Sample Marketing Essays on Facebook Matrix

Facebook Matrix

  1. Identify three main proxies in the data that you would like to use to measure user engagement? (5 points)

The three main proxies that would be vital for measurement of user engagement from the data provided include: reach, comment and share

Explain why do you think so? (5 points).

The above mentioned data is appropriate because it helps us to measure the appeal and quality of the content posted on Facebook.

What are some other proxies (name at least two) for user engagement that are currently unavailable in the data? (5 points)

Some other proxies that could be added to the metrics include: negative feedback and “People Talking About This.”

  1. Identify elements of a status update via linear regressions that seems to boost user engagement. Based on your answer in Q1, please present the snapshot of regression results and explain how you use these results to reach your conclusion. (hint: for simplicity, no need to log-transform on variables) (20 points)

In this case, the user engagement will be the dependent variable while the elements of a status update will be the independent variable. The dependent variable will be the values in the Input-Y Range while the independent variables will be values for Input-X Range for the linear regression analysis.

Linear regression for photo versus reach;

Linear regression for photo versus comment;

 

 

Linear regression for photo versus share;

 

 

Linear regression for video versus reach;

 

 

Linear regression for video versus comment;

 

 

Linear regression for video versus share;

 

 

Linear regression for link versus reach;

 

Linear regression for link versus comment;

 

 

Linear regression for link versus share;

 

 

 

For the above figures, the Multiple R which is a correlation coefficient and significance F are essential predicting tools. Multiple R should be between -1 and 1, with 1 being a strong positive relationship and -1 being a stronger negative relation. On the other hand, significance F should be below 0.05.

Hence, videos and photos are the best for improving user engagement because links have significance F that is very high than desirable. Also the Multiple R values for links are too low.

  1. Does the inclusion of video increase or decrease user engagement? (5points)

To determine whether there will be a decrease or increase in user engagement, the Square R is used. There is a positive change in Square R which implies that inclusion of videos will increase user engagement.

  1. Do special offers & contents increase or decrease user engagement? (5 points)

The regression data for special offers and content increases or decrease in relation to user engagement is illustrated below: Action and product element are considered;

 

Multiple R is too high which implies that there will be an increase in user engagement

  1. Is a paid post more or less effective than an organic post in increasing user engagement? (5 points)

Paid post versus user engagement;

This figure shows regression for a paid post; the multiple R value is comparable higher than that for organic posts. Thus, paid posts increase user engagement.

  1. Does timing (i.e., day of week, month) of the status update matter for user engagement? Please use regression results to justify your answers. (10 points)

We shall use an element such as comments to see how it varies with time.

Post-week versus comment;

 

 

Post-hour versus comment;

 

The above data indicates that timing matters a lot, for instance, there is a significance difference in the values of Multiple R in both of the above figures.

  1. The data does not indicate explicitly which variables incorporate calls-to-action. That said, which variables could you use to crudely proxy for calls-to-action? Please explain. (10 points)

Since the data presented does not explicitly incorporate calls-to-action, we would use click-rate and submission rate as a reliable means for calls-to action. Click rate would normally show the percentage of people who viewed the post and then clicked on it, this is different from those who just clicked but did not view. On the other hand, submission rate would show people who clicked on a post and then submitted a form. The metrics provide clear information that directs a call-to-action.

  1. What are your recommendations for future social media posting on Facebook? Please explain as specifically as possible how you come up with these recommendations? (10 points).

From the findings in this work, our recommendation for future social media posting include:

  • Identify key success metrics; all the possible metrics should be analyzed to determine the most effective elements to include in media posting; this enables accurate postings which can reach the targeted people.
  • All metrics should involve a call-in-action. Call-in-action will enable appropriate actions for a specific metric.
  1. Do you believe that your recommendations for Sephora’ Facebook postings also apply for its postings on Instag ram? Please explain. (10 points)

Facebook and Instagram are similar in different aspects such as the profile, however, an example of a difference include the number of users for the social sites. Facebook has more users than Instagram. As such it is possible to analyze key success matrix in Facebook as well as Instagram. Further, a call-in-action for specific metrics can be implemented in Instagram.

  1. Please list at least three potential caveats or concerns that might contaminate the effectiveness of your recommendations. How would you investigate and address these concerns in the next step? (10 points)

One of the concerns that might contaminate the effectiveness of our recommendation is that while measuring key success metrics is possible, setting of unclear goals can give contradictory information. To investigate the impact of unclear goals, specific goals can be set while the output for a particular variable is monitored. The challenge would be addressed by evaluating inconsistencies in the variable that is monitored. On the other hand, too many objectives may not clearly show the trend a specific proxy, this can be investigated observing trends for numerous objectives versus simple and few objectives; this can be addressed by compressing complex objectives into simple and clear objectives. Lastly, call-in-action tends to be more of experimental and may not give a true and consistent trend because of the external factors. Call-in-action can be investigated by monitoring the outcomes of various proxies with time. To address this concern, call-in-action can be utilized for elements that show a consistent trend.