Welcome back.

I hope that Regu's content on regression and related techniques makes sense to you.

I hope it's pretty clear now how we can take a bunch of data,

from let's say period one, whether it's again, past behavior or

marketing activities, competition, whatever.

To predict something about period two, whether it's the number of purchases,

whether someone stays with us or not.

It's really, really important to be able to do that.

And fortunately those techniques are common.

They're very accessible.

You don't necessarily have to have special software.

You can do it in something as simple as Microsoft Excel.

In fact I'm going to talk about an example where people were doing that kind of

thing, and long before they had any kind of the computational

power that we have today or even the rich data that we have today.

I want to take you back to the late 1960s, the early 1970s,

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

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

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

granularity about what particular customers were doing.

And a desire to know what each and every one of those 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, let's just throw in tons and tons and tons of data.

Because part of it is the data was limited.

Part of it, as I said, is that our computational power was limited so

we had 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 data set,

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, period two would 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 to find out which

bits of data were most predictive.

And they'd do it over and over and over again on lots of different data sets, for

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 suggests

that they're going to become a more valuable customer.

Generally we're talking about a purchase, so that's R, 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 that data that we've been collecting, not really sure what to do with it.

Whoa, there's real value there, we can really predict stuff and

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