IP addresses are one of the most important components of the modern internet. They are used to identify computers and servers that are connected to the internet. They are what enable two devices to communicate with each other online.
However, there are more advanced uses of IP addresses, especially in data science. In this article, we will discuss the applications of IP location in various data science projects. Let’s start.
Applications of IP Location in Data Science Projects
The use of data and data processing to gain meaningful information is known as data science. So, given below are some of the common data science projects that can utilize IP location data.
Marketing has some of the most uses for data science. One of the top uses is audience segmentation. Big corporations and companies have lots of audiences. They cannot use the same type of marketing for all of them.
A huge audience means you have people from all walks of life coming to your brand. That includes both the old and the young.
An approach that works for old people may not necessarily have similar success on young people and vice versa. You need separate tactics for both demographics.
The same goes for location. People from certain states or countries may have specific likes and dislikes. IP location data can help segment audiences based on their location and tailor their marketing accordingly.
Geofencing is a technique where imaginary boundaries are created on a digital map. IP addresses that originate within the boundaries are able to access particular services and data, while those outside cannot.
International corporations use IP locations to provide geofenced services and goods.
Some of the popular services include:
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Regional pricing. In international markets, regional pricing is crucial. This is because different regions have different buying power. Something considered cheap in one place might by too expensive for another because of their weaker currency. IP geolocation is very useful for providing regional prices.
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Location-based Offers. Companies use their customer’s regions to offer special promotions for that region. This helps in global marketing campaigns as it let’s you customize your offers for the local audience.
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Region-locked goods. Some online sellers have a limit on where they can deliver. If they can only deliver products to a particular region, they can use IP geolocation to determine whether a client falls in the correct region.
These are just some uses of IP location. Data science is related to these use cases as it is paramount in determining whether these services should be offered or not. It helps to find out whether there is enough demand for their product in a region and if it’s feasible to offer these services.
Content localization is another use case for IP geolocation. As is the case with geo-fenced services, data science is essential for determining whether content localization for a region is worth it.
Content localization refers to creating content for a specific language or dialect that is native to a region. Multinational companies have websites in multiple languages. This is because of content localization.
By localizing content for specific regions, companies make it easier for more people to engage with their brand.
Targeted advertisement refers to creating ads specifically for one type of audience. We touched upon this in the personalization section as well.
Different audiences react differently to various stimuli. That’s why they need personalized content for engagement, and the same is true for advertisements.
An advertisement that uses the language that your audience prefers and promotes products that they want/need will perform very well. Of course, the data used for creating this profile is collected from the customer’s activities on your platforms as well as publicly available and shared data on other platforms.
IP location has an important role to play in fraud detection. Data science is used to determine whether fraudulent activity is going on or not based on IP location. Given below are some common use cases for fraud detection and prevention.
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Login attempts from an IP location that is very different from the one normally used by a customer is a clear sign that they are either using a VPN or their account credentials have been stolen.
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Offices use geofencing to detect fraudulent attempts to log in from outside the designated area.
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Banks use IP location to determine whether certain online transactions are fraudulent or not.
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In some cases, multi factor authentication kicks in if a login attempt or transaction attempt is made from an unusual IP location. For example, tools like Whoer allow analysts to quickly verify IP geolocation data and detect proxy or VPN usage before incorporating that information into data science models.
Those are some ways in which IP address geolocation is used for fraud detection and prevention.
Data science is used to determine when activities happen outside of their usual pattern and whether that is a mistake or an actual fraud going on.
Techniques for Working with IP Location Data
We have learned how location data obtained from IP addresses is used in data science to perform a variety of functions. But how is that data obtained and processed? Given below is a high-level view of the methodology.
First off, we have to obtain IP location data. This data is kept in publicly available databases. Popular tools like IP location finder can access these databases and extract the geographic information associated with the IP address.
By integrating the API of such a tool into your data processing system, you can simplify the acquisition of IP location data.
IP location is not the only data that will be in the pipeline. You will also need complementary data from other sources to make the audience personas and tie an individual’s data and behaviour together.
The obtained data needs to be cleaned. Cleaning refers to identifying and removing noisy data. Noisy data refers to information that contains errors, irregularities, or irrelevant variation.
Noise makes it difficult to analyse and process data to gather meaningful conclusions and insight from data.
Data is considered noisy if it has:
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Errors
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Duplicate values
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Irrelevant information
Both automated techniques and manual reviews are used to deal with noise.
Finally, with the data obtained and cleaned, it is time to analyse and visualise it. There are various methods for data analysis that work in different situations. Some common methods are listed below.
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Descriptive Statistics. These include mean, mode, median, standard deviation, etc. They are used for data summarization.
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Regression Analysis. This includes linear and logistics regression models. They are used to model relationships between variables.
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Neural Networks. These are applications of machine learning. Deep learning neural networks are used to detect complex patterns and make predictions.
The analyzed data is then visualized via a graph or chart. The most useful tools for doing so include Matplotlib, Seaborn, Tableau, etc.
The visualized data helps humans see the patterns in the data and draw their own conclusions. This does not mean that the conclusions drawn by the machine are irrelevant. Both types are used to determine the best course of action.
Conclusion
Now, you know what IP location data and how it is a worthwhile addition to data sets used in data science. We covered various applications of data science that rely on IP geolocation data to work. We also briefly covered how data analysis and visualization occurs.