How do you combine digital metrics with other data?

1 Why combine digital metrics with other data?

Digital metrics — such as page views, bounce rate, conversions, and social shares — can provide you with valuable insights into how your content is reaching and engaging your audience. However, they can also be misleading, incomplete, or irrelevant if you don't consider the context and purpose of your content.

For example, a high bounce rate might indicate that your content is not relevant or engaging enough, or it might mean that your content is so informative and satisfying that users don't need to explore further. Similarly, a low conversion rate might reflect a poor content quality or design, or it might result from external factors, such as competition, seasonality, or user preferences. Therefore, it's important to combine your digital metrics with other data sources that can help you understand the why behind the what of your content performance.

2 How to collect other data sources

Collecting other data sources that can complement your digital metrics is essential. Qualitative feedback such as user surveys, interviews, reviews, comments, ratings, and testimonials can provide insight into the opinions, emotions, needs, and pain points of your audience. Additionally, tracking user behavior like actions, interactions, journeys, and patterns can help you understand how your audience uses your content and what motivates them.

Market trends such as industry data, competitor analysis, customer segmentation, and keyword research can reveal opportunities, threats, and gaps in your market. Aligning your content with business goals like vision, mission, values, objectives, and strategies is paramount. Tools like SurveyMonkey, Google Analytics or SEMrush can be used to collect data sources, while platforms like UserTesting or SimilarWeb can be used to analyze them.

3 How to combine digital metrics with other data sources

Once you have collected your digital metrics and other data sources, combine them in a way that can help you answer your key questions and make informed decisions about your content. To do this, there are various methods and frameworks that you can use. Popular and effective ones include data visualization, data storytelling, and data analysis.

Data visualization involves creating charts, graphs, tables, dashboards, and reports to display and compare data clearly. Tools like Google Data Studio, Tableau, or Power BI can help with this. Data storytelling involves crafting narratives, stories, and case studies to communicate and explain data in a compelling way. Platforms like Medium, LinkedIn, or SlideShare can be used to publish these stories.

Data analysis involves applying statistical, mathematical, or logical techniques to interpret and evaluate data systematically. Excel, R, or Python are tools that can be used for this purpose. Additionally, platforms like Coursera, Udemy, or DataCamp can be used to learn and improve data analysis skills.

4 What are the benefits of combining digital metrics with other data sources?

Gaining a deeper and broader understanding of your audience, their behavior, needs, preferences, and feedback is one of the main benefits of combining digital metrics with other data sources. You can also identify the strengths and weaknesses of your content, its quality, relevance, design, and impact. Additionally, combining digital metrics can help you discover the opportunities and threats in your market, niche, competitors, and keywords. It can assist in aligning your content with your business goals and strategies. Ultimately, this can help you improve your content performance, optimize your content strategy, and increase your content ROI.

5 What are the challenges of combining digital metrics with other data sources?

Combining digital metrics with other data sources can also pose some challenges for your digital publishing content and business. Data overload, inconsistency, or incompleteness can make data collection, combination, and interpretation difficult and time-consuming. Additionally, data bias, errors, or misinterpretation can affect data quality, validity, and reliability. Fragmentation or isolation of data can prevent integration, collaboration, and sharing. Data privacy, security, or ethics can raise legal, ethical, and moral issues and risks.

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?