# How To Calculate Lifetime Value – The Infographic

One way to analyze acquisition strategy and estimate marketing costs is to calculate the Lifetime Value (“LTV”) of a customer. Roughly defined, LTV is the projected revenue that a customer will generate during their lifetime. In this graphic we’ll briefly cover how to calculate LTV and how to use LTV to help solidify your marketing budget. Special thanks to @avinash.

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20 years as average customer lifetime is a lot! now I can see with numbers why Starbucks is a very successful company. Very interesting! Thanks for sharing.

Hi Judit:

Great point – Starbucks does have an amazing customer retention rate. Just goes to show – focusing on customer satisfaction goes a long way!

Hi Briac:

Thanks so much for the comment – definitely appreciate your pointers! I think your math is very insightful – certainly helps to clear the waters a bit.

There’s lots of little ancillary factors that go into calculating LTV – too many to include in a small infographic. Additionally, the purpose of the infographic was not so much to give a precise LTV to an average Starbucks customer as it was to simply illustrate a (simplified) method of calculating LTV – and how LTV can be useful when putting together a marketing budget.

From our research, we estimated that the LTV of an average Starbucks customer was roughly between $5,400 and $25,000, as illustrated in the infographic. Consequently, the break-even range for Starbucks’ marketing budget per customer lifespan had to be less than $25,000 (high estimate). Any number less than $5,000 is less than $25,000, so a marketing budget of less than $5,000, as you mentioned, would probably be more accurate.

Again, we’re working with rough estimates. :)

I would probably not average all 3 LTVs and just pick the lower figure to keep my calculations on the conservative side.

I suppose the downside to that is you might not be throwing enough money at customer acquisition as you should, but I figure when you’re starting out you might want to start conservative until you have a better feel of what’s working and what’s not.

The wide range of results from the different formulas, small sample size, and some of your assumptions about customer retention rate, profit margins, etc. were questionable. Briac, above, has some good points as well. Nevertheless, it was a fun exercise, and the variables are things that all new ventures should consider as they talk with investors.

FWIW, I used a different set of values for r (60%), c (2) and t (10) and ended up with an average LTV of $3,046.

I 100% agree with Briac. Averaging the LTVs makes absolutely no sense. Those metrics are measured in different ways for different purposes. Would it make sense to average daily unique visitors with monthlies? Each represents something distict and aren’t meant to average together.You should pick the ONE LTV that suits your particular business need.

Great work, very useful information!

Calculations here are flawed: in addition to the mixing of revenue and profit (mentioned above), there are 2 other issues I see at a glance:

1) There’s a functional relationship between customer retention rate and life time of a customer. You can’t assume 75% retention and 20 year relationship. That’s what the (r/(1-r)) term in the third LTV equation is: the expected number of periods that a customer sticks around, given that they drop out at the rate of (1-r) per period. So, the expected number of years for 75% retention is (.75 / (1-.75)) =4.

2) Since the third equation is (correctly) accounting for the expected number of years and then multiplies that by the profit, it should be profit *per year* not *per “lifetime”*. In the example above, that means the final value will be about 1/20th of what you calculated. (assuming the 75% retention rate, avg annual spend, and discount rate given).

Steven is right. As soon as you average the revenue and the profit, this methodology implodes. Nice graphic though, you should adjust and republish.

We use the following

Churn = (Current + New – Previous customers)/Previous

Lifetime = 1/Churn

ARPU = Revenue/Current Customers

Referrals per customer = k

kLTV = 1/(1-k)*Lifetime*ARPU

Note that s*c ~ a so the difference between simplified and custom is one is total revenue and one is profit.

I have noticed that smart real estate agents all around you are starting to warm up to FSBO Promotion. They are acknowledging that it’s more than merely placing a sign in the front property. It’s really about building associations with these dealers who sooner or later will become buyers. So, whenever you give your time and effort to aiding these vendors go it alone — the “Law involving Reciprocity” kicks in. Interesting blog post.

I really appreciate the effort that went into producing this infographic. I assume that in order to determine what the average lifetime span of a customer is, it really comes to basic business knowledge and market intelligence?

I am sorry to write this, but at least one of the LT methods above is bullshit. I’d say the first and the third, because they don’t take the margin into account but use only the revenue. In fact, second method is the same calculus as the first, just multiplying with p.

Actually, Starbucks shouldn’t spend more than $588 to acquire each customer. Based on the numbers provided in the infographic the LTV is $588. The three formulas used are partially right and partially wrong. This is how I got the $588 as the LTV for Starbucks:

(52 x s x c x p) x [ r / (1 + i – r)]

(52 x 5.90 x 4.2 x 0.213) x [ 0.75 / (1 + 0.1 – 0.75)] = $588.12

What’s wrong with the formulas/calcs in the infographic?

1) It’s not including any cost….it should be profit (excluding marketing).

2) It’s not taking into account the value of money along the time….the formula should discount anual profits ($274.45) for 20 years.

3) The “m” used is incorrectly including the profits of 20 years. The “m” should be the profit of one year (in average if you prefer). The other bit of the formula is called the multiplier factor, and it basically discount perpetuity profits (m) at 10% discount rate plus taking into account the churn effect.

Hope this is helpful. If you believe I’m missing something, please, let me know.

I think a great follow up post would be host to calculate the “constants.” I would like to hear the rational behind how they calculate customer retention and average life span.

Jason- I see your point.

The $14k comes from the average of the 3 computation of LTV, the simple LTV, the custom LTV and the traditional LTV.

The problem with the simple ($25k) is that it doesn’t take into account margin (21.3%), it’s just based on pure revenue (ignoring the company costs).

If margin was considered the simple LTV would be $5382

Which bring the average of $7408, so about 1/2 of those $14k

Maybe it doesn’t matter that much but I think you should know.

Confused with the formulas of LTV

Say if I have either 0 or very small churn then I will get very high LTV as per following formulas:

LTV = 1/churn or MRR / %churn

Can anyone help me how to get rid of these formulas

If churn is close to 0 LTV necessarily has to become very large, no way to get rid of this reality.

BTW the:

LTV = 1 / churn

formula is just a first order approximation (for which error becomes important when churn>10%) of the exact formula:

LTV = -1 / ln(1-churn)

Hi

So the following is what I do not understand –

1) In an ideal world, no churn is applicable, also discount is 0%. Now CLTV (margin based) is simply 52*24.3*20*0.213 = 5382.9

2) After applying discounting and retention (<100%), how come it becomes 11,535??

Should it not be less than 5382.9 after discounting and retention???

3) Why do we consider lifespan and retention together. Would it not be sufficient to consider only retention?