Admittedly, this graph isn't quite as pretty and perfect as the other graph.

Especially towards the let side of it.

Those people who haven't made purchases for awhile, the model thanks to be killing

them off and underestimating how many purchases they will make but it's not bad.

It still does a pretty good job.

Considering this is a very simple model that we are running and

projecting over a five year period, I think we can live with those errors.

Yes, I have all sorts of other academic papers that close that gap further

to change the story with the coin flips and so on.

If you are interested, I could point to some papers for you.

But I think you get the basic idea.

The model did a very good job at predicting the number of

purchases on the basis of recency or frequency.

But let's roll it all together and make overall statements about purchasing for

the customer base as a whole.

And that's what you see in these two pictures over here.

The graph to the left shows you the cumulative number of purchases.

So of these 11,000 people, let's just watch them buy, buy buy.

As time goes on, you can see where we break out the six year

model calibration period from the five year forecast period.

And in that five year forecast period,

it's very hard to see any difference between the lines at all.

The models nearly perfect.

It becomes a little bit less perfect when we look at it on a year by year basis.

That's what the graph on the right is showing and

this is actually a very diagnostic graph.

In any one year the model's going to be a little too high or a little too low.

Again, it's not necessarily our goal to hit the mark in every single year,

it's our goal to map out a pretty good trajectory over the next five years or

maybe beyond.

If we were standing at the end of 2001, at the end of our model calibration period.

Remember 2001 was a horrible year.

9/11.

The recession starting.

What are we doing with those factors in this model, we're ignoring them,

completely ignoring them.

We're just saying, people just flipping their coin, same as ever.

This shows you the power of this as if random story,

that we can ignore very impactful factors.

And if someone stood over here in 2001 and said this is my prediction for

what the trajectory will look like over the next five years, and

we watch the way that trajectory emerge from 2002 to 2006, it's pretty amazing.

So again, sometimes we're too low, sometimes we're too high, but

to get an overall feel for what the purchasing would look like for

that group of customers.

It gives you a good feeling in the belly that if we wanted to project beyond 2006

into 2007, 8, 9, if we wanted to make statements about overall customer

lifetime value, we could do a pretty good job of that.

That's the analysis I am going to share with you for this one data set and

I think you'll get a pretty good idea about it.

In the actual paper that we wrote, we show all kinds of other pictures,

all kinds of other diagnostics and all kinds of other details and stories.

I think you get the highlights.

I just want to spend time showing you one other data set.

So I want to show you the same kinds of things.

I'm not going to give you all the parameters.

I'm not going to show you the estimating spreadsheet.

I just want to talk about the same kind of recency, frequency trade off table.

This is going to be with another non-profit, but one, we we're watching

the watching the donors, the customers for a much longer period of time.

You can see that in this chart, right over here.

The same basic idea that we have recency on the top.

We have frequency on the rows.

Just many, many more opportunities.

But you can read it the very same way as the table that I showed you with Bob,

Mary, and Sarah and so on.

In fact, we still have the equivalent of the Bobs down at the bottom right.

So these are the folks who have donated every single opportunity since they were

acquired.

You can still see the trade offs between the Marys and the Sharmilas.

In this case, the Sharmilla one is very interesting.

Here are folks who made, out of a total 19 opportunities, they donated,

out of the first 18, they went 18 for 18 and then they skipped.

So if you went 18 out of 18 and

then you skipped, it's not a matter of, I forgot to send you the check.

Something more fundamental happened.

Of course in our model we would say you died but there's a very,

very large chance that you dropped out.

We don't know for sure, it's impossible for us to know for sure.

But there's a much higher likelihood that if you went 18 out of 18 is skipped,

there's a much better chance that you dropped out

compared to our original Sharmilla, who went five out of five and then skipped.

Again, I hope you have this intuition.

It's not about the model.

It's about understanding these patterns of future looking behavior.

As you can stare at this thing you can see the way that we framed as a heat map

to really understand who the best customers would be and

you could really start to see those trade offs between recency and frequency.

Once again how recency Trump's frequency.

The last thing that I want to show you, one of the other nice diagnostics that

emerges by this buy diadem model is this idea of how alive are the customers.

Let's not only look at how many purchases we think they'll make, but

let's look at the likelihood that a customer like this

with this particular RF pattern would indeed be alive.

And so that's what we see in this graph over here.

Now if you look at it, on the right side every body alive, and that makes sense.

If you made a donation in the last period, regardless whether it

was your first donation since being acquired, or if you're a Bob and

you've done it at every single time we know that you had to be alive.

There was 100% chance that you were alive last time, and so

there's only a small chance that you might drop out as we move to our next period.

So, you see a slightly different pattern here.

Once again, if we look down at the bottom of the table,

you can look at the Shamillas and see, you know what,

if you went 18 out of 18, but you didn't donate in the most recent time.

You're chance of still being alive is fairly low.

And then you can look at the other end of the table.

You can look at the Sarahs and say, the Sarahs,

they're probably not around with us.

If you haven't donated for the last 19 years, you're probably gone but

on the other hand, it might surprise you to note that while the Sarahs,

there's not a lot left in them, but those Sarahs are actually more alive, or

at least have a greater chance to be alive,

than someone who used to donate a few times but hasn't been around for a while.

If you used to donate, but

now you've dropped out, kind of like the Sharmilla story,

then you are deader put it in quotes, than someone who's just never donated at all.

because with a Sarah, we don't know if it's that she's gone or

if maybe she's actually alive, but her donation propensity is just so,

so light, so rare, that it's going to be a while before we see her do anything.

So our guess is that the salaries are actually more allied

than someone who used to donate but isn't around anymore.

They've given us good reason to believe that they've dropped out.

And once again, it's not just to show off one particular model, but these

are forward-looking insights that emerge from customer-level datasets all the time.

And again, happy to share much more information about it but

I think this is just a really nice