Rube Goldberg’s famous “Self Operating Napkin”, 1931 (Source.)

Introducing the Lightspeed standardized e-commerce model

Sharing our attempt at the simplest possible financial template for your e-commerce startup

Alex Taussig
Nov 26, 2018 · 10 min read

For the uninitiated, building an e-commerce financial model can feel a bit like constructing a Rube Goldberg machine. Named after a prolific early 20th century cartoonist, these devices famously accomplished simple tasks in the most complex way possible.

In this case, the “simple task” at hand is calculating revenue and cash flows over a couple years, but the inputs to and structure of the model can vary greatly. Without a standard model, results are often difficult to justify to others and can be unreliable. Too many founders end up with a model that’s about as easy to follow as an OK Go music video, but a lot less enjoyable:

There must be a better way!

A few of Lightspeed’s e-commerce portfolio companies.

Nearly all of them struggled to forecast financials at the earliest stages. Part of that struggle is the inherent uncertainty in any early stage business, but another major factor is the structure of the model used. Some forecasts rely on arbitrary assumptions, like monthly revenue growth. We think there’s a better way to forecast; it all starts with cohorts.

Cohorts are ground truth for e-commerce.

We’ve previously written about how we use cohorts to estimate lifetime value (LTV), and how graphs of cohorts (aka “spaghetti graphs”) are the best tool to understand long-term customer engagement. We haven’t seen anyone provide a solid framework for integrating cohorts into a forecast; so, my partners Jeremy Liew, Natalie Luu, and I built one with fictitious data.

You can download it here. We summarized our approach below and left a more detailed user guide in an appendix for the extra curious.

It all starts with cohorts.

These new users then follow a standardized cohort behavior. After the month of first purchase (=month 0), they appear again along a baseline retention curve that levels off to a steady state. Note the model uses “in month” retention and not cumulative retention. “In month” retention is equivalent to the percentage of the cohort which shows up to purchase in a given month. We also allow for some improvements in that baseline retention for subsequent cohorts. You can see how the cohorts stack up in the classic “triangle view” below, with month 0 justified left:

A zoomed out image of the “Cohort Model” tab, which shows the left “triangle view” of successive cohorts.

The magic happens in the column marked “Total Transactions,” which executes a sum along the hypotenuse of the triangle. This crucial step is the linkage between “cohort time” (i.e. month 0, 1, 2, etc.) and “calendar time” (i.e. Jan 2018, Feb 2018, etc.). For example, the total transactions in Jun 2018 is the sum of month 0 transactions for the Jun 2018 cohort, month 1 transactions for the May 2018 cohort, month 2 transactions for the Apr 2018 cohort, and so on. I’ve highlighted the relevant cells below:

Moving from “cohort time” to “calendar time” by summing along the hypotenuse of the Jun 2018 cohort.

You can see some other calculated values in this tab as well: weighted average CAC (aka “blended CAC”), 3-year cumulative transactions, and repeat rate. 3-year LTV and LTV/CAC are also calculated here, but require input from the P&L. We also placed the average order value (AOV) assumption on this tab, which will play into the calculation of revenue on the P&L tab.

Constructing the P&L

From here, we added various assumptions about shipping revenue/costs, return/discount rates, and COGS components — each as a % of AOV. To model Operating Expenses, we included assumptions for monthly headcount and salary, as well as other line items for overhead. We admittedly focused less on these fixed cost elements, which are harder to model in the abstract.

Rolling it up

Extra credit? :-)

Similarly, if you find errors in the model, or have a better approach, feel free to send me a note. We can’t promise to get back to everyone, but will do our best to crowdsource the best possible model.

A special thanks to Jeremy Liew, Natalie Luu, Janou Gordon, and my other Lightspeed team members who worked on and reviewed this model.

Update (12/4/2018)

Shout out to Kelsey Ditto at Affirm for spotting a small error on the “Summary” tab, and to Steven Cruz at Plated for expanding our model to include a balance sheet and cash flow statement. Steven’s version allows you to play with assumptions around cash conversion cycle, which is essential for any e-commerce business.

We’ve incorporated Kelsey and Steven’s changes in the new model below. Use this one for all your financial planning needs!

Update (6/24/2020)

Appendix: a detailed user guide for our sample model

I. Cohort Model tab:

  • Each row represents the cohort that starts in the month in column A.
  • We make assumptions about Ad Spend (column D) and Paid CAC (column E). Dividing these two gives us the number of Paid New Buyers (column F).
  • We then make an assumption of the ratio of paid to organic new buyers and that gives the number of Organic New Buyers (column H). Total New Buyers for that month (column I) is paid + organic.
  • Column Q is the number of new buyers that month (copied from column I). Then each column after that is the number of transactions from that same cohort in each subsequent month. For example, Q18 shows how many new (aka first-time) buyers there are in Sept 2018. R19 shows how many transactions there are from that same set of buyers (who bought for the first time in Sept 2018) one month later (in Oct 2018). R20 shows how many transactions there are from that cohort two months later (Nov 2018) and so on.
  • We calculate the number of “month n” (Mn) transactions by multiplying the Number of Original Buyers (column Q) by the Cohort Retention Assumption (Row 5) and then modify this by a retention change factor (1+ column C). We included the retention change factor to allow retention to improve (as product improvements kick in and we have more to sell) or deteriorate (in which case column C would become negative), perhaps because we have saturated our core user base and are now attracting less high quality customers. And of course we round to the nearest integer because you can’t have fractional buyers!
  • This model holds true for every cohort of new buyers starting from Jan 2018. However, we treat the set of new buyers who bought before Jan 2018 a bit differently because we’re lumping multiple cohorts all together, so they won’t have the same cohort retention as a regular cohort. That’s why we use row 5 to calculate the number of transactions in a month instead of row 4 — it is a flatter curve because it’s a mix of multiple cohorts.
  • The Total Number of Transactions (in purple, column K) for any given month is then the number of new buyers in that month + the number of M1 buyers from the month before + the number of M2 buyers from two month before + (you get the idea):
Total Number of Transactions in a given month
  • If you double click on any cell in column K you can see how this sum looks like a staircase as it traverses each cohort.
  • If you sum the total number of transactions in a single row from new buyers (M0) through M35, you get the total number of transactions from that cohort over three years (column K).
  • To get 3-year LTV (column L), you divide the Total Number of Transactions (column K) by the number of New Buyers (column Q) and multiple this by the Average Order Value (column B) x the average contribution margin (looked up from the corresponding month in the Monthly model). Strictly speaking, we should vary AOV and contribution margin over the period, but this is good enough as an approximation.
  • By dividing the Total Number of New Buyers (column I) by the Ad Spend (column D), you get Weighed Average CAC (column J). By dividing 3-year LTV (column L) by CAC (column J), you get The LTV/CAC ratio (column N).
  • Repeat Rate (column O) is just the number of transactions that are not from new buyers, divided by the Total Number of Transactions.

II. Monthly Model tab

  • Forward and return shipping are calculated by the Average Shipping Charge (column A) x the number of relevant orders shipped.
  • If you’re doing free shipping and returns, you can just set shipping charge to 0 in cells A10 and A11.
  • The one place you may choose to overwrite formulas with assumptions is in the fixed overhead links (Rows 40–42). If you want to make a more fine grained month-by-month estimate of costs (e.g. if you know you are expanding to a bigger office with higher rents, if you have a PR push planned for a particular month etc), you shouldn’t hold the numbers constant as the model currently does. If you do make those changes, I suggest that you change the font of any cell that you overwrite to blue and give it a yellow background to match the other assumption cells. That way you can easily find hard coded values and change them later on.
  • Returns is calculated in row 52 as a %(column A) of Total Transactions.
  • Gross revenue is a sum of product revenue and forward and return shipping revenue.
  • Net revenue is Gross Revenue less Discounts and (Markdowns and Returns).
  • All the items in the Cost of Goods Sold (COGS) calculation are a product of the Number of Transactions and the cost per line item in rows 69 to 74. These in turn are calculated as a percentage of AOV from lines 83–88. The one exception is that product costs are offset by a certain recovery factor (line 94) x the number of returns x unit product costs. This reflects that there will be some costs to remediate returned products before they can be sold again, so you don’t get to offset product costs with the full cost of returned goods.
  • Gross Profit is obviously Net Revenue less COGS.
  • Personnel costs are calculated as salary x number of FTEs (lines 59–64) for each month.
  • Personnel costs (line 37) are a ratio (column A) of salary and represent all costs variable to headcount, e.g. health insurance, travel & entertainment, 409A etc.
  • Ad Spend (line 39) is transposed right from the assumptions on the Cohort Model tab.
  • Other G&A line items are either held constant (column A) or there is also a variable G&A factor to account for other expenses that will grow with sales.
  • Operating income is Gross Profit less Operating Expenses.

III. Summary tab

IV. How to customize this model to your company

  • The assumption numbers we put in are for testing purposes only. You should replace them with your own real data.
  • To calculate your own cohort retention numbers (lines 4 and 5) refer to this blogpost and the linked Google Doc. For Line 5, you’ll need to make some adjustments to your cohort retention calculations to reflect that these are a combination of multiple cohorts so will flatten out relative to a true monthly cohort
  • If your e-commerce company is pre-launch, for those months before you are selling, you can zero out Ad Spend (column C). If your e-commerce is post-launch, use your actuals instead of assumptions for the months that you have data.

Lightspeed Venture Partners

Lightspeed is a multi-stage VC firm focused on accelerating…

Alex Taussig

Written by

VC at Lightspeed. Consumer tech enthusiast. Partner to AWA, Daily Harvest, Faire, Flockjay, Zola, thredUP, RentJuice/$Z, $TWOU, $CBLK. Reformed physicist.

Lightspeed Venture Partners

Lightspeed is a multi-stage VC firm focused on accelerating disruptive innovations and trends in the enterprise and consumer sectors. In the past two decades, Lightspeed has backed 400 companies and currently manages $10.5B across the global Lightspeed platform.

Alex Taussig

Written by

VC at Lightspeed. Consumer tech enthusiast. Partner to AWA, Daily Harvest, Faire, Flockjay, Zola, thredUP, RentJuice/$Z, $TWOU, $CBLK. Reformed physicist.

Lightspeed Venture Partners

Lightspeed is a multi-stage VC firm focused on accelerating disruptive innovations and trends in the enterprise and consumer sectors. In the past two decades, Lightspeed has backed 400 companies and currently manages $10.5B across the global Lightspeed platform.

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