Best Practice

Using data to boost ticket sales

05.12.2019

The effective customer-lead and risk-scoring approach - in soccer and other industries.

Even non-IT industries like soccer are trying to increase their financial success with the help of digital processes. There are practical examples that show how recognizing the data available can contribute to success. Other industries can also benefit from the approach of customer-lead and risk-scoring.

Chants, swinging flags, excitement and awesome goals: This is how you imagine a successful afternoon soccer match. But the reality is sometimes different: poor athletic performance, bitter defeats and relegation to the lower league. It is no wonder why ticket sales decline and some fans do not renew their tickets for the new season. After all, a city has many other attractive leisure activities to offer.

Many soccer clubs are thus always thinking about ways to increase their ticket sales. Price adjustments are often unsuccessful in the medium term, especially if the athletic performance is not great. Available data sources can help make marketing more professional. For example, data can be used to identify trends and patterns of sales in order to optimize the fan approach.

Understanding fans to increase sales

A striker must not only keep an eye on his goals, but on the opposing goal as well. Likewise, the club must know its goals. In addition to television money and sponsorship, a secure income generated from season ticket sales is an essential source of income and is therefore one of the most important pillars for every professional soccer club. Data can be used to help stabilize or even expand this strategically important pillar, even in difficult sporting times.

The concrete approach here is to get a better understanding of the fans, especially the season ticket holders. Based on the example, we should ask a few questions: Which fans buy season tickets? Who do not renew their season ticket? What are the reasons for buying or not renewing? 

Answers to these questions can be used to identify new potential season ticket holders, to recognize, in good time, season ticket holders that are at risk of not renewing their tickets and to control the communication with these target groups accordingly. The concrete result of such an analysis should, for example, be in the form of an assessment (scoring) of the current season ticket holders with respect to the risk that they may not renew the season ticket. A list of potential new customers (leads) and their purchase probability can also be useful. Such results form the basis for a marketing campaign.

Implementation: The truth lies on the field

Even the best tactics are of no use if the players on the field cannot implement them. After the content and scope of the application case have been clearly defined and its added value has been presented, the implementation begins. It must be determined which resources are necessary and whether the club has access to them or has to rely on external partners.

Our example

Our example

Our example includes the following areas:

  • Ticketing data (ERP/ticket provider)
  • Membership data
  • Socio-demographic data (CRM)
  • Access control data (scan of tickets at the stadium entrance)

In addition to the data, appropriate tools, technologies and people or skills are required to connect, prepare, save, model and analyze the data accordingly in the last step. The analysis should start with an illustrative (descriptive) approach followed by an investigative (diagnostic) view. Based on that, predictable (predictive) statements can be made.

In practice

In practice

In practice, based on our example, we should ask the following questions:

  1. What happened, or, in other word, how did the ticket sales change?
  2. Why did something happen, or, in other word, why did the season ticket holders renew or not renew?
  3. What will happen, or, in other word, who will not renew their season ticket?

Conception and implementation must be carried out taking into account the GDPR (General Data Protection Regulation), i.e. it must be earmarked with the consent of those affected. For modeling, the season ticket holders’ data can be linked to various data sources in order to combine the information contained therein. If necessary, the data must be cleaned and processed in advance. The subsequent analysis and pattern recognition can determine the most important influencing factors for the purchase of season tickets in comparison to normal single tickets or for the non-renewal of season tickets.

Factors influencing the purchase a season ticket

Factors influencing the purchase a season ticket

The analysis identified the following decisive factors that contribute to the purchase of season tickets:

  • Age of the person
  • Distance to the stadium
  • Number of single ticket purchases
Factors influencing the non-renewal of a season ticket

Factors influencing the non-renewal of a season ticket

The decisive factors influencing the non-renewal of a season ticket were the following:

  • Number of matches attended
  • Distance to the stadium
  • Years in possession of a season ticket

The table never lies

Every striker wants the coveted trophy - but most goals do not result in a trophy. Such rankings are not only used to evaluate players, but also fans. This is because a scoring model can be developed based on the results of the previous data analysis. There are two perspectives: The Lead-Scoring lists the fans and visitors in the databases according to their similarities with season ticket holders and thus represents promising new customers and season ticket holders. On the other hand, Risk-Scoring assesses current season ticket holders with respect to the risk that they may not renew their tickets.

This scoring can be improved with further information about the respective person in order to address fans and customers. The aim is to create content and offers that are as personalized as possible to optimally meet the wishes of the fans.

Examples of the results of Lead-Scoring

Examples of the results of Lead-Scoring

  • Upselling offer of season tickets to fans who bought several day tickets in the first half of the season
  • Upselling offer of season tickets in combination with a discount for children's season tickets for day ticket buyers with children
Examples of the results of Risk-Scoring

Examples of the results of Risk-Scoring

  • Coupons for season ticket holders who live close to the stadium but have made little use of their season ticket in the current season
  • Parking voucher (free parking space for some games) for season ticket holders who live far away from the stadium and who have rarely used their season ticket in the current season
  • Cross-selling offer for changing seats of season ticket holders whose seats are in a stadium area that is associated with high-risk of not renewing their tickets.

In the end, results are what matter

The approach shown has already been successfully carried out by several soccer clubs.

They benefit from the following advantages:

  • Decrease in the decline of season tickets (the churn rate has been reduced by up to 20%)
  • Increase in the number new customers (the rate of new season ticket holders has increased by up to 30%)
  • Increase in fan loyalty through positive feedback with more individual communication
  • High ROI through quick implementation of the use case with clear added values

Other industries can learn from this

This approach can be used as a starting point for other industries to start or expand their data-driven marketing and sales campaigns with a success measurement or automation, for example. The Customer-Lead and Risk-Scoring can be transferred to other B2C and B2B areas to support marketing and sales processes.

Here are some thoughts:

  • What kind of data does the company have about customers?
  • Does the company use this knowledge for marketing and sales?
  • Which customers are the most important ones?
  • Which group/industry accounts for 80 percent of sales?
  • Which customer groups have been lost, kept or acquired in recent years and months?
  • How does the company communicate with these groups?
  • How does the company assess its success?
Lead-Scoring

Fazit

The application example shows that data can be a decisive factor in professionalizing marketing and thus optimizing customer communication, even in small industries and companies such as soccer clubs. Even if the analysis results described are soccer-specific, the data-driven approach for marketing and sales campaigns can be applied for all industries. Companies do not always have to turn to “Big Data Science” in order to get concrete benefits. With simple customer lead and risk scoring, companies can find concrete approaches for new sources of income and increased sales.