27 July 2018

You can’t do marketing if you don’t know the lifetime value of your customers

Calculating the lifetime value of your customers is an essential step in (data-driven) marketing.

How come?

Well, think of a toothbrush company that sells its toothbrushes at a price of 2 dollars. This particular company uses an entire sales team to cold call their customers and hard sell them the toothbrushes. You can imagine that you have to sell a lot of these toothbrushes to break even: pay for the training, the salaries and being able to pay your own salary. Pretty easy to see this doesn’t fit.

To put it simply

‘If you don’t know what you can spend, you can’t judge if campaigns are working or not, and thus you can’t scale’

It sounds a bit stupid, right?

To put it in perspective, let’s take an actual company as an example: UBER. The customers of UBER are generally expected to use the product more and therefore spend a lot more than the 2 dollars you would normally spend buying a single toothbrush every year.

Let’s say on average an UBER customer pays 10 dollars per use and uses the app 8 times, resulting in a LTV of 80$. Comparing this to the consumers of toothbrush company, which have a LVT of 2$, UBER could easily budget way more. Logical right?

“Depending on the LVT you should choose your marketing strategies”

This illustrates how important it is to know the Lifetime value of paying customers and thus how much you can spend on your marketing strategies and still break-even.

As shown, the amount you can spend on marketing and still be profitable does vary a lot. Preferably you want to know exactly how much you could spend but if this is impossible you should estimate the amount. This is still a good way of calculating because at the end we are working with averages.

But how can you know?

The call to action

You want to calculate the LTV of a customer

This is the key: you want to accurately calculate your customers Lifetime value. Knowing that, you can feasibly choose which marketing channel you want to pick and how much you can spend on it.

You are probably thinking right know: ‘But isn’t every customer totally unique?’

Well they are! That why it is so important to work with averages. Knowing the average makes way more sense because at the end you are choosing strategies not based on individuals but on the sum of your customers.

So back to the UBER example! UBER customers do vary a lot in use and therefore spending patterns; some people will download the app and use the service once, while others will depend on UBER every day.

But UBER does have an average LTV for all downloaders, and probably even for every city or other specific characteristics.

Find this data for your own customers and use the relevant data to your advantage!


Split up the data per channel or another variable


Well imagine this; what if you would have not one, but two acquisition channels? For instance, content marketing and Facebook Ads. Because content marketing is cheaper, more efficient and unlike conventual ads not seen as an interruption, these channels differ in quality and therefore in value.

So the above game plan does not fit well in this situation; a customer being brought in via the content channel could be worth 90 dollars. But, because customers being brought in via the Facebook ads are ‘worth’ way less, this means that you cannot spend the 90 dollars on Facebook ads and still be working efficiently or even reach break-even.

Take away leads from different channels are almost certainly of different quality and therefore different value.

So, you have to split your costs and Lifetime Values per channel.

This makes sense, right? Just like UBER splitting up their LVT’s per city and because of that using different marketing strategies.

To sum it up!

  • A business needs to make money, so you can’t sustainably spend more than you earn, duh!
  • Having accurate figures, such as LTV, allows your marketing team to make far smarter decisions and be way more efficient.
  • These tactics will be a great start to making your enterprise become more data-driven in marketing