Big Data
Any action by a user on the Internet is not a secret behind the seven seals. You can track everything from online shopping to likes thanks to the Big Data concept. The result – you learn more about your target audience and make personalized offers. More precisely, the machine does everything for you: it analyses and even makes the best decision.
Say, is this fantastic? Of course, the mechanism is not so common yet, and not fully debugged, but the first steps on the way to it are precisely made.
If we are talking about big data, it is not how much data you have collected, but how you use it. In general, Big Data is a universal method.
What is Big Data
Large transport companies, online stores, telecom providers, SaaS services, banks – in short, companies with a large customer base are collecting a huge amount of information.
This is not only personal data (name, email, phone, gender, age, geography), but also IP address, time of visit, number of visits, requests on the site, purchase history, etc. Each company has its own specifics and unique data, which are available only to it.
That is, this is not only the data that each business accumulates in the CRM-system. This is all that the company may know about customers, and it can be measured in terabytes of information in individual cases. Usual databases can’t process such volumes. At least because the data regularly changes and arrives – vertically (+ new client) and horizontally (+ additional information about the client).
In addition, they are diverse and unstructured, as they are presented in completely different sources, for example:
- Blogs and social networks;
- Audio and video files;
- Corporate databases;
- Sensors, measuring devices and sensor networks.
This is Big Data. Something more abstract than physical documents, so a person can’t manage them either. Machine algorithms come to the rescue.
Data Mining or how big data is collected and processed
Where’s the big data coming from?
- Firstly, it’s your website and all the contact details you capture.
- Secondly, counters and analytics systems (Yandex.Metrika, Google Analytics).
- Thirdly, social networks, forums, blogs, mobile applications.
Here are the main solutions of Big Data market:
- Database management systems (Sap, Oracle, Microsoft, IBM and others) that store and process information, analyze indicator dynamics and provide results in statistical reports;
- Algorithms that analyze Big Data and extract useful data (interests, intentions, consumer preferences) from them. They build predictive analytical models to prepare marketing campaigns and identify the most relevant advertising methods
- Off-the-shelf services that allow you to personalize your advertising campaigns.
These include:
- RTB advertising procurement management services that predict the actions of the target users and target advertising in online channels.
- Services of product recommendations, which show products on the site, the most interesting for a particular user.
- Content personalization services that show users the most appropriate versions of the resource pages.
- Mailing list personalization services that send targeted emails.
How Big Data Technology Works and What is Data Science
The practical essence of this approach is to minimize human involvement in the decision making process. This is what the concept of Data Science is based on.
According to this concept, a statistical model manages large data. It finds hidden interconnections in the data and predicts as accurately as possible (due to objectivity and a wide range of data) the behavior of a particular user – whether he will buy a product, subscribe to the mailing list, or be interested in the article.
At the same time, there is a continuous process of self-learning. That is, the machine itself learns (principle of Machine Learning) in real time and creates algorithms to optimize business processes.
It independently determines and prompts:
- What, where and when to offer the user for maximum conversion probability;
- How to increase cross-selling and additional sales;
- Which products are the most popular and why;
- How to improve product/service to suit the needs of CA.
In retail, machines can make the following decisions:
- Where to open the next store;
- What marketing campaigns to conduct;
- How to forecast sales in the future period;
- How to highlight the “core” of the audience;
- How to raise / lower prices in the next month;
- How to optimize the marketing budget;
- How to identify customers who will leave in the next month.
In marketing it allows to segment the target audience, develop creativity and personal offers for each segment. Unfortunately, at the moment, this process is only partially automated.
The Future of Big Data
Experiments with big data are continuing.
Yandex is one of the pioneer companies that not only teaches the concepts of Data Science, but also actively uses them in the development of their own products.
It is available in different countries. There is no need to sort the material by topics and other parameters and customize the display to specific user categories. Everyone will read articles that are of interest to them and get a new selection of similar ones. The system simply offers what he or she is most likely to like.
The point is that machine intelligence is not aimed at averaging. It does not aspire to create a limited number of segments, as its capabilities allow offering personalized content to each of several billions of users.
A foreign analogue can be called alexa.com – a rating of the most visited sites around the world and in different countries separately (samples by country are paid and cost money).
Automatic data collection and statistical models allow you to include sites that do not participate in other ratings.
Even in its current form, it makes it possible to identify leaders in various niches and model their promotion strategies and traffic sources with the help of other services.
The ambitious goal is to create and train such a tool that would use internal algorithms to find a target audience for the minimum set of parameters given by a person and select the creativity for advertising campaigns.
Suppose you select 5-10 users – and the machine finds thousands of similar and configures them targeting. The advantage of machine intelligence is that it takes into account factors that even an experienced specialist can overlook, not guess about them.
Learn to distinguish which decisions are better made by a person and which ones are better made by a machine, and do not confuse these two classes. If with the same type of tasks (choose the design of the button) algorithms cope better, more creative (to construct a site from scratch) can only a person. Teach not only people, but also algorithms.
Keep in mind that although the algorithms are great at answering questions, but can not ask questions themselves. Although it may be a matter of time, too.
By the way, the question about the “confrontation” between man and machine intelligence is being raised more and more often.
About segmentation, marketers with axes, burning budgets and whether the button “Bring me customers” will appear in the near future.
