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Soylent Blog - Pricing Soylent Efficiently

Soylent Blog / 2 years ago

Pricing Soylent Efficiently

You’ve got it. The game-changing product that could take your company to the next level. All you need to do is figure out how you will price it. Simple enough – nothing a leadership meeting can’t solve right?

And then this happens:

“Our margins should be as low as possible. That way we can capture the market and maximize our revenue growth. That’s all the VC’s care about anyway!”

“No way.. our products deliver tremendous value to customers. We should price them at 2-3X what our costs are. That way we can take our profits and reinvest in marketing and R&D. Look at Apple.. they are making a killing off this strategy.”

Sound familiar? It’s the classic debate about optimizing for growth or profitability.

It came up time and time again in our strategy discussions and was leading us nowhere. We needed to turn the philosophical discussion into a data-driven one.

Reframe the Problem

The first step to solving our margin problem was to stop talking about margins!

Margins are a useful accounting concept, meaning simply sales minus the direct cost of those sales. Margins, however, ignore a crucial dimension – time. Time is the major difference between profits on paper and cash available to run the business. As they say, you can’t pay the bills with margins.

Ignoring time in our financial analysis would be akin to a physicist who couldn’t see time as the fourth dimension. This physicist probably wouldn’t have a long career.

Take Amazon (AMZN) for example. Most people recognize AMZN as a “low margin” business that has disrupted traditional retail, but most people don’t know that Amazon could have negative margins and still generate significant cash flows.

How is that possible? It lies in how they manage their working capital and how efficiently they convert that capital into cash. We can quantify it through the Cash Conversion Cycle (CCC).

According to Investopedia:

The cash conversion cycle (CCC) is one of several measures of management effectiveness. It measures how fast a company can convert cash on hand into even more cash on hand. The CCC does this by following the cash as it is first converted into inventory and accounts payable (AP), through sales and accounts receivable (AR), and then back into cash.

To visualize, let’s imagine Apple selling iPhones. The cycle starts when Foxconn manufactures iPhones and delivers them to Apple. Inventory is created. Apple owes Foxconn for the production, but no cash will be paid until weeks or month later. These debts are called “accounts payable.” Now Apple sells the iPhones to end users. Retail customers buying online pay Apple cash right away. However, for larger wholesale clients like Best Buy, Apple may wait 30 or more days to collect cash on these sales. These are our “accounts receivable.”

To bring in cash on the iPhones, Apple must hold the inventory and collect on the receivables from selling that inventory. Cash goes out when they pay off the payables from producing that inventory. The difference between the inventory + receivable hold time and payable time represents the number of days cash is tied up while running the business. Just like in golf, the lower the number, the better.

Here’s a visualization for our business:

I encourage you to read this article for a full explanation of the CCC and how it’s calculated. Here is the calculation done with public financials for AMZN over 2014:

AMZN holds inventory for 39 days, and it takes them 21 days to collect on receivables. They have a whopping 78 days to pay suppliers, though. This puts their cash conversion cycle at negative 18 days, something you will rarely find in a physical goods company.

AMZN essentially gets 18 days of interest-free loans just by running their business. Even if AMZN takes in less than they pay out (negative margins), those 18 days of free money can be invested in more sales or financial instruments (stocks, bonds, etc) and give positive cash flow! Interest-free loans are incredibly valuable to any business. Just ask Warren Buffett – the concept of insurance float is what fueled Berkshire Hathaway’s rise from a $14M company into one worth over $350B.

Now for the bad news. If you are a young company like Soylent selling physical products, you may not have negative working capital dynamics. You may want to be careful when someone suggests you should do X because that’s what AMZN does, or do Y because Uber just dropped prices 25 percent.

In our early days, Soylent had the wonderful problem of negative working capital from pre-orders and shipping delays. As we scaled, though, we built inventory, and our cash conversion started to look more like a traditional physical goods producer.

We used the upcoming release of Soylent 2.0, our latest ready-to-drink product, to build a tool that would help us better understand our company’s cash flow profile. I’ll now walk you through how we built the tool and used it to drive key decisions for the launch. I’ll also share a fictionalized version of the complete tool we used in our original analysis.

Building the Cash Flow Model

We wanted a granular (weekly) model that would map out all the cash flows of a new product release over a 52-week period. The goal was to transform a simple margin analysis into a cash flow model that could be incorporated in our company’s operating model. Just a quick disclaimer before we continue - we’ve replaced actual data with fictionalized numbers to illustrate how we used the tool in our analysis.

Key Assumptions

We’ll start by defining our inputs and assumptions. We first looked at costs, since these were the most clearly-defined inputs:

Manufacturing costs are our biggest cost driver and fall into three categories: materials, packaging, and processing. Materials are raw food ingredients like algal oil and powdered soy protein. Packaging includes the drink bottle, sleeve, and outer box. Processing is the tolling fee paid to the manufacturer for production of the goods.

Not every physical goods business will follow this split, so you may want to explore more or less detailed breakouts of your manufacturing costs.

Freight are the costs to get product from the manufacturer to the distribution centers (DC). Shipping & Fulfillment are costs to get product from the DC to the end customer. We used historical data based on our existing product line (Soylent 1.5 Powder) to estimate unit costs for the launch of Soylent 2.0.

Credit Terms

Credit terms define how much time you have to pay suppliers after goods and services have been received. Extending credit is effectively an interest-free loan from the supplier. Early in a start-up’s life, most suppliers won’t take on this risk, but as a company matures and scales, the doors begin to open.

Our experience is a little negotiating - along with professionalized financials and forecasts - can go a long way in securing terms for your company. As we saw with the AMZN example above, credit terms can be one of the biggest drivers of your cash flow profile.

We inputted credit terms for each cost driver listed above:

Sales Projections

Now, we’ll turn to the demand and pricing side. These are among the most important model inputs, but also the easiest to abuse.

Retail unit pricing is user-defined and one of the key inputs we manipulated in our scenario analysis:

Next, we needed an initial sales forecast for the launch. You may have sophisticated forecasting software or methodologies, but we decided to keep it simple.

Our team looked at our existing business and used a percentage of that to gauge initial unit demand. We then modeled a week-over-week growth rate and plateau period to build out the sales forecast. Be careful here, though – put too high of a number for growth and you may soon find your product launch overtaking global iPhone sales!

Pulling it all Together

Now we have everything we need to build a cash flow forecast for our launch. Well, almost… We need to make one more key assumption. We are going to assume we have “perfect visibility” into future demand. This allows the model to intelligently order inventory and maintain a consistent buffer.

While this assumption makes the model work, anyone who has worked in forecasting will tell you reality is often quite different. Configuring the model to work with forecasting variance is a different discussion for a another day.

The model works by placing purchase orders on weeks offset by the lead time. The model orders exactly enough inventory to sustain the business before the next production run is received, while maintaining the set inventory buffer. It then adds receipts from product sales, deducts costs of manufacturing & distribution, offset by credit terms, and sums it all up to tell us if we are in the black or red for the launch year:

A few things to note here:

We were able to leverage organic marketing channels like press outlets and our Discourse forum to market Soylent 2.0, and we used a world-class formulator to save on development costs. As a result, our marketing and product development spend were a fraction of the direct costs to manufacture. Your business, however, could be different and require significant expenditures in these areas.

Scenario Analysis

Our last step was to use the tool to deliver key insights for our team. We wanted our tool to:

For pricing, we wanted to find the point where our product would deliver exceptional value relative to competing products. At the same time, we also needed sufficient cash flows to fund our aggressive growth strategy and invest in technologies that will change the world. Our team looked at a few scenarios, and agreed on $29 per 12 bottles on subscription and $34 for single orders.

You could try playing with a few scenarios for your business. Look at a range of price points. See what happens if you drive 20 percent cost reductions in manufacturing or shipping costs. If you are currently paying deposits on manufacturing runs (negative credit terms), see what happens if you get NET 30 credit terms on everything. This could be the difference between netting or losing a few million dollars.

We concluded our scenario analysis by prioritizing each cash flow driver by:

A key insight that came out of this analysis was that our packaging costs had a high likelihood of near-term reduction and also a massive effect on cash flows. We also found credit terms were a significant cash flow driver but our terms were the best we could likely get in the near-term.

The second tab of the model sheet contains an example of this analysis. Doing this exercise will position your team so cash flow optimization can begin the moment you launch.


At Soylent, we are passionate about open-sourcing our data and leveraging it so all our teams can make better decisions. We’ve recently formed a new team, Team Analysis, to build out our data infrastructure and champion data-driven decision making. If you share our passion for data, APPLY NOW.

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