A glossary of definitions

1. Sentiment Analysis

How do people feel about your brand? Happy? Sad? Ambivalent? Sentiment Analysis identifies how people perceive a brand. How do we do it? We use Natural Language Processing on Facebook Brand Posts, Fan Posts and Twitter mentions and find indicators that will tell users with a certain degree of accuracy if their brands evoke a Positive, Negative or Neutral sentiment. We’ll not only tell you the overall sentiment shown towards your brand but also break down the sentiment on a per post level, allowing for further customization. Also, on your individual Posts, it’s possible to change the sentiment (in case you think the processor didn’t classify it right, owing to slang, lingo etc).

2. Share of Voice

Share of Voice gives you a definitive look into how much your brand dominates on a social network as you go head to head with our competitors. We display your Share of Voice in the form of a pie chart; users can also type in a keyword specific to their industry (for example, retailers might want to type in ‘summer sale’) to calculate how much the term is used by competitors.

3. Average Reply Time - Twitter

Does your brand hit reply at lightning speed or does it take its time? A brand’s Average Reply Time is the average time it takes a brand to respond to a Tweet from a customer that mentions the brand’s Twitter handle. However, note that the responses are calculated only for original Tweets directed to a brand; this does not include Retweets.

4. Average Response Time - Facebook

A brand’s Average Response Time is the average time it takes a brand to respond to a comment on a Brand Facebook Post or User Post on the brand’s Facebook Page. However, note that these responses are calculated only for Posts on the brand Page. Fun fact: If you do a stellar job, they’ll even stamp your Page with a ‘Very Responsive’ badge.

5. Groups
We know benchmarking with numerous brands all together can be painful, so we decided to make things easier by sorting them into Groups! Your brand can customize these groups, putting them into categories labeled ‘Competitors’ or ‘Group 2’ or ‘Group 3.’ This way the user can add brands to particular Groups based on specific aspects they’d like to benchmark on. For example, a retail chain can benchmark with direct competitors in the Competitors Group, with other branches belonging to the same chain in Group 2 and with luxury brands they’re hoping to emulate Group 3.

6. Campaigns

What do brands and politicians have in common? They campaign...a lot. A brand is said to have started a ‘Campaign’ when it posts content about the same theme or using the same hashtag for two or more Posts or Tweets. We give the user information on Campaign Publishing Volume and Campaign Engagement.

7. Facebook Insights

We’ll give you insights on your Facebook Insights Page like

Reach: How many people have your Brand Page reached? Page Reach denotes the number of people your Page has been served to - this includes fans and people who have seen Promoted Posts. We further break down Page Reach by Gender, Age, and Country.

Impressions: We’re counting the number of (good) impressions your Page made! Page Impressions denotes the number of times your Page has been displayed - this includes followers and people who have seen Promoted Posts. We further break down Page Reach by Gender, Age, City, and Country.

8. Proactive Tweets

Proactive Tweets refer to original Tweets authored by a brand; therefore it does not include Replies to other Tweets and Retweets.

9. Clicks

Curious if your Campaign was a hit? Just figure out the number of Clicks, which is literally the number of clicks on a specific, actionable bit.ly link in a certain piece of content. Getting these numbers could help brands measure the success or effectiveness of a campaign or direct purchase through to a website.

10. Moving Averages in Twitter Engagement

Moving Averages in Twitter Engagement plots a brand’s performance over 7 days and 30 time periods. This helps smoothen out those slight fluctuations in data and give you a birds-eye view of changing trends; of course, huge hits and misses will show up on this chart.

11. Unmetric Score

The Unmetric Score has two components - the Audience Strength and Engagement. The Audience strength represents the size (share of market, presence) of the brand within the social network, and Engagement represents the brand’s engagement with customers (interaction, velocity, virality). These components are built from a number of underlying metrics including (but not limited to) Total Fans, People Talking About, Engagement of the brand, Sentiment of posts, Number of Admin Posts, Fan Growth rate for Facebook. These metrics are run through a number of statistical filters (normalizing across the sector, normalizing variances between metrics, outlier filtering, weighted averaging etc) to produce a single benchmarkable score.The number is normalized to give brands a score between 0 and 100. Hypothetically, the best performing brand within the sector is assigned a score of 100. All other brands within the sector are scored relative to this. The scores are unique to each sector and cannot be compared. A score of 80 in the Aviation industry is different from a score of 80 in the Banking industry.

12. Reach and Impressions

Reach: This refers to the number of unique people who viewed a piece of social media content.Impressions: This refers to the number of times a piece of social media content is viewed. If the same person has viewed a piece two times, the impressions will be counted as 2. These metrics are important for benchmarking as they indicate how many eyeballs various social media campaigns are grabbing. Impressions are always equal or likely higher than reach. Building an algorithm to estimate reach and impressions is a challenge because of the limited availability of markers that impact reach/impressions. Existing solutions simply measure this based solely on the absolute number of audience interactions a post receives and the number of followers for the brand page. However, this simplistic and grossly exaggerated method doesn’t account for differences based on audience size, sector or industry and type of post. Our approach addresses these issues and captures complex patterns in audience interactions and provides the most accurate estimate of reach and impressions.

13. Promoted Post Detection

A Promoted Post is a post with some social spend behind it. Today, most brands allocate a budget for social spend. And we’d all be kidding ourselves if we didn’t give some of our Brand’s Posts a boost every now and then. But like most marketers, we’re concerned with benchmarking. So how are we supposed to figure out if our competitor’s campaign is performing well because they've made mind-blowingly good content that’s gone viral or if there’s some green enhancing their performance?We analyzed thousands of Posts from multiple industries and Brand Pages of sizes ranging from thousands to millions of Fans. With exploratory research, we identified patterns in Likes, Comments, Shares and Fan Count over time that differentiate Promoted Posts from Organic posts. We look for these same patterns in velocity of engagement to predict whether a Post is promoted or not. With a robust sample dataset of Facebook Posts, our proprietary machine learning algorithm has an overall prediction accuracy of a whopping 95%.

14. PTAT (People Talking About This)
PTAT is the total number of unique people who have interacted/engaged with a page over the past 7-day period. The interactions/engagement could be in the form of Page Likes, Comments, Shares, responses to events created by the page, mentions of the page, tagging the page in a photo, etc.

​Note: PTAT has been deprecated by Facebook in 2014. But we still do get this data via the API and that's the reason we are still making it available as a metric within the Unmetric platform.
​We won’t be carrying this over to the new Benchmark product within Falcon.

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