No Sales For 12 Hours? WTF??

Stats delayed perhaps?

Confucious once said, "Sometimes man lose... sometimes Manwin..."

sometimes that happens...drink 20 beers and smoke a fat doobie.

once you come back to reality sales will be back too

Doubt it but let's hope - 12 HOURS (at this point) is unheard of and statistically virtually impossible. LOL


Hahaha!! Damn that old Chinese dude may be on to something....:D


Too early for drinkin' but never too early for doobies (tho 'wake 'n bake' is not my fave thing).

Sometimes huge gaps between sales DOES occur, I know this, but I believe that 12 HOURS is my personal record since making steady sales from August '09 til now. So who knows. I blame the damn Google Penguins!!

Need more traffic brah

ah thats why sales are up today

It there a "white envy" meaning in English?

Hey MisterPeabody it's the n-th thread on gfy where you complain that there's no sales over a certain period of time... relax and sales will come...

I don't get why anyone watches stats on an hourly basis. Maybe daily. For sure weekly.

I haven't had any for like 15 minutes and I am fucking pissed!

Actually it's only my 3rd such thread, and one of them was more of an examination rather than a complaint but your point is well taken.


I do NOT look at stats on an hourly basis. That would be obsessive and nuts.

Oh wait......LOL

No, this is from midnight to noon without a single sale. Form hits steady, traffic steady, just never saw zero sales overnight like this, on a Friday. So was wondering if it was because of the Google Penguin 2.0 thing or (more likely) people getting ready for Memorial Day Weekend or even beginning the Holiday early.

I know, I'm obviously nuts to question these things. :D

It depends on the amount of sales you make I'd say.

No sales at all in a 12 hour period says a lot about the size of the network and the amount of regular sales Peabody normally gets. So tommorow there could be double the amount of sales and it evens out again.

Midnight to noon can actually mean 9pm for some programs to 3pm for others depending on where you are in the world.

Excellent point Roald! PeabodyCash does well over a sale an hour, on average, sometimes 3-4 or more depending on traffic. I have 32 paysites, remember. :D

If the traffic were down I wouldn't say anything, or if there was something up with form hits or processer stats, but it's not any of those things. Maybe just a weird time, that's all.

Oh look - 2 sales came in just now. See? All is well. LOL!!

What I'm saying is that unless you push a few 100 Join's a day you'll always see fluctuations like this.

So log off, go for a drink and check back on Monday

Have a good weekend

Ahhh I got it now. Thanks Roald, I know you know what you are talking about. Not that others in this thread don't, just sayin'. LOL

100's of joins a day.....that's the Goal all right!!

MisterPeabody I didn't meant to sound like I'm criticizing you, it was more like "don't worry be happy".

I would suggest studying this

http://en.wikipedia.org/wiki/PID_con...troller_theory

In simple terms life, with everything involved (sales, sex, chick, booze etc) is not a linear function and in fact has a lot of oscillations... or like Heisenberg said it here

http://en.wikipedia.org/wiki/Uncertainty_principle , you can never determine two arbitrary variables with maximum precision ... you either determine one or the other ... you know you have the content but you don't know exactly if sales will come...

No sales in 12 hours may mean that you'll have 24 sales in the next two hours...

(have drank a couple of glasses of wine :D )

That's "only" 3 joins per site Pea, should be doable

Keep on working!!!!!

Is traffic hitting the joinpage? maybe the cc scrubs been raised

No offense taken at all! I do understand fluctuations and the law of big numbers etc. But getting 24 sales in 2 hours would be as strange as 12 hours with no sales. LOL

This is the very first time, in almost 5 years of doing good, consistent business, that I have gone this long without a single sale while traffic and form hits remain "normal" so I thought it warranted a thread, asking if anyone else has seen this or can explain it as part of a Holiday Weekend trend. I do appreciate the wise answers!!

RIP Mister Peabody, you had a decent little run but it's all over now.

lol

You are so bad. lol

It all depends on how much traffic you get and your normal sales volume. I watch our stats nearly incessantly. 12 hours of no sales due to a bug or some other change would cost us a lot of money.

Bingo.


LOLZ Yah, I'm starting the 'wake' now, New Orleans-style WooHoo!!

when having a particularly bad period of time, there are a few things I check for, and generally in this order.

1) has there been any downtime? check servers and host, see if anything has been down at all during that period.

2) Physically go to a join page and a pay page, make sure all is working.

3) Check real time traffic, see if something is way off.

if the sites have been up during the period, join page and pay pages are functioning fine, and traffic seems normal, I then

4) Check join attempts, has a normal amount of people been trying to join? if the first 3 were fine, but your join attempts were down quite a bit, you have a situation where things just suck, and chances are your join success ratio is not off by much. But, if you have a normal amount of attempts, but no sales, then there is a chance you have an issue, at that point:

5) check your decline reasons, were they because of over limit cards, fake info, or is your processor spitting back an error.

not sure who you process with or where, but back in the day, Epoch had a stat known as "Epoch Ratio" which was just the number of people who attempted to join over those who were successful. When I had a bad sales hour, or hours, or day, it was the first thing I looked at, if the ratio was off (ratio of 6 to 1 instead of 4 to 1) I called them to complain, if the ratio was normal, I looked internally for an issue.

Thank you so much for taking the time to write and post this! The basic answer to all your questions is 'everything is normal'. I am not seeing an increase in denials, or a decrease in form hits. Looks like a bloody normal day to me! LOL

So it may just be Memorial Day Weekend flucuations, tho this of course does not explain the rest of the world.

But I love your checklist. It is similar to my own!

Nice list Phil and makes a lot of sense.

The people are upset with you

I forgot 1 thing, never compare today to yesterday.

always compare a Friday to a Friday, or a day of the month like the 24th to a day of the month like the 24th of the previous month.

today has nothing in common with yesterday, but generally lots in common with 1 week ago today.

as far as the 24th to the 24th, you are looking at the times of the month when people get paid, or when people's wives see their credit card bills, then obviously there are circumstances to consider like holidays.

there is a bunch of things to consider with respect to the last part. If your demography is primarily women, they prepare for holidays well in advance, so today is a normal day, if your marketing to guys, they don't prepare and are all packing like mad men today. Here is a more important one, in general, guys surf porn all day, at work, then at home, However many of them only purchase when at home, they surf tubes and sites at work, then join in the evening so they can see the full vids in higher quality when they don't have to be wearing pants. Monday is a stat holiday and people have to work today if they want to get paid for Monday when they are not at work. So a lot less guys at home actually joining, but still surfing from work because they don't actually want to be there today!

in the end, like Roald said, shit happens, but before you accept that, you just need to make sure the shit was not your fault, or the fault of anyone who takes a piece of your pie!

Nah....he has cool sites! ;-)

Doesn't seem right.

I've learned this as well, comparing dates. This is especially true when calculating rebills and forecasts.

But agreed, there are so many factors that it is always a challenge pinpointing causes but narrowing things down to "what you can control" vs. "what others control and you do not" is a perfect way to look at things. This is why, ultimately, I take the 'go grab a beer and chill' advice to heart the most because I know "my shit" is all working properly.

(Sales returning to normal, too.)

Jammin' over here. They must have passed yours to us. ;-)

high fives! Yes indeedy. Looking forward to that day alright!!

Exactly. Today though there is a high decline rate across all mids.
ds

One of the best (although complicated) reads on this subject; http://www.buildinganempire.com/poisson2.html

Your sales have been temporarily redistributed in the form of advertising.

testing 123

Need better SEO to get more traffic which in turn will generate more sales. =D

Relax Swirlsgirl will be here shortly to explain it all to you.

more ass please

Never despair, Peabody ...

MrPeabody shoot me an email icq or skype.. sales at amateurdough.com 174228679 or skype pornguy_epic about this topic.

WTF you're shaving yourself

all your sale belong to us

Bump for Sale ?

Hehe Fiddy pretty easy Formula to verify sales!


More Building an Empire

The Poisson Distribution Revisited: The Mathematics of Signup Ratios

Updated Oct 16, 2001

"If your experiment needs statistics, then you ought to have done a better experiment." - Ernest Rutherford

I love being able to use mathematical or scientific methods to learn something about my adult sites. One of my favorite mathematical tools is the Poisson Distribution. The Poisson distribution was derived by the French mathematician Siméon Poisson in 1837. This particular distribution describes the probability that an event will occur under the conditions that the event occurring is very low and the number of opportunities for that occurrence is very high.

Doesn't that sound like the perfect tool for analyzing the distribution of pay site signups? A small probability of an event occuring (signup ratio) combined with many opportunities for that event occuring (many visitors).

The formula for calculating probabilities with the Poisson distribution is this:

Probability = (e^-m) * (m^x) / x! where m is the expected number of occurences and x are the various possible occurences (0,1,2,3, etc.)

Let me explain the notation:
e is a mathematical constant. If you have a scientific calculator, it will have a value for e. e is approximately 2.718

The caret (^) symbol means to raise it to a power. e^2 means "e squared" or "e*e". e^3 mean "e cubed" pr "e*e*e".

! means factorial. Multiply by all integers down to 1 .. so 3! = 3*2*1 and 4! = 4*3*2*1 , one exception: 0! = 1

So once again,

Probability = (e^-m) * (m^x) / x! where m is the expected number of occurences and x are the various possible occurences (0,1,2,3, etc.)

Note that to calculate the expected distribution of signups, you only need to know two numbers in order to use this formula: The average number of signups and the case in particular that you want to look at.

This can sound confusing in theory but is easier once you try a few examples. Let me take a concrete example and show you.

Example 1. Assume that you normally convert a program at 1 in 1000 and want to know what the odds are that you will get 0 signups out of your next 1000 hits. In our formula above m (the average value) is 1 (we get 1 out of that 1000 on average) and x (the particular occurence) is 0.

Plugging into the formula these values (m=1, x=0), we get:

Probability = (e^-m) * (m^x) / x!
Probability = (e^-1) * (1^0) / 0!
Probability = (.37) * (1) / 1!
Probability = (.37)
This means that if we expect 1 in 1000, that there is a 37% chance that we will go 0 for our next 1000.

Example 2. Let's try another example. If we normally convert 2 in 1500, what are the odds that we will get exactly 3 signups in our next 1500 hits. Now "m", the expected number of occurences is 2 and "x" is 3.

Plugging into the formula these values (m=3, x=2), we get:

Probability = (e^-m) * (m^x) / x!
Probability = (e^-2) * (2^3) / 3!
Probability = (.135) * (8) / 3*2
Probability = .18
There is an 18 % chance that we will get exactly 3 signups.

Example 3. Let's look at a full distribution. Say that we normally convert 1 in 1000 and want to know what the probability of getting exactly 0,1,2,3, and 4 signups. Here m = 1 and we will look at the cases of x being 0,1,2,3, and 4. Probability = (e^-m) * (m^x) / x!
Probability(x=0) = (e^-1) * (1^0) / 0! = .37
Probability(x=1) = (e^-1) * (1^1) / 1! = .37
Probability(x=2) = (e^-1) * (1^2) / 2! = .19
Probability(x=3) = (e^-1) * (1^3) / 3! = .06
Probability(x=4) = (e^-1) * (1^4) / 4! = .015

If you expect 1 in 1000, then the odds of getting exactly 0 are 37%. The odds of getting exactly 1 are 37%. The odds of getting exactly 2 ar 19%. The odds of getting exactly 3 are 6%. The odds of getting exactly 4 are 1.5%.

If you look carefully, you will notice that the 1000 from 1 in 1000 never appears in the formula. In other words, if you expect 1 in 10,000 the odds of getting exactly 0 are 37% also. The odds of getting 1 in 10,000 when you expect 10,000 is also 37%.

You will also notice when you are looking at the whole distribution for multiple cases (0,1,2,3,4,...) when you expect "1 in something" that you have a constant term

(e^-1) * (1^x) = .37
no matter what x is because one raised to any power is 1. This constant term is divided by a number that is x!. Since x! grows very fast, the probability of getting a large number of signups when only 1 is expected drops off very quickly as you try large values of x.

If you expect 1 signup in any amount of hits, the odds that you'll get exactly 1 signup in that amount of hits is 37% and the odds that you'll get exactly 0 is also 37%.

How to use it There are many ways that you might be able to use the Poisson Distribution in a useful way. Say you join a new webmaster program and you need to convert it at 1 in 1200 for it to be useful to you. You send 1200 visitors and want to know whether that is enough to have fairly tested it. From the example above, you know that you will get 0 out of any random 1200 37% of the time, a fairly large occurence, so you really need to stick it out a little longer.

How about if after 3600 visitors sent you still don't have a signup? You are still wondering whether you have sent enough traffic to have given it a fair chance. What are the odds that you will have gone 0 for 3600 by chance if the program is going to convert 1 in 1200 for you overall? Here you expected (needed) to have 3 signups (3600/1200) so m is 3. The case you are interested in is for x=0.

Probability = (e^-m) * (m^x) / x!
Probability(x=0) = (e^-3) * (3^0) / 0! = .05
In other words, in 5% of the cases where we look at any random set of 3600 visitors, we will have 0 signups. It's not impossible that you might end up doing 1 in 1200, but the odds have slipped pretty far by then.

No mathematical distribution can make a decision for you, but they can give you a very good idea of when enough is enough. The question then becomes, what probability level is enough for you? You might also invoke it for those cases when you suddenly have a really bad day and would like to know if it is within the realm of possibility that you are just having a bad day. A .05 probability means that it is going to happen once every 20 trials. So if you normally get 3 signups in 3600 hits sent per day, you are going to get 0 signups once every 20 days. This is not because there is anything wrong with your sponsor, it's just the way the distribution works. If you average 5 signups per day with a sponsor you will have a 0 signup day once every 5 months on average. They are rare, but they will happen.

Always remember that even a slight adjustment in your marketing approach might make 1 in 1200 become 1 in 900. A tool like the Poisson Distribution is useful only when all other factors stay constant. The poisson calculator

Sending enough hits to test a sponsor There's a concept in statistics called standard deviation. The idea is that 68% of the time a quantity will be within one standard deviation of the average. 95% of the time one expects to find the quantity within two standard deviations of the average. When a scientist measures some quantity they quote it with an error measurement which is one standard deviation. No measurement is certain. I will just refer to the standard deviation as the error, but you will know that technically I mean the standard deviation. I've also left a few technical details out such as how confident we are in our measurements because well, I'm only going to take this so far.

In the Poisson Distribution the error is simply the square root of the average. In other words, the error is the square root of the number of signups. If you send 4 signups to a sponsor the error is 2 signups because 2 is the square root of 4. You can say you measured 4 +/- 2 signups. With all other things being equal, 68% of the time you can expect to attain between 2-6 signups by sending the same number of visitors. You can take that further and say that 95% of the time you expect to attain between 0 and 8 signups for the same number of visitors sent.

If you send 10,000 visitors to a sponsor and get 9 signups, then 68% of the time when you send 10,000 visitors, you expect to attain between 6 and 12 signups (6 +/- 3) and 95% of the time you expect to attain between 3 and 15 signups. The interesting point of all this is that the error depends only on the number of signups and not the number of visitors. Most people think, ok, I'll send a certain number of visitors to a sponsor and see how many signups I get. What you should be doing instead is saying ok, I'll send a certain number of signups to a sponsor to test them.

Why don't we use the "bell curve" instead? You might guess that a graph of the signups for every 10,000 hits won't exactly look like a bell curve. This is because you can't get less than 0 signups but there is a small probability of getting a lot of signups. This is what skews the graph and makes it non-symmetrical. We must use the Poisson Distribution instead of the Normal Distribution, often refered to as the "bell curve". The error estimation method I used here is a little different than the true error estimate. They're very good though. The error estimates I used are symmetrical when we just said that they aren't. As one increases the number of signups measures towards infinity, the Poisson Graph eventually looks exactly like the Normal Distribution. Here the error estimates become the same. The Poisson Distribution is just a special case of the Normal Curve.