It was the dawn of what today we would know as direct marketing.

It really was when a lot of these ideas of when customer analytics where born.

It was the first time that we really had any kind of

granularity about what particular customers were doing and

a desire to what know each and every one of customers would be doing next.

And for how long, and for how much money?

And so it became very important for companies to come up with

what we like to call KPIs, key performance indicators.

Can we look at some indicators of what people had been doing in the past in order

to make some accurate statements about what they're likely to do in the future?

And again, this is just a natural area to run something like a regression model and

indeed, regression models were used for this kind of purpose.

But it wasn't this just throw in tons and tons and tons of data.

Because part of it was the data was limited, part of it as I said,

is that our computational is limited so we have to think very carefully.

It was very, very important for us to come up with just a few measures that

would be fairly predictive of what customers would be worth in the future.

So our forefathers in direct marketing,

they basically did the kinds of things we've been talking about here.

Let's take our dataset, let's chop it into two pieces.

Let's collect some data from period one to see which elements of that period

one data would be most predictive of what people did in period two.

And again in period two, we'll be looking at how many purchases they made or

what was the dollar value of those customers?

And they ran lots of models to try and find out which bits of data were most

predictive and they do it over and over again.

Lots of different data sets, lots of different products,

lots of different geographies, lots of different customer segments.

Because we wanted to find a few of those explanatory variables

that were pretty robust that time and time again would prove to be predictive.

And this is where our forefathers in direct marketing came up with the idea of

RFM, recency frequency monetary value.

What they found time and time again, back in the 60s, early 70s.

And we still see true today here in the 21st century,

is that you can give me these three summary metrics.

You give me recency, frequency, monetary value.

You tell me the last time that someone made a purchase with me or

did some other kind of economically valuable activity.

Maybe they took a sales call, maybe they visited the website.

So they did something that suggest that they

going to become a more valuable customer.

Generally, we're talking about a purchase.

So that's our, that's recency.

Now, tell me about frequency.

Tell me how many purchases they made or how many

economically beneficial activities they did over a set period of time?

Let's say the last year or two.

And third would be monetary value and I think that's pretty much self explanatory.

So when they did those economically beneficial activities, what was

the overall or the average monetary value of each and every one of them?

So if you can give me RFM,

recency frequency monetary value, I can make a very accurate statement

about what that customer's going to be worth in period two.

And again,

this was one of the first areas where regression analysis was used in marketing.

It was one of the first ways for folks in marketing to say, you know what?

All of that data that we've been collecting,

not really sure what to do with it, woah, there's real value there.

We can really predict stuff.

Then we can start to change our business to take advantage of these insights about

what's likely to happen in the future, not just what happened in the past.

So I just want to put RFM out there as just one very nice example

of an application of the kinds of things that I was talking about.

And now I want to go one step further.