[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!
[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)
[4/7]3b-NDR mashes together churn with monetization. Both important/distinct. Need to supplement with active customers and new customers acquired to have the data required to triangulate a model that makes this distinction.
[5/7]4- Irony: the more you enhance NDR, the more back end loaded the payment profile of your CFs, making valuation sensitive to perceived changes in LT competitive position and discount rates. Akin to duration risk in fixed income, as I've said before. Shifts risk profile.
[6/7]5- To understand the underlying framework behind customer-driven valuation for subscription firms, read my seminal paper with @faderp about it: bit.ly/2N8P3sq
[7/7] All told, great to see investors waking up to the importance of customer metrics and their role in valuation. It's a cause that we/@rgmarkey and a growing chorus of other investors are waving the flag about. It's a revolution...
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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.
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.
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.
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]
We took a deep dive into unit economics at @lyft. Main take-aways: constructive unit economics stemming from positive CLV's, low and stable CAC, but with risk in that the value is coming from a fairly small proportion of each cohort: bit.ly/2UqpdVk
The key take-home metrics: (1) they spend $32 to acquire customers then make ~$51 after acquisition, implying a CLV of $19. (2) CAC has been hovering around $32 the past couple of years. (3) Lyft may be losing money on 80% of its customers, but making it up on the others.
(4) A lot of uncertainty in CAC due to non-disclosure -- incentives are split out across two line items with no split of new versus existing customers, but sensitivity analysis suggests it is almost surely $21 - $54. (5) Seems like the @Uber mishaps in 2017 really helped Lyft.