Unmasking the Secrets Behind Explosive Online Growth with Social Analysis

Executive Summary

- Catalyze Research was tasked with analyzing Aetheria’s social media metrics to improve engagement, reach, and number of followers.

- Twitter's API was used to collect real-time data, which was analyzed through lemmatization and regression models to identify effective strategies.

- The analysis of diverse tweet data uncovered patterns in engagement, reach, and follower growth influenced by factors like posting time, day of the week, and tweet elements, showcasing effective strategies for maximizing social media impact.

Introduction

Tracking online growth without social media metrics can be incredibly difficult. Social media metrics provide valuable data on how users are interacting with a certain cryptocurrency project, how many people are exposed to the project, and what types of content are driving engagement.Without this data, it can be challenging to determine the success of social media marketing efforts, which can make it difficult for a project to adjust its strategy or allocate resources effectively. Social media metrics can also help identify areas of opportunity and highlight potential issues or challenges that need to be addressed.

Leveraging its expertise in social analysis, Catalyze Research was tasked with tracking the online growth of Aetheria’s presence in a quantitative manner. In doing so, the two most important metrics that required our attention were engagement and reach.

We take our clients' confidentiality in utmost reverence. While we have changed their names, the results presented herein remain undeniably authentic

Engagement refers to the level of interaction that user shave with Aetheria’s online content. This includes likes, comments, shares, clicks, and other actions taken by the user. Engagement is crucial because it shows how users are interacting with the Aetheria brand, and it can help the Aetheria team understand what types of content resonate with their target audience.

Reach, on the other hand, refers to the number of unique users who actually see Aetheria’s online content. This is important because it determines how many potential customers are being exposed to Aetheria. Reach is determined by both impressions and interactions, and it can be affected by the level of engagement Aetheria’s content is receiving.

Engagement and reach are important metrics because they directly impact the success of Aetheria’s social media marketing efforts. Platforms like Twitter prioritize posts with high engagement, meaning that engaging content is more likely to be seen by Aetheria’s target audience.

By analyzing social media metrics such as reach, engagement, and changes in follower counts through the framework that Catalyze Research developed, we have provided Aetheria with data-driven insight and solutions that would help them optimize the operations of their social media account.

Problem Statement: How can Aetheria collect, analyze, and leverage social data for profound content insight to enhance its engagement and amplify its influence?

While social analysis may appear feasible on the surface, there are many practical limitations that hinder Aetheria’s discovery of potential strategies it could employ. One such limitation is the quality of the data that these tools generate. While they can provide a large quantity of data, searching through it can be time-consuming and inefficient. This is because the data retrieved from these sites is generated by a select group of the population, which may not represent the whole population of people consuming the products or services.

Another limitation is that data for social analysis doesn't always represent what a true user thinks and believes. It shows what people are publicly projecting about themselves, which is often very different from what they actively believe. Additionally, social analytics tools cannot identify trolls from genuine users, which can lead to misleading insights.

Lastly, social platforms are also noisy places, with a large quantity of data that may not be relevant to our set objective. As a result, it can be challenging to find the exact truth needed to derive value from the datasets. Social analytics tools do not allow users to set specific goals when retrieving data, making it difficult to tailor searches to specific areas of interest.

Objective: Establish a comprehensive approach for collecting and analyzing social data to enhance reach and engagement

To collect and process data relevant to the objective of this case, we utilized Twitter's API, a powerful tool that allowed us to gather real-time data from the platform. Once we obtained the data, we proceeded to transform the tweet data into a string format, which enabled us to easily extract useful information such as links and tags used in the tweet. To ensure that the data was as clean and concise as possible, we removed all special characters and emojis, as well as any expressions of date and time.

Next, we employed lemmatization, a technique that simplified the data by grouping together words with similar meanings. By doing so, we were able to produce a final dataset that was optimized for analysis.

To make sense of the data we had collected, we generated statistical values that were plotted as violin plots, allowing us to visualize the distribution of engagement levels according to the days and times when tweets were uploaded. Using a basic regression model, we analyzed the difference in engagement based on whether links or tags were used, as well as the impact of specific word choices and the type of tweet.

Additionally, we tracked changes in the number of followers by analyzing the types of tweets shared. To ensure the accuracy of our analysis, we excluded tweets that were posted less than an hour before the next tweet, as this could lead to skewed results.

Overall, through careful data collection, processing, and analysis, we were able to derive valuable insights that shed light on the most effective ways to engage with our target audience on Twitter.

Impact

The analysis of the collected data unveiled diverse outcomes in terms of engagement, reach, and follower growth for various tweets. Our findings revealed patterns in tweet performance based on factors such as posting time, day of the week, and involvement of tweet elements such as links,tags, and key word choices.

In terms of engagement, tweets exhibited a range of scores, with certain tweets outperforming others. Notably, factors such as posting time and specific days of the week appeared to influence the level of engagement received. Additionally, the presence of links and tags in tweets showed a positive correlation with increased engagement, while the choice of specific words had varying effects on average engagement scores. Moreover, the type of tweet, such as events, partnerships, or call-to-actions, had an impact on the level of engagement generated.

When it came to reach, tweets demonstrated varying degrees of reach, with different posting times and days of the week affecting the overall reach achieved. The inclusion of links and tags in tweets positively influenced the reach, while word choices within the tweets played a role in expanding or limiting the reach. Moreover, the type of tweet had varying effects on the reach attained.

Regarding follower growth, certain posting times and specific days of the week were associated with higher average growth in followers. The presence of links and tags in tweets appeared to negatively impact follower growth, as did the choice of specific words within the tweet content. Additionally, tweet types, such as performance reports and partnerships, demonstrated differential effects on follower growth.

Overall, the analysis revealed insights into effective strategies for enhancing engagement, expanding reach, and fostering follower growth. These findings provide valuable guidance for optimizing social media performance and maximizing impact.

In summary, through the framework created as a combination of social analysis and statistical/regression models, Catalyze Research concluded that engagement and reach are typically proportionate to one another, but they are inversely proportional to follower growth. For instance,using links can result in higher engagement and reach, but can lead to lower follower growth. Nevertheless, there were instances when this trend is broken,with tweets posted on Saturdays having tendencies of increasing engagement,reach, and follower growth simultaneously.