[1/8]Given all the uncertainty swirling around it, I put together a detailed analysis of $DASH's unit economics. As it turns out, a relatively simple model captures most of the customer dynamics (great fits!).

Key insights:

bit.ly/3lqUAfk
[2/8]
1/ Exeptional repeat buying -- e.g., customers do ~2.6 orders in yr 1, 3.5 in yr 2, even more thereafter. Very back end loaded.

2/ Variable margin is thin but expands over calendar time and tenure: < 0% in yr 1 to 10% in yr 3 => valuation sensitive to margin assumptions.
[3/8]
3/ Even w/ short horizon CLV estimates, unit economics look exceptionally strong. I infer that $DASH gets a return on CAC of 250% and ~900% over 3 and 5 yrs, respectively. Very good, significantly de-risks their unit economics despite back end loaded monetization.
[4/8]
4/ COVID has been a huge shot in the arm, but as with other companies (bit.ly/33z5326), bigger boost for acquisition than retention. I infer ~200% increase in business from new customers relative to baseline due to COVID, vs 70% increase from existing customers.
[5/8]
5/ If their customer acquisition data can be trusted, $DASH is spending about $6 to acquire customers, making back $21-60 after acquisition over a 3-5 year horizon...
[6/8]
6/ ...but I think there may be some sort of data error in a key chart in the filing which could change these inferences. As a result of this, everything (CAC and CLV) could be 4x larger.
[7/8]
7/ If their customer acquisition data is to be trusted, might bode less well for overall valuation -- already acquired more of their TAM, which would create headwinds for acquisition, and/or drop goodness of customers due to possible abuse of new customer promo discounts.
[8/8]
8/ A more detailed analysis that is significantly more comprehensive and applies to the entire restaurant meal delivery category will be coming soon.

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More from @d_mccar

29 Oct
@patrick_oshag Golly - there's a lot that one could say, but let me try:
1/ The paradox of great NDR: great for valuation, but leaves you extremely sensitive to changes in long term competitive position and the discount rate, a variant of duration risk.
@patrick_oshag 2/ Some of the best opportunities in B2C could be those for which we see a "retention smile" -- NDR that falls then rises again. During the growth phase, aggregate-level retention measures *must* by construction look really bad, yet sales growth will be surprisingly resilient...
@patrick_oshag ... over time as those older cohorts get better and better. You'd only see this when you peek under the hood at the cohorts.
Read 7 tweets
15 Oct
[1/7]"The Rise of ‘Retention Rates,’ the Software Metric Investors Are Obsessing Over"

Great, timely article. A few comments:
1-I lament that there is no universal standard for calculating NDR. We need standards for comparability/adoption. Regulators!

bit.ly/316guNq
[2/7]2-Weird how they discuss importance of revenue growth versus NDR. The latter drives the former. Would be more conceptually distinct to compare gross customer acquisition [units or acquired MRR] vs NDR.
3-While good, NDR isn't great for many reasons:
[3/7]3a-What about the C3? Dominates NDR because it's cohort-specific. Article mentions an issue with Twilio. Issue solved w/ C3. Need an article on C3's! (wait: bit.ly/2kpqf7p)
Read 7 tweets
5 May
$W earnings!!

A few thoughts:
1/ This was not a good Q. Adj EBITDA margin was -5.5%, FCF performance similarly poor, weak margins, rev growth slowest ever?

Little/no CAC leverage despite March pop. No leverage on variable headcount despite cuts. See images.
2/ The stock is up because QTD sales are +90% vs +60-80% consensus from crcd panel data. Very strong but unsurprising. As per my comments to @laurenthomas, 86% of the market was shut down, $AMZN de-prioritizing furniture, and WFH purchasing: cnb.cx/2YB2zi3
3/ They will have a good Q2. Surely won't need as much ad spend to drive it -- only game in town! Higher revenues + lower ad spend + probably better GM due to price increases may even lead to US adj EBITDA profitability.
Read 6 tweets
14 Jan
1/Working paper thread! “Assessing the Role of Customer Equity in Corporate Valuation”. TLDR: customers deserve a role in valuation, CE was prescient but ~no uptake outside marketing. We show why and propose CBCV as solution. bit.ly/2tXVOth (LI: bit.ly/2QQFVxL)
2/Traditional financial data is getting less relevant while customer data is getting more relevant. Investors have a blind spot and insights through customer data could be the solution. I think this is our field’s biggest opportunity to expand the role of marketing by far.
3/Customer equity (CE) has been far and away the most popular concept linking customer activity to valuation. CE is, loosely, the sum of the CLV’s of a company’s customers. There has been a flood of articles and books written about it since Blattberg and Deighton’s seminal work.
Read 11 tweets
13 Jan
A few quick thoughts on $CSPR:

1) As mentioned earlier, no cohorted data, but no surprise there (bit.ly/30e9DQu).
2) Only repeat purchase data points: 14% of customers returned within a year of original purchase, 20% of customers in DTC were repeaters.
3) Not surprised they quoted repeat figures in terms of customers, not sales. Repeat orders at dramatically lower AOV's (mattresses => pillows). As per @second_measure, their annual *dollar* retention is 6% (h/t @ataussig). They do not have repeat business right now.
@second_measure @ataussig 4) AOV math: AOV is $710 in 2019 so far in e-comm => 440K orders placed L9M. But we know new/repeat order AOV is different. Assume initial orders are 80% of total at higher AOV, other 20% are repeats at 10% of that AOV (ala SM data). Then initial AOV = $867, repeat AOV = $87.
Read 7 tweets
3 Dec 19
With customer-based valuation drivers, ROIC, and other related measures, it's generally wise to assume regression towards some sort of global average. If so, the billion dollar questions become (1) what the global averages are and (2) how fast the shrinkage will occur. [1/5]
It's striking how often this is ignored in valuation (by myself included!). But this is old news to any hierarchical modeler, and of course to folks like @mjmauboussin (and folks like Rappaport and Modigliani/Miller) who have more diligently thought about it/how to model it.[2/5]
The tough part is that to do shrinkage estimation for overall valuation in a truly Bayesian way requires bringing all the relevant companies into a single joint valuation model, and shrinking off the right things. I see this on narrow problems, not multiple DCF models. [3/5]
Read 5 tweets

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