Case Study: Analytical Tool for Ticket Sales Service – Hundreds of Event Advertising Budgets Managed in Real Time

Service: Web analytics.

Niche: Ticket sales for events.

Results: Automation enabled the budget to be spent more efficiently.

The Client is one of the leading sites for event ticket sales in Ukraine. The service offers tickets for events at hundreds of concert venues and hosts.

The Challenge

Event ticketing campaigns have several specific characteristics:

  • Each event has an 'expiry' date, i.e., the date on which the campaign is turned off.
  • An event relies on the venue's specific capacity; if all the tickets are sold out, the advertising campaign has to be stopped.
  • As the venue is tied to a particular city, location needs to be considered when it comes to the geography of promotion.
  • Each event has a clear advertising budget, and exceeding this budget affects the event's profitability.

Due to these restrictions, keeping track of advertising campaigns isn't easy. Therefore, we decided to automate the control of the budget expenditure for all the events that we promoted for

The Solution

  1. First, we identified the problem and started to use analytical thinking to solve it.
  2. We clearly defined the data used in this project. We asked ourselves, "What type of data will help us solve the problem?"
  3. Then, we gathered the sources necessary to manage the data. Here is what we used:
  • A Google Table directory with information on each event — ID, event page link, advertising budget allocated in the context of sources, and promotional tools.
  • Landing pages of events — parsed dates and venues.
  • Google Ads and Facebook — information about spending budgets through APIs.
  • Google Analytics — data on the number of transactions and other e-commerce information.
  1. The next stage was tool collection. During the development, we used only free tools.
  • Data collection and transfer engine, as well as parsing landing pages, were written in R.
  • Google Table was used for data visualization and storing background information about events.
  1. During implementation, the R-script ran daily and performed the following actions:
  • queried the event directory;
  • collected data from the event landing pages;
  • gathered data on tickets sold from Google Analytics;
  • requested data from advertising platforms for the past year and separately for the last five days;
  • correlated the information by event ID;
  • calculated additional metrics;
  • sent the result to Google Tables;
  • recalculated visualization and reported based on the uploaded data.

Some considerations to keep in mind:

  • All the names of advertising campaigns and groups are regulated and contain event IDs to link data from advertising systems to the project directory.
  • Data on ticket sales in Google Analytics contains the event's name from the directory and is linked to it using this field.
  • The parsed data is linked through the URL of the event specified in the directory; this way, the data obtained from all these sources are linked together.
  1. The viable solution was delivered to the customer.

The Results

Overall, automation greatly helped contextual advertising specialists work with

  1. Convenient visualization made it easier to control the remaining budget in advertising campaigns.
  2. It is now possible to monitor each event's outreach dynamics on two systems: Google and Facebook.
  3. The contextual advertising team saved an average of 3 hours per day that would have been spent on summarizing the expenditure rates and the dynamics of the advertising budget for other professionals.
  4. Specialists can now identify campaign downturns in a particular platform and compensate for these downturns more quickly.


Ilya Strizhak, Middle PPC Specialist:

The tool allows you to work more accurately with the budget distribution and control the campaign balance for each event in different systems. This innovation makes it much easier to monitor the effectiveness of event promotion. Manual management of all this data would otherwise require a separate specialist.

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