At their heart, nearly all e-commerce businesses are database businesses. Oddly enough it’s a side of the business that tends to be ignored. There is a virtual obsession with customer recruitment and conversion rates but almost no discussion about database marketing.

This is ironic because the database is where all of the profit comes from. In an efficient market, which the e-commerce world tends to be, it will be impossible to recruit profitably. So logically profit has to come from the database.

Currently most e-commerce businesses market to their database mainly be email so I will focus on that for now.

So the strategy should be to maximise the value of the database. I think that is quite an uncontroversial statement. I would however suggest that the vast majority of marketers don’t do this.

The first thing is to think of what the value of the database currently is. In investment there’s a very simple Net Present Value (NPV) calculation:

NPV Formula

This is just a sum of the cash flows from the database with the future cash flows discounted back to its present value. NPV is very widely used for valuing assets.

The tricky bit though is working out what the future cash flows from the database are likely to be. I would divide this into two parts:

1. What is the value of leads?
2. What is the value of customers?

Valuing leads should be based on i) the likelihood of them turning into customers and ii) when that is likely to happen. This is generally a fairly simply calculation providing the database has about 6 months of history. (Leads tend to go off faster than fish.) The more metadata you have the more accurate this calculation is likely to be.

Valuing customers is about estimating lifetime value. There are several ways of doing this. One method would be to add up historical lifetime values and use survival analytics (we’re Python developers at Move Fresh so we like Python Life Lines for this) to estimate future lifetime values of current customers.

Another approach would simply be to dump the dataset into a machine learning API and see what comes back, although you would need a good few years for this to work. This has the advantage that many more variables could be taken into account.

Once you have your estimate of cash flows you can then calculate the NPV of your database at will. This gives you a very different way of thinking about your business.

For recruitment campaigns, instead of thinking of Cost per Customer Acquired you can think of cost of the recruitment campaign v. increase in NPV of the database.

It also helps you understand how to email the database. The value of sales of an email campaign must be greater than the decrease in NPV of the database as a result of sending the campaign out. Thus you should be well on the way to maximising the value of the database without killing the golden goose by over mailing.

Finally, it will also help you to understand how much capital is sensible to allocate to marketing. If a marketing campaign results in £3 of NPV for every £1 of spend then that would be very powerful.

In my next post I’ll cover tactics.