Data analysis is a critical step in any research or business project. It involves processing, interpreting, and summarizing data to extract valuable insights that can inform decision-making. However, to carry out an effective data analysis, it is essential to have a plan in place to guide your efforts. In this article, we will discuss the steps involved in preparing an analysis plan and provide best practices for analyzing data.
Step 1: Define Your Objectives
Before you begin analyzing data, you need to define your objectives clearly. What are you trying to achieve? What questions are you trying to answer? What insights do you hope to gain from the data?
Depending on your objectives, you may need to conduct different types of data analysis. There are three main types of analysis: Attribution, Validation, and Discovery.
- Attribution is used to determine what impact a campaign or initiative has had on visitation to your brand or trade channels.
- Validation is used to confirm what you already know about your business.
- Discovery is used to identify new areas for consideration.
Selecting one of these types of analysis will guide your investigation and ensure that you stay focused on your goals.
Step 2: Identify Your Segments
Next, you need to identify the segments or groups that you want to analyze. Which audiences do you want to observe behavioral data for? Many begin with demographic audiences and look at different age groups, income levels, or geographic locations. While others choose to create segments based off questions in their survey for data analysis. These are typically audiences based on stated behaviors or opinions. For example, you can observe visitation trends among those who answer the survey question about the health of the US economy. The selections would isolate trends between those who feel the economy is on the right track versus those who feel it is on the wrong track.
Make sure that your segments are well-defined and that you have the necessary questions to analyze them. Your analysis plan should identify the specific questions or variables that you will need to observe your segments (see our article on Questionnaire Best Practices to help you identify audiences with existing questions).
Step 3: Select Your Categories
Once you have identified your segments and analysis type, you need to select the categories that you want to analyze. Categories are the different aspects of your data that you want to examine. Studio provides three channels for data observation: brick & mortar locations, apps or websites. Within each of these channels, you can choose to analyze a wide variety of categories such as fast food restaurants, grocery stores, big box retailers, social media, photo and video apps, travel, etc.
Choosing the right category is crucial when working with behavioral data. To ensure success, you need to select a category that aligns with your company's trade channel or objectives. For instance, if you are an advertising agency tasked with driving traffic to a large bank, you should start by analyzing the brick and mortar channel and monitoring visits to banking centers. Additionally, you can examine app and website usage for banking categories and identify whether your company received any visits from these sources.
If your intended category is not readily available or your specific brand is not found, there is an option to create a category via custom selections (view our article on creating custom categories). This option is very helpful if you would like a specific competitive set than relying on the brands that Studio provides as a default.
Step 4: Customize Your Analysis
Every project is unique, and sometimes customization of analysis is necessary to meet specific needs. There are a variety of ways to customize data on the Studio dashboard to ensure that you get the insights you need. Beyond categories and survey questions, many like to begin with the data timeframe filter. For instance, depending on the research objectives, it may be essential to observe data before or after the survey. With Studio, it's possible to filter traffic based on one, seven, or fourteen days before or after an event. In most attribution cases, the goal is to validate whether a campaign or event led to an increase in visits. Therefore, it's advisable to select the filter to observe data one, seven, or fourteen days after the campaign or event. Furthermore, it's not necessary to analyze data for a single period only. The ability to analyze all segments provides insights into trends and long-term impacts following a specific milestone.
Another popular option is to filter results by demographic variables: age, gender, region or income. All of these options can be selected as a survey question or as a filter on top of your question by visiting the “venue metrics” table and selecting the appropriate button. Visit our article on adding demographics to surveys or adding demographic trellis to results for more information on this option.
Step 5: Set Your Thresholds
In order to ensure that your analysis is meaningful, it is important to establish clear thresholds for your results. Since Studio data relies heavily on visitation, it is possible that your results may occasionally show values lower than 10% across various channels and categories. As a result, it is necessary to determine the minimum percentages that should be displayed as results. Our internal experts typically use a 3% cut-off as the standard minimum, but it is important to note that every study is unique and you may need to adjust your thresholds based on your analysis or your audience's preferences.
Another threshold to consider is the differences between two comparison groups, such as Audience A and Audience B. Since statistical testing is not conducted on the results, our internal experts suggest that a minimum threshold of at least +/- 5% should be considered significant. However, some analysts prefer to set the threshold as low as 2% in order to identify meaningful differences between groups. Ultimately, setting clear thresholds can help you sift through your results and focus on the most relevant findings.
Conclusion
Analyzing data can be a daunting task, but with a well-defined analysis plan and the right tools, you can extract valuable insights that can inform your decision-making. Remember to define your objectives, identify your segments, determine your analysis type, select your categories, set your thresholds, and customize your analysis to suit your needs. With these steps in mind, you can be confident that your data analysis efforts will be effective and efficient.
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