I am a publisher that is contracted to serve an ad to only US users. We are serving this ad using geoIP database from MaxMind to identify IP addresses.
Based on DoubleClick metrics, we are showing that of 3200 impressions served, 7% are still being served outside the US (Germany, France, India, Italy, UK with the most impressions).
Is there a better technology to geo-target ads to the US?
There are several commercial geolocation technology provider. For free one, you can consider the http://lite.ip2location.com
MaxMind precision service costs $0.0004 per lookup ... $20 gives you 50000 ip lookups. That is VERY cheap and much more accurate than goeiplite ..
GeoIP2 coun try costs $0.0001 so $20 gets you 200,000 lookups.
The only thing you need to block bots and spiders - or they will eat through your queries :D
Here: https://www.maxmind.com/en/geoip2-precision-services
I tried GeoIP2lite and it was horrible in tems of accurancy, but I needed CITY / ZIPCODE level - so I'm paying $10 / month for a much more accurate precision service
If you are serving ads, $20 is nothing for you
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I am planning to start development and I don't know which method of analysis to take as a basis. Perhaps you have some ideas?
In the Forex market:
Volume = the number of price changes over a period of time.
In the stock market:
Volume = trading volume * price.
For example, the volume for a particular stock is 1000 shares at $10. The Volume indicator would then be as follows:
Volume = 1000 * $10 = $10,000.
The difference in calculations is due to the fact that Forex market is decentralized as opposed to stock market.
And that's why I don't understand how to calculate volume on forex to be accurate.
There is no unambiguous way for the MT5 trading platform to allow you to develop a universal way of calculating market volumes.
The forex market is decentralized.
Experiment! You can take your formula as a base.
I am planning to start development and I don't know which method of analysis to take as a basis. Perhaps you have some ideas?
In the Forex market:
Volume = the number of price changes over a period of time.
What about volume = 10 lots # 1.2USD
So in case of eurusd 1 000 000 € exchanged for 1 200 000 $
Anyway simce the data coming from the metatrader is crippled and unreliable it doesnt matter how good your calculation is.
So just calculate the ticks per unit of time to have an idea of level of activity for a specific pair.
If you are starting developing I would not raccomend meetatrader since it has lost popularity for many bad and good reason..
The best option for easy start is Pinescript or Python(adv), these will bridge you on different market rising a lot your chances to make a profitable robot. The forex market is competely controlled by central banks and it is programmed such a way that the speculator has always the biggest risk with the lowest chance of making any gain
How much bandwidth and storage do I need to store a small online shop, with let's say 200-250 products and how should I know how many visitors my site has monthly? Can you give me some real example, in order to make myself an idea? If you already have an online store or already existing online stores (Stradivarius, Zara, something smaller?)
Thanks in advance:)
Simply put:
Domain name - pays yearly ~ $10-40 (reasonable).
Web hosting - pays monthly ~ $3-10.
I wouldn't worry about disk space and traffic. For example some hosters give 50GB space + Unlimited traffic for ~$6/mo.
Shop engine - mostly free, 50 - 250 MBytes space. Generally your products require MySQL database space, which is commonly sufficient for your store size in hosting plans priced in part 2.
WooCommerce, PrestaShop and OpenCart will require less space,
Monsters like magento will require more space.
Statistics - To monitor bandwidth you can use Google Analytics.
SEO - if your web store is not popular, there is no point in having it.
If you are a startup you can first leverage free online shopping platforms like ebay, and later trouble yourself with self hosted web shop.
P.S. Estimations on traffic are 1-2MB / per page. If we assume 25% cache hits for images (user should see new content every other link), and interest rate like 7 products per user.
We can very roughly estimate, that one user produce ~11MB of traffic. I.e. 1.1GB per 100 users.
P.P.S. If you optimize content it will be significantly less.
I'm about to embark on a website build where a company wants to reward new visitors with a gift. The gift has some monetary value, and I'm concerned about the site being gamed. I'm looking for ways to help reduce the chance that any one person can drain the entire gift inventory.
The plans call for an integration with Facebook, so authenticating with your FB credentials will provide at least a bit of confidence that a new visitor is actually a real person (assuming that scripting the creation of 100's of FB accounts and then authenticating with them is no simple task).
However, there is also a requirement to reward new visitors who do not have FB accounts, and this is where I'm looking for ideas. An email verification system by itself won't cut it, because it's extremely easy to obtain countless number of email address (me+1#gmail.com, me+2#gmail.com, etc). I've been told that asking for a credit card number is too much of a barrier.
Are there some fairly solid strategies or services for dealing with situations like this?
EDIT: The "gift" is virtual - like a coupon
Ultimately, this is an uphill, loosing battle. If there will be incentive to beat the system, someone will try and they will eventually succeed. (See for example: every DRM scheme ever implemented.)
That said, there are strategies to reduce the ease of gaming the system.
I wouldn't really consider FB accounts to be that secure. The barrier to creating a new FB account is probably negligibly higher than creating a new webmail account.
Filtering by IP address is bound to be a disaster. There may be thousands of users behind a proxy on a single IP address (cough, AOL), and a scammer could employ a botnet to distribute each account requests to a unique IP. It is likely to be more trouble than it is worth to preemptively block IPs, but you could analyze the requests later—for example, before actually sending the reward—to see if there's lots of suspicious behavior from an IP.
Requiring a credit card number is a good start, but you've already ruled that out. Also consider that one individual can have 10 or more card numbers between actual credit cards, debit cards, and one-time-use card numbers.
Consider sending a verification code via SMS to PSTN numbers. This will cost you some money (a few cents per message), but it also costs a scammer a decent amount of change to acquire a large number of phone numbers to receive those messages. (Depending on the value of your incentive, the cost a prepaid SIM may make it cost-prohibitive.) Of course, if a scammer already has many SMS-receiving PSTN numbers at his disposal, this won't work.
First thing I wonder is if these gifts need to be sent to a physical address. It's easy to spoof 100 email addresses or FB accounts but coming up with 100 clearly unique physical addresses is much harder, obviously.
Of course, You may be giving them an e-coupon or something so address might not be an option.
Once upon a time I wrote a pretty intense anti-gaming script for a contest judging utility. While this was many months of development and is far too complex to describe in great detail, I can outline the basic features of the script:
For one we logged every detail we could when a user applied for the contest. It was pretty easy to catch obvious similarities in accounts by factoring the average time between logins / submissions from a group of criteria (like IP, browser, etc - all things that can be spoofed so by themselves it is unreliable). In addition, I compared account credentials for obvious gaming - like acct1#yahoo.com, acct2#yahoo.com, etc. by using a combination of levenshtein distance which is not solely reliable - as well as a parsing script that broke apart the various details of the credentials and looked for patterns.
Depending on the scores of each test, we assigned a probability of gaming as well as a list of possible account matches. Then it was up to the admins to exclude them from the results.
You could go on for months refining your algorithm and never get it perfect. That's why my script only flagged accounts and did not take any automatic action.
Since you're talking about inventory, can we therefore assume your gift is an actual physical item?
If so, then delivery of the gift will require a physical address for delivery - requiring unique addresses (or, allowing duplicate addresses but flagging those users for manual review) should be a good restriction.
My premise is this: While you can theoretically run a script to create 100s of Facebook or Google accounts, exercising physical control over hundreds of distinct real world delivery locations is a whole different class of problem.
I would suggest a more 'real world' solution in stead of all the security: make it clear that it is one coupon per address. Fysical (delivery and/or payment) address. Then just do as you want, maybe limit it by email or something for the looks of it, but in the end, limit it per real end-user, not per person receiving the coupon.
I have a geo-sensitive webapp for which I send a request's IP to a remote, commercial ip-to-location service, and get back the country, city, ISP, etc. for the IP.
I currently cache the IP lookups in my database in order to make subsequent lookups faster and free (the commercial service charges per lookup).
I wonder if I can further optimize my caching by assuming that the first 16 bits (i.e. the aaa.bbb in a aaa.bbb.ccc.ddd addresss) always have a uniform location. That way I can have at most 2^15 records to cache.
I don't mind so much about uniformity of ISP but that info would be helpful as well.
I'd recommend going down to at least /24 resolution. Oftentimes a /16 will tell you the ISP but not the city, or vice versa.
If you want a good idea of what the maps really look like, you can spend 49 USD on a developer license to Geobytes's GeoNetMap database. A developer license allows you to download the entire map from IP blocks to locations as a bunch of CSV files, but doesn't cover deploying it onto a production server. Geobytes has the added advantage of being entirely local, so lookups are liquid fast.
MaxMind also has a free downloadable map offering, although it is somewhat cut down from the full map, producing approximately double the error rate.
No, it's not safe. For example, if you do a GeoIP lookup on 216.34.181.45 (Slashdot) you get Mountain View, California. If you do a lookup on 216.34.1.1 you get Chesterfield, Missouri.
With respect to your caching, keep in mind that IPs can move around spatially. If an ISP goes bankrupt and its block gets bought by someone else, that block of IPs will move location.
I'm setting up a iPhone tracking system for my friends, so they can submit their location to my website by their iPhone, anywhere, anytime - by WiFi or cellular data.
The website will use Google Maps for their coordination's so that my other friends can track where they are, however, it is the accuracy of the IP to coordinates to Google Maps is what I'm concerned about, exactly how accurate is it to use Google Maps that would track down the locations by an IP address?
I was thinking about 95%, but this was tested in a village which was quite fairly accurate, but what happens if it was in a city? Would this cause unaccurate locations?
Any kind help appreciated.
IP geolocation is really hit-or-miss, depending on both how the user's ISP assigns IPs and on the IP geolocation database you're using. For instance, I made a simple PHP script, IP2FireEagle, which looks up your IP. I found that the database kept placing me 10+ km to the west of where I really was. Updating my entry in Host IP wasn't the greatest, as it soon got reverted, presumably by someone also occasionally assigned that IP by my ISP! That being said, I found that Clarke has very accurate coordinates (not that this it's using IP geolocation per se but rather Skyhook's API and their WiFi geolocation database).
If it's a website for your friends and you know they have iPhones, I would suggest using its browser's support for navigator.geolocation.getCurrentPosition(). That is, get the location via Javascript and submit it to your server via an AJAX call. Even better since you want to use Google Maps, they give you a short tutorial on how get your friends' locations and then update a map.
Excerpt From:
http://www.clickz.com/822881
IP targeting has been around since the early days of ad serving. It's not very hard to write code that will strip the IP address from a request, compare it to a database, and deliver an ad accordingly. The true difficulty, as we shall see, is building and maintaining an IP database.
One of the first applications of information in an IP database was targeting to specific geographic regions. Most commercial ad management systems have IP databases that can make geographic targeting possible. However, there are a couple weaknesses in this method. The first (and biggest) problem is that, for various reasons, not all IPs can be mapped to an accurate location.
Take all the IPs associated with AOL users, for instance. Anybody who has seen a WebTrends report knows that all AOL users appear to be coming from somewhere in Virginia. This is caused by AOL's use of proxy servers to handle their web requests.
In the interest of saving space, we won't get into the reasons why AOL makes use of proxy servers. The important thing is that AOL does use them, and as a result, all its users appear to be accessing the web from Virginia. Thus, it is impossible to attach meaningful geographic location data to an AOL IP, and those IPs must be discarded from any database that wants to maintain a reasonable degree of accuracy.
Other ISPs and networks may use a method known as dynamic IP allocation for its users. In other words, a user might have a different IP address every time he visits the Internet. You can see how this might affect the accuracy of a database.
But the real difficulty in discerning geography from an IP address has to do with the level of specificity that a media planner might expect from this targeting method. The first few geo-targeted campaigns that I put together early in my career had to be accurate to the ZIP code level. This level of specificity is not practical via IP targeting.