How can you use AR data to engage customers?

1 Types of AR data

AR data can be divided into two main categories: user data and environment data. User data refers to the information about the users who interact with your AR content, such as their demographics, preferences, behaviors, feedback, and satisfaction. Environment data refers to the information about the physical and digital context where your AR content is displayed, such as the location, lighting, objects, sounds, and network. Both types of data can help you understand how your AR content performs and how it affects the user experience.

2 Sources of AR data

AR data can be collected from a variety of sources, depending on the platform and tool used to create and deliver AR content. SDKs and APIs are software tools that enable the integration of AR features into apps and websites, often providing analytics and reporting features to track key metrics such as user sessions, interactions, events, and conversions. Additionally, AR devices such as smartphones, tablets, headsets, and glasses have built-in sensors like cameras, microphones, accelerometers, and GPS that capture data about the user and the environment. This data can be accessed and analyzed using native or third-party apps and tools. Lastly, AR platforms and services are online platforms that offer ready-made or customizable solutions for various use cases and industries. These platforms typically have dashboards and tools to monitor and manage AR campaigns, content, and data.

3 Benefits of AR data

AR data can provide you with valuable insights that can help you improve your AR strategy and achieve your goals. For instance, it can enhance customer engagement by allowing you to understand what customers want and need from your AR content. Additionally, it can increase customer retention by measuring customer loyalty and satisfaction. Finally, it can optimize AR performance by evaluating how the content performs in different environments and scenarios, which will allow you to troubleshoot any issues and improve your AR content quality and functionality.

4 How to analyze AR data

AR data can be analyzed using a variety of methods and tools, depending on the type, source, and format. For example, data visualization is the process of presenting data in graphical forms, such as charts or images. This can help identify patterns, trends, and outliers. Data mining is the process of extracting useful information from data using statistical or machine learning techniques. This can help discover hidden relationships or generate new insights. Lastly, data modeling is the process of creating and testing data models that represent the structure, behavior, and interactions of your data. This can help define and validate data requirements and optimize data architecture and processes. Popular tools for these methods include Tableau, Power BI, Google Data Studio, Python, R, SAS, ERwin, SQL, or UML.

5 How to apply AR data

AR data can be applied to various aspects of your AR strategy, such as content creation, delivery, and evaluation. For example, AR data can be used to inform and inspire your AR content creation process, such as choosing the right format and features for your AR content. Additionally, AR data can be used to optimize and personalize your AR content delivery process, such as selecting the best platform and time for your AR content. Furthermore, you can use AR data to measure and evaluate the effectiveness and impact of your AR content, such as tracking key performance indicators (KPIs). Finally, you can use AR data to compare and benchmark your AR content against competitors or industry standards.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?