Pricing strategy #6: you can’t afford to over-rely on cost-plus pricing vs. science
If you’ve been following this series, you’ve heard us talk quite a bit about cost-plus pricing. It’s the “we’ve-always-done-it-this-way”, gut-instinct approach to running a wholesale distribution or retail business. But today we’ll show you why over-reliance on cost-plus pricing is just plain dumb.
Cost-plus pricing has severe limitations. Introducing machine learning-based science into your pricing strategy will simplify the many uncertainties of today’s supply chain while improving a long list of financial metrics for your business.
What is Cost-Plus Pricing?
Cost-plus pricing comes in many forms under different names, including margin targets, margin or markup by segment, margin pricing. We like to call it “peanut butter pricing” or “guess-casting”.
A good way to illustrate cost-plus pricing is the example of holding a garage sale…
In my garage sale days, I’ve tested two different pricing approaches – one where I put price tags on everything and one where I ran out of time setting up, and just made up the prices in my head.
[Now I’ll admit, in the latter scenario, my off-the-cuff prices often corresponded with how nice of a car the shopper pulled up in, or how excited they were about a particular item]. Moral of the story… I made a whole lot more money in the flexible-price scenario!
You know why? Because by taking a moment to assess the market before setting my price, I could strategically increase my margins. I could raise prices when I knew that a willing customer was there. And I could decrease my prices when I needed to deplete the inventory easily – whereas at the higher price point, I’d lose the sale because my neighbor down the street was selling a similar item (or substitute) for less.
In this example, cost-plus pricing is spreading set prices across everything without knowing the ‘margin science’ behind it.
Why it Doesn’t Work
Whatever you call it, over-reliance on cost-plus pricing causes problems.
First, cost-plus pricing fails to connect prices with willingness to pay.
For example, one product in a category is commoditized and highly competitive and, therefore, highly price sensitive. Another product is unique; it has no direct competitive substitutes and, therefore, it is less price sensitive.
Cost-plus pricing takes an inflexible formula and spreads it across all items regardless of price sensitivity, product substitutability or other factors. This directly limits the control you have over your margins.
Cost-plus pricing also involves a whole lot of guessing, which leads to price inconsistency. Having overpriced and underpriced products causes lost sales and damage to your reputation with customers.
For example, let’s say the 9-foot ladder is your biggest seller. So you buy more from your supplier or manufacturer, which drives down cost. Without even realizing it, your 9-foot ladder becomes lower priced than your 6-foot ladder. You’ve underpriced your best seller and your customers don’t understand your pricing. So they stop buying from you. Part 1 of this series goes deeper into how you can establish price consistency. Read here.
Benefits of Pricing Science
Why spend so much time collecting competitive pricing constantly, guessing at good margin, and wasting valuable historical prices and demand? The benefits of using pricing science are similar to using statistical forecasting methods to supplement (not replace) demand management, inventory optimization and order management.
Science-based pricing solutions allow two significant, margin-boosting outcomes:
- Measure customer willingness-to-pay by product and location or channel. This allows you to achieve better prices by quickly identifying underpriced or overpriced items.
- Forecast the impact of price changes on demand, revenue, profits and other financial metrics.
Machine Learning: Built to Handle Post-Pandemic Life
With the pandemic, large-scale weather events and other seismic shifts redefining how distributors and retailers conduct business, the new normal is too much for humans to handle alone. We’ve crossed the chasm into a world where science and automation are needed for survival. A cost-plus pricing strategy can’t handle post-pandemic planning.
Machine learning-based pricing tools don’t just optimize “A-items” – they actually analyze price performance and make recommendations to boost margins across the entire assortment, including the long-tail items which no one usually touches.
This is especially important in an environment like today’s where so much is shifting on the demand front – businesses that didn’t sell much online before are now having to put their prices out there for the world to see. They must pivot quickly on what they sell, as well… Items that used to have very low demand – such as janitorial and sanitation products or home gym equipment – have suddenly bubbled up as A-items. Sometimes without any obvious signs.
Price Optimization solutions that integrate with supply chain planning deliver early demand signals and insights you need to react profitably to sudden or insidious changes. These solutions deliver advanced calculating power to constantly evaluate the impact of a price change across the entire assortment, so you can quickly:
- Identify mis-priced products,
- Strategically deplete inventory to raise margins,
- Get the upper hand in supplier negotiations, and
- Drive revenue opportunities through pricing that is both competitive and profitable
Read More from This Series
Read this series from the beginning