We find our information on websites and buy tickets through ticketing systems instead of at the box office. We tell friends about performances, concerts, and exhibitions at parties, but also via messages and chats. In short, we create an enormous amount of data through our online presence, and your audience does too. By collecting (a portion of) this data, you can get to know your audience. Read below how to do this.
How to approach data analysis
Formulate your goal
Every data analysis begins with formulating your goal. Define this goal by also consciously deciding what not to do. The more the goal aligns with the objectives already present within your organization, the more effective it will be. Below, we help you get started with two possible goals.
Come up with questions
Based on the goal, come up with the questions you want to answer with the data. Data by itself does not provide insights. A list in Excel with all sales data from the past 5 years does not yet tell you where to find your audience. Insights from data only arise when a question is posed to the data. The more concrete the question, the more concrete the answer and thus the actions you can take. Below the step-by-step plan, you will find two examples.
Come up with questions that align with your goal. Only then can you extract valuable information from your data.
Choose a data source
If you want to learn more about the needs of a new or existing audience, segmentation can be a good tool. If you want to know when to send an email, link the transaction data to the data from your newsletter. In short, there are many possibilities. Don't hesitate to ask for help; there is a lot that can be done. By hiring external expertise, the effectiveness of the answer can be significantly increased.
Evaluate the results
If done correctly, the results will provide a concrete answer to your question. With this answer, you can ask a new question or set up an action.
Two examples: repeat visits and new audience
Cultural institutions often have the following goals: reaching the existing audience again or tapping into a new target group. Data analysis helps you understand these issues. Below, we provide two examples that show how such research can proceed.
Goal 1: Increase repeat visits among the existing audience
In several areas of the cultural sector, we see that visitors are returning less frequently. Possible questions for this goal are: What is the ideal time to email my current audience (for example, just after they have visited or a week later)? You can investigate this with A/B testing. Which target group attends which programming? Work with segmentation models. Is there something they are currently missing, causing them to visit less often? You can find this out through a survey.
Goal 2: Understand the needs of a new audience
If you are working on understanding the desires and needs of a new audience, it is important to look at sources outside your own data. This audience is not yet known. Questions that can be asked are: Which target group is present in my city but not yet in my institution? What are their characteristics? Are there colleague institutions serving a different target group with whom we can collaborate? You can investigate this through segmentation; the Cultural Target Group Model (opens in new tab) can help you with this.
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