Audience Segmentation: Technical Side

Content Personalization is a burning topic for the majority of newsrooms. Starting with segmenting audience, we have to proceed to the final result - personalized content. However, no human can handle such enormous amount of big data. Here’s the moment when the machine does everything.

Today, IO Technologies team will spread the light on this question and tell how the things are sorted out by the machine.

Personalization API

It allows data processors collecting information and delivering it to servers for analyzing. Specialists also use APIs for distributing personalized content to website visitors.

Data is captured from DMPs (data management platforms) and it creates initial images of readers. Such images usually contain geographical and demographic data. DMPs organize users by groups and audiences, they are the first step towards creating the fully personalized user experience. Where do they collect users’ data?

• Web applications data.

• Data from CRMs and transactional systems.

• Search engines, ads campaigns data.

• Information from data providers and other third-parties.

All this data is already analyzed, segmented, statistically correct and ready-to-use.

After collecting data from every possible source, ‘Personalization API’ distributes customized and personalized to each visitors group. Readers are offered content relevant to their interests and preferences.

Machine learning

It is when a machine, but not an editor decides which materials are suitable for a specific group of visitors. Machine learning can seem similar to AI, as it bases decisions on statistics, and actually, it is part of AI that develops machines’ intelligence.

As NVidia states, “Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world”.

In general, a computer analyzes data and creates recommendation without human help. Such work can’t be done manually, that’s why editors use computing power to perform effective data mining and precise analysis.

Collaborative filtering

Collaborative or social filtering is one of machine learning techniques. It uses other visitors’ recommendations for creating suggestions for readers. Common interests are the base for social filtering, however, there are two types of it:

• User-based (similar user histories).

• Item-based (similar articles lists).

Far-famed websites, such as Amazon, Netflix, IMDB, iTunes use collaborative filtering, trying to predict user behavior taking into account not just his / her own browsing history.

Synergy of humans and machines

Technology helps achieve great goals without enormous effort. A machine helps use data and analysis instead of gut feeling that makes the final result more precise and focused.

Why not use these opportunities for creating an effective data-driven business?

Entrepreneurs who personalize content using modern computing techniques will skyrocket in the nearest future, and we hope you will be among them.