5 Ways To Use Twitter Data To Determine A Hit (Or Miss)

Let’s face it, even with all of the gnashing of teeth regarding changing viewing habits and outdated methodologies, TV ratings still provide the benchmark for measuring a success or failure in terms of viewership and demographics, both of which drive the almighty advertising dollar. 

But while both the legacy and simplicity of the ratings system will likely ensure its place moving forward, the public social web provides new and compelling ways to determine an audience’s reaction to your content, characters, and competition… all in real time.

Twitter data is probably the best place to start when it comes to an analysis of public reaction to your programming. To understand the big insight that can be gleaned from Twitter, one must first understand the big data behind it.  Every day, approximately 400 millions tweets are posted to Twitter, with more daily users than last year’s Super Bowl had viewers. That’s every single day. In essence, Twitter has become an always-on focus group for every media organization, and new Social Analytics tools have made it easy to mine public conversation for actionable insights into your current and future investments.

Here are five ways you can use Twitter analytics to complement the data you’re already receiving:

1. Dive deeper into mentions.

You’re likely doing some form of this now. On the surface, mentions simply tell you whether or not the audience is talking about your show. A deeper analysis would give insights into volume, pace, and trends of that conversation. Are people simply talking about your show, or is the conversation dominated by a specific character, scene, or plot twist? This level of analysis could influence any number of items including casting, character relationships, and future writing efforts.

2. Understand sentiment.

An analysis of Twitter data could also enable you to understand the audience’s overall attitude towards the program. Are they happy with the addition of a new character or upset about the departure of an existing one? Have there been any significant events that have tainted an actor’s reputation… and are they now a liability to the show or movie? How does sentiment compare to last week’s, last month’s, or last year’s?  Present and past sentiment data can be a great predictor or an audience’s future reaction. 

3. Do a competitive analysis.

Using Twitter data, you can overlay competitive information to better understand how your lead actor or actress is comparing to another in their time slot. You can also analyze how the audience is reacting to different cast members within the same show. This is a deeper level of analysis than simple viewership, and much more easily actionable.

4. Identify other key terms, themes, or topics around your content.

Deeper analysis gives you the story behind the data. Let’s say your lead character has had a spike in mentions and sentiment; are there any themes, trends, or hashtags associated with the increase? This analysis could provide insights into everything from product placement to story development. 

5. Look at geographic patterns.

Finally, there’s a huge opportunity to analyze Twitter data for insights into geographic patterns. Where is the conversation happening? Is there a specific city, state, or region that is responding more or less favorably? This information could help with everything from promotional ideas to syndication opportunities.

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