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Why and How People Follow on Twitter: We Analyze 100K Followers
By: Chris Bolman
[Note: This post originally appeared on Chris's startup marketing blog.]
Last year, I launched a social marketing automation app [re: BuzzFork] that's quietly grown to be the largest third-party "Twitter marketing" software - I use that term loosely - on the web. To give you a sense of size, BuzzFork is now generating millions of impressions and hundreds of thousands of engagement events (follows, retweets) across Twitter for users in 41 different countries. Because impressions create conversions much like a more traditional ad network, this also means we're now spinning off large volumes of data about social engagement, Twitter behavior and follower interactions.
This is our first data-driven look at some of the insights BuzzFork has collect, and we're going to start by analyzing a random data slice of 100,000 Twitter follows captured on BuzzFork.
First, some stats about our overall sample population of followers:
a. The average follower has 3,011 followers of their own on Twitter, although the median follower count is much lower at 388
b. The average follower has a Klout score of 38.9
c. The top 3 most commonly occurring Twitter bio keywords/strings: 1. social (3,857 unique appearances), 2. university (1,940) and 3. media (1,844)
d. The top 3 most common locations (according to their profile): 1. London (2,742 profiles), 2. Boston (2,239) and 3. New York (2,133) (somewhat surprisingly San Francisco came in sixth)
All time for this study is set to Eastern Standard time. Also, with follows being drawn randomly throughout most of the year, our data set shouldn't need any seasonality adjustments. Nonetheless, if you see any ways to improve this analysis (or any flaws in my methodology, whatever they may be) feel free to shoot me an email.
Lesson #1: Users with Less than 1,000 Followers are Three Times More Likely to Follow You
Trying to get influencers to engage with you on Twitter? The odds aren't necessarily in your favor, particularly if you're in the early stages of brand-building for your own account. All other things equal, Twitter accounts with more than 1,000 followers are 62% less likely to follow you than users with <1,000.
Lesson #2: You're 14% More Likely to Get Followed on a Weekday vs. a Weekend
Out of every 100,000 followers distributed throughout a week, this data says the average weekday will see 14,819 follows vs. an average of 12,952 on a weekend day. That means 14% more follows happen on a typical weekday when people are presumably spending more time on their computers and mobile devices and less time off-line engaging in leisure activities. Overall, approximately 74% of all follows captured in this data set took place between Monday and Friday. The distribution of follows throughout the week was also fairly consistent: Thursday was the highest days for new follows but still only 5.9% above the weekly average, while Sunday following volume was only 10% below average.
Lesson #3: Twitter Engagement is Continuous, with Follower Spikes Around Human Activity Patterns
As this distribution chart of Twitter follows per second shows, Twitter is truly "always on," with engagement throughout the day looking more like a jagged, organic stream of peaks and valleys than a clear, clean pattern. Interestingly, with 78% of Twitter's monthly active users (MAUs) outside the US and 76% of MAUs on mobile, almost 71% of daily follows happens outside of 9am - 5pm EST, proving that Twitter's immense global scale drowns out the typical US work day in terms of driving social interaction. While Twitter engagement seems to be slightly higher in the afternoon (58% of follows happen between noon and midnight vs. 42% between 12:01am and 11:59am), it's difficult to derive actionable recommendations for your social strategy from such a noisy distribution. One last observation worth making about this particular chart is the obvious spike that happens between 1:00-1:30pm EST. Could this reflect US workers on the east coast returning from lunch breaks and checking their Twitter feed? Could that be coinciding with commuters in Europe getting home from work and checking Twitter around 7:30pm (or children finish their homework after school, then turning to social media)? Impossible to say just from this data-set, but the sharp short-term uptick in follows is hard to miss.
Lesson: #4: Twitter Users Engage Around Social Media, Games, Business and Personal Fulfillment
What are the topics you can talk (re: tweet) about (and/or reference in your bio) that are most likely to get someone to follow you? Here are the four leaders:
1. Social media itself ("social," "social media," "social media marketing")
2. Sports and games ("game," "games," "league," "sports," "football," "soccer")
3. Business (particularly technology and marketing-related topics and keywords)
4. Personal fulfillment and care ("care," "health," "spirituality," "religion")
In some ways these findings aren't terribly surprising. As one might expect, Twitter users have a strong positive bias toward social media usage, as well as marketing and technology. High-volume amplification and social TV conversation around live sporting events like the Super Bowl are also pretty expected social media behavior. However, the fourth highest source of social solidarity on Twitter suggests a large share of its users also see social as an extension of their moral compass. Twitter users are highly engaged around topics of health, personal care and the changing -- and often polarizing -- healthcare landscape. So for those quick to dismiss "viral media" outlets like Upworthy, take heed, they seem to be giving Twitter audiences what they want. And despite Twitter's secular, Silicon Valley roots, spirituality and religion play a non-trivial role in how users interact with others on the platform.
Overall, talking about one of these four topics is 93% more likely to attract new followers to your account than tweeting about something else, nearly doubling your engagement prospects, according to this analysis.
Lesson #5: Talking About TV Doesn't Translate to Engagement
One final finding of note is that while social TV represents a relatively large share of voice on Twitter, it doesn't contribute much to follows, suggesting social TV audiences trend toward broadcasting, not listening, and the quality of conversation is relatively low by engagement standards. In fact, tweeting two or more consecutive posts about a TV show reduces your profile's rate of new follower acquisition by 39% over the next hour. This lines up with earlier Twitter analysis by Dan Zarella of Hubspot, which showed tweets that include TV social network startup @GetGlue (now tvtag) capture only 1.24% click-through-rates (CTRs), compared to a platform average CTR of 2.11%.
Surprised? Intrigued? Unimpressed? We'd be happy to hear about it on Twitter or in the comments.
Our Favorite Website Today: Kanye West vs. Ad Agency Creative Directory
They dress twenty years younger than their age, frequently deliver jaw-dropping, head-scratching "pearls of wisdom" and think they're genius taste-makers... yep, you guessed it, Kanye West and agency creative directors.
Our favorite website find of the day pits the two against each other in the ultimate, ego-driven face-off for cultural attention: Kanye vs. Creative Director. Play along or submit your own legendary Ye or creative director one-liner, but beware, with perplexing gems like "I AM THE ONLY AUDIENCE I WRITE FOR" and "PEOPLE ARE SO, SO DATED AND NOT MODERN," a perfect score is no small feat.
While Cyber Monday shoppers bought up iPads and Macbooks in droves, Apple did some purchasing of its own, acquiring social search and data analytics aggregator Topsy for a reported $200 million+.
Topsy, known by many as the "Google for Twitter," offers tools to analyze tweets, social data and consumer sentiment. We've featured Topsy hashtag and keyword data previously on our own blog to look at trending terms and topics at events like Advertising Week and the World Series.
While Apple has confirmed the acquisition, its representatives have declined to comment on how the company plans to use Topsy. Nonetheless, we see Topsy factoring into Apple's plans across a variety of potential avenues and product applications:
Market Research and Forecasting. Compared to Google, Facebook and even Microsoft (with Bing), Apple lacks strong social data aggregation capabilities. Because Apple has chosen to own the hardware, rather than the network (like a Google or AT&T), it doesn't acquire as much social interaction data outside of platforms like iTunes and its App Store. Tapping into Topsy gives Apple a better sense of what's trending, how to identify social influencers and a robust platform for making sense out of social data.
Twitter Music (or Spotify, or newcomer Google Play) Caught Apple's Attention. Apple's iTunes radio currently lags far behind Pandora and Spotify in terms of consumer attention, as well as traction. Although Twitter's failed Twitter Music experiment died on the vine (no, not that Vine), Apple management may still be intrigued at the possibility of integrating social data more deeply to make iTunes radio a more data-driven and shareable media experience. Apple's purchase of Topsy could be a key element of a strategy to recover listener share lost to streaming providers like Spotify, Pandora and newcomer Google Play All Access.
App Store Analytics. Most quickly think of the Twitter firehose in conversations about social big data, but with over 2 billion downloads per month, the App Store throws off a considerable amount of download and interaction data, despite very limited external data transparency. Topsy's team may be coming on board to help Apple execs make more sense of that data at scale for strategic forecasting and product purposes.
Apple Worries About Being Left Out of Mobile Ads. According to the Wall Street Journal and other media sources, "Apple has been wrestling with a tepid response from marketers for its iAd platform, which was launched in 2010 to sell ads within mobile apps on iPhones, iPads and iPods" and other mobile devices. Although mobile ads seems like a relatively low priority for Apple, Twitter's acquisition of MoPub may have inspired Apple to open up its checkbook to balance the product roadmap scales for mobile ad insights and analytics.
Topsy is a Defensive Move to Keep Safari Competitive as a Web Browser. Google's Chrome is now used more than Safari, Firefox and Internet Explorer combined, and in recent years both Mozilla and Microsoft seem to have invested significantly more in trying to stay afloat. Overall, Safari's share has treaded water, and the likely reason is that Safari is the native search engine browser in Apple's mobile device and tablet product line.
Acquiring Topsy could bring Apple valuable search IP as it looks to stay competitive in a browser world that's rapidly transitioning toward mobile (where rival Google and it's Android platform is uniquely positioned).
While the Topys acquisition certainly isn't a game-changer for Apple, it's an interesting pickup at the intersection of social data, real-time analytics and mobile insights, representing a data parsing engine that could contribute meaningful value to the App Store, iTunes Radio, Social Search (to contribute to Safari) and general corporate forecasting. Will it make Apple a more transparent and social company? Probably not, but it may indicate a shift in strategic sentiment at Apple, where hardware has primarily reigned as the prized priority.
As we first announced in part one of yesterday's customer referral program creation tutorial, BuzzFork's customer loyalty program is now live with a simple mission in mind: share BuzzFork, get rewarded.
Now, sharing BuzzFork with friends, colleagues and social media connections can earn you free campaign impressions and even $10 for you and the friend you refer.
Growth Hacking Experiments - How We Set Up Dropbox-Style Referral Links (Part 1)
If you scan the volumes of growth hacking literature on the web, there's a lot of good data and post-mortem analysis on how startups like Dropbox, Paypal and Uber used referral marketing programs to accelerate early user adoption and brand-building. As a result, we're going to assume if you've read this far, as a baseline, you agree with us: IF you have a good product, a customer referral program is an effective way to incentivize your existing user base to do some of your marketing for you. But we're not here to repeat why customer referral programs are a tasty growth hacking recipe: instead, we want to walk through how to bake the cake, structure and implement one.
[disclaimer: BuzzFork is a Django shop, so our code examples are Python. I've been fiddling around with a referral program module adaption for Express node.js, but no promises when that gets done. Unfortunately, if Rails is your bread and butter I've got nothing for you (other than brotherly love), although maybe this guy does...]
Let's begin, shall we?
Program Structure
In terms of the structure our referral program, we decided not to think too outside the box: we've already been handed a growth-oriented industry blueprint by growth hackers at startups like Dropbox, Paypal, Uber, AirBnb and Groupon, so why reinvent a working wheel?
Here are the basic principles/rules we opted for:
Two-sided benefit stream: both referrer and referee get a benefit. I asked SaaS expert David Skok of Matrix Partners whether his data and experience suggested a two-sided referral program outperformed a referrer-benefit-only one, and he confirmed it consistently did. Check.
$10 worked for Paypal and Uber, and since we're in Uber's price-point ballpark (despite a polar opposite SaaS offering aimed at growth hackers, social media managers, agencies, startup marketers and emerging brands), we decided to launch with "give $10, get $10" too. We also polled some existing users and friends, with the general agreement being that $10 was a psychologically significant threshold where it was actually worth doing something, without being so lavish an offer we feel like we're leaving a lot of value on the table.
Offer a separate benefit to brand new users who aren't as familiar with the product and haven't demonstrated a willingness-to-pay yet. For free users (non-subscribers), sharing BuzzFork gives them extra free trial time.
Referral Program Architecture
Ok, we've got our program model, so what steps (features) do we need to implement? How do we break our goal down into actionable, programmable steps?
Here's what we need our system to accomplish:
We need to generate a user-specific, unique URL for each user when they sign up (existing users will be handled separately). The URL will be made unique by a URL parameter code (and can also be passed through a trackable, 301 redirect-generating shortener API like bit.ly). Here's an example of a code generator which generates (returns) a 40 character code that will be appended as a URL parameter to our link:
Then we define a url where "code" (as path) gets added to our domain:
We want a one-to-one relationship between each user and their sharing URL, which we'll talk more about below.
We then want to track whether a browsing session came from a referral link. What we need is a way to listen and record within our app if an HTTP request came from a referral link.
When we record a browsing session that came from a referral, we'll want to connect it to the user who created/shared the link.
We need to define a schema for our "reward(s)" so it can be redeemed, credited and messaged.
When a sharing reward is earned, we need to be able to credit it to the referrer. In our case, this means posting two $10 discount codes to Stripe, one to the referrer and one to the referree. We'll also display the rewards our referring user has earned within the app, similar to Dropbox's rewards section.
Referral Program Dependencies
To recreate our rewards system in Django (while using modular libraries and writing as little new code as possible), we used:
Django==1.5.1
Anafero (anafero==1.0.1, in our case)
django-celery==3.0.17
A celery broker and back end (we run Redis)
Django-Stripe-Payments // if you're using Stripe for payment processing
Referral Program Implementation (Models)
After a quick analysis of existing open source code, we identified Eldarion's Anafero library as a good starting module to build our referral system on top of. Django devs can install Anafero into their own project with a simple "pip install anafero" from the command line, followed by adding the app to settings.py like so:
Then, simply add Anafero's middleware and url configs as detailed in its documentation. If Django isn't your framework of choice hopefully this can still serve as a useful "how we tackled/thought about the problem" resource that you can adapt to Rails, Meteor or whatever the kids are into these days.
Anafero works by establishing a one-to-one relationship between your app's user profile object and a Referral object, then uses a record_response method to listen to user sessions and check for a referral_response to see if a browsing session is linked to an existing user referral. Signal responses are handled by the receiver user_linked_to_response. So let's walk through how to integrate this basic structure into our app's models (models.py):
1. If you're using Anafero, be sure to import whatever objects you need from anafero.models (i.e. Referral, ReferralResponse)
2. Next, we specify our rewards (the different types of perks our users can qualify for by sharing their affiliate URL):
3. Third, we create our referral reward database model, which establishes the following fields: (a) referral type, (b) user [using models.ForeignKey to establish the relationship to our user schema], (c) response, (d) reward amount, (e) date credited, (f) date redeemed and (g) a Boolean indicating whether or not the reward was redeemed. This enables us to create ReferralRewards that can be displayed and redeemed (credited to a user's account).
We also create custom methods for reward redemption (redeem) and a conditional reward label.
4. Our next step is to add two new fields to our user profile model to establish the one-to-one relationship between the user and Referral (thereby connecting them to their referral code and link):
5. Then we define a new method get_referral_link which we'll rely on to generate the sharing URL on the client side within our HTML template using an <input> element with value="{{ user.get_referral_link }}" [note: if you're using social auth to create your user profile this might look something like {{ user.facebook.get_referral_link }} instead]. This method also checks to see if we have a shortened URL available, otherwise, it returns us the un-shortened referral URL:
If you're using anafero, that's all the model customization that's required after importing Referral and Referral Response. If you're not using anafero because you're rolling your own or working in another framework, you'll also need to create schema and class methods for Referral and Referral Response. See here for how anafero implements this.
Ok, at this point, we've now accomplished three key things:
1. We've installed our modules and dependencies.
2. We created a ReferralReward model.
This now gives us our app three objects total that we can work with: (a) Referral, (b) Referral Response and (c) ReferralReward.
3. Finally, we linked Referral and ReferralReward to our user schema so that -- logically -- users can refer and be referred (and rewarded).
If you're following along in Django, all that's required now is a syncdb (and possibly a quick south migration) and our database is primed to support our referral system. Now that we've got our models set up, our next steps are to (a) configure our receivers, (b) set up our views, (c) add referral sharing into our templates and create the UX around it, then (d) track, award and log rewards. In part 2 of this tutorial, we'll focus on signaling, views and templates so we can display functioning, sharable URLs to our users just like Dropbox. Finally, in part 3, we'll look at reward implementation, crediting discounts to Stripe and tracking reward redemptions/credits.
Social Selling Takes Center Stage at DreamForce 2013
Salesforce's DreamForce 2013 conference kicks off in San Francisco today, and has already begun to livestream on Salesforce Live. One of the larger tech conferences of the year, with over 120,000 visitors and keynotes from Salesforce CEO Marc Benioff, Sheryl Sandberg, Marissa Mayer and Deepak Chopra, DreamForce focuses on the intersection of sales, software, data and social media.
Social selling will be a front-and-center topic throughout the week, as sales teams, marketers and CRM providers look for ways to leverage social data and digital relationships to accelerate and optimize lead generation, customer acquisition, customer service and customer retention. Giving weight to the social selling trend is ongoing research and data that shows that, put simply, social selling works: a 2013 Aberdeen study found that sales reps who leverage social selling in their sales process are 79% more likely to attain their quota than those who don’t use social selling in their sales process (15%).
Moreover, a Sales Management Association survey of 140 companies this past spring found that 96% of their sales teams' use LinkedIn at least once a week, spending an average of six hours per week on the professional networking site. However, despite spending nearly an entire workday each week prospecting for leads on LinkedIn, nearly 80% of teams surveyed had no formal social sales strategy, instead leaving it up to individual sales reps to manage their time, tactics and accounts.
Clearly there's a disconnect, a disconnect companies like SalesForce, Nimble, Hubspot and Marketo are looking to turn into an opportunity to unite social media marketing and the sales process. We've also highlighted how integrating social with sales not only drives more website traffic and inbound leads but also directly increases sales for both B2B and B2C businesses in past blog posts. At DreamForce, dialogue around social selling will be amplified all week long, with panels focused on using social marketing and social data for lead generation, prospect profiling, relationship building, competitive intelligence, and better sales team productivity.
To join the DreamForce 2013 conversation on social yourself, the official hashtag is #DF13. For a list of other hashtags in use at the conference, visit SalesForce's DreamForce 2013 social guide.
Vine Releases Time Travel, Makes it Even Easier to Become Social Video Famous
Yesterday, everyone's favorite six-second social video app Vine released a new update that lets mobile videographers save and work on multiple Vine posts at once. The upgrade also gives Vine creators the power to edit and arrange shots inside individual posts on iOS and Android. Vine calls the new features "Sessions" and "Time Travel."
Together, "Sessions" and "Time Travel" enable aspiring Vine creators to maintain more of a "Vine portfolio," in addition to allowing users to edit posts before sharing them.
So far, the new feature release from Vine has been getting positive reviews, with top Vine user Ian Padgham tweeting out:
The new Vine update is AMAZING! Check it out :) #loop by @origiful https://t.co/hy0YCxRfaq
— Ian Padgham (@origiful)
October 24, 2013
Vine use has grown 403% between Q1 and Q3 this year, making it the world's fastest growing app, according to Mashable.
With Fall in full swing, we dug into our database using Heroku's awesome data clips feature to look at social keyword trends on BuzzFork for October 2013.
Our key takeaway: sports makes Twitter users get social. In fact, 23 our of the 50 highest-performing BuzzFork keywords were sports-related.
Source: BuzzFork.com.
With the Boston Red Sox en route to the World Series, both "Sports" and "Boston" have been trending keyword themes on BuzzFork this month. And although Boston isn't exactly known for its friendly hospitality, New England sports-themed keywords generated 1,591 Twitter followers across BuzzFork, proving Boston fans show strong social solidarity on #gameday (at least when the home team's winning).
Interestingly, although only a small-number of vertical-specific keywords were used, Video Gaming also showed high social engagement, with 1,154 new follower conversions stemming from a relatively small keyword universe.
Looking at the top 10 performing keywords (with at least 100 impressions), three brands were high-performing campaign targets: Nike Running (10.1% conversion rate), question-and-answer social network Quora (8.7%) and landing-page A/B testing SaaS service Unbounce (7.8%).
Source: BuzzFork.com. Includes only keywords with >100 impressions.
A new study released by social publishing tool-creator Shareaholic shows Facebook, Pinterest and Twitter are dominating social media referral traffic to websites. According to Shareaholic's data "these" three social media power players collectively accounted for 15.22% of overall internet traffic last month. Given their community and share-friendly nature, it’s no surprise that they top the list in traffic referrals and have grown more than 54% each in share of overall visits." Overall, referral traffic volume from Facebook grew 58.81%, Pinterest by 66.52% and Twitter 54.12%.
Shareaholic's analysis also confirms some of our own earlier research that Google+ still isn't competitive with Facebook, Twitter, Pinterest and LinkedIn in terms of social sharing volume. Read the full study here.
The Evolution of How Consumers Interact with Brands is Fast and "Radical"
New data from Global Web Index (GWI), identifies and highlights how quickly and dramatically the way consumers interact with brands is evolving. According to GWI, "The fastest growing brand behaviours include “retweeting a branded micro-blog post”, up 28% since Q1 2013, followed by “uploading photo / video to a branded social network page/group” and “sharing content in a branded community” – both up 22%. This demonstrates... the big impact for brands from social media is users sharing branded content, underlining the power of human distribution and making it clear why content should be at the heart of every future brand strategy."
According to GWI, 218 million internet users are now uploading photos or videos to branded social network pages or groups, while 170-175 million people are now retweeting brands and/or sharing content within branding communities like Facebook pages or Tumblr blogs.
At today's Advertising Age Digital conference in San Francisco (official hashtag #aadigital), Kevin Weil, Twitter's VP of Product for Revenue gave an update on the company's strategy for social selling, describing how Twitter Ads' conversion funnel is evolving to include brand lift studies, sentiment analysis, lead-generation and offline conversion tracking.
Weil shows how Twitter Ads spans the digital marketing funnel on the below slide:
"The distance between an impression and a conversion is very small," noted Weil at Ad Age Digital, as he talked about Twitter's new ad products that include lead generation cards and the social TV ad-targeting program, Twitter Amplify.
Vindu Goel, a technology reporter from the New York Times, reported via Twitter that
Twitter drives offline sales. Kevin Weil says 8 pc lift from exposure, 12 pc from engagement, 29 pc from followers. #aadigital
— Vindu Goel (@vindugoel)
October 15, 2013
Overall, Twitter Ads' product progress is encouraging and reinforces the social tech titan's commitment to building a powerful and multi-faceted platform for digital marketers. A 29% increase in purchase likelihood from a person who follows a brand on Twitter is a meaningful statistic, one that bodes well for the future success of social selling strategies on Twitter.
After discovering wickedly-cool source code search engine Nerdy Data, we decided to put our free credit trial to use to find out something cool, evergreen and social. So what big data-based question did we try to answer in less than 10 search queries? How about... how does the market share of social sharing break down among social networks and social widget sharing providers?
To accomplish this, we analyzed different sharing providers' javascript and ran search queries for each one. Now, off the bat we should note some limitations to our micro study. First, the results won't be perfect due to tag and script customization. Since we could only use exact search on the trial version, any dev who included a deprecated meta attribute like type="text/javascript" in their script tag or placed a meta-attribute like "data-send=''" ahead of or within our search query would have been omitted from our results. Second, we're not totally confident Nerdy Data has actually crawled the entire big, broad internets as we know them, although they are clearly already crawling and indexing millions of pages. Third, social sharing has recently evolved to become more and more browser-based, via Chrome apps and Firefox browser plugins. Since these widgets are browser-based rather than website-based, our study can't identify them.
Methodology shortcomings aside, here's how Nerdy Data ranks the presence of sharing buttons, like/+1 buttons and sharing widgets across the internet:
Findings:
We think it's pretty interesting that there now *seem* to be more Google +1 buttons (judged by the page presence of apis.google.com/js/plusone.js) across websites than Facebook like buttons. This suggests web-masters, startup engineers, bloggers and SEO agencies have picked up on the high reported correlation between Google +1s and higher search rankings [speaking of which, if you enjoyed this and other posts on our humble blog we'd appreciate your +1 on the right-hand side of the page], even if Google still denies a direction relationship.
Source: SEO Moz
However, despite a major influx of Google +1 buttons, digital content creators don't seem to be sharing much to Google+ directly via Google's own sharing tools. For example, our search for <g:plus action="share"></g:plus> returned only a paltry 348 results (websites). Now again, customization will reduce the number of results this query returns (we definitely would have gotten more results just searching for "<g:plus" or even "<g:plus action='share'"), but that's still a really low number, suggesting most internet users are sharing to Google+ via third party widgets like ShareThis, AddThis and Shareaholic, or directly from content nodes like YouTube. By comparison, we counted 388,330 Twitter share and follow buttons using Twitter's developer javascript tags, which means publishers clearly want your help distributing their message on Twitter.
ShareThis appears to be the dominant market share player among third-party, on-page sharing widgets. The social widget and analytics provider was identified on over 350,000 websites, nearly 10X more than runner-up AddThis.
Source: ShareThis
Again however unfortunately our study suffers from the short-coming of not being able to identify browser-app sharing widgets, making it harder for us to pin-point a representative sample for other social sharing tool-makers like Shareaholic (where we found 3,885 sites sending requests to their API, although the company itself says it's in use by approximately 200,000 publishers).
We're mighty sad that we ran out of credits before we could get to Pinterest. If Nerdy Data is willing to kindly bestow us some more credits (or a discount to their $99/mo plan), we'd love to dig deeper into this data-set.
Questions? Comments? Responses? We'd love to hear from you. If not, feel free to do one of the platforms we mentioned above a kindness and share this post.
8 Growth Hacking Strategies to Increase Your Website Traffic
BuzzFork founder and technical marketer Chris Bolman shares 8 actionable growth hacking strategies to grow your website traffic, improve user/customer acquisition and increase lead generation. Growth hacks include suggestions and references to Github, Google News, Friendbuy, BuzzFork, Quora, Medium, Visual.ly and more.
8 Marketing Growth Hacks to Increase Your Website Traffic
Want your latest Facebook post, Tumblr note or tweet to get the maximum amount of viral lift? Then write simple, clear sentences.
The more complex your social media post is, the fewer shares you'll get. Write plainly and simply. #scichat pic.twitter.com/f8xDOUFTu5
— BuzzFork (@BuzzFork) October 11, 2013
Complicated writing has a negative relationship (correlation) with sharing. The most shared social media posts have a student reading level of 2nd to 6th grade, and 8th and 9th grade. This data science also proves what we've always long suspected: everyone hates on 7th graders.
A Better Twitter Growth Hacking Mousetrap: "Conversion Identities"
Today we're excited to announce the release of a new analytics upgrade for BuzzFork: "Conversion Identities".
Conversion Identities now allows you to see, rank and analyze who's engaging with your Twitter campaigns and learn more about your new followers' interests. Not only does it allow you to better understand your best-performing targets (i.e., you can move beyond conversion rates to also measure the quality of your conversions), but it offers an insightful opportunity-window to be more social with your new Twitter followers and start relevant conversations. The "social authority" scores featured within conversion identities are Klout scores, via our latest API integration with Klout.
We're really excited to bring this new, powerful feature to all BuzzFork subscribers, and we hope it leads to new levels of engagement and usefulness for digital marketers and social media growth hackers.
An new social TV study led by Frank N. Magid Associates and first reported in Forbes has led to a fascinating and potentially meaningful initial conclusion:
By more actively participating in social media conversations about TV, Twitter users are, on average, less likely to be cord-cutters.
This finding is particularly surprising given that Twitter users, are, on average, more tech-savvy, more affluent and younger than the general TV-viewing population. Thus, despite the fact that youth, technology adoption rate and affluence all tend to positively correlate with greater use of streaming and over-the-top digital video services like Netflix and Hulu Plus, Twitter users seem to go against the trend. "Only 1.7% of the Twitter users in Magid’s survey expected to drop TV subscriptions in the next 12 months, versus 3% of non-users," writes Forbes. "This even though the Twitter users were on average younger — meaning they should’ve been more inclined to cord-cut, not less."
Although the sample size may be too small to draw broad-based conclusions (only approximately 2,400 people were surveyed), the Magid study gives Twitter some additional momentum in pitching its social TV advertising solutions to media buying brands and agencies, while lending further weight to its current lead among social networks as the true "real-time conversation companion."
Infographic: First Nielsen + Twitter Social TV Study Coming Soon
Nielsen will publish its first report with TV rankings using Twitter data on Monday, October 14th, say journalists. The revised ratings data incorporate new social TV stats like the number of tweets about a show and the size of the audience that sees them.
Interested to learn more about what the Nielsen + Twitter partnership means for the social TV ecosystem? Then click "read more" to view an epic infographic about Nielsen SocialGuide:
Too small? Check out the full-size, high-resolution infographic here.
