Platforms & Asymmetric Pricing
The 4th in a 4 part series on platforms as a business model in logistics.
Imagine it: you’re launching a new logistics platform startup. Your LinkedIn title says that you're disrupting a trillion-dollar industry. Your company’s landing page says technology is changing the game. The about-us page explains your commitment to sustainable growth, work-life balance, team diversity, and so on in this vein. You give TED Talk style presentations to VCs, listen to the Acquired podcast for business history, read We’ll Be There Soon for logistics insights, and follow Ryan Petersen on Twitter.
Its all been whiteboards and pitching. But now the time has come to price the logistics platform. If this was normal big-ticket enterprise software, you’d use internal guidelines on pricing and negotiate the deals privately one by one. But this platform is going for large scale adoption: you are changing the world after all. That means you are tempted by the typical approach of consumer software and have some public pricing plans that the user chooses from. How to proceed?
The rest of this article is going to explore this question: how does pricing a platform differ from pricing typical enterprise software as a service (SaaS)? In my view, there are important points where they deviate because of:
higher potential for demand elasticity,
greater importance of customer count,
longer expected customer lifetime, and
the ability of pricing in one customer segment to impact the other customer segments.
I’ll address each through the lens of specific decisions to make.
Public or Private Price?
Should a logistics platform make its pricing public?
There are two ways a prospective member can pay to join your platform. The first is to check out a clear public pricing page on the website: people can buy via self-sign-up. The second is to be forced to contact a salesperson and go through enterprise sales negotiations. If you hide the price, forcing the buyer to negotiate for a specific agreement, you can execute differential pricing. This can extract maximum revenue per sale: that is the upside. The downside is that such negotiations are slow, expensive, and reduce total sign-ups. Let’s look at these deeper from a platform perspective:
Enterprise negotiations are slower than self-sign-up. No matter how you approach it, they add a step in the process. More often they add many steps. As a platform, the number of members impacts the attractiveness of the platform to other members. Being slow doesn’t just shift revenue to the future it lowers the likelihood of ever getting the revenue at all. Bottom line: platform network effects mean you value speed to reach N member count, and therefore should eliminate delays instead of maximizing per-customer revenue.
Enterprise negotiations are more expensive than self-sign-up. Not just for the vendor but also the buyer. In SaaS it wouldn’t be unusual to see >10% of first year revenue for a newly negotiated contract paid out as salesperson compensation. We can assume that something like that amount is being dispersed within the buyer to the procurement staff who made the other half of the negotiations. That’s ~20% of the first year’s revenue that was consumed because the vendor obliged a negotiation. I don’t think this amount is more impactful for platforms than for non-platforms but eliminating it would provide room to discount. And that is important because of demand elasticity, discussed later in the article.
As a thought experiment imagine a self-sign-up form for Netflix that has, as a middle step, a requirement to get some other person in a different location to login as well and click “approve”. We can assume this doesn’t just slow down the process: it kills conversions. Some prospects won’t want to engage someone else, others will try but fail to get their attention, and many more will intend to but then become distracted. Here is what we know for sure: forcing your potential customers to start formal negotiations will certainly lower total demand for your solution. Like point 1 above, this funnel drop-off matters more for platforms because part of what a platform offers its members is a network effect from other members. If you lose X number of members because they drop-off at the price negotiations step, you also reduce the overall attractiveness of the platform. For this reason it is better to optimize for member count instead of revenue-per-member.
I think these points suggest strongly that logistics platforms should run with public pricing, not privately negotiated enterprise deals.
Will You Serve Tapwater?
What consumption metrics should drive fees for a logistics platform?
Years back when I wrote a guide to SaaS pricing it was about what the total price should be. But that leaves aside an important nuance: on what basis is that total price tallied? Just as every building has a meter tracking its water or energy usage, every SaaS will place a meter on some dimensions of its customer’s consumption so as to justify the price they charge.
We create meters on consumption of SaaS for three reasons:
For any business with non-zero incremental costs, fees per cost-adding increment are a defensive mechanism to avoid losses. Example: if you sell transport optimization software then some form of “solve” activity will generate computing costs. If solving doubles, the business has some compute cost doubling, and they need a way to charge fees to recoup this unless they like the calculated risk of covering it from other revenue.
When a customer gets more value, the vendor will try to extract more revenue as well. This is value-based pricing, and tracking some metrics of consumption is a proxy for it.
Demand is elastic to price model, for the reasons of risk-reward implications and also as a counter positioning to competitors. For example, a “pay if it works” pricing model makes consuming a SaaS product low risk hence it boosts demand. Likewise, if a competitor requires an upfront commit, then a price model that is per-usage brings higher demand (while perhaps not adding demand for the market overall, it gains market share for the per-usage option, i.e. it is effective counter positioning).
The downsides of price models that meter consumption are simply that they can get the tradeoffs wrong. A well-built pricing model links revenue to costs, captures increasing revenue in line with increasing value-creation, and counter-positions to competitors. But by the same token a poorly built model gives unexpected losses, leaves money on the table, and reduces demand for the product.
Logistics platforms have the same challenge to establish pricing models as other SaaS models, but with two additional nuances. First, if you want to execute value based pricing, and platforms add value through network effects, you effectively have a price model that can grow fees based on other members behaviour not the single customer's consumption. Second, platform network effects mean demand elasticity of one member “side” can impact the demand for the other sides. This has unexpected implications which matter enormously in practice. For example: it may yield higher total demand (and total revenue) to create a very attractice pricing model for one side, whose high demand then creates high demand for another membership side, and this second membership side is given a pricing model that captures enough revenue for the platform to make up for foregone revenue from the first member side.
Together, these point to developing a pricing model that is strategically cohesive across members, i.e. not a price model per member class in isolation. My suggestion is a price model creation process as follows:
Pick your platform's hard side. For these members:
List the ways that their usage could trigger incremental costs. For each way, add a pricing model metering that charges exactly the expected cost (and no more). This results in pricing model that achieves zero contribution margin percentage. It does not cover fixed costs yet.
In what way would consumption be measurable as a proxy for irreplacable value? This means value to the member of the platform that cannot be captured in some other method (i.e. some other platform, for example). For each pattern, add a pricing model metering that charges 25% of the expected irreplacable value.
Inspect the resulting price model compared to the most important competitor's pricing model. Which model will be most attractive on the basis of risk-reward or time-phasing? If your model is not the winner, is there a polymorph such that the same revenue can be earned per customer but with a more attractive positioning compared to competitors?
Having fixed a draft pricing model for the hard side, switch to the next hardest side of members.
Repeat steps 1a, 1b, and 1c above for this member class.
Now, inspect how much additionally this group would pay to boost the size or usage intensity of the other platform members. Find an “willing to pay X to see other member count or usage increase by Y” formula.
Add an additional pricing metering that achieves charging X for Y, and discount the other member's pricing model by X for Y.
Repeat step 2 for each platform member side in decreasing order of difficulty to attract to the platform.
While the process is dense to read, it offers a clear way to approximate the usual SaaS pricing but also including the multi-sided nature of platforms and their network effects impacts on pricing viability and demand elasticity. In effect from step 2 onward you have the trigger to assess to what degree one set of platform members will pay to boost the platform's overall membership, and then redistributes this value out to the other members on the basis that demand is indeed elastic to price.
Network Effects on CAC and LTV
How to balance CAC and LTV for platforms?
At its core, business success can be checked with straightforward math:
We spend money to get a new customer.
Then, we generate a certain amount of money from that customer until they leave.
Subtracting the cost of serving the customer, we get an aggregate contribution the customer made to the company profitability.
The first amount is referred to as Customer Acquisition Cost (CAC) and the second amount is the lifetime revenue from the customer which, when subtracting the cost to serve the customer in step 3, is referred to as Customer Lifetime Value (LTV). You want LTV > CAC, and any sane business will track their will track their LTV/CAC ratio. These form the core unit economics behind how accretive new sales will be, and when the customer's fees will overcome the cost spent on everything that went into winning them (i.e. CAC Payback, calculated as the average time needed for the contribution margin from a single customer to equal the CAC).
Whereas the pricing model design described in question #2 is about maximizing contribution margin from each member, the exercise to balance CAC vs. LTV is about optimizing cash intensity. Any LTV>CAC would result in a profitable business in the long term (assuming cost of capital is also factored). But some LTVs are in fact spread over very long time horizons, and hence the sales payback period is years: meaning the company is consuming cash intensively while it acquires customers (i.e. while it grows). Many businesses that grow quickly and have losses are cash intensive simply because the CAC Payback period is large. Uber, Peloton, Door Dash, and Blue Apron are examples of such businesses. Because of network effects, platforms need to treat these CAC, LTV, and CAC Payback metrics more carefully than a traditional SaaS business.
As an example of how platforms have it different, consider a negative LTV/CAC ratio. This would imply we spend more money to get the customer than they will ever pay us in fees. That sounds crazy and unsustainable. But this may be okay (even optimal) for platforms because, again, network effects mean changes in one member side will impact the unit economics for the other members. To estimate LTV one needs to consider the following:
The cost to acquire the customer
The cost to serve the customer
The price the customer pays for the service
What services this customer will buy in the future. For example, will they upsell or cross-sell from the current ones?
How long they will remain a customer.
How much pricing power the platform has on this customer, along with future expected pricing changes (up or down).
The customer's influence on all the factors above for other platform members.
Did you see that last line?!
Point 7 means that you cannot really optimise the cash intensity of a platforms business (i.e. its CAC and LTV) without intentionally assessing how the customer's usage or presence on the platform effects other customers.
I'm suggesting that the best platforms likely end up with high asymmetry. That asymmetry is in the price paid, but also in factors like cost to acquire customer, cost to serve, cross or upselling intensity, churn rate, and pricing power.
Wrapping Up Pricing
I'd written years ago about how to price enterprise SaaS, and I believe in the importance of negotiations for maximum value capture in privately priced deals. But platforms seem to be a different beast, and if you are going to play the game on the field you'd be best served to notice key places where traditional enterprise SaaS diverges from an ideal logistics platform pricing strategy. In brief, I called out the following:
Put up public pricing and a self-sign-up option. Don't offer or entertain private negotiations: show people an easy way in and reinforce that its indeed the entrance. This is rationale because it increased demand via both lower friction and lower costs; network effects then amplifies the additional demand.
Price the hard-side of the platform to zero contribution margin. Then do the same for the other sides, intentionally cannabalizing their network effect demand to discount the hard side. Add a metered pricing that attempts to capture 25% of the unique value created in the platform. This approach achieves both demand maximization and value-based pricing in a complex multi-sided situation.
Rather than optimize the CAC against LTV on a customer segment basis, do so on a total platform basis by again engineering maximum use of network effects. Like any optimization process, it seeks minimuma and maxima, hence it will move the member segments apart in hwo they are managed.
Points 2 and 3 above lead to highly asymmetric outcomes for the platform members. I think this will be one hallmark to look for in well planned & executed logistics platforms. At its core, what it derives from is the differential negotiation leverage and desirability of one member group compared to another. These differences can be ignored with poor platform pricing (example: all accounts cost $100 per user), or maximally leveraged with nuanced pricing models.
Wrapping Up the Series
This was the last in a four part series on platforms as a business model for the logistics sector. It connects with a three hour podcast in which I discussed the same topic with my friend Märten Veskimäe. Check out the podcast at Logistics Tribe on Apple or Spotify. If you like this kind of analysis, sign-up to the newsletter.
End Notes:
There are also factors to pricing like billing cycle (pre-pay with credit, post-pay, once per day-month-year, etc). and commit cycle (monthly vs. annual subscription, etc). I leave these out as they seem not uniquely interesting for platform models.
Sources:
https://en.wikipedia.org/wiki/Cross_elasticity_of_demand#Potential_strategies
https://en.wikipedia.org/wiki/Price_elasticity_of_demand