Snapchat Video Metrics Analysis

Introduction

When assessing data within the social media landscape, it is extremely important to understand the nuances of the platform you are dealing with. Snapchat, in particular, is extremely nuanced in the sense that typical metrics one would assume to see within the social media space (i.e clicks, click-through-rate (CTR) , reach, impressions per session (IPS), etc.) are either not readily present, or are represented by other categorizations/combination of different metrics. These differences are partly due to the natural changes in data collection one may find from platform to platform, but more specifically, are due to the static nature in which videos are viewed, distributed, and maintained across the platform.

For the purpose of this project, the context being assumed is Snapchat as a novel revenue stream and platform for a content creator/distributor, where further insight is required to understand how the metrics available through the platform are related to viewership (in this case, the KPI "unique viewers"). Furthermore, this question becomes increasingly more interesting when considering whether any of these metric relationships differ in strength or nature when isolating for the overly successful episodes (i.e "banger" episodes).

Purpose

To evaluate Snapchat Video performance data to uncover insights regarding:

• Metric correlations to viewership across different dimensions (timeframes, individual snapchat channels, "banger" vs. regular episodes)
  • How do the following KPIs relate to unique viewership?
    • If there is significant relation between variables, what is the shape of this relationship? (i.e linear, polynomial, non-linear, etc.)
    • Does the nature of these relationships change over time? (i.e strength of correlation, direction or shape of relationship)
• Uniqueness of "banger" episodes
  • Are there changes in these metric relationships when filtering for outlier (banger) episodes?

Approach

Data

• Real-world dataset, previously used in a professional setting and altered to appropriately translate to github.
• 4 data samples are used and filtered based on the following:
  • 2021-2022 Data (see data/yearly_updated.csv): inaugural episode-level data from Sept 2021-Mar 2022
  • 2022 Data (see data/2022_updated.csv: Inaugural episode-level data from Jan 2022-Mar 2022
  • Bangers 21-22 (see data/in_bangers.csv: data pertaining to episodes individually designated as "bangers" based on pre-determined critera of stakeholders
  • Bangers 2022 (see data/bangers_2022.csv: "" filter from Jan 2022-March2022

Glossary

• Channel
  • Referring to a specific Snapchat channel where episodes are released according to the Channel's aim/purpose
• Episode/Story
  • A single video (story) posted on a Snapchat channel that is divided into individual "snaps"
• Snap
  • A short video clip which makes up a piece of an entire video episode/story
• Engagement metrics
  • Referring to the subscribers added, screenshots, and shares metrics
  • Each metric represents a number attributed to a specific episode at a specific time (for example: number of shares on an episode)
• Viewership
  • Here, relating specifically to the metric "Unique Viewers"
  • Unique viewers = the number of unique snapchat users who interacted with a given episode or story
• Unique Completers
  • Number of unique Snapchat users who meet a certain retention threshold on a given episode or story.
• Unique Topsnap Views
  • Number of unique Snapchat users who interact with/view a given snap within an episode or story.

Correlation Analysis

(See Correlation_analysis folder for more)

• Pearson/Spearman correlation values are computed and compared across different time frames/data samples to assess metric to viewership relations.

Regression Analysis

(See Regression_analysis folder for more)

• A collection of different linear & nonlinear regression models are fit to the data to determine the shape and predictability of metric to viewership relationships.
• Only metrics with strong correlations to viewership are selected for regression (i.e correlation coefficient > 0.70).

Results

Correlations

(See Correlation_analysis folder)

• Metrics with strongest correlations to viewership:
  • Unique Topsnap Views and Unique Completers (0.93-0.98)
  • Subscribers added (0.89-0.92)
    • Strongest correlated metric to viewership out of engagement metrics (i.e subcribers, screenshots and shares)
    • Most consistently correlated metric to viewership out of the engagement metrics.
• Shares
  • Only strongly correlated with viewership when assessing episodes overall, and not for banger episodes
• Screenshots
  • Only strongly correlation with viewership when isolating for individual channels

Relationships

(See Regression_analysis folder)

• Unique Topsnap Views & Unique Completers
  • Very close linear relationship to viewership (R2 = ~0.90)
  • Potentially perceived as an obvious finding as both metrics are by-nature viewership metrics.
• Subcribers added
  • Moderate linear relationship with viewership (R2 = ~0.80) - best modelled by a RLM to account for outliers.
• Shares
  • Asymptotic relationship with viewership, best modelled by the Michaleis-Menten equation or GAM (0.78-0.79 correlation predicted y to actual y)
    • This relationship is only valid when assessing overall data, and not banger episodes specifically
  • Indicates a threshold value where the increase of shares becomes less relevant to the increase of unique viewers
    • i.e as the number of shares increase, the rate of UV increase slows.
  • Outlier influence
    • Asymptotic relationship & Michaelis-Menten model is still valid with removal of the most extreme outlier.
      • (See Outlier influence section in Regression_analysis folder)
• Screenshots
  • Linear relationship with viewership when assessing individual channel data
    • Strong linear correlations in all individual channels except for 1 (channel_2d)
  • The strength and rate of this relationship is unique to each channel, indicating a potential profile pertaining to each channel where screenshots interact with viewership at different rates.

Bangers

• Metrics most strongly correlated with viewership become even more strongly correlated when isolating for banger episodes
  • (See table under All Datasets section in Correlation_analysis)
• No significant correlation with Shares
• Strong correlations with screenshots only exist when data is filtered by individual snapchat channel.
  • (See table under Data by Individual Snapchat Channel section in Correlation_analysis)
• Although the strength of correlations/relationships change when analyzing across different datasets, the shape of these relationships remain constant
  • i.e shares to viewership is always a nonlinear (asymptotic) relationship when looking at all-channel data across different time-frames or banger/overall data, regardless of the correlation strength or performance of the regression model.

Summary & Insights

• Only metrics that are consistently strongly correlated with viewership are:
  • Unique topsnap views
  • Unique completers
  • Subscribers added
• All strongly correlated metrics have a positive linear relationship with viewership with the exception of shares
  • Shares is the only metric with a nonlinear (asymptotic) relationship with viewership
• Correlation strength and the slope of the linear relationship between screenshots and viewership are channel-dependent
• The shape of metric to viewership relationships remain consistent across timeframes and datasets, regardless of correlation strength or regression performance.

Future Analyses

The nonlinear relationship with shares and viewership is only statistically satisifed when applied to overall data (not to bangers) - we see this relationship happen specifically when performing regression on these smaller datasets (for obvious reasons). One may be able to find more adequate models with use of a larger dataset. Thus, future inferences will look to perform the same analyses at a future date when more data is available.