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Blue Apron Prices IPO At Bottom Of Deeply Discounted Range

Courtesy of ZeroHedge. View original post here.

Perhaps impacted by Amazon's Whole Foods' deal, Blue Apron just priced its IPO at $10 (30 million shares).

Blue Apron Holdings #IPO$APRN $IPO priced 30mm shares at a price of $10.00, and will debut tomorrow (6/29).

— IPO Boutique (@IPOBoutique) June 28, 2017

The food-kit-delivery company slashed its marketed initial public offering price by 34% Wednesday, cutting the range from $15 to $17 a share to $10 to $11, the second biggest cut in five years.

Perhaps the crushing discount on the IPO is because of this…

Via Daniel McCarthy, Assistant Professor of Marketing at Emory University,

Good companies can acquire many customers cheaply, retain existing customers for extended periods of time, and generate a lot of revenue while those customers are alive. Putting it simply, the litmus test of any company’s financial success is the ability to acquire many high lifetime value (LTV) customers. Being LTV-centric is at the heart of being customer centric.

Does Blue Apron, which recently priced its IPO at a very healthy ~$3 billion implied valuation (or almost 3.5 times trailing twelve month revenues), pass the test? In my last note on Blue Apron, which was recently cited in the Wall Street Journal, I showed that while Blue Apron disclosed nothing explicitly about its customer retention, and very little about how its customer acquisition cost (CAC) has been changing over time, it disclosed just enough to use the modeling approach that I advocated in a recent journal article to “back out” what these figures are most likely to be. The conclusion: Blue Apron doesn’t retain customers for very long, and the cost to acquire customers has been on the rise lately. These are important ingredients to the overall customer-based corporate valuation recipe. At the same time, there is a lot more that we can learn from Blue Apron’s S-1 disclosures.

I went back and built a much more complete model to leverage all the data that Blue Apron has disclosed. I explicitly model how customers are acquired, how long they remain customers before churning, how many orders they make while they are retained, and how much they spend on each of those orders. This more general model allows us to incorporate all the metrics that Blue Apron has disclosed, such as six-month cumulative revenue for annual customer cohorts. It allows us to refine answers to previous questions, such as what Blue Apron’s retention curve looks like, and answer new ones, such as how the post-acquisition profitability of customers has been changing over time, and whether younger customers generate more revenues as they age or not (e.g., that the customers who stick for a long time around reorder a substantial amount).

The results continue to suggest challenges ahead – retention is even weaker than I had originally estimated it to be, new acquisition cohorts are generating less revenues than old ones, and as customers age, they spend less and not more with the firm. In recent months, I estimate that Blue Apron is losing money on ~70% of the customers that it acquires. I dive into the model briefly next, before expanding on these conclusions.

The Model

My model for the acquisition and retention of users remains the same, using only the cost per acquired customer, historical marketing expense, and active customer data as inputs. However, I built additional models for how many orders customers make while they are alive, and how much they will spend on a particular order. I estimate parameters for each of these models so that what we expect the data to be is as consistent as possible with the disclosed data. As before, wonkish comments are provided below.

The resulting relatively simple composite model does an excellent job of fitting the observed data. As shown below, it provides a very reasonable fit to all the data – the number of active customers, total customer acquisitions, orders, revenues, and cumulative revenue per acquired customer metrics. I provide a series of charts summarizing this performance below, all of which are accessible in Excel spreadsheet form here (download), if you would like to examine the numbers yourself. On to the charts!

Quarterly total number of active customers:

Aside: Total active customers must be larger than total subscribers, and it is unconventional for a subscription business such as Blue Apron to report the former instead of the latter. Blue Apron defines active customers to be the total number of customers who have placed at least one order during the quarter, regardless of whether or not that customer has churned by the end of the quarter or not, from what I can tell. Active customers is a more appropriate metric for (and traditionally only disclosed by) non-subscription businesses such as social networking companies, mobile gaming companies, and e-commerce retailers (e.g., Facebook, LinkedIn, Zynga, and Amazon’s e-commerce business).

Cumulative customers acquired, Q1 2014 to Q1 2017:

Quarterly total orders:

Quarterly total revenue:

Cumulative revenue per acquired customer for customers acquired between Q1 2014 and Q1 2017, 6 to 36 months out:

Cumulative revenue per acquired customer over next six months for customers acquired in 2014, 2015, and 2016:

The fact that my relatively simple model is consistent with the data along so many key dimensions at the same time provides some comfort that we can trust the results of the model. Let’s discuss those results next.

The Results: Anti-stickiness – Low Retention and Declining Revenue per Customer, Over Time and Across Cohorts

Here is a summary of what I found from the deeper dive:

1.   The retention curve is worse than I originally had estimated it to be. While my substantive conclusion remains the same, I estimate that 72% of customers will churn by the time they are six months old. Because Blue Apron cannot retain customers for extended periods of time means that CAC is effectively part of cost of goods sold. CAC should go down relatively sharply over time as a percentage of sales at healthy businesses, as sales are increasingly derived from loyal customers who have been around for a while. When customers churn out very quickly, that pool of loyal customer revenue remains small, making CAC effectively variable in nature.

2.   The revenue that Blue Apron is generating from more recently-acquired customers is less than from customers acquired in the past. Every new acquisition cohort generates, on average, about $7 less in revenues over the next 6 months than the cohort which preceded it, which adds up quickly over time. In other words, while the cost to acquire new customers is going up, the go-forward value of those newly acquired customers is going down. Both trends are driving LTV lower over time. I suspect that this is due at least in part to the vast sums of money that Blue Apron is spending upon subscriber acquisition expenses (SAE). It is very common to see LTV go way down when SAE goes way up.

3.   While customers are alive, the amount of revenue that Blue Apron generates from them tends to go down, not up, over time. This makes it unlikely that long-time loyal customers will “bail out” the firm because they are also high spenders, a common trend at mobile gaming companies, for example – in fact, we infer that the opposite has been taking place. As customers get older, they place fewer orders on average, which is only slightly offset by a marginal increase in spend per order over time. Customers are not “sticky.” Moreover, at subscription-based businesses like Blue Apron, there is only so much that big spenders can spend, while there is no such upper bound at non-subscription businesses.

4.   70% of recent Blue Apron customers will not break even. We estimate that CAC in Q1 2017 is $147. To break even at this CAC, new customers must generate at least $565 of net revenue (i.e., gross revenue minus returns and promotional discounts), assuming Blue Apron’s variable contribution margin is equal to ~26%. The chart above shows that newer customers must remain subscribed for about 4.5 months to generate this much revenue. However, almost 70% of customers churn by this time and thus do not break even. Even though Blue Apron turns a profit on the remaining 30% of customers, the break-even point is moving farther away with every new cohort due to declining revenue and growing CAC for newer customers.

In summary, this customer-based analysis spells trouble for Blue Apron, with important measures of customer health in decline. Amazon’s recent acquisition of Whole Foods is likely to make it even more difficult to keep those Blue Apron subscribers coming back. I recommend that Blue Apron redouble its efforts upon activities that will make customers “sticky” in the long run. Investors are clamoring for customer metrics so that they can go beneath surface-level financial metrics to better understand Blue Apron’s underlying unit economics. I hope that this analysis takes investors a step closer to what they are looking for, and that Blue Apron will begin disclosing a few more.

A big acknowledgement goes to Valery Rastorguev. All errors and omissions are mine.

Wonkish Comments

1.   The model for the acquisition and retention of users over time is essentially unchanged from our previous analysis, except for two factors:

  • I re-incorporate marketing spend into the acquisition process. More than ever, the data demanded that this covariate be included because it had a strong positive relationship with customer acquisition. While I would not read too deeply into its coefficient estimate, including it gives us a much “cleaner read” into the company’s retention trends, as well as the evolution of CAC over time. It implies acquisitions are more front-loaded than previously estimated, which as I suggested in my previous post, led to worse retention trends.
  • I accounted for the aforementioned fact that Blue Apron reports active customers and not total subscribers.

2.   The total orders model assumes that customers by definition place an order the week they are acquired as customers. They will make purchases in subsequent weeks with some probability, which is a function of how tenured the customer is, and when they were acquired as a customer. The model allows for seasonal fluctuation in the order rate over time.

3.   The spend per order model assumes that the expected revenue derived from a particular customer’s order is some function of a time-invariant baseline, how tenured the customer is, and when they were acquired as a customer.

4.   The retention, order, and spend processes are assumed to be independent of one another a priori (but not a posteriori, if individual-level data were available).

5.   The variable contribution margin is assumed to be equal to Blue Apron’s most recent gross margin (31%), less 5% of sales from non-SAE operating expenses which are assumed to be effectively variable in nature. This is substantively consistent with Lee Cower’s assessment of fully loaded contribution margin. Had I optimistically assumed that no non-SAE operating expenses are effectively variable in nature, the current break-even point is closer to four months.

6.   I agree with Lee Cower’s comments regarding margin improvement over the past few years. However, I also agree with his opinion that gross margin improvement appears to have stopped and thus is unlikely to resume in the future.

7.   The proposed model’s implied six-month retention rate of approximately 30% is consistent with the retention rate estimated by business intelligence firm SecondMeasure, and implies that business intelligence firm 1010data’s 10% retention rate estimate is pessimistic. A helpful benefit of methods such as the one proposed in this note, which leverage first-party disclosures, is that they do not rely upon data from a panel of users which may not be representative of the overall customer base of the focal firm in question.

*  *  *

But apart from that we are sure BNob Pisani will see it a success tomorrow morning when it opens…


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