Alex Schultz: APIs

Affiliates more Profitable than Malware

Sunbelt Blog is a really fascinating blog for anyone who works in the internet industry. As an affiliate I find it a really fascinating blog since the kind of people who produce malware and so on also are often affiliates. In the early days of running my own site I promoted a company who produced a software that when installed on someone's computer replaced ads on any site with their own ads. If someone came to my site, used my bandwidth, read my content they wouldn't see my adverts and wouldn't earn me a penny if they had installed any of this company's software. The company was called Gator and is now called Claria and have been through a number of legal cases on this which means what they do is legal although distasteful to me.

Anyway something that is very common in the malware industry is to get someone to install a piece of software that does something to your PC in order to get hold of a pornographic video or some other slightly illicit goody. They may hide the fact this download is making you part of a botnet but in the latest example shown on the sunbelt blog they get you to download google pack. The 2 incredible parts of this is that the Google affiliate program is paying more than malware pays and that what appears to be a relatively experienced naughty group would risk attacking an affiliate program. Usually these programs are on top of their affiliates enough that they would catch this, stop it and would simply not pay the affiliate. I wonder why Google appeared an easy target. Was the payout big enough to be interesting, the conversion to Google pack so great that it was worth doing or does Google not have a great affiliate fraud monitoring system in place yet?

Tickle.com - impressive revenue generator

I am a little bit of a sucker for anything that makes me feel clever and so for the first time in a very long while I clicked on a banner ad yesterday. The advert took me to tickle.com and an IQ test.

This IQ test was pretty cool and took 12 pages to complete. Once I had completed the test it through me into a string of 6 co-brand sign ups before I got the results of my test. Each page carried 3 cpm banners from rotating sources. I can get a $1cpm so I expect they should be able to get the same or better. Just the banner ads earned them $0.036 from me doing the test. Each of the 6 cobrands make >$1 per sign up so again low balling it let's say 1 in 10 signs up to only 1 that's another $0.1 a visitor from one test their earning is at least $0.14 and they claim almost 0.5billion tests served, that's $70MM in revenue with the conservative estimates above.

This however isn't the end of the game. Today I received the following email:

Another smart move, I clicked and visited their site, being pushed through multiple pages of co-brand sign ups again, returning to the site and being merchandized a bunch of other pretty cool looking tests, which the internet marketer side of my brain stopped me from taking but I strongly considered.

Tickle.com is owned by Monster.com so it isn't that surprising that they are really professional and good at what they do but it's pretty cool to see such a smoothly operating affiliate/cpm business model out there where arbitrage traffic purchasing is a real opportunity. Great job Tickle.com and keep it up.

Awesome Discovery

A friend from work accidentally opened my eyes wide last week at eBay live in Boston by creating a potential new design for my cocktail recipes website.

The eye opening event was using some stock photography from iStockPhoto (an example of which is above). The concept of iStockPhoto is simple photographers and designers offer up licences to use their photos and designs through iStockPhoto for a small fee and people like me can buy them for PPT use, posters (up to 500k reprints) and websites. The photo above cost me $1 and I just integrated another $1 photo into my facebook app newsfeed posts to have an even greater impact when my news feed stories appear.

I am a terrible designer but a pretty good coder. I thought my websites would forever be doomed to look rubbish because of this but now thanks to iStockPhoto I have hope. Look for improvements in the future and even new projects!

In the Facebook Directory

So I have been working on my facebook cocktail application for a little over a week now having started it on the memorial day weekend. It allows you to do everything my cocktail site does and a little bit more including recommending a cocktail to a friend and seeing the most recently popular cocktails and the top 12 users yesterday. I actually have a bunch more ideas of what it could do like little graphs of the popularity of the cocktail over time and so on.

What is exciting is that in 3hrs I have added 300users to the application, now obviously rates won't stay that high but realistically most of the east cost wasn't browsing facebook when the app was approved and neither was the UK. It will be really interesting to see how things go over the next 24hrs.

The best news is I have log files tracking everything going on so I will be able to note down and describe what the growth of a facebook application looks like!

More posts to come down the line but I want to leave you with a thought. For my cocktail recommendations engine I now have a track... at a user level... of every cocktail viewed and rated, and at what level (pretty much tied to demographic too if I can get my ass in gear and pull that data). This recommendation engine can get much, much smarter :D

A Beautiful Internet Marketing Experience

This has to be one of the most beautiful graphic integrations I have seen. Smirnoff Black Cherry on Pandora. It really set me longing for a smirnoff black cherry and coke which I will be having this evening in the bar. Very cool indeed and I can only hope that I will be able to produce a calibre of IM similar to this as I progress in my career!

 

The Cocktail Recommendation Engine is Live

Finally with a month of data under my belt I launched an AB test on the cocktail recommendation engine on Friday. The cocktail recipe the test group will see is shown below. Early indications are great with initiall navigation for visitors entering the site on these pages clearly influenced by the cocktail relationships shown. I will give more details of how to produce an accurate AB test via Google Analytics in another post (cos I am still figuring out a few of the kinks).

% Returning Visitors

This is such a cool metric and one which I truly love. How many of the visitors to my site are returning and how many are new visitors. Ideally I love to see the absolute number of returning visitors growing faster than the new visitors to the site simply because that shows the site is getting more sticky and people are enjoying it so much they are coming back of their own volition. As a site owner I feel this is a metric you should keep a close eye on.

The left image shows 2006 April returning visitor rates for my cocktail making site and the right image shows 2007 April returning visitor rates for the same site. In that time I have tripled the site traffic but grown the returning visitor rates 50% faster. This is awesome news and I am really excited to see that the site is getting better and more sticky.

The whole question is how to make that EVEN better and draw more people back to my site. Returning visitors are hugely valuable since they are free traffic and engaged. It's worth investing time and money in increasing this metric even though it's a really hard one to move.

Cocktail Recommentation Engine: Part 3 - The First Results

The following table is a subset of the output results from the cocktail recommendation engine (be warned some rude words in some of the cocktail names). I have tried to drill the list down to just the best matches so bear in mind there are probably 30% more matches thrown out which appear to have statistical significance but I can find no reason for the relationship (beyond sometimes they are next to each other in the large cocktail A-Z list).

Cocktail One - the cocktail someone is on, Cocktail Two - the cocktail I recommend, Probability - the probability that if someone is on cocktail one and visits another cocktail cocktail two will be that cocktail. There had to be at least 10 relationships in total recorded between cocktail one and another cocktail in my db for a cocktail to make this table.

I am hoping that as I get more data this table will grow more accurate and I will have fewer "wtf?" moments looking at the list. I also want to add two more probabilities to the calculation:

1. Probability if they visit 1 they will like 2
2. Probability if I display 2 on 1 they will click on it

Anyway for now enjoy looking at the cocktail relationships and expect more data soon.

Cocktail One Cocktail two Prob. The Refresher Tom Collins 0.76 throat cramp Toxic Waste 0.67 Raunchy island sex Red Headed Slut 0.5 three wise men Three Wisemen 0.5 Tomate Momisette 0.43 Wicked one Screwdriver 0.35 Amaretto Rose Amaretto Dream 0.33 Purple Haze Radiation 0.33 Strawberry shortcake StrawBerry Shortcake 0.33 Toxic Waste Titanic Sinkers 0.32 martini spiller Niks Shot of Aussie 0.32 The Orville three wise men 0.31 3 Headed Parrot A-Bomb 0.3 BABY GUINNESS Baby Guinness 0.3 Flaming Shooter Flatliner 0.29 tequila slammer!!!!!!!!!!! TEQUILA SUICIDE ! ! ! ! ! ! ! ! ! ! ! 0.28 see you at the hospital Tequila Sunrise 0.28 Absolut Nut Absolut Quaalude 0.27 Sex on the beach Sex On The Beach 0.27 suicide surfer on acid 0.26 Tom Collins Orange Fizz 0.24 Kick in the Willy martini spiller 0.24 Slippery Dick Slippery Nipple 0.24 Multiple Orgasm Multiple Screaming Orgasm 0.24 Absolut Stress absolutly mistafying 0.22 death cock sucking cowboy 0.22 A strawberry with milk A-Bomb 0.22 Cheeky Vimto Layered cheeky Vimto 0.21 Tequila Sunrise throat cramp 0.21 Sex On The Beach Sex on the beach 0.19 Screwdriver Bay Breeze 0.19 Arsonist awamawama ding dong 0.19 Absinthe Drinker Absinthe Minded 0.19 nipple twister Nymphomaniac 0.19 Jolly Green Giant Flaming Lamborghini 0.17 Flatliner Flaming Lamborghini 0.17 Zipper Zombie 0.17 Absolut Nut Level 28 0.17 Absinthe Drinker Royal Absinthe Fizz 0.17 Long island ice tea Long Island Iced tea 0.16 Slippery Dick cock sucking cowboy 0.14 blowjob Blow Job 0.14 Blow Job Clit Licking Cowgirl 0.14 Wet Pussy Wet Dream 0.13 Pina Colada Pina Colada (Virgin) 0.13 Screaming Orgasm Sex on the beach 0.13 Orgasm Cocktail Screaming Orgasm 0.13 B52 Blow Job 0.13 Slippery Nipple cock sucking cowboy 0.13 Sex on the beach (shooter) Sex on the beach 0.13 Black Russian Black Russian - proper 0.13 Brain Damage brain haemorage 0.12 Alabama Slamma Alabama Slammer 0.12 cock sucking cowboy Clit Licking Cowgirl 0.12 Mai Tai Mojito 0.12 Sex on the beach Slippery Nipple 0.12 Pornstar Purple rain 0.12 Pornstar Quick Fuck 0.12 Baby Guinness Bad Babysitter 0.11 Arsonist red , white & Blue 0.11 Baby Guinness A-Bomb 0.11 Baby Guinness Billy Badass 0.11 Screwdriver Wicked one 0.11 Angel's Kiss B52 0.1 Blow Job blowjob 0.1 Clit Licking Cowgirl A-Bomb 0.1 Clit Licking Cowgirl cock sucking cowboy 0.1 Flaming Lamborghini Deep Throat 0.09 A-Bomb Abbey cocktail 0.09 Sex On The Beach Sex on the beach (shooter) 0.09 Blue Lagoon blue lagoon special 0.08 Slippery Nipple Sex on the beach 0.08 Clit Licking Cowgirl Orgasm Cocktail 0.08 Clit Licking Cowgirl Deep Throat 0.08 Screaming Orgasm Sex On The Beach 0.08 B52 Baby Guinness 0.08 Screaming Orgasm Blow Job 0.07 Blow Job Blue Lagoon 0.07

Huge steaming piles of data (Project Cocktail Part 2)

Today has been quite challenging in project cocktail. The main issue is working out how much information to store and what granularity to summarize it on. Data is pouring in rapidly, I have defined over 5000 directional cocktail relationships and probably 10% of my cocktails within one day have at least one relationship I would define as relevant (I shall work on statistical significance fairly heavily later on).

The issue is that every row in my database is currently taking up 46bytes and I am adding c. 7.5k rows a day. The index on the database is then adding another 25% to this and there is little I can do here since I am required to create a primary key. Therefore daily I am creating 0.4MB of data in unsummarized form. The machine I am using has 100MB of storage and so I could store up to 250 days of raw (unsummarized) data or 2.17million rows (handy cos right now my cocktail DB could theoretically create 2.17million different directional relationships with different likelihood factors).

In the short term I am going to do nothing more than summarize on a month basis and depreciate the value of prior months over time but in the long term I want to know if recommendations should differ significantly by various user related variables and hence I want to store those variables so that in the future I can have a significant dataset to query and work out the impact of those variables. My target is to make this engine slightly scary at predicting what cocktail you might want to see next and to hit the scary threshold will take a little more than the #1 most likely relationship to deliver. So all things considered I have 0.5TB of data storage going spare right now in 5 mySQL databases... let's go for the big data :) and see what comes out!!!

Tomorrow I will be helping move 100 rowing boats so that my new boatclub can undergo an awesome renovation over the next 3wks which means I will have to take a break (and let the data gather into an even higher volume). Hopefully Sunday I will be able to do some analysis and maybe even build a mock up that shows what the output could start to look like. For now goodnight!

Recommendation Engine: Project Cocktail Part 1

Ok so how does one build a recommendation engine?

For the last year I have been tweaking improving and revising my cocktail recipes website with the prime goal of driving more traffic to the site. I have certainly acheived some of that with >1500 visitors daily on most days at the moment (still not huge but really massive growth). What is noticeable though compared to my paper airplanes site is a distinct lack of loyalty in my user base. With paperairplanes.co.uk ~32% of the visits daily are from returning users but with cocktailmaking.co.uk only ~9% are.

I need to make the site better and so my first attempt is to produce a cocktail recommendation engine. Phase one of the project just released to the site and I wanted to chat about that a little bit here.

I am logging every chain of two cocktails viewed by a visitor to my site within 2hrs. My thinking is that time spent and number of clicks browsing both degrade the correlation between cocktails in a user's browse path and so two cocktails viewed back to back in a short time period are likely to be very closely related. If I observe this interaction over millions of such pairs (data I can gather in just a few months) the most correlated cocktail combos should stand out from the mix at least for the top 10% of cocktails viewed on my site. That is my thinking and I will start to share the results on here as the data gathering proceeds.

The wonders of having a website with enough traffic to make analysis interesting :)