How machine learning helps the development of the event industry

JULY 4, 2017
Machine Learning is perhaps one of the main technological trends of the recent times. According to a research, about 60% of companies use this technology somehow in their business. Obviously, this is not just a beautiful term, but a reality and even a necessity. Following the trends, Radario applies the elements of machine learning to introduce new functions of the marketing platform. Let’s speculate on this and on how machine learning as a whole can change the event-industry.

According to a study by MIT Technology Review and Google Cloud, machine learning provides real competitive advantages and is used for a wide range of projects from image recognition, emotion and behavior analysis to word classification, data mining and processing. Experts argue that 45% of those who have already started using machine learning have achieved their goal, expanding the analysis of data and its depth.
It’s noteworthy that machine learning is not artificial intelligence, but only a part of it. In the process of machine learning, the system can be automatically improved as new data and new experience are obtained. In this case, the algorithms (or functions) for the work are specified by the developers. These are the rules or directives that the computer follows when processing information and performing operations.
In fact, we witness the use of machine learning for processing and analyzing large amounts of information every day. These are recommendations from online stores and services; Web search results that adapt to your browsing history and audience; spam filtering in e-mail; the change in the prices of retailers, based on demand predicting and   so on.
Suggested segments in the Radario Marketing Platform
We implement such a system in the Radario marketing platform. Now the system can offer the recommended audience segments to the organizers, which are created automatically by analyzing the set of filters that each particular viewer falls under. The technology allows us to help our clients run more effective marketing campaigns targeted at specific groups of users. The users of our system can see automatically configured segments and use them for their own purposes, with the base being more than two thousand viewers.

It seems quite simple, but from the technical point of view, it turns out somewhat more serious. Take, for example, a segment of loyal users. First, we determine which filters will show loyalty.

This can be the time since the last purchase, the number of transactions, the size of the average check, the frequency of purchases and so on. After that, we need to determine which dimensions of every parameter individually and collectively label the customer as loyal. To do this, we take all spectators from the database and plot a graph where the X axis is the filter values, and the Y axis is the percentage of buyers that fit the filter with this value. Based on data processing from the largest organizers working with our platform, we understand that the number of loyal audiences will not exceed 10% and determine how the graphics of loyal users will look. After that, the system checks the data itself and allocates to the segment those viewers whose graphs are the closest to the parameters set by us.
For a better understanding, we can give a simple example. Say, we are talking about an apartment. We know how many square meters it has, the floor it is located on and the distance from the house to the subway station. All this is described by numbers, which can have an infinite number of combinations. At the same time, we have a database of one hundred apartments with all these data, as well as information on the cost of each of them. We have to determine in which way all these parameters affect the price of an apartment . So, essentially, we do the same thing. We teach the system to find out in which way initial parameters affect the behavior of clients, and then we apply this pattern to other users, and, by the similarity of the data, we predict their behavior.
— Alexey Morozkov, engineer and developer of the Radario Marketing Platform:
Despite the prospects of such technologies, it is still too early to talk about the machine learning boom in the event industry. However, many experts predicted that it was in 2017 that the market would begin to pay special attention to this issue. Everyone agrees that machine learning should help to uncover the full potential of the data accumulated by the event organizers.

As an example, we can cite several directions for the use of machine learning in the event industry, formulated by the CEO of Eventgrid Nirazh Shah:

1. Planning and Decision Making

Making predictions for decision making is one of the most popular ways to apply machine learning. Computers can read and analyze much more information than the human brain and therefore the use of a large amount of data certainly gives a competitive advantage.

2. Dynamic Pricing

Hotel managers already use machine learning in a key aspect of their business, price optimization. With the help of algorithms, you can determine the optimal cost of a room at any time by analyzing the demand, supply and competitive situation among other hotels in real time. Such dynamic pricing can also be used in event management.

How can the event organizers find out the optimal price for each type of ticket? What is the best time for sales at starting prices or burning sales? Thanks to algorithms, you can answer these questions and predict a rush of buyers (that is, when it is better to hold discounts), or a decline in sales (it’s time to stimulate them). Such an analysis will help to optimize the profitability of activities.

3. Analysis of moods

It is another popular way of using machine learning. Known also as intellectual analysis of opinions, mood analysis allows organizations to find out the audience’s opinion about a particular product based on text analysis, which are usually web comments, social networking messages and online conversations. The analysis of moods allows you to judge the feelings of your audience, their likes or dislikes. Thus, the event organizers can use analyzing the moods to find out which artists to invite for an event next year, which speakers and which topics to choose for a future conference.

4. Classification of content

Machine learning allows us to organize, mark and classify a huge amount of content. This library is accessible to the viewers who are looking for information on various topics, and algorithms will help them to find what they need. This is what YouTube does, for example. It uses algorithms to offer viewers associated content and provide the most relevant query results. This artificial sense of understanding and personalization, achieved by machine learning, can be used in the event industry. If you are an organizer with a considerable track record, imagine that all the previous master classes, seminars and presentations organized by you are sorted and labeled. Visitors, participants or buyers of a paid subscription could thus access this content library, where they could use the algorithms of recommendations to find interesting playlists and the desired content.

5. Advanced segmentation of the audience

Data allows you to learn more about customers and better understand how to establish contact with them. Data analysis can help event organizers to attract new customers, and the process of segmenting the audience through machine learning will be maximally automated. A potential customer will be assigned to this or that category and attached to the corresponding marketing campaign without you or your team members being involved, which will save your time and resources.

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