Snorkel Dive / Stickiness

I’m an MIer and do not do the deep dives that you guys do. I’m a numbers guy and try to divine stuff without actually being there. I try divine stuff by looking at the numbers.

Anyway, I’m always concerned with “stickiness.” Take BYND. They’re hot, but anyone like Tyson can just come in and take it away. I was reading a recent SA article on TEAM. They spend very little on sales people, because the product sells itself. This means that the product must be awesome- a sign of stickiness.

I looked at (Sales+Gen Administration)/Sales, TTM. One would expect that established SaaS companies such as MSFT would have low numbers and it’s number 2, out of the 70. There are ones that keep showing up on IBD, week in and week out are the ones I consider “established.” They already have the ball already moving along. VEEV is number 7, PAYC is number 14. (I missed both boats.)

The second column is the Rate of SGA fall- How much did the percentage fall, compared to the previous 12 months. SGA/S (TTM)- SGA/S (PTM). A “large” negative number means that the number is falling fast, like DOCU, ZS and MDB.

Here’s the list. The data source is Portfolio123/ CompuStat. (Sorry in advance about the formatting.)

Ticker SGA2Sales%TTM Rate of Fall
TRHC 25.99 4.31
MSFT 31.76 -1.72
EBIX 34.81 5.08
SQ 39.45 0.19
HSTM 41.12 -2.27
RP 42.47 -3.34
VEEV 44.62 -1.67
APPF 44.72 -0.08
BLKB 47.16 2.79
ROKU 47.63 3.49
UPLD 48.56 -4.34
JCOM 48.63 -0.58
ALRM 50.25 0.87
PAYC 51.25 -2.26
SPSC 54.14 -5.63
TTD 55.54 0.74
ADBE 55.55 1.63
FIVN 55.85 -3.47
QLYS 56.13 -5.31
AAXN 56.85 1.73
LOGM 57.12 -2.11
PCTY 57.38 1.89
ZIXI 58.69 -3.02
QTWO 61.86 1.58
BNFT 63.12 2.3
SHOP 64.45 0.41
MDSO 67.66 1.96
CRM 68.25 0.99
UPWK 72.47 1.5
MIME 73.12 -1.95
CSOD 73.28 -4.56
TWLO 74.15 0.99
NOW 77.36 0.04
DBX 77.82 -34
RNG 78.13 1.45
ZM 78.89 -5.87
ECOM 80.12 -4.05
LPSN 81.46 7.41
ZUO 81.62 2.25
AYX 82.43 -9.16
TEAM 82.75 -1.61
WIX 84.08 -6.38
PFPT 84.27 1.88
WDAY 85.02 1.47
APPN 85.48 3.58
COUP 85.49 -5.43
FEYE 86.89 -4.29
WK 87.89 -5.29
HUBS 88.46 -1.82
BL 88.56 -0.87
ZS 89.47 -8.32
TWOU 89.85 -2.68
NEWR 90.49 -5.03
ZEN 90.92 -2.86
EVBG 91.32 3.72
BOX 92.16 -9.39
INST 93.38 -2.88
SPLK 95.04 1.03
TLND 95.93 0.16
DOCU 100.53 -25.83
MOBL 102.64 -10.65
MDB 103.73 -22.08
OKTA 103.96 -3.29
CLDR 106.37 -15.32
YEXT 106.88 -4.5
ESTC 108.19 4.52
SMAR 112.96 -11.18
NTNX 114.73 27.9
PD 117.66 -5.02
WORK 122.5 -21.15

DoesMIWork

6 Likes

Here’s the formatted data. The second column is the percentage of sales spent on selling the product (plus general and administration costs) and the second shows the trend compared to the year before, all TTM. A negative number on the second column shows that the percentage spent on selling the product is less than the PTM, implying that the product is selling itself.

	Ticker	SGA2Sales%TTM	Rate of Fall	
	TRHC	25.99	4.31	
	MSFT	31.76	-1.72	
	EBIX	34.81	5.08	
	SQ	39.45	0.19	
	HSTM	41.12	-2.27	
	RP	42.47	-3.34	
	VEEV	44.62	-1.67	
	APPF	44.72	-0.08	
	BLKB	47.16	2.79	
	ROKU	47.63	3.49	
	UPLD	48.56	-4.34	
	JCOM	48.63	-0.58	
	ALRM	50.25	0.87	
	PAYC	51.25	-2.26	
	SPSC	54.14	-5.63	
	TTD	55.54	0.74	
	ADBE	55.55	1.63	
	FIVN	55.85	-3.47	
	QLYS	56.13	-5.31	
	AAXN	56.85	1.73	
	LOGM	57.12	-2.11	
	PCTY	57.38	1.89	
	ZIXI	58.69	-3.02	
	QTWO	61.86	1.58	
	BNFT	63.12	2.3	
	SHOP	64.45	0.41	
	MDSO	67.66	1.96	
	CRM	68.25	0.99	
	UPWK	72.47	1.5	
	MIME	73.12	-1.95	
	CSOD	73.28	-4.56	
	TWLO	74.15	0.99	
	NOW	77.36	0.04	
	DBX	77.82	-34	
	RNG	78.13	1.45	
	ZM	78.89	-5.87	
	ECOM	80.12	-4.05	
	LPSN	81.46	7.41	
	ZUO	81.62	2.25	
	AYX	82.43	-9.16	
	TEAM	82.75	-1.61	
	WIX	84.08	-6.38	
	PFPT	84.27	1.88	
	WDAY	85.02	1.47	
	APPN	85.48	3.58	
	COUP	85.49	-5.43	
	FEYE	86.89	-4.29	
	WK	87.89	-5.29	
	HUBS	88.46	-1.82	
	BL	88.56	-0.87	
	ZS	89.47	-8.32	
	TWOU	89.85	-2.68	
	NEWR	90.49	-5.03	
	ZEN	90.92	-2.86	
	EVBG	91.32	3.72	
	BOX	92.16	-9.39	
	INST	93.38	-2.88	
	SPLK	95.04	1.03	
	TLND	95.93	0.16	
	DOCU	100.53	-25.83	
	MOBL	102.64	-10.65	
	MDB	103.73	-22.08	
	OKTA	103.96	-3.29	
	CLDR	106.37	-15.32	
	YEXT	106.88	-4.5	
	ESTC	108.19	4.52	
	SMAR	112.96	-11.18	
	NTNX	114.73	27.9	
	PD	117.66	-5.02	
	WORK	122.5	-21.15	

DoesMIWork

4 Likes

Hi DoesM1work

It’s certainly interesting data as well as a valuable goal to research and understand but I’m not sure that you can attribute or correlate a large falling % with stickiness and or a product selling itself (which I actually don’t think are the same thing).

The obvious correlation appears to be companies with high % oF SGA vs sales have larger negative numbers (falls) and ones with Low % seem to have more flat YoY movements).

To me a declining number represents companies demonstrating some operating leverage or that have hit their maximum investment levels that weren’t sustainable.

Of course there are still plenty of issues with this for instance:

  1. it doesn’t consider companies deliberately going full throttle and reinvesting ahead of the profit curve to gain a landgrab ahead of the competition or capture growth whilst available (Amazon, Shopify, Uber etc)

  2. it doesn’t consider whether companies are bringing in 0 vs a billion $ of future deferred revenues that are not counted in TTM sales but partially (with the exception of the variable incentive payout) included in the SGA

  3. it doesn’t account for quirky year on year situations (such as what’s happening with Nutanix)

  4. as you point out it doesn’t necesarily account for the maturity of the underlying business

Leverage is an important attribute and a declining % in the ratio is probably the right metric.

For stickiness I would think the retention rates and promoter score are the better metrics. Together with a defined moat and a high cost of switching.

For selling itself maybe the dollar expansion rate would be a better metric or even a low SGA % and/or a low gross margin but then it doesn’t account for the items above. David Skok of matrix group and Hubspot talks about solutions that sell themselves and the massive difference in profitability that nil to light touch to heavy touch selling models achieve (it isn’t a linear scale).

Ant

5 Likes

2 other considerations…

In a macro level obviously different industry sectors have a completely different cost to serve and margin basis.

On a company basis the 2 key metrics to compare on a more unit economics basis is the customer acquisition costs vs customer lifetime value. That would get you closer to the “product selling itself” as well as “stickiness” factors.

Unfortunately these aren’t metrics that every company produces on a like for like basis but if they did or someone could calculate it across the board as per your own table that would be an awesome resource. It would help also differentiate the attractiveness of Amazon, Shopify etc on a unit eceonomics basis rather than on an enterprise basis whilst locked in the gorilla game.

Ant

2 Likes

PLEASE END THIS VERY OT THREAD. Look, I know that it was done with good intentions but a list of 70 companies with a couple of numbers after them is not what our board is about, It’s about analyzing individual rapid growth companies. Thanks for your cooperation.
Saul

3 Likes