Social Media ROI – The Believers and Non-Believers

It appears the Social Media ROI conversation is heating up – and predictably it has split into two camps, the Believers and the Non-Belivers.

The non-belivers are adamant that you simply can’t value conversations. The believers say you can because relationships are valuable. They are both wrong (and right).

Let me try to clarify things – conversations have zero tangible hard value – you can’t put a specific dollar figure on the value of any conversation. In that respect the non-believers are correct. The believers tell you that conversations are valuable because they affect some other valuable thing – and they are correct (however they insist on pointing to the wrong affected things).

Here is a great example:

Shel Israel – who is very bright and who I have immense respect for – is one of the non-belivers. KD Paine – of whom I have no previous knowledge – is one of the believers (at least for the purposes of this post).

Ms. Paine wrote a post taking issue with Mr. Israel’s assertion from his blog that there is value in conversation that can not be measured.

The way to measure the value of conversations is to first measure the degree to which people trust you, and the degree to which your stakeholders are satisfied and committed to your relationships. Find out just how valuable people think those relationships are. Then start a conversation and measure how much MORE valuable people think the relationship after you’ve been talking with them awhile.

I think we would all agree that these statements are accurate – but do they assign hard value to conversations? No, in fact this is simply a list of other intangibles that conversations affect. Ms. Paine lists tangibles (i.e., lowered costs across several functions) but never comes out and says that conversations lower costs in functions x, y and z.

It isn’t that Ms. Paine is terribly far off – it is just that she refuses to make a well formed argument that translates to a hard dollar ROI. She simply refuses to connect the dots and commit to a hard dollar outcome from her “conversations”.

On the other hand, Mr. Israel posits this thought experiment via Tweet:

Screen shot 2009-12-17 at 9.00.19 AM.png

Screen shot 2009-12-17 at 9.00.42 AM.png

Mr. Israel makes the opposite mistake – he is essentially telling us that unless you can directly connect an action with it’s effect you can’t call it an ROI. If that were true about 85% of corporate spending would stop today.

So, just for fun, I’ll complete Mr. Israel’s thought experiment:

First let’s figure out the investment:

  • Assume you need 12 pair of pants that cost $125.00 each – total cost of pants: $1500.00
  • Pants maintenance (i.e., dry cleaning) costs of $18.00 per week – total cost of maintenance: $936.00
  • Total sunk cost for pants (for one year) is: $2436.00

Now, let’s talk about the return side of the equation:

  • Assume an average deal size of $25,000.00
  • You take 42 “business meetings” per year and you currently (wearing pants) convert 62%.
  • That means you win 26 deals per year @ 25k each – for a total of: 650k/year

Here is where the fun begins:

  • Let’s make the conservative (and if you disagree with this being conservative please speak up) estimate that not wearing pants to business meetings would lower your conversion rate by 8%.
  • Now you only convert 54% of your opportunities.
  • You now generate 22.5 deals per year at 25k each – for a total of: 562k

Net change in outcome metric: -88k

ROI on Pants – for an investment of $2436.00 you (conservatively) generate an additional $88,000.00 per year. That is a return of 3600%.

Then non-belivers will read this an suggest that there may be hundreds of reasons that you didn’t win those deals – and they’d be right. But that isn’t the point. The point is that the proximate cause of losing those deals was – with a very high degree of probability – the fact that you were sitting in the meeting with your junk hanging out.

ROIs are built on proximate causes the vast majority of the time – and that isn’t a scam, it simply reflects the reality that most of the things we do directly affect input metrics, not our hard dollar output metrics. In other words, we do things to improve important measures that have no direct tangible dollar value because those metrics have a proven affect on measures that do.

The takeaway here is that we should stop trying to assign hard dollar values to Social Media metrics/measures and get busy showing how they affect the hard dollar metrics for your business.

Social Media’s Big Problem – Marketers

I hate to say it, but Social Media (and Twitter in particular) has a big problem… and that big problem is marketers.

I know, I know, marketers made Social Media – and setting aside weather or not that is true, let’s focus on the facts.

  1. Nearly any relatively popular topic is quickly overrun with marketers trying to get their message into your stream.
  2. The line between spam and marketing is non-exisitent in Social Media.
  3. Any analytical analysis of a topic is becoming more and more difficult as the topic gets filled with marketing.
  4. Traditional marketing approaches work in Social Media… get your message/link in front of enough eyeballs and some percentage will click.


Case in point. I own a Social Media solutions company – justSignal – and we have a Signal set up to track everything people say is “great” (via a variety of term searches and exclusions using our proprietary filtering mechanism) on Twitter. We’ve just released our SignalLinks Analytic in beta (you can learn more about SignalLinks here). So today I decided to look at SignalLinks for our everything great on Twitter Signal.

The results were disappointing to put it mildly. There is no authentic user voice in this data… only marketing and/or spam (you find the line there).

Here are the most mentioned links over the last 30 days:


























silence_noise_143829_tns.png Let me make one thing perfectly clear – I belive that Social Media provides the best opportunity for opt-in targeted marketing. But when the Signal is so clogged with marketing and/or spam that adds zero value the only effect will be user apathy.

From a development/partner point of view, some of Twitter’s actions to “curate” seem rather annoying – but from an end user’s point of view they are doing exactly what they need to do. After all, at some point Twitter will launch their business model, and the two best bets are:

a) Targeted opt-in Ads

b) Analytics

Both of those revenue paths are put in serious jeopardy if users become apathetic because their Signal if full of marketing noise.

TweetsForBoobs – How justSignal Helped Make it Happen

200910090957.jpgReposted from the justSignal Blog (

Sometimes the best way to illustrate how justSignal can accelerate your strategy is by providing concrete examples of how others have accelerated theirs.

TweetsForBoobs is raising money for the Susan G. Komen foundation by encouraging folks to tweet the #tweetsforboobs hash tag on Twitter. It is the brain child of Chase Granberry and Josh Strebel – justSignal (and I for that matter) claim no credit for the idea or it’s successful execution.

In the interest of full disclosure, we did donate justSignal to TweetsForBoobs.

TweetsForBoobs needed four things in order to complete their vision.

  1. A way to capture Tweets about the site and with the hashtag
  2. The ability to put those Tweets on the site in real time.
  3. A way to count how many times a Twitter User used the hashtag.
  4. A way to measure the effectiveness of their efforts.

justSignal, because we focus on the complete Social Media Lifecycle, was uniquely suited to get them there – fast.

TweetsForBoobs was able to create a Signal that pulled in the content they were interested in. They were also able to – using our Exclusion Engine – remove spam and re-tweet bots from their Signal.

In order to create an engaging user experience on the site TweetsForBoobs dropped in and customized our real-time Twitter widget.   

Using our API service, TweetsForBoobs was able to pull in all mentions of the hashtag and the site in near real-time. This enabled them to count how many times each user tweeted the hash tag and update the site with current pledge totals.

Finally, TweetsForBoobs wanted to have some information (analysis) that gave them some indication of how the campaign was going. Since every Signal comes with our SignalReports (SignalMeter, SignalDensity and User Activity) they had basic who, when, and how much information.


That is execution of the complete Social Media Lifecycle enabled by justSignal.

  1. Signal – Getting the #tweetsforboobs content.
  2. Engagement – Using our Widgets to put the content on the site, and using the API to create site specific information.
  3. Analytics – Using our SignalReports to gauge the effectiveness of the effort.

What makes TweetsForBoobs even more interesting is that they clearly show the benefit of our approach to data and analytics. When Chase wanted to understand the “Reach” of the hashtag he wasn’t confined by the data we provide “out of the box”.

The data is yours, when you have a question we don’t answer – because it is the really, really important kind, those specific to your business, company, product or service – you have access to the entire data set and the unfettered ability to mine out what is important to you.

You can read Chase’s excellent summary of the data and his thoughts on justSignal at the Authority Labs Blog.

What about action?

That is where you come in… head over to TweetsForBoobs or just grab your favorite Twitter client and send a tweet with #tweetsforboobs in it. Every time you do you pledge $1.00 to the Susan G. Komen foundation.

It is ALIVE – Twitter Search is Back To Full Speed

Not sure what was going on, and there was no mention of it on the Twitter status blog, but it now appears that Twitter Search (and the search API) is back to full speed. There is no question that all day yesterday and and today up until 12:00 Pacific Twitter search was not indexing all Tweets.

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Unless of course all the Twitter users just took an 30 hour nap…

We will keep an eye on it and keep you updated here.

Posted via email from justSignal Status & Updates Blog

Something is DEFINITELY Not Right with Twitter Search

I get hourly emails that show me (graphically) how much justSignal “collects” total from each of the services (Twitter, Blog Search, Backtype, etc). The thing about these graphs is they are amazingly consistent over time – that is why we use them… you can quickly see a change in the normal trend.

Yesterday I noticed that Twitter’s graph looked funny and that the volume was way down from a typical day (on a typical day we collect ~150 thousand tweets). This morning that trend has continued:

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A normal day is a nice smooth curve up from about 2 or 3 am through mid day… like this:

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Also worth noting, our total volume of Tweets collected yesterday was the lowest it has been in 60 days. Normally I would chalk this up to normal variation, but the percentage change from Monday to Tuesday was 50% – and that is unprecedented day over day change. You should also note that provides an exact tweet for tweet match (in our limited sampling) to what we are collecting via the API – so this appears to be a global Twitter Search issue.

This seems to be affecting Trackers with high volume traffic on Twitter – so many of you will not notice any change. If however, your tracker regularly pulls in 10,000 tweets per day it appears that your volume will be cut (roughly) in half.

Obviously all of this is anecdotal and I’ve received no confirmation from Twitter that there is any issue. We will stay on top of it and keep you updated.

NOTE: No, we are not being rate limited – we are, in fact, using less than 10% of our whitelisted request limit per hour. All requests to Twitter Search API are returning normal response codes and, in most cases, tweets – just not nearly as many as just 2 days ago.

Posted via email from justSignal Status & Updates Blog

The Growth of @DbacksBooth on Twitter

I’ve been watching the Dbacks broadcast team begin to use Twitter to interact with their audience, and I’ve been using justSignal (shameless plug for my product) to track their efforts. Over the last 60 days I’ve watched the effort from the Booth and (I presume) Fox Sports AZ intensify. They’ve been requesting Keys to the Game and answers to the AFLAC trivia question via Twitter.

The early efforts were marked by significant inconsistency and mixed messages both on air, and on Twitter. Just look at the gaps in the graph below.

That being said, it appears that on August 7th or 8th they began to consistently engage. What impresses me is how quickly the efforts bear fruit. 

Download now or preview on posterous

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Look at the growth and the consistency of the growth between August 7th/8th and the 29th. There are consistent jumps of 10 – 20 responses per game. 

Two takeaways from this data:

1) Consistency of interaction on Twitter seems to outweigh the on air requests. 
2) A very small investment of time from the broadcast team (4 or 5 tweets) generates a relatively large and consistently growing response.

I know in terms of Fox Sports AZ viewership the numbers are pretty small, but at this point the real goal should be to move the trend line in the right direction. If you look at the trend line for the responses to @dbacksbooth on Twitter it tells you everything you need to know – steeply up and to the right.

They are on to something… and the small investments they are making in Social Media will pay large dividends if they keep this up. The real question is, what will they do with this engagement? I have a few suggestions if they’d like to give me a call.

Posted via email from briantroy’s posterous

Make Mark Reynolds an All Star – More Analysis

Following up yesterday’s post with more data about the effectiveness of the get out the vote campaign to make Mark Reynolds of the Arizona Diamondbacks an All Star.

First let me say this:


If you haven’t yet (at least 300 times) stop reading this and go vote RIGHT NOW!!!

Ok… now that we’ve taken care of the important business, let’s get to the data.

Here is the updated Tweets Per day including the full day yesterday (July 8th, 2009)


Nice spike in volume on July 8th… it looks like the Twitter campaign for Mark is building some steam. Let’s look deeper into the numbers to see if we can attribute this surge in Tweets to the Vote for Mark Reynolds campaign.


Yep, that is impressive. More than a third of the tweets yesterday mentioned Mark specifically. I think it is safe to say the Vote for Mark Reynolds campaign is driving the increased volume… what else can we see…


Again, a nice chunk of the Tweet volume were “vote” tweets. This is a nice indication of the viral effect. Or to put it in non-Social Media terms, people are clearly talking about the Vote for Mark Reynolds campaign.


This is the graph the Diamondbacks (and more broadly) MLB should be focusing on. Look at the growth in the number of Twitter users (aka people) participating in the conversation… being engaged with the brand. The raw numbers aren’t as important as the trend line… and the trend line is phenomenal.

The million dollar question is, what now. Can the Dbacks and MLB sustain this level of engagement, or will we see a corresponding drop off starting at 4pm today when voting ends? Stay tuned…

One other thing… I personally would LOVE to see this data correlated with the actual votes cast by day/hour. If you’ve got a spare second send @MLB a message on Twitter… point them to this post and tell them you’d love to see that information too!!! I’d be happy to help them put it together.

Twitter, @replies and Multicasting

Twitter made a change to how @ replies are handled yesterday and the response has been, well, loud. Essentially you used to be able to see @ replies to everyone you followed, even if you didn’t follow the sender. Under Twitter’s new rules you only get to see @ replies to people you follow IF you follow the sender.

To many this may seem like a trivial change, but to those who use Twitter to discover interesting people… it is a very big deal. There has been rampant speculation (ignited, near as I can tell, by this post from Jesse Stay of SocialToo) that this change “kills” Follow Friday.

I’m including my comment on Jesse’s post here:

Ok… just to clear this one up. I checked (as you know I use justSignal to track Follow Friday for

I have every Follow Friday tweet from 4/13 – 5/13 (last 30 days). There are a total of 771,244 Tweets. Of those 220,166 BEGIN with @username. That is 28.5% of the Tweets.

So definitely NOT most… but a very significant percentage.

Also, we’ve added User Filters to justSignal on FollowFridays. What does that mean? You can filter the total Follow Friday Tweet Stream by what it is about people you want to discover. Want to find PR folks to follow… add a User Filter for PR…

Follow Friday is most certainly NOT dead.

The short version of my take on this is that there are much, much better ways to find interesting people to follow than to rely on the people you follow to tell you about them. Don’t get me wrong, I believe in the power of recommendations, but I also believe in the power of context.

You see, my fictional Twitter Friend Joe is really, really into late 13th century birth control. I – for whatever it is worth – find his updates about walrus skin condoms entertaining. Beyond that Joe and I don’t have much in common. There is nothing atypical about this Twitter relationship… in fact, if I had to bet with my own money I would guess that 90% of Twitter relationships are pretty similar to this. So, Joe’s Follow Friday recommendations are only valuable to me in a very, very narrow window of my interests… so if I don’t see them all am I really missing anything?

What I really want is to see the recommendations from the 10% of the people I follow with whom I have a much broader affinity AND people recommending others because they are really smart about (CONTEXT). Whatever my context of interest happens to be right now.

That is why we’ve added User Filters to the justSignal Tracker on FollowFridays to allow you to filter the live Twitter Stream for your context, your 10%, or whatever else makes Follow Friday work for you. That is what we call – Getting Signal.

Multicasting & @ Replies:

UPDATE: 05/13/2009 @ 4:10PM Pacific Time

I blew it… the below examples misrepresent what the @ reply option did. I’m not going to redact it or change it… because:

  1. I blew it… and that happens and is ok with me.
  2. The example of Tweet distribution as it relates to a multicast system vs. a non-multicast system is still 100% relevant and correct.


Biz made an update to the Twitter blog letting us know that the change to the @ reply system wasn’t really about user confusion – but a technical issue. This is a reason that actually makes sense. It makes sense for one simple reason – the “web” and Twitter were not built to efficiently implement multicasting. The problem with these @ replies is they create a burst of Tweet traffic… why?

If you create a normal Follow Friday tweet like:

#followfriday @joe @mary @steve @mike @larry @meg @biz @wally

That tweet will be sent to joe, mary, steve, mike, larry, meg, biz and wally. Let’s say each of them are followed by 400 people. And let’s say 60% of those people have “show me all replies to those I follow” checked.

This tweet will be sent 1,928 times:

  • 8 – The original 8
  • 240 – Joe’s followers who’ve checked show me all replies
  • 240 – Mary’s followers who’ve checked show me all replies
  • 240 – Steve’s followers who’ve checked show me all replies
  • 240 – Mike’s followers who’ve checked show me all replies
  • 240 – Larry’s followers who’ve checked show me all replies
  • 240 – Meg’s followers who’ve checked show me all replies
  • 240 – Biz’s followers who’ve checked show me all replies
  • 240 – Wally’s followers who’ve checked show me all replies

Now lets say we create the following Follow Friday tweet:

#followfriday @scoblizer @mashable @aplusk @oprah @cnnbrk

Again, let’s round things off and say each of these people are followed by 700,000 each and 60% have “show me all replies to those I follow” checked.

This tweet will be sent 2,100,005 times:

  • 5 – The original 5
  • 420,o0- – Scoble’s followers who’ve checked show me all replies
  • 420,000 – Mashable’s followers who’ve checked show me all replies
  • 420,000 – AplusK’s followers who’ve checked show me all replies
  • 420,000 – Oprah’s followers who’ve checked show me all replies
  • 420,000 – CNNBRK’s followers who’ve checked show me all replies

YIKES!!! Now imagine that tweet getting re-tweeted a couple hundred times. This is both why Twitter (I’m speculating here but with a high degree of confidence) “edited” trending topics to exclude Follow Friday AND why the @ reply change was made.

Why Multicast Matters:

Today Twitter (because they don’t implement efficient multicasting and instead rely on publish/subscribe architectures) has to effectively send each of those 2,100,005 tweets serially, one at a time. This consumes massive amounts of computing resources.

Multicast would allow them to send it once to many destinations – thereby removing that bottleneck from their scalability and tweet processing systems.

Those of you who are familiar with large scale real-time communications systems (i.e., VoIP) will immediately recognize this problem – it was central to creating a large scale VoIP service platform in the late 1990’s. The VoIP community resolved it (not without much effort) and now a very small system can efficiently multicast hundreds of millions of status updates/changes per hour.

I totaly buy Twitter pulling @ replies due to technical issues – as exposed by Follow Friday combined with celebrity adoption and massive follower counts. But if you are going to be the real-time web backbone disabling useful things because they create too much load instead of implementing a more efficient architecture isn’t the right answer.

justSignal Upgrades, or My Lame Attempt to Explain Ignoring My Blog

I’ve gone through another one of those spells where I just didn’t write anything here – and to say I’ve been busy isn’t enough – hell I’m always busy.

I also can’t claim lack of interesting content – I could write six really compelling pieces on ASU Startup Weekend alone.

So we’ll just agree that my energy for writing posts took a bit of a vacation… but now I’m back.

Before I get back to my regular banality I need to get a bunch of housekeeping taken care of, so this post will catch you up on the justSignal updates, upgrades and new features.

Details after the jump…

Continue reading “justSignal Upgrades, or My Lame Attempt to Explain Ignoring My Blog”

Another justSignal upgrade. Twitter real-time upgrade.

We had a list of items we wanted improved and added to the justSignal Twitter real-time widget. And since I have no ability to procrastinate we are launching those changes over the weekend. Here is a rundown of the changes:

  • Allow for variable length hash tags to be inserted in the tweets (used to be fixed at 6 characters).
  • @names are now links.
  • Added HTML elements for easier customization via CSS.
  • Delete old tweets when there are more than 350 in the widget.
  • Added date on tweet time.
  • Added “logged in as” and a Logout button.
  • Added the ability to re-tweet directly from the tweet (no copy and paste, just click on RT).

Re-Tweets are a big deal. So is the capping of the number of tweets in the widget (prevents memory issues). I’m also pretty happy about the improved HTML which will allow you to customize nearly every aspect of the widget via CSS (thanks to Josh Strebel for his guidance here).

For those of you who need to see it now… here is a screen shot of the base widget (not customized).

justSignal Twitter real-time Widget
justSignal Twitter real-time Widget

We still have much, much more up our sleeve(s)… so stay tuned.