Social Media Analytics: Twitter: Quantitative & Qualitative Metrics
Twitter is amongst new media channels that are challenging how we communicate, with whom we communicate and perhaps most fundamentally how we (Marketers) influence people.
Sadly execution and analysis of these new social media channels has been hobbled by old world thinking. When it comes to marketing because of the old world thinking from the worlds of sTelevision and Magazines, and when it comes to measurement because of the world of traditional web analytics.
These new channels, Twitter and Facebook and YouTube and Tumblr and, yes, even blogs, are very distinct customer / participant experiences. Stale marketing or measurement thinking applied to them results in terribly sub optimal results for all involved.
So in this post my hope is to share with you what is unique about measuring one such channel, Twitter. The blog post is also sprinkled with my own words of folksy wisdom as to how you should use the channel for maximum impact.
My new book Web Analytics 2.0 covers social media measurement, but I am going to cover something very different in this post.
First: An Ode to New Thinking:
One common thing between the all tools in this post is that they were built by "outsiders".
One of the things I love and adore about Twitter (besides all that connection and conversation) is how its open API has lit a fierce fire of innovation when it comes to analytics. Anyone and their brother and ma-in-law can develop a tool, and they have! Much to the benefit of the rest of us.
Perhaps the most beneficial thing to me is how much out of the box innovation this has brought.
For example just look at traditional web analytics tools, there is absolutely no fresh thinking when it comes to Social Media Measurement. Their constant focus is on "let's figure out how to collect and report ever more data and not bother with a truly immersive understanding of these channels and what makes them unique". That mental model is, sadly, extremely clear in the metrics and analysis they provide with "twitter integrations".
While there is some stale thinking in the new twitter tools, most of them have a lot of fresh thinking from people untainted by Omniture or CoreMetrics or WebTrends or, ok ok ok, Google Analytics.
I consider this massive proliferation of new thinking to be a gift from God.
To all of you developers who are toiling out there, you have my love and gratitude.
In this post four twitter analysis tools that while not yet fully developed show sweet signs of:
1. Truly understanding the medium and uniqueness of the channel and
2. Are not just reporting "hits", rather coming up with clever metrics.
Quantitative Metrics / Analyses.
Most twitter analytics tools just do data puking. They find numbers that can be computed and then proceed to puke at you as many as they can find, with wonton disregard of value being provided or outcomes being measured.
Here is one of the mild ones:
You must pause and think: So what is this saying? What action can I take?
Always, always, always ask that question when faced with tools that simply puke data out at you (twitter or Google Analytics or whatever).
But as I mentioned at the start of the post one of the beauty of twitter's open API is that there are a few pockets of truly innovative thinking.
Here are some that I humbly believe look promising. . . .
Klout. Twitter Analytics.
Klout is a wonderful little tool that measures Klout Score, a proxy for "influence":
It is easy to understand the market demand to boil things down to one number, but this is perhaps the least useful thing in Klout.
While on the surface they might seem useful, I am always suspicious of compound metrics. They can be subjective, inapplicable to many and efficiently hide the insights you need to understand what actions to take. [See more here for Compound Metrics: Four Not Useful KPI Measurement Techniques]
Mercifully there is so much more to Klout than that.
Klout measures a bunch of lovely metrics, specifically applicable to Twitter, that are grouped into four buckets: Reach, Demand, Engagement (!!) :), Velocity.
There are two lovely things about these computations.
1. Joe and team have wonderfully avoided the temptation make these compound metrics (as in Reach = Followers / Total Retweets * Friends + Pixie Dust). The factors used are laid out as individual metrics making it easy for you understand the data and pick metrics that work for you.
2. (My favorite) The metric definitions are not "crap". This seems like such a low bar to meet, sadly far too often metrics out there (not just for twitter) are just plain shoddy.
For example here are some clean definitions from Klout:
# Engagement
* How diverse is the group that @ messages you?
* Are you broadcasting or participating in conversation?
# Velocity
* How likely are you to be retweeted?
* Do a lot of people retweet you or is it always the same few followers?
# Reach
* Are your tweets interesting and informative enough to build an audience?
* How far has your content been spread across Twitter?
* Are people adding you to lists and are those lists being followed?
When I use Klout I simply pick the metrics that are most important to my own twitter strategy.
I would suggest that this is very very very very important, pick what is right for you rather then following a lemmings like strategy of "I am going to use metrics Y & Z that someone recommends".
Here's an example: I don't care about Follower/Follow Ratio. I think it is disingenuous to follow everyone who follows you just for appearances sake when you have no intention of reading what they all say. Why be fake?
As you might have read in the new book I like "Message Amplification" in Social Media, and hence I do care a lot about Total Retweets.
[Sidebar: my favorite twitter metric is: # Of Retweets Per Thousand Followers, it's a measure of efficiency and value provided and people voting with their clicks, all rolled into one!]
I care a lot about Follower Retweet % ("Do a lot of people retweet you or is it always the same few followers?") because I want to appeal to more people than my mom, dad, and best friend!
One of the biggest mistake companies and brands make about Twitter is that they think it is one more "shout channel" like TV and Radio and Magazine ads or Press Releases. Twitter is not that. Twitter is a "conversation channel", a place where you can find the audience relevant to you (and your company and products and services and jihad) and engage in a conversation with them. It is not pitching, it is enriching the value of the ecosystem by participating.
Hence I like the metric Messages Per Outbound Message , as a primitive measure of the fact that you are participating in a conversation and not just yelling.
With Klout I can choose the metrics that best reflect my personal twitter strategy, I can easily find them and I can monitor my progress (using a handy dandy graph) and ensure my strategy is a success.
Your strategy might be different. Walk up to the buffet and pick the metrics that will help you best measure your own success.
[ Contest: Notice the metrics I have deliberately ignored: # of followers, # of retweets, @ mention count etc. Can you guess why? :) The person with the best guess gets a copy of Web Analytics 2.0! Contest closed, thanks for the entries!]
At the bottom of the Stats tab Klout also includes a handy dandy Analysis table with trend indicators. . . .
As an Analyst it might be of some value to look at the trend pointers at the bottom (clearly I am doomed!), it might be cute to put this into a PowerPoint slide for the HiPPO's who might like the Chinese fortune cookie messages for each metric group.
Ok, ok, I am just teasing the Klout team, I know it is very hard to "wordify" and programmatically make valuable recommendations. :)
GraphEdge. Twitter Analytics.
The reason I believe GraphEdge is interesting is that it has a set of really cute metrics that help bring a different perspective to measuring Twitter.
If want to contrast the difference in thinking applied compare some of the metrics below with, for example, what Omniture is touting with its Twitter "integration". The difference between the old web analytics thinking and a new person's could not be more clear.
[Allow me to rush and add that while Omniture has a hack to bring some twitter data into Site Catalyst to do something, Google Analytics has nothing. Not even something that is not useful. So perhaps GA stinks even more.]
Here are some, IMHO, differentiated metrics. . . .
Legitimate Followers:
If you have spent any time on Twitter you know that spam accounts are a problem so it is very nice that the first thing you see in GraphEdge is not a follower graph but rather their attempt at telling you how many legitimate followers you have (and the trend over time, cropped out in the image below). . .
To identify "legitimate" they use the following filters, direct quote:
Any of your followers who are following more than 2,000 people are considered not-Legitimate… they’re probably not really monitoring your feed, so we don't count them as "Legitimate".
Users who have been suspended by Twitter can’t read your tweets (and probably weren’t interested in the first place!). We don’t consider these Legitimate Followers.
It is ok to argue with their filters, but it's a fine start and I think good enough.
Klout also measures something called "Reach", which is also their way of identify if you've got people or bots following you.
Churn Rate:
In my days at DirecTV one of the metrics that the company was obsessed with atleast then and rightly so, was Churn Rate. It reflected the value of not just going after new customers but doing all that was possible to take care and love the customers we already had. Makes sense?
So I have always had that obsession with tracking Churn, simply to try and understand why people quit. The hope is if I can understand why then I can do something to fix the problem.
[By now I am sure you get the feeling that I am treating twitter analysis like I would business analysis. Twitter is my brand channel and I take this very seriously. It is perfectly ok to use Twitter to tell people where you are and what you are doing and not care about analysis.]
I have not really found any decent tool to track unfollows in twitter (yes I have tried the normal ones and they are either flaky or just outright stink). Hence I was happy to have this in GraphEdge. . . .
Yea!
Actually ouch! 291 un-follows!! So sad.
Atleast now I know.
It is nice to have the over all trends on the right in the above image, as well as for the period you choose smarter metrics like growth rate.
GraphEdge will also show a list of your new followers and un-follows (so you can send them bad vibes! Kidding, Kidding. :).
[Qwitter was one of the first tools I used to track unfollows, sadly it does not work any more, and it had a great feature: It would try to guess and report on which tweet resulted in the un-follows. Nice.]
And here's what we were on the quest of. . .
I will admit to not being charmed by having three different lines above, they clutter the left and the right, and get in the way of understanding the data. But you I suppose you can learn to ignore two of them.
Here's the definition from GraphEdge for Churn:
The number of removals (un-follows) over the average size of the existing base (followers) during the period measured:
Drops / (Current Followers – ((Adds – Drops) / 2))
As always look at the trends, the longer term the better. And remember that history is littered with companies that were growing just fine but they still died a painful death because of Churn Rate.
Loyalty:
Slightly along the same lines GraphEdge has a metric called Loyalty. At the moment I think it is too limited in what it actually measures, and it only starts measuring once you join GraphEdge. But there is kernel of promise in the metric, keep an eye on it.
Lastly… while most people overestimate their "twitter power" (I can bring you down with a single bad tweet Avinash!) I think a few people also underestimate their reach, if they participate in twitter in the right way.
Looking at the second level report can give you a feel for your network size.
Followers' Friends is an "incestuous" number, it shows all your followers and the people they are following. If you have ten followers and they all follow each other that's 100 Followers' Friends. Feel free to be proud of this number, but then promptly ignore it.
Unique Names is are the unique twitter account id's in the network, less the "illegitimate" ones. Think of this as something close to, but not the same as, the Unique Visitor concept in web analytics.
This is a useful number.
Think of it this way: If you say something of incredibly profound :) . . .
. . .and a whole lot of other people who follow you think that and retweet it then you have a theoretical capability to reach 1.2 mil people (Unique Names in your Second Level network).
Now the reality is that that will rarely happen, if ever, but in our profoundly hyper connected world Unique Names is a good number to keep on your horizon.
Remember success in twitter comes from participating in the conversation and giving something of value, not by running "social media campaigns". If you don't internalize that be ready for a reality where both your Followers, and Second Level Network size, to be small potatoes.
Qualitative Metrics / Analyses.
Now let's tackle the much much harder analysis to do in any filed, analyze the data from a qualitative perspective.
Linguistic Analysis:
I have given up on "Sentiment Analysis". Well atleast for now. Everyone over-promises and massively under-delivers.
On paper it seems like such a great thing to want to have, this is a social / conversation medium after all. But most tools I have had the good fortune to try are simply either glorified versions of Google Alerts even if they promise you buzz metrics and the moon.
Now sentiment analysis is a very hard problem to solve. For example I just analyzed my account using an expensive "social media sentiment buzz analysis tool" and it marked this tweet from today as Negative:
avinashkaushik: There in nothing quite like AC power in your seat for a 10 hour flight. Oh and 20 hours of pending work to do.
Perhaps the tool does not find my dry wit as funny as I do, but it's hardly "negative"!
With all that context I think TweetPsych holds a lot of promise.
Tweet Psych uses the Linguistic Inquiry and Word Count (LIWC) method and the Regressive Imagery Dictionary (RID) method to build a psychological profile of a person based on the content of their last 1,000 tweets.
Dan Zarrella, founder, says: "I think the possibilities of a system like this are enormous, from matching like-minded users to identifying users that exhibit certain useful or desirable traits."
I am not sure I understand perfectly how it works (I need to send this to Joseph Carrabis!) but the analytical techniques looks very promising. . .
Hmm… interesting. I do like talking about "learning, thinking, knowing etc"! :)
As always rather then looking at my data in isolation I compare / contrast it with my friend who is a web analytics twitterer. . .
Now I understand a lot better how I am doing and how he is doing.
Remember there is nothing wrong or right here, we are both just very different people with different twitter strategy and what Tweet Psych's linguistic analysis algorithms helps us understand if our psychological profiles are aligned with our twitter goals.
Tweet Psych also provides you with Primordial, Conceptual and Emotional Content analysis, here's mine. . .
Nice.
Use this type of analysis to understand at a deep level what attributes are being associated with your brand, and if they are reflective of the goals that you set for yourself.
Content Visualization with Stream Graphs:
Stream graphs can be very good at visualizing data, content specifically. Twitter StreamGraphs is delightful for:
1. its visualization of highly associative words with the word you are querying and
2. viewing streams (tweets) for any associative word (hence sweet filtering, something so darn hard to do with twitter content)
Here's today's view of the data for my account (searching for @avinashkaushik). . .
Please click on the image for a higher resolution image.
You can choose the associative word stream that you are interested in most, click on it and at the bottom you can see the tweets. The size of the stream and shows you strength.
Once you choose the stream you can also click on the dates on the x-axis to filter down to the tweets for that particular stream for that particular date.
Sweet.
The stream graph for avinashkaushik would be different, as it looks for mentions. . . .
Please click on the image for a higher resolution image.
It is analyzing the last 1000 tweets and such a great way for me to understand the content, and filter down and review the relevant tweets easily.
Twitter StreamGraphs helps you visualize content in a very unique way and solves a very important problem to boot.
Parting Words of Wisdom.
I hope if there is one thing I have convinced you of then it is that you need to be a lot more critical when you think of analyzing these new media channels.
It is important to put aside stale (certainly current web analytics) thinking.
It is important to participate in these mediums so that you'll truly appreciate what their real strengths are.
It is important to question metrics that have cute names, dig one step deep, just one single solitary step, to check if the metric definition passes the BS filter.
It is important to choose the metrics that help you measure your unique goals.
Finally it is important to realize there are no short cuts. Be willing to work hard. Be willing to put in the sweat equity. Be willing to try 45 things (tools / metrics / strategies) to find the 3 that work for you.
Good luck!
[Missed the contest? Go back and look for the red parenthesis, you'll win a copy of Web Analytics 2.0! Contest closed, thanks for the entries!]
Please share your feedback on this post via comments. Got any other tools that you love and adore? Please share them – with a quick comment on Why you love them. Got a piece of analysis that you think is profound? Please share that with all of us as well.
Thanks.
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November 24th, 2009 at 03:43
[...] This post was mentioned on Twitter by KPInomics WA blogs, AllThingsM. AllThingsM said: Social Media Analytics: Twitter: Quantitative & Qualitative Analysis http://bit.ly/7RHUqp [...]
November 24th, 2009 at 04:39
Wonderful collection of tools Avinash, thank you!
(I love neoformix work, too bad it doesn't seem to catch Hebrew words)
November 24th, 2009 at 06:19
More tools in the microcosm of social measurement!
Love what the community is doing in developing the latest and greatest reporting for the platform. As time goes on, these only get more and more sophisticated. It's exciting to see how things are evolving to quickly.
Twitter Streamgraphs was a new one for me, thanks Avinash. Interesting stuff.
Regards,
Garry
November 24th, 2009 at 06:29
Avinash, thanks for the post. We have struggled to change the way we think about analytics when it comes to our social communication platforms, including Twitter and our blog. These tools certainly have hope of becoming useful and I'm hoping some of the bigger analytics players out there pay attention to what these companies are doing.
/kff
November 24th, 2009 at 06:30
Howdy,
Thanks for the nod, Good Sir. Interesting stuff, to be sure. My concerns are the amount of input required to come to conclusions. People are psychologically "static" for brief periods of time and applying the information presented here denies cognitive v emotional decision making which is highly dynamic.
My processes for purchasing a book are highly personal, for dinner-fixings are highly social (family). The static personality of "me" shifts for each purchase event and must be addressed and communicated with separately for each event.
And never the less, interesting stuff.
Thanks for the nod. – Joseph
November 24th, 2009 at 06:48
Thanks for your insight into these tools. There are so many out there, crunching numbers, with little analysis of their usefulness.
You might also check out http://www.analyzewords.com/index.php which @TheNextWeb just reported on.
November 24th, 2009 at 07:01
Thanks for the great post, Avinash.
I would have loved to know more about your thoughts on metrics using Twitter Lists, for e.g. Lists/Followers ratio. A preliminary analysis I performed to assess correlation between L/F ratio and Klout score yielded surprising results (http://bit.ly/1gxcZx).
November 24th, 2009 at 07:20
Avinash, once again your level of analysis makes people want to further improve their understanding and skills. It also makes it very clear that using tools you can work smarter with the data and spot points and trends that might not have been noticed without a tool.
The GraphEdge app looks awesome, im going to give it a try.
I have found twitalyzer.com does have some Google Analytics integration but unless you are actively promoting your website/blog and tracking with GA it doesn't usually have enough data to be significant.
Also congrats on the new book!
November 24th, 2009 at 07:21
Avinash,
Well thought out post on the challenges facing businesses/marketers trying to analyze Twitter. At the core you need to know who are you reaching and does it help improve your business revenue? At the end of the day, customers need to buy for companies to thrive, regardless of how much you may be participating.
And try not to give up on sentiment analysis. The amount of text used in analysis is a huge part of how automated sentiment works. 140 characters is not a lot of text, and people tend to be very forthcoming with their sentiment right out the gate. They don't have the space to explain, elaborate or get distracted from their main point. Tweets like "XY is the best product ever" doesn't get lost in translation. But they do love their sarcasm which can be a big challenge for computers.
The best sentiment goal may be to strip away the neutral to focus more time on the really positive and the really negative about your company. And without a doubt, accuracy can never be 100% with either humans or computers.
Enjoyed the post. And you are right, there are no short cuts for any of it!
Regards,
Christine
November 24th, 2009 at 07:23
Great post, thanks for sharing these tools!
November 24th, 2009 at 07:44
Thanks for the analysis and round-up, Avinash. I've thought about the Twitter analytics problem for quite some time, which is why we built Tweeb.
Tweeb is focused on individuals and provides a simple Twitter analytics dashboard. Because of the mobile nature of Twitter, we wanted to initially launch on the iPhone, so that people's stats can travel with them.
The app has currently been submitted to Apple and should *hopefully* be approved this week. Follow @tweebapp to keep posted.
November 24th, 2009 at 08:04
I havn't read your complete post yet but couldn't help myself to react on your contest…
sorry for the spelling, I am not native english
The metrics you ignore are "just" metrics. They have to be analyzed knowing all other things that happen to your stream.(and who knows everything? Not me and I guess not even you Avinash)
One example: Are there a lot of phony followers ? (they don't read or react to your tweets)
that number may varry intensly over time (both the exact number and the % of the total followers)
My guess is you don't want to measure the quantity of x (followers,messages,….)
but rather the quality of your messages.
for example: you said you like # Of Retweets Per Thousand Followers.
what is the difference with # of retweets some might ask?
well it is HUGE!
as you gather more followers, is is only logical that # of retweets would increase. If 1000 followers retweet 2 times you might expect 2000 to retweet 4 times (mind you, this is a estimate)
But that is not nesscacarrily so for # Of Retweets Per Thousand Followers. That number might actually go down!
the number per 1000 will only go up if you said something interesting.
It measures the willingnes of your followers to retweet and not the # of retweets.
And that willingness is a metric you can compare over lots of time as is.
And that is something verry interesting!
to be short the metrics you avoid have no value(meaning) on themselves over time. And there are metrics that have value over time on themselves.
It is those meaningfull valueable metrics that are interesting! And those are the metrics we should pay attention to!
now, off to read the rest of your post!
November 24th, 2009 at 08:15
Great post. I jotted down those apps and I'll give them a try.
As far as your contest, you are looking into engagement, not fluff like followers and false sense of reach.
November 24th, 2009 at 08:31
Per contest: # of followers and # of retweets does not measure for engagement but only measures the distribution of message. There is no conversion metric in # of followers and retweets, meaning it does not give an idea of the conversations being built. The commodity is in the engagement, otherwise they may un-follow you. That's my best guess. I am currently reading your first book and greatly look forward to the second! Thanks!
November 24th, 2009 at 08:33
Thanks for this rundown of some quality tools, metrics and visualizations. It seems like every day there is a new analytical program popping up and some times its hard to cut through the hype and look past the pretty diagrams.
I think you ignored # of followers, # of retweets and @ mention counts because in isolation these metrics are meaningless. If I said I had 2000 followers, what could I do with that?
None of these metrics give a sense of the quality of my twitter audience. Ratios and percentages using these building block metrics are much more informative. I could have 2000 more followers than another guy and think I'm doing swell, but if my goal was around engagement, the number of followers isn't a meaningful measurement of engagement.
Perhaps if we compare the percentage of followers that retweet, his might be higher and therefore he followers are showing greater engagement than my much larger follower base.
November 24th, 2009 at 09:27
Avinash,
As usual,great blog post!
My guess as to why you have left out the metrics #of followers, #retweets and @mention count is that these metrics are meaningless just by themselves and without context, and cannot be used for comparison across different profiles. They do not indicate anything about the users engagement on twitter. We cannot compare two users just based on these metrics, and draw insights that because one user has higher numbers for these metrics they are engaging better on Twitter.
For instance # of retweets does not indicate much without taking into acount your number of followers, and a KPI like #of retweets per thousands followers brings in greater context and is great for comparing a profile to a benchmark.
Similarly #of followers will be more useful if used in context again with other metrics like follow/#followers, and your reach.@ mention can be factored in with a KPI like@mention per thousand followers.
So we can use these numbers to create fantastic metrics but on their own they can be quite misleading!
November 24th, 2009 at 09:37
A very practical and useful post Avinash! The most interesting blog post on Twitter Analytics I have ever read. It will add tremendous value and guidance to my Social Media Analytics adventure. Thank you always Avinash.
November 24th, 2009 at 09:46
Avinash – this was a very interesting (and useful) read indeed. I totally agree with you on the need to change our 'way of thinking' from the previous mental models we have on analytics.
One thing I have noticed in terms of tools/metrics vis-a-vis measuring Social Media is that most [not all] tools out in the space currently are what I call "Vanity" tools — it tells you how cool YOU are, how strong YOU are, how much power YOU have etc. What we need are tools and metrics that are more 'humble' and stresses the true quality of one's interactions and engagements with their customers/followers(both ways) — of course, for the sake of completeness, we should have some vanity metrics too. Anyway, that is why I found your post intersting — definitely some new perspectives in there.
On the cognitive aspects — it is a difficult concept to fathom (maybe easier at an individual level or a dyadic level). But from a practical business standpoint, I think there are two barriers to surmount – one, folks shift their cognitive behavior constantly depending on the context, interaction, influences etc. and two, the law of diminishing returns dictate that we have to make sense of how a collection of individuals are behaving towards your business (call it cognitive segmentation).
Enjoyed the post.
November 24th, 2009 at 10:08
A decent article Avinash, although I wonder how long you've been playing with Klout before you wrote about it (sometimes its satisfying to people like me to know about a beta tool – then watch as an industry great writes about it and compare the failings we have with the failings he/she had).
# of followers/# of retweets/@ mentions/etc: useless by themselves because each of these items lack CONTEXT.
November 24th, 2009 at 10:09
You ignore # of followers, # of retweets, @ mention count etc because they’re just numbers – numbers in a report. You need to ask a second question to really begin analyzing.
November 24th, 2009 at 10:45
[...]
Google’s web analytics guru, Avinash Kaushik, has written an excellent blog post examining some tools for analysing Twitter stats. If you’re interested in finding out who’s influential on Twitter, and understanding how to reach the right audiences, it’s well worth your time to read his somewhat lengthy article.
Be warned – this is far more rigorous and in-depth than the usual light and fluffy garbage that gets written about Twitter measurement/analysis, you should only read if you’re seriously interested in understanding how to analyse and measure Twitter activity.
[...]
November 24th, 2009 at 11:24
Avinash,
Great post. Lots to think about here. I complete agree with the kudos being sent out for the innovation that we are seeing and the fresh perspectives being brought to analytics. I found the Twitter StreamGraph to be crazy cool. congratulations on the creation of some wicked good content that is likely to infest (in a good way) the minds of analysts globally. This is a post will get some great mileage.
There is so much to like that you covered here. My only concern is that we're having to pick and pull from multiple tools to paint the picture that we're trying to paint. (Taking the best of 8 worlds, each with their own version of reality) Many similiar tools report common metrics, like followers, with different values due to methodology differences. I completely buy into the concept of multiplicity to get a more comprehensive view. I get a little nervous when the tools we are pulling from are similar, but one reports a single metric that we want that the other doesn't. I think that this is probably just reflective of the early stage that we are at with trying to measure social media. The dust will settle and we will have fewer and more reliable tools in time.
Can you ask your Google friends to deliver a single tool that gives us all of the meaningful metrics you outlined in your post? :) For Tomorrow? :) :)
Seriously, thank you for consolidating all of this information into a meaningful post. Very good stuff.
Alice Cooper's Stalker
November 24th, 2009 at 11:26
Avinash: As usual, good post with great process of thinking this through.
Curious about part of this post. You state "I am always suspicious of compound metrics. They can be subjective, inapplicable to many and efficiently hide the insights you need to understand what actions to take" You have been pretty consistent in this area which is one reason why I love reading your blog.
Their Influence metric (Score)"is derived from measuring 25+ variables" Does that not contradict your feelings towards large computations?
I liked your comment about "Most twitter analytics tools just do data puking" Have you used Twitalyzer much lately? It is far from data puking :-)
http://www.twitalyzer.com/twitalyzer/profile.asp?u=AvinashKaushik
FULL DISCLOSURE: I work with Eric Peterson on the Twitalyzer solutions. As you know, Eric, like you knows a few things about digital measurement :-)
November 24th, 2009 at 11:47
Hey Avinash – Great and insightful post today and Klout looks really good!
I wanted to know if you could talk a bit more about %Overlap in GraphEdge. Is a high % of Overlap good or bad? Is a low % of Overlap bad or good? Should I even care about that at all?
Let's say that you Tweet about your the launch of your new book, Web Analytics 2.0 (which is awesome BTW). Then, the 25 other followers that I am following who are also following you re-tweet your tweet, and I wind up with a full page of tweets of the exact same thing, over and over, and over again!
I know that kinda combines %overlap and re-tweeting (not necessarily one in the same) but the one frustrating thing about Twitter for me is the constant re-tweeting…I have so much overlap that the same message is repeated 10 times each time. So this is why I'm wondering about %overlap and whether too much overlap can actually be a bad thing.
Thanks again for a wonderful post!
November 24th, 2009 at 15:26
Enjoyed this post.
Twitless is worth a look at for seeing who follows and unfollows on a weekly basis (sends it on a tweet and on an email)
I find it strangely reassuring that the unfollows are people I can understand unfollowing – perhaps they've followed on the basis of a single conversation and been disappointed thereafter at the lack of common ground.
November 24th, 2009 at 18:57
Avinash,
Good post. Some really good information about non-standard Twitter metrics. I do wonder though, if there isn't some way of being able to take into better account the quality vs. the quantity.
I will check out several of these apps.
November 24th, 2009 at 19:21
This is a very helpful post. I don’t have a lot of analytics or number-crunching experience, so some of it was over my head (note to anyone who wants a marketer to buy your analytics products/services), but I’ll try to dig in more later.
I think one of the most important things that analytics tools can offer is a CLEAR definition and description of how they arrive at their stats. Having created about 50 twitter accounts, I can say that not all accounts are created equal and you would want to analyze each based upon your goals for that particular account.
In some cases, link click-throughs might be all that’s important (which makes me wonder why none of the analytics tools have created their own URL shorterners w/ stats such as Bit.ly or Cli.gs). For another account, conversation threads might be the biggest measure of success. In still other cases, retweets and pass-alongs could be the primary objective.
What I see missing from the analytics tools is a plain-English explanation of how non-analytics pros can use these tools to their advantage, i.e., to demonstrate how tangible goals have been met (or not) to clients or employers. Marketers are likely the intended audience for these tools, so analytics tools should address their pain points.
@CarriBugbee
Social Profiles: http://www.carribugbee.com
November 24th, 2009 at 20:03
[...] Social Media Analytics: Twitter: Quantitative & Qualitative Metrics (tags: socialmedia twitter research business roi strategy measurement metrics analytics) Twitter Updates [...]
November 24th, 2009 at 20:32
Unlike the world and thier mothers it seems, we still have not got heavily involved with twitter yet.
I think it certainly suits some businesses and types of 'information givers' but can also be detrimental to others I believe. (I conclude that when reading about what some company director has eaten for lunch)
In reality there is only a small team doing all the website stuff and sitting there writing 2 line posts and reading another hundred 2 line posts that I often have no idea what they are actually about seems quite a chore. It seems another position is needed in company now just to sit at pc all day twittering.
I always thought I was 'quite' up to date with seo etc but nowadays with the boom of all this social media we have got a bit left behind in some areas. (better to do 3 things right than 10 things average?(!)
Fortunately now there are tools that collate a lot of this info so If a new post is made on the blog for example then it gets mentioned in twitter etc so it will interesting to keep my eye on this avenue as a potential income stream.
Your new book got delivered today (or at least they tried) so hpefully I will reading this over the coming weeks and seeing where exactly I am missing out (if at all).
Maybe then some of these tools you mention can be added to our arsenal with genuine direction.
November 24th, 2009 at 23:52
Hey Avinash,
Thank you for taking the time to look at what we are working on here at Klout. We like to think our Klout Score is a helpful benchmark for individuals/companies to understand the relative impact they are having across Twitter. I agree with you though that by looking at the individual factors that make up the score and really thinking about what you are trying to accomplish you can fine tune your behavior and be most successful.
I also wanted to say thank you again for the feedback you've given both publicly and privately on our metrics. It's been a huge help in shaping the way we think about measurement.
One side question, we've been capturing social graph data for the last 18 months and have often debated about showing churn (especially who the most influential people that have unfollowed you are). Our concern has always been whether or not that data was too painful/confrontational. I feel like it can be nice to know who unfollowed you but sometimes you don't necessarily want someone to know you unfollowed them. Curious how people feel about churn/unfollow data in general.
November 25th, 2009 at 00:34
The winner of our little contest is E. Mills!
His/Her answer was very close to my own thought:
I don't like those metrics is that they are aggregated metrics that simply report on "activity" (and they don't even do that in context) and don't report either on customer behavior or outcomes. The latter two are the most important to any business (and hence to me). Overall atleast if they had some context perhaps it might add some marginal value (like increase in # of followers per day, rather than total).
E. Mills gets a copy of my new book, Web Analytics 2.0.
I would be remiss if I did not mention the contest entries from Romy, Alli and Tony. All three of them were close and were it not for the time stamp of their comments any of them might have won. Thanks so much for playing along guys!
Avinash.
November 25th, 2009 at 00:45
Liz: Nice find!
Analyze Words seems to use one of the two algorithms that Tweet Psych is using, LIWC, but the output is differently organized.
I am not happy that is says my "Arrogant/Distant" is High! But "Spacy/Valley Girl" is Low. :)
Mahendra: Lists are too new to be incorporated into computations of various twitter tools, but I am starting to see it being mentioned. I did not include it because I am not sure there is a good use of it yet.
That said my working hypothesis is that Lists are a great "vote of confidence" by the community and in as much it would not be unusual if being on a high number of lists correlates with higher "influence".
Christine: I am actually surprised that sentiment analysis, or simply clustering the data, has not made a lot more progress..
For example there is a keyword tree for one word referring traffic to my blog: http://kaushik.net/avinash/juice_ga_keyword_tree.png
I has uploaded about 10k words and even this simple tree for one of those keywords, "Avinash", is pretty cool in understanding the data.
I am surprised something simple like this does not exist! :)
Jey Pandian: I have been using Klout for around six months. I first ran into an early version somewhere in Q1 of 2009 and included it in my book, written mostly in July 2009.
I have had a chance to talk to Joe, the founder, a few times during these months and share my feedback on how to evolve it. I have been happy with the changes and they have many more cool things to be released in the future.
Everything in the world of Twitter measurement is in Beta. The whole darn ecosystem is in beta! :) That should not stop us from making the right moves even this early on. Tools like Klout, Tweet Psych etc are making those right moves. IMHO.
Alice Cooper's Stalker: I am afraid we don't have a choice. We have to embrace the fact that we won't have "one tool" for the conceivable future.
Trust me, this is very hard even for me to accept. I come from the traditional business intelligence world, our daily quest was to get to the "single source of the truth".
It is simply impossible on the web because the world is changing too fast.
Success will now come from having people who have flexibility and ability sown into their their DNA, people who can do a rapid analysis of data is captured in each tool, how the metric is defined and then go use the data today to take action, even as they know in their heart they might have to quit that tool in three months.
It is hard. But it is not impossible.
Jeff: Hmm…. I am confused…..
I said under the picture with their formula this: "It is easy to understand the market demand to boil things down to one number, but this is perhaps the least useful thing in Klout." And then as you'll note I don't talk abut it at all in the post and focus on other metrics they compute that I find interesting.
It was my subtle way of saying… don't use this influence thing.
Perhaps I should have been even more obvious.
Like everyone else I have used Twitalyzer!
Joe Teixeira: A large overlap indicates a more "incestuous" connections in your network, i.e. your followers (and you!) are just following each other.
This is good in the sense that it is a more cohesive community (and you might not need a lot of "pimping" to reach everyone! :)).
It is bad in the sense that a large overlap shows a limited 2nd-level reach (limits the value of a retweet – which you really do want to spread your awesomeness to new people).
I would much rather have a low overlap.
Joe Fernandez: I understand your concern with id'ing unfollows.
I am more of a tough love kind of guy but in the grand scheme of things it is not super super important to know who unfollowed. But that's a different issue from measuring churn rate. You can still show the churn rate and suppress the names of people who churned. I think that the metric is worth having so people can get a holistic view of success.
Thanks everyone!!
-Avinash.
November 25th, 2009 at 04:52
As usual, great work and great finds Avinash. Thanks!
Nikki
November 25th, 2009 at 07:39
[...]
Social Media Analytics: Twitter: Quantitative & Qualitative Analysis
I saw this post, recommendations for measuring the impact of Twitter, make the rounds on Twitter and Tweetmeme yesterday. It’s worth having Avinash Kaushik’s blog in your RSS reader and often times, the comments are just as valuable as the blog posts themselves.
[...]
November 25th, 2009 at 08:15
[...] My good buddy Avinash does a fabulous job on this post explaining Social Media Analytics: Twitter: Quantitative & Qualitative Metrics. [...]
November 25th, 2009 at 13:48
[...] Avinash Kaushik reviews some social media analytics tools. Four Twitter analysis tools to be exact. As usual he's pretty outspoken about tools that just spit data at you. Alway ask, what action can I take in light of the data. So for these four Avinash had this to say …show sweet signs of [...]
November 25th, 2009 at 21:30
[...]
If you’re not using any metrics to measure your twitter account, see our recommendations ( 10 free Twitter analytics tools to help you gauge your tweet effectiveness) If you’re only using one site, you should read this article to dig a little further on the strengths and weaknesses of some of the leading twitter analytic options.
[...]
November 26th, 2009 at 01:32
My 2 cents (with humility and some apprehension)
*Number of retweets per thousand followers is a good metric.
However, due to the nature of twitter-
this would probably not be fair to someone who tweets less Vs someone who tweets more often.(Assuming ceteris paribus).
So if A has 1000 followers and sent 10 tweets a day out of which 1 gets RT-ed, my ratio is 0.1 RTs pe thousand followers
and if B has the same 1000 followers and send 100 tweets a day of which 10 gets RT-ed, my ratio is 1 RTs pe thousand followers
I think (number of updates / number of retweets*100) could be a good indicator.
In the above example, this percentage would signify that A is actually equal to B-
because the propensity of his tweets to spread is equal to B- irrespective of the number of followers (that is 10%)
This number would signify more closely what is the propsensity of messages to spread and perhaps also the quality of tweets sent (a larger score would mean more of my tweets are found valuable)
"I don’t care about Follower/Follow Ratio-
I think it is disingenuous to follow everyone who follows you just for appearances sake when you have no intention of reading what they all say. Why be fake?"
Quite true. On its own self this ratio is probably not so useful. But maybe this is not a metric to be seen in isolation- rather in a combination with other metrics.
At the least, it could 'flag' certain cases:
*There could be some more ways to interpret this metric- significantly more people following you then you following them could denote less 'socialbility' but more 'authority'. I mean if I am a celeb/authority – I cannot meaningfully listen to all- so I have a high follower-follow ratio. On the flip side, a low ratio could imply a twitter-er who follows people recklessly (in the hope that they would follow you back)- and in turn follows a significantly larger number of people than other people follow them.
This metric- coupled with a metric like, say- "rate of follower adoption" could give more meaningful insights into the "genuine authority/sociability" IMO
It is quite simplistic yet not entirely useless IMO- if we know what are we using it for (like most other metrics)
Shalabh
Chasing The Storm
November 26th, 2009 at 17:59
[...]
Avinash Kaushik – Measuring Social Media:
How should you track success on sites like Twitter? In a forward-thinking post on social media analytics Avinash offers several suggestions.
[...]
November 26th, 2009 at 21:19
Very well explained Avinash with pics and examples. Great post
November 27th, 2009 at 03:41
Avinash, great post. Tried to retweet but the button does not seem to be working.
November 27th, 2009 at 06:32
[...] Social Media Analytics: Twitter: Quantitative & Qualitative Analysis (Occam's Razor) [...]
November 27th, 2009 at 10:50
I really enjoyed reading this post and totally agree with it.
November 27th, 2009 at 12:53
Hi Avinash,
Very informative post. But I would like to add two cents of my own.
Agreed, the accuracy of linguistic analysis tools is not cent percent; yet there is a lot of work being done on it. Matter of fact, there are researchers working on extracting more subtle sentiments (e.g. in your case, wit, in other cases, sarcasm). It might take a while before it hits the mainstream; but it cannot be written off.
And it was refreshing to know that people are working on psychoanalysis of a person based on his written work. That is something that holds a lot of promise. Just thinking about it from the perspective of an HR person/recruiter.
PS: Even twitter was written off; now there are companies earning their livelihood just by using twitter APIs.
November 27th, 2009 at 16:31
[...] Klout - I am not going to write much about clout because Avinash Kauushik has given a very detailed explanation on Klout in this post twitter quantitative qualitative analysis. [...]
November 28th, 2009 at 06:13
Another informative blog post – thank you. I appreciate the detailed exploration of Twitter measurement tools. I have also purchased a copy of your latest book and look forward to reading it.
Admittedly I did have a few issues with understanding parts of your post due to your sentence construction. These issues decrease your blog's readability and thus impede understanding of your (quite valuable) message.
The reason I'm taking the time/effort to note these issues is out of hope that you will both correct them and also proofread more thoroughly in the future. I can only assume that you are as eager to post to your blog as we all are to read your posts.
Here are a some samples:
1. 'The in their thinking from all the constant …'
[I am not sure what you are saying at all.]
2. 'In this post four twitter analysis tools.'
[This is not a complete thought. Perhaps 'In this post I discuss four Twitter analysis tools.']
3. 'Each while not yet all developed yet show sweet signs of..'
[This is a bit convoluted]
And some minor issues…
1. 'Mercifully there is so much more to Klout than the that.'
[extra 'the']
2. 'atleast'
[Is two words not one. This appeared multiple times.]
3. Please capitalize proper nouns: Twitter, Facebook, YouTube, Tumblr [unless the particular company does not itself capitalize it own name]
November 28th, 2009 at 06:44
hmm… based on the high level of thought required around detailed analysis who really cares for a few mistakes.
@jhay feel free to go thru my blog posts and correct them if you have that much spare time…
Its always preferred the data is correct and accurate as for some of the issues highlighted are secondary.
Avinash your knowledge is much more important than correct use of capitals for nouns and we appreciate your time to get data correct and useful.
Seriously if newspapers and media companies can type mistakes it must prove we are all human.
@Avinash as you know the community appreciates your efforts to share knowledge and best practice and a I know of several people are already very appreciative of this particular post.
November 28th, 2009 at 11:25
Your presentation is excellent. I found your analysis is reasonably correct. It is really great post and gives me to think in the direction which you aimed for. Thanks Avinash.
November 28th, 2009 at 11:45
Jon: Thanks for pointing out the errors in sentence construction, they are terrible mistakes!
The only time I have to write is late in the night and with my many jobs and (one :)) family the posts tend to be rushed.
My goal is to just get something out on time, but of course it should not be at the cost of quality. I've fixed errors.
Once again I appreciate the help.
-Avinash.
November 30th, 2009 at 02:24
Great post, Avinash. Thank you for the information! I agree with you on Follower Retweet % but what do you think is a healthy % to achieve? I understand that the important thing here is to be able to monitor progress over time but still wanted to know.
Also, I have used Twitalyzer and have found it useful. What is your take on that?
November 30th, 2009 at 10:03
[...] This post was mentioned on Twitter by Three Deep Marketing. Three Deep Marketing said: Great post from @avinashkaushik – Social Media Analytics: Twitter: Quantitative & Qualitative Metrics – http://bit.ly/5rJyFy [...]
November 30th, 2009 at 12:02
[...]
Social media, however, is still a challenge to set up the right success metrics. There are some great tools out there, like Scout Labs and Radian6, for measuring social media statistics. Regardless, it's still important to identify what you want to get out of a social media campaign first and then pick the right tool.
Avinash Kaushik, on his blog, brings some fresh thinking to analyzing social media from a quantitative and qualitative perspective. He points out that these new channels are a "distinct customer/participant experience" and that a new and fresh approach to measurement is needed for optimal results.
[...]
November 30th, 2009 at 14:10
Nice post – very comprehensive. I want to try them all!
Would love to know your take on The Whuffie Bank.
Slightly different take on measuring influence, and more about social value capital.
http://www.thewhuffiebank.org
Aerin :)
November 30th, 2009 at 15:26
Aerin: The whuffie bank is a very interesting idea, not the least because of their noble non-profit motivations.
From a measurement perspective it is simply yet another Compound Metric like "clout" or "influence" or …. Whuffies. There are five different things that go into computing the metrics:
http://www.thewhuffiebank.org/static/faq
(Click on How is Whuffie Calculated)
But I am unsure of the comfort levels with some of their assumptions (I do not believe having a hashtag in a tweet makes it any more valuable or simply the presence of a link in a tweet means your tweet is of high quality!). Other assumptions are good and now universally accepted (ex: retweets are good).
In the end I think about how to use the information provided to be better (for a personal gig or my business) and in that sense it is very hard, very very hard, to know from compound metrics what to do. My whuffie balance is 4,027. What do I do? There is a trend included but I am not sure how to parse out how to get better. Atleast without the underlying data.
This is not a problem unique to The Whuffie Bank, you'll see it in most twitter analytics tools. But one must give them credit for trying to take a very unique approach to monetizing reputation.
Avinash.
November 30th, 2009 at 21:04
[...] Social Media Analytics: Twitter: Quantitative & Qualitative Metrics (tags: twitter tools metrics statistics analytics) [...]
December 1st, 2009 at 08:25
There is actually now some GREAT tools for tracking Social Media effectiveness, example radian6.com can help show all the different places that your viral campaigns reach.
This has become very effective in transparency for clients ( and we know how much they love that…) and also to price up your work!
Jimmy
December 1st, 2009 at 08:47
[...]
After reading Avinash’ post about cool Twitter tools I tried to find out what tools are availabe for free to monitor Twitter and other social media.
Who does what, where and how? Are you measuring up against your competition? There’s a lot of social media buzz going on. What are they talking about your brand and your company?
[...]
December 1st, 2009 at 08:56
[...] Social Media Analytics: Twitter: Quantitative & Qualitative Metrics – Avinash [...]
December 1st, 2009 at 09:57
[...] Original Post: Social Media Analytics: Twitter: Quantitative & Qualitative Metrics [...]
December 3rd, 2009 at 01:31
[...] 3 décembre 2009 · Laisser un commentaire Social Media Analytics: Twitter: Quantitative & Qualitative Metrics – http://www.kaushik.net/avinash/2009/11/social-media-analytics-twitter-quantitative-qualitative-analy... [...]
December 4th, 2009 at 02:43
[...] Social Media Analytics: Twitter: Quantitative & Qualitative Metrics – Avinash Kaushik, sozusagen der Web Analytics Held aus dem Internet, teilt seine Erfahrungen mit Social Media Analysen in Bezug auf Webmetrics in einem ausführlichen Beitrag. [...]
December 5th, 2009 at 07:00
[...]
This article was inspired by a blog post by Avinash Kaushik “Social Media Analytics: Twitter” and he has a new book out titled Web Analytics 2.0 that is worth reading, which covers social media measurement.
It is important to realize there are no short cuts. Be willing to work hard. Be willing to put in the sweat equity. Be willing to try many tools and metrics and strategies to find the three that work for you.
[...]
December 6th, 2009 at 02:24
Great article – twitter analytics simply explained, without the hype.
December 6th, 2009 at 03:07
[...]
Just got sent this excellent article from my DCU Marketing masters classmate – Antonio Minuta – via facebook and reposted from Avinash Kaushik’s blog. Avinash explores twitters analytic’s tool in a in clear no nonsense way, which is great, as I’m a big fan of the non-smoke-and-mirrors-approach when it comes to social media. I particularly like how he brought to my attention that twitter is evolved and does not merely track numerical data with charts & graphs that ‘puke data’ data back at you but it measures activity via its klout analytics tool in 4 key areas – Reach, Demand, Engagement and Velocity.
[...]
December 6th, 2009 at 11:49
[...] FREE tools, code for Twitter, blogs and Facebook: Tips and tricks Twitter #analytics – why both quantitative AND qualitative #metrics could still fail you [...]
December 9th, 2009 at 05:39
Loved the post. I am myself trying to write a post on manually getting to know how important a person's tweets are. Maybe then my group can automate the same process
December 9th, 2009 at 08:19
Avinash,
Great post with an incredible amount of information. Thanks for your insight and I appreciate your no-nonsense humor!
December 11th, 2009 at 14:55
Avinash,
I'm learning a lot from your blog. I love Twitter and recommend it to our clients. It's taking time for many people to understand it's value. So thanks for this info.
~Wendy
December 12th, 2009 at 20:08
[...] 衡量社会化媒体不是没有办法，同样也不是没有工具。这两个帖子（一个是我的，一个是Avinash的）一定能够帮助你，我觉得值得仔细研读：Sidney的IWOM监测与分析：理解和实践，Social Media Analytics: Twitter: Quantitative & Qualitative Metrics。 [...]
December 13th, 2009 at 22:25
[...]
I am not sure, if any application yet calculates all the metrics mentioned by me but in the future I expect something on these lines. I would like people to go through a brilliant post by Avinash Kaushik which made me think about this post.
[...]
December 14th, 2009 at 04:55
Your post provoked a lot of thought about Twitter Analytics. As I had already told, I went on to write about some factors which can be considered while designing a tweet weight calculating tool.Thanks a lot to this post, my way of thinking about tweet weights have changed.
December 14th, 2009 at 20:46
Hello, Kaushik. I am your big fan. I read so many your blogs. Thanks your work.
Can I ask you a question? My question is about Google Analytics.
My company's website change its track code , the code is following:
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-XXXXX-X']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script');
ga.src = ('https:' == document.location.protocol ?
'https://ssl' : 'http://www') +
'.google-analytics.com/ga.js';
ga.setAttribute('async', 'true');
document.documentElement.firstChild.appendChild(ga);
})();
The result is the whole wensite data is growing rapidly, however the data of sub domain have not growing, even some of them decresed. I am totally confuse, and I don't know why. Can you fingure it out? Thank you very much.
December 15th, 2009 at 00:53
[...]
Avinash Kaushik writes on his blog about the limitations of these analytics tools.
"Most twitter analytics tools just do data puking. They find numbers that can be computed and then proceed to puke at you as many as they can find, with wonton disregard of value being provided or outcomes being measured…. You must pause and think: So what is this saying? What action can I take?"
There are some new services being developed attempting to answer this question. Avinash cites several of them, such as Klout. These services focus on analyzing users on Twitter, however, not the links themselves. We at PreferenceSet are working on our own solution to this problem, specific to online fundraising over social networks.
[...]
December 15th, 2009 at 10:29
A simple 150 word update in twitter and followers are great. But understanding the true nature of followers is quiet a hassle. I had never even thought about that.
December 16th, 2009 at 08:22
Thank you for such this article.
As a self proclaimed SEO Wannabe (trying to become a SEO newbie) this is definitely information that I needed. In a world where all your SEO interviews end up asking questions about social networking it is awesome to have something more to say than… well I tweet :)
No wonder my DREAM BOSS/MENTOR added this article to my recommended reading. You have definitely inspired me to get up and head over to B&N for a copy of your book.
If your writing style is anything like what I see here, I am sure it will be a great reference.
I'll definitely be back. *adding you to my SEO bookmarks*!
Thank you again.
- The SEO Wannabe
Did I mention who have a new follower to add to your analytics? :)
December 16th, 2009 at 08:26
[...]
First things first I have to try and get in 5 articles from my required reading.
Social Media Analysis: Twitter – Wow! Excellent post from Avinash Kaushik
I love his info and his writing style.
[...]
December 17th, 2009 at 09:28
[...]
Twitter ha cambiato il modo in cui le persone comunicano tra di loro e si influenzano.
Per poter comprendere realmente i valori distintivi di questo canale, non dovremmo limitare l’analisi ad una semplice raccolta di dati. Avinash Kaushik, nel suo post, presenta i 4 strumenti utili per analizzare Twitter al fine di capire che cosa rende unici i social media.
[...]
December 21st, 2009 at 09:02
[...] Social Media Analytics: Twitter: Quantitative & Qualitative Metrics Yes, throw the baby out with the bath water! Here's how Twitter measurements are re-shaping the way we analyze social media [...]
December 22nd, 2009 at 22:54
[...]
The most detailed measurements are of limited use to us, in any practical way, without the next two steps in the process — Analysis and Interpretation, and Action. Sounds a bit scary, doesn’t it? Like you’d need to put a half-dozen statisticians on the job…
I much prefer the way analytics expert Avinash Kaushik says it:
Now, that's concrete. Two simple questions that give focus to this whole business of measuring social media — and, most importantly, help to distinguish between truly useful metrics and, well, measurement for the sake of measurement. That's something we can work with!
[...]
December 26th, 2009 at 12:20
[...] This post was mentioned on Twitter by AxsysTechGroup, TSNN_com_US. TSNN_com_US said: Social Media Analytics: Twitter: Quantitative & Qualitative Analysis http://ow.ly/Ihwr [...]
December 29th, 2009 at 00:29
Hi Avinash… have been reading the your blogs … they are quite informative … thanx.. for putting so much effort and give us insights about social media…
are you speaking at the eyefortravel "social media strategies for travel" I think they and the travel industry need your guidance on SM .. big time…
Thanx again….
January 4th, 2010 at 08:40
[...]
You can’t ignore social media anymore. To web analysts, social media is just another platform that can be measured independently using external sources, or analyzed with software by segmentation. Avinash Kaushik goes into greater detail on this subject, and while I agree his equation of influence on Twitter is useful, “influence = RT’s per 1000 followers”, a greater holistic metric is needed that can be applied to all other social media platforms.
[...]
January 5th, 2010 at 13:34
[...]
I took the simple approach to social media metrics in a recent posting but jumping across to the “Occam’s Razor” blog I get another view from Avinash Kaushik, the evangelist at Google.
Avinash provides an analysis of some of the current analytic tools around for Twitter, but he does point out that he picks out the metrics important to his personal strategy.
[...]
February 6th, 2010 at 05:31
[...]
业界有谁？
如果大家看过Avinash的这篇文章：Social Media Analytics: Twitter: Quantitative & Qualitative Metrics，那么大家一定会跟我一样印象深刻——原来光是监测Twitter的第三方工具就有这么多！
这些工具都是典型的垂直型的网站分析工具。它们的功能非常针对性的细分，而且即插即用，大多数也能够实现功能模组的定制化。
[...]
February 9th, 2010 at 00:15
[...]
衡量社会化媒体不是没有办法，同样也不是没有工具。这两个帖子（一个是我的，一个是Avinash的）一定能够帮助你，我觉得值得仔细研读：Sidney的IWOM监测与分析：理解和实践，Social Media Analytics: Twitter: Quantitative & Qualitative Metrics。
但是，这些还不够，为了衡量效果，我们需要利用Control Group的方法。即，给社会化媒体的一部分人（曝光组）群进行营销活动，而故意不给另外一部分人（控制组）进行营销活动，然后通过对比来衡量营销活动到底有没有促进认知和偏好。
[...]
February 24th, 2010 at 19:52
[...]
12. Además de conectar con la comunidad es importante para mi saber si mis mensajes se están propagando. En la medida que la gente haga ReTweet de mis mensajes tendré mayor posibilidad de acceder a grandes audiencias. Lean acerca de ReTweets por Millar de Followers (RTPM) en el blog de Avinash.
13. Aún cuando logre crear una red grande y logre hacer que la gente propague mi mensaje, lo que busco que lean el contenido que envío. Los servicios como bit.ly o tr.im son una excelente forma de saber si estás o no logrando este alcance.
[...]
February 26th, 2010 at 12:44
Great article avinash, I wish there was a clear way to consolidate all this information!
March 4th, 2010 at 21:05
[...]
Social Media Analytics: Twitter: Quantitative & Qualitative Metrics | Occam's Razor by Avinash Kaushik
Excellent round up of tools to analyse infleunce and activity on Twitter. To many people analysing Twitter seems like a tedious activity, that verges on glorifying and over-egging something that is simple and small in scale. In all honesty I shared the same thoughts—and then clients started to ask us questions about the influence and reach of people on Twitter.
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March 16th, 2010 at 08:01
Great article avinash, I wish there was a clear way to consolidate all this information!
April 20th, 2010 at 20:48
[...] Q4: Do you think social media engagement analytics are valid? RE: Avinash Kaushik’s posts on Measuring Online Engagement: What Role Does Web Analytics Play? http://bit.ly/d4dETE and Social Media Analytics: Twitter: Quantitative & Qualitative Metrics http://bit.ly/aktwtr Q5: What's your most vexing marketing/web analytics [...]
May 15th, 2010 at 00:05
[...] Social Media Analytics: Twitter: Quantitative – (Tags: Twitter Netzwerkanalyse Monitoring ) [...]
July 7th, 2010 at 06:38
[...] Social Media Analytics: Twitter: Quantitative & Qualitative Metrics As usual, Avinash puts together a great article that outlines his thoughts on Twitter metrics. He reviews both how to measure and draw analysis from the data. [...]
July 8th, 2010 at 10:17
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Ein sehr guter Beitrag zu quantitativer und qualitativer Twitter Metrics von Avinash Kaushik.
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July 13th, 2010 at 16:57
Social Media for sure has a growing influence with the rise in influence, use and exposure of major websites such as Youtube, Twitter, Facebook and other social websites with other uses. Through these platforms, we are now in an age in which you can keep more and more up to date with both people and businesses.
It also has a significant value in the amount of traffic it can bring to businesses websites.
August 2nd, 2010 at 05:03
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Both measure “engagement”, but they are called what they actually measure. That’s what I recommend. More here: Social Media Analytics: Twitter: Quantitative & Qualitative Metrics.
In each new medium (like social now) we’ll get even more opportunities to measure if we are doing this right. Unique metrics for unique “engagement” processes.
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August 5th, 2010 at 12:27
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Social Media Analytics: Twitter: Quantitative & Qualitative Metrics
As usual, Avinash puts together a great article that outlines his thoughts on Twitter metrics. He reviews both how to measure and draw analysis from the data.
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August 18th, 2010 at 14:21
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It’s not surprising that useful social media measurement tools lagged behind. Mega blogger and analytics virtuoso Avinash Kaushik put it this way:
“These new channels, Twitter and Facebook and YouTube and Tumblr and, yes, even blogs, are very distinct customer / participant experiences. Stale marketing or measurement thinking applied to them results in terribly sub optimal results for all involved.”
I love that phrase, sub optimal results. But that’s always the results of unmeasurable, blunt instrument marketing. It was bound to happen when traditional measurement tools are grafted on to new media.
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November 26th, 2010 at 09:25
[...] The presentation can be hard to follow at times because the slides are not visible on the video. Even so, it is worth watching as Avinash is a great speaker. Many of the topics he discusses here are also covered in his blog post, Social Media Analytics: Twitter: Quantitative & Qualitative Metrics. [...]
December 1st, 2010 at 10:14
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Hearing people say wonderful things about your company is always awesome. But money talks, too. If we have to rely on one type of data to inform our marketing strategy, we want the data that delivers real proof.
For an outside (and unbiased) opinion, check out this blog post about quantitative and qualitative measurements by Avinash Kaushik, a social media analytics expert (and our hero!). It’s from way back in 2009, but it’s still right on target.
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December 1st, 2010 at 16:23
this is a great post Avinash, and we very much enjoyed your presentation at Twtrcon.
would love to somehow entice you to update/reprint this post on our blog at http://oneforty.com/blog, or to simply build a toolkit of the apps you mention in the post, reusing the copy you have already written about each one.
i think you'll find we're equally in love with Twitter's open API and developer community. :-) we'd love to know how you feel about our contribution to it — we've centralized listings, taxonomy, ratings and reviews for everything in it.
FWIW, currently 250+ of the @TwitterAPI apps that we have claim the category analytics. http://oneforty.com/search?query=analytics
warmly,
@pistachio
December 30th, 2010 at 22:25
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在做社会化营销的时候，你一定有一个跟别人独一无二的品牌、产品名称（或是产品昵称）或是代号什么的，它们都是被传播的对象。而通过搜索引擎的帮助（比如Google Search Insight）就能知道它们引发人们兴趣的情况到底如何。
还记得我翻译的Avinash的文章吗？Avinash：互联网品牌营销的监测和分析（上），Avinash：互联网品牌营销的监测和分析（下），这个时候真的可以派上用场哦！
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January 15th, 2011 at 06:56
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The chapter is filled with technical details, tools to support your analysis and insights into how you can take actionable steps in response to your collected data.
I hope that is a helpful overview that will allow you to decide whether this book might be helpful to your Social Media Analytics efforts. Avinash tackles the topic of Social Media Metrics in his own blog in a lot more detail.
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January 26th, 2011 at 06:09
Exchanging tweet and maintaining a relationship to several individuals will surely earn you trust and respect. Consequently, once others begin to notice that you are a trustworthy and valuable user, they will surely interact with you, and the virus-like marketing rewards will undoubtedly ensues.
January 26th, 2011 at 08:26
I am not sure I can agree completely with comment 103; we have about a year's worth of data detailing emotional valences individuals have to those they follow and who follow them. There is no question that a relationship is maintained, there is evidence that trust and respect do not naturally follow from that relationship. In many cases relationships are positively or negatively reinforced when personal meetings occur, or when trust inherents are altered within one's social framework.
And, as always, just my opinion (okay, my opinion backed by about a year's worth of data) on this.
Joseph
January 26th, 2011 at 08:58
Joseph: Thank you for pointing out the key nuance between the real world and the social world of limited knowledge about the other person.
I could define relationships in the social world, for the most part, as "relationships", and not really Relationships (at least not as in after getting to know the person with say one in person meeting).
The nice thing is that for brands and people there is an opportunity in the social world to start a "relationship" and if they are authentic about it then convert it into a relationship in the real world. :)
Thank you again, it is such a delight to get your esteemed input into these complex issues.
Avinash.
January 26th, 2011 at 09:29
Howdy,
1) Quite correct. Our data is for individual to individual relationships, not for brands-individuals. Our research was to recognize individuals who actually influenced others to take action versus individuals who…umm…didn't.
2) "…esteemed input…" Me? LOL! Thanks.
March 10th, 2011 at 14:20
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Social Media Analytics: Twitter: Quantitative & Qualitative Metrics | Occam’s Razor by Avinash Kaushik
Social Media Analytics is a complex challenge. By looking at Twitter we identify the quantitative and qualitative analysis that can be done to identify true success.
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March 14th, 2011 at 09:55
[...] Avinash Kaushik admits he’s suspicious of compound metrics like the Klout Score because “they can be subjective, inapplicable to many, and efficiently hide the insights you need to understand the actions you need to take.” He contends that the overall score is the least useful part of Klout. Within a business context, he explains that the most useful portions are the subgroups of metrics in the following graphic (shown as the links in blue under each main category.) Kaushik recommends using the individual metrics within each of the categories selectively, depending on your marketing objectives. The text describes the insight that each category provides. [...]
March 15th, 2011 at 01:47
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Muchos son los expertos que han advertido de los problemas del análisis de sentimiento y principalmente del análisis semántico realizados mediante software. Avinash Kaushik, quizás el autor más relevante del mundo en analítica web, ya nos advierte en Social Media Analytics: Twitter: Quantitative & Qualitative Metrics que es preferible no utilizar herramientas de análisis lingüístico porque los resultados no son fiables.
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March 31st, 2011 at 08:59
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Finally I love the entire social media data explosion. Social media is a lot less exciting than we think (and I say that as someone who has over 30,000 followers on Twitter!), it is also distracting many marketers for strategies that they should be paying attention to. But there is no doubt that social media throws a big wrench in the way we have measured success, or how we have defined success. I love proliferation of tools and attempts to figure out how to measure what matters (I attempted to in my blog post Social Media Analytics).
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March 31st, 2011 at 09:46
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3. SAS Enterprise Miner: SAS Enterprise Miner, a module in SAS, was the first software I used for performing data mining tasks. It is extremely fast and user-friendly. However, I must admit that it reminds me software like Amos, now included in PASW (formerly SPSS) for Structural Equation Modeling (SEM) tasks, where you move the “little truck” to build your model and don’t really understand what you’re doing at the end of the day. Furthermore, it costs a lot but to my knowledge, SAS is the only software platform integrating data mining tasks with web analytics and social media analytics.
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April 8th, 2011 at 04:50
I think it is important to be able to measure social media's effectiveness, but I think the alot of web creators are spending too much time on these sites. It all depends on how you use them.
For dialashop I used twitter to announce new pages and useful information. I do not try to get personal. I do get visitors from it, but not a large amount. I think its best to use social media as a platform to announce new imporant pages and info.
If people see the tweets are of quality then they will follow you. If you are trying to work out which is the best, try each one and see which gives the best results. Remember at the end of the day your main site is the most important.
April 29th, 2011 at 06:04
Great post Avinash. Thanks for sharing the Twitter metrics.
Will share this post to my twitter followers.
May 6th, 2011 at 12:57
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[5]http://klout.com
[6]http://export.ly and http://www.kaushik.net/avinash/2009/11/social-media-analytics-twitter-quantitative-qualitative-analy... http://en.wikipedia.org/wiki/Twitter
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