How do you deal with data quality and reliability issues when collecting and analyzing social media metrics?
Social media metrics are essential for measuring the performance and impact of your social media marketing efforts. However, collecting and analyzing them can pose some challenges, especially when it comes to data quality and reliability. How can you ensure that the data you use to inform your decisions and strategies is accurate, consistent, and relevant? Here are some tips to help you deal with data quality and reliability issues when collecting and analyzing social media metrics.
Before you start collecting and analyzing data, you need to have a clear idea of what you want to achieve and how you will measure it. Define your social media goals and align them with your business objectives. Then, choose the metrics that best reflect your progress and success. Avoid vanity metrics that only show superficial numbers, such as likes and followers, and focus on meaningful metrics that show engagement, conversion, retention, and loyalty. For example, you can track metrics such as reach, impressions, click-through rate, bounce rate, time on site, leads, sales, referrals, and retention rate.
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Followers and follower growth rate can be helpful metrics for goals related to brand presence and awareness. Mentions are also a strong indicator for tracking brand awareness. When it comes to building brand authority and thought leadership, I look at social media metrics that show whether posts are resonating with my audience. For example: - Amplification rate - Engagement rate - Video views - Clicks
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Aligning with the business goals, campaign objectives and the audience you are presenting to is key. Many may not like to present the so-called vanity metrics, but if those are the metrics that the key decision-makers are interested in, they have a place in the report. As long as these metrics are supported and contextualised in light of the engagement metrics, they can tell a meaningful story.
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I'd recommend these 10 steps: -Define clear objectives to align data collection with goals. -Choose reliable data sources with established methods and reputation. -Validate sources through thorough research and due diligence. -Scrutinize data collection methods for adherence to best practices. -Implement data cleansing to remove duplicates, spam, and irrelevant content. -Establish data validation protocols to identify and address inconsistencies. -Monitor data integrity throughout collection and analysis. -Use statistical analysis to identify outliers and detect trends. -Combine quantitative and qualitative analysis for context and validation. -Transparently document limitations and assumptions for accurate interpretation.
Once you have defined your goals and metrics, you need to decide where and how you will collect the data. There are many sources and tools available for collecting and analyzing social media data, such as native platforms, third-party tools, web analytics, surveys, and customer feedback. However, not all of them are reliable, accurate, or compatible. You need to evaluate the quality and credibility of your data sources and tools, and check for any biases, errors, or inconsistencies. You also need to ensure that your data sources and tools are integrated and synchronized, so that you can access and compare data from different channels and platforms.
After you have collected the data, you need to clean and validate it before you analyze it. Cleaning your data means removing any irrelevant, duplicate, incomplete, or inaccurate data that might skew your results or cause confusion. Validating your data means checking that it meets the standards and criteria that you have set for your goals and metrics. For example, you can use filters, segments, and categories to organize and refine your data, and use quality assurance methods, such as cross-checking, testing, and auditing, to verify and correct your data.
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Maggie Langley 🟪
CEO OfficeHounds Social Media Agency helping businesses get found online since 2009
One of the biggest challenges for marketers is dealing with click fraud and engagement from fake or irrelevant profiles when buying social ads. You have to keep an eye on your important conversion metrics and the quality of those conversions. You may count a conversion as an email subscriber, but if your ad gave you a bunch of subscribers that never buy, you have a problem. Followers and engagement stats are also impacted. For example, if the only comments you get on an Instagram post were sent by an automation triggered because of a hashtag, these comments skew your engagement results and can give a false impression of success. Cleaning the data would be laborious, so you can explain what you think the data means in the report.
Once you have cleaned and validated your data, you can proceed to analyze and interpret it. Analyzing your data means applying statistical and analytical methods, such as descriptive, inferential, predictive, and prescriptive analysis, to identify patterns, trends, correlations, and causations in your data. Interpreting your data means extracting insights, conclusions, and recommendations from your analysis, and relating them to your goals and metrics. For example, you can use dashboards, reports, visualizations, and storytelling techniques to communicate and illustrate your findings, and use benchmarks, comparisons, and evaluations to measure and improve your performance.
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An underrated statistic: lowest-performing posts. Don't just note wins, note losses. It can be much easier to find patterns of lowest performing posts. Oftentimes, you'll be able to predict what performed badly (maybe because you've been telling leadership this all along). Top performing topics tend to be obvious. Everyone knows what the wins will be. However, if you know what NOT to do, you can start pruning your strategy with overall engagement rate increases. That's significant algorithmically.
Finally, you need to monitor and update your data regularly, as social media is a dynamic and evolving environment. Monitoring your data means tracking and measuring your data over time, and detecting any changes, anomalies, or opportunities in your data. Updating your data means adjusting and refining your data collection and analysis methods, tools, sources, goals, and metrics, according to the feedback and results that you get from your monitoring. For example, you can use alerts, notifications, and feedback loops to stay on top of your data, and use experiments, tests, and optimizations to enhance your data quality and reliability.
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In my experience building my own data and database is the most reliable and significant information I look at. After one year even the data I collected is 10-20% inaccurate. The world and technology is changing so fast assimilating and dissecting what is important is ever more reason to hire a professional to help.(Hiring the right professional is a job in itself) Sifting out the most important data and responding swiftly is paramount because the next thing is just around the corner.
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