Why Telfar’s Dynamic Pricing is Fashion-Forward but Monetization-Backward

James Wilton • April 25, 2023

Telfar Clemens, the mind behind hit clothing brand Telfar, recently made headlines announcing a new ‘dynamic pricing’ strategy that flies in the face of traditional fashion pricing, charging less for more popular items. Should other businesses follow suit and discount more when demand is high?


From the article, “there will be a dynamic pricing tool on the website that ensures the most popular, fastest-selling products are cheaper. The whole experience is designed to flip the script on the fashion industry, where brands tend to charge more for popular items. And it reinforces Clemens’ mission of making his products affordable, so they are accessible to anybody who wants them.” 


Different, eh? To be clear, this is dynamic pricing, but it’s unconventional dynamic pricing. A conventional dynamic pricing model for fashion would suggest that price would go up as demand goes up (so long as supply stayed consistent). Telfar are flipping it, and raising supply and lowering prices when the demand increases. This aligns with their operations – more demand means materials will be ordered in higher quantities. That unlocks volume discounts, so unit costs go down, and savings can be passed on to the customer. Neat. 


I want to like this because 


(a) it’s really interesting and potentially disruptive, and 


(b) it’s anchored around a social conscience, and there’s not enough of that in pricing. 


My problem with it? I just can’t see it working. 


What’s the problem? 

Luxury goods – and fashionable clothes are luxury goods to an extent – are an interesting case because they can have negative price elasticity. This means that demand increases as the price increases, because then the goods are seen as more exclusive and therefore more desirable. In other words, when fewer people can afford a specific garment, people want it more because now having it makes them “special.” A kind of wearable status symbol.

 

So, given that frame, Telfar’s strategy is a bit counterintuitive. They want to reduce the price of popular items so more people can afford to buy them. It remains to be seen how that is going to mess with customers’ perception of the value of those garments.   


Can you imagine? “I bought this, but now everyone has it. And they paid less for it than me(!) So, do I still want it as much?” 


Unless you’re under the age of ten or trying to blend in, people tend not to want to wear exactly the same clothes as other people. It can be embarrassing to turn up to an event in the same outfit as someone else. The phrase “b*tch stole my look!” is going to be on everybody’s lips if that look is more available the more that other people “steal” it. 


At the opposite end of the spectrum, if I purchase something that nobody else does, under Telfar’s model I will pay a high price for it. But then I also know that nobody else wanted it, so do I get the same sense of esteem from being the sole purchaser? It’s not that only I could afford it, or that it was limited in quantity and I was one of the lucky ones that found it. It’s that only I wanted it. 


The only thing that says about me is that I have non-mainstream tastes. Some people might want that (e.g., to be cool, edgy and unconventional, perhaps), but then if everyone is looking for unique clothing items hoping that other people don’t like them, then many people will buy them for that reason. And then they’ll go down in price! 


Final thoughts 

I challenge Clemens’ notion that fashion pricing is illogical. It’s extremely logical, because it involves aligning pricing to broad perceptions of value. If you turn the model on its head, as in this case, you end up getting stuck odd circular arguments (as I did) because it pulls away from buyer behavior, and it’s illogical It’s a great pricing strategy for grabbing attention, but I’d be surprised if it is successful. I’m all for fashion being unconventionally dynamic. But any dynamic pricing for fashion should remain conventional. 

By James D. Wilton May 28, 2025
Outcome-based pricing (OBP) is one of the hottest topics in AI and SaaS monetization today. Instead of charging customers for access or usage, vendors charge based on measurable results. The idea? Customers only pay when they see real value. It sounds like the ultimate pricing model - perfectly aligned incentives, no wasted spend, and a direct link between cost and benefit. So why don’t more companies use it? Because in reality, OBP is much harder to execute than it looks. It’s been around for decades, but few companies truly succeed with it. That’s because OBP introduces complexity, risk, and friction that can make it more challenging than traditional SaaS models. Here are the five biggest pitfalls of OBP - and what to do about them. 1. Defining the Right Metric is Harder Than It Looks The biggest challenge in OBP is choosing a metric that accurately reflects value - without creating unintended consequences. If the vendor defines success too loosely, customers will feel overcharged. If the metric is too restrictive, vendors won’t get paid fairly. Example: Zendesk’s AI Ticket Resolution Pricing Zendesk introduced AI-powered customer service pricing based on resolved tickets. But customers pushed back - because Zendesk’s definition of a "resolution" didn’t always match what customers considered a real resolution. The lesson? A pricing metric must be: Meaningful to the customer (aligned with their definition of success). Tied to the vendor’s real value-add (not just surface-level activity). Difficult to game or manipulate (or customers will optimize against it). 2. Attribution is a Nightmare (Even with AI) Choosing the right metric is only part of the battle - there’s still another problem: Can you prove that YOUR product drove the result? In many cases, multiple factors contribute to an outcome. If revenue grows, was it because of the AI-powered sales tool, better sales reps, or an overall market uptick? Example: IBM Watson & Salesforce Einstein Both were positioned as transformational AI platforms, but customers struggled to isolate the AI’s impact. They could see business improvements, but couldn’t confidently say, “Watson/Einstein was responsible for X% of that success.” Notably, neither IBM nor Salesforce uses OBP for these products. Why? Attribution is too difficult. If vendors can’t prove they caused the outcome, customers won’t want to pay for it. A better approach: Control more of the process (the more your product influences the outcome, the easier it is to claim credit). Use proxy metrics (if direct attribution is hard, find leading indicators that correlate with success). Offer hybrid pricing (mix base fees with OBP so revenue isn’t fully dependent on attribution). 3. Baselining Gets Messy, Fast Even if a vendor picks the right metric AND can prove attribution, there’s yet another challenge: How do you measure improvement? The problem: Many OBP models assume a static baseline - but in reality, customer environments change over time. Example: Fraud Prevention in Financial Services Some AI vendors charge based on the reduction in fraudulent transactions. But this raises tough questions: What’s the starting fraud rate? (Pre-existing fraud levels may fluctuate.) Should the baseline reset each year? (If the vendor permanently reduces fraud, do they still get paid for maintaining it?) The lesson? Customers won’t want to pay for improvements they believe they would have achieved anyway. And vendors need a way to continuously justify their impact. A better approach: Define clear baseline periods (e.g. compare against the 6 months before implementation). Adjust pricing over time (the vendor’s impact might be front-loaded, requiring a different model in later years). Use tiered pricing (higher fees early, lower fees as impact normalizes). 4. Revenue Delays Can Kill a Vendor Even if everything else works - the metric is solid, attribution is clear, and baselining is fair - there’s still one big problem: Vendors often don’t get paid until months (or even years) after delivering value. This creates massive cash flow risks. Many SaaS companies depend on predictable, upfront revenue to fund operations. But OBP means revenue recognition is delayed, making forecasting difficult. Example: Riskified’s Outcome-Based Model Riskified, a fraud prevention platform, only gets paid when transactions are successfully approved without fraud. This aligns incentives - but it also means their revenue is inherently unpredictable. The lesson? While this approach works for Riskified, not every vendor can afford to wait for long-term verification before getting paid. (Note: Investors may not love it either - Riskified trades at just 1.89x EV/Revenue, a very low multiple for a SaaS company.) A better approach: Charge a mix of fixed fees + OBP to ensure steady cash flow. Offer performance tiers (higher base fees for lower-risk customers, full OBP for riskier bets). Use milestone-based payments - instead of waiting for full verification, charge in phases. 5. Customers Prefer Predictability - Even Over Potential Savings Even if an OBP model delivers better value, many customers still choose predictable pricing over variable costs. Why? Most businesses prefer stable, budgetable expenses over a fluctuating fee - even if the predictable price is technically more expensive. Example: Conversational AI in Customer Support A vendor offering an AI chatbot asked customers to choose between: Payment based on how many conversations the AI fully handled (OBP model). A flat subscription fee. Most customers chose the flat subscription. The lesson? Even if OBP is theoretically the best model, buyers often prefer predictability. The existence of an OBP option, however, can signal vendor confidence and reinforce the value of a fixed-price plan. A better approach: Give customers a choice (some will prefer OBP, but many want predictability). Use OBP as an anchor (show the OBP price, but steer customers toward a fixed option). Cap OBP costs to reduce buyer anxiety. Final Thoughts: OBP Works - But It’s Not for Everyone Outcome-based pricing sounds great in theory, but it’s tough to get right. When structured poorly, it leads to: Customer friction (over unclear metrics or unfair pricing). Revenue instability (due to attribution and baseline issues). Delayed payments (which can crush cash flow). The best OBP models: Pick the right metric - aligned to value and hard to manipulate. Solve the attribution problem - proving the vendor’s role in success. Balance cash flow - with a mix of fixed fees and variable components. OBP isn’t broken - but it’s not a magic bullet. Companies that embrace it need to go in with open eyes and a clear strategy. What’s your take? Have you seen OBP succeed or fail? Let’s discuss.
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