their competitors are doing. Others want to focus on pricing by individual or cluster of individuals (often referred to as Bunching) or by-products. The most sophisticated Price Optimizers use a combination of all of them.
If you’re not ready to tie in all your prospect/customer data, you may still be prepared to optimize your prices with your Product Data. With AI/ML-powered Dynamic Pricing, you can set and reprice products by oodles of variables: margin, sales velocity, timing, inventory levels, new product status, price/product history, standards (MAP/MSRP rules), conversion rate, number of transactions, offline support (catalogs, radio and TV ads), competitive pricing and info, level of ad support, geography, additional fees, product reviews and ratings, influencer sentiment, and more.
Incidentally, no matter what your Price Intelligence vendors tell you…. There’s no shame in starting small when it comes to Price Optimization. For most companies I see, establishing a solid foundation with just Product Price Optimization and then tying in the Customer info typically works best. It will be faster and easier to spot errors and build your disruption techniques. You’ll also be able to read and react to the results quicker and more precisely.
Hint: If you’re overwhelmed by all this, just set up a system to predict what and when to discount. Then, look at how much to discount.
We have clients who use AI-enabled Price Optimization techniques and never reprice a thing. Most of these folks manufacture or sell products with set pricing, but they find value in the data they get about the overall market and specific competitors they’ve identified. Optimization tools also help them track what’s happening in third-party marketplaces and Paid/Social advertising.
Some folks use competitive intelligence to track significant competitors’ prices across multiple channels or build a proprietary Market Price Index. (MPIs show you what price position you are in relative to your competitors.) Some use the competitive info they gather to police their products – ensuring that everyone is selling at the price(s) they’re supposed to. Others use it to determine how their peers are discounting, if there’s a difference in their catalog or brick-and-mortar pricing vs. their online pricing, how often their competitors change their prices, and more.
What competitive intelligence should you collect? Easy. What you’re going to use. You can collect all sorts of things on all sorts of levels, from SKUs and brands to categories and channels, but none of it matters unless you use it.
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