How do you deal with data quality and reliability issues when collecting and analyzing social media metrics?

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

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.

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.

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.

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.

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.