Check out these examples:

Web-driven voice advocacy through the “Make America Happen” Campaign

AFSCME sends emails to their list to drive constituents to web forms for congressional voice advocacy.

Make America Happen Campaign Results:

Over a two week span when the Obama Jobs and Recovery package was being debated in Congress, AFSCME sent out three standalone emails that generated approximately 1,000 calls to Congress in support of the bill. Additionally, AFSCME has been building their text messaging file by converting email subscribers into text subscribers as an optional check box in the web form. In the future, they will drive response via SMS.

Advocacy at Live Events

At their recent conferences, AFSCME asked their audience to text “happen”  69866 to make calls into Congress.

Results: They have seen response rates as high as 53% for people in the room texting in (225 person conference, 119 opt-ins) and 42% response rates on broadcast text-to-call messages sent later that day to generate calls. (119 opt-ins, 50 calls generated)

Targeting state legislators

AFSCME has also used mobile to target state legislators. This strategy gets their affiliates involved and targets legislators who get calls less frequently, which means the calls are typically more effective.

Thanks so much to Tracey Conaty, Assistant Director of Online Communications, at AFSCME for sharing the results and to our friends at the Watershed Company for all their hard work!

Mobile Commons has a similar application that our customers use to make their own SMS lookup services. Organizations can quickly create a service that users can text to and get back information quickly. For example, text ‘FISH SALMON’ 30644 and you’ll get a response letting you know if there are any health or environmental concerns with it (courtesy of Blue Ocean Institute). Text SHOP and the name of a company to 30644 to find out if they are LGBT friendly (courtesy of HRC).

We often get requests to make services based on location; in other words, a private-label Starbucks locator. Some organizations have faked it by uploading a database of zip codes. This works great but takes a lot of work and doesn’t handle people texting in their address. As we mentioned a couple days ago, we’ve been working hard integrating location information into everything we do. Today we are very excited to announce that we have added automatic location information to our SMS database applications.

How does it work? First, you upload your database as a spreadsheet with two columns: input and output. Check the box that says “Treat inputs as geographic locations”. That’s it!

Now you can instruct people to text your keyword plus their address. We automatically find the closest geographic match and text it to you. They can text in their full address, city, zip code - it doesn’t matter. We can handle it all.

This is best illustrated with an example. Let’s create a service to find the closest Mobile Commons office via SMS. Here’s our sample CSV file:

"input","output"
"86 Chambers St, New York NY 10007","The closest Mobile Commons office is at 86 Chambers St, New York NY 10007"
"100 Bush St, San Francisco CA 94104","The closest Mobile Commons office is at 100 Bush St, San Francisco CA 94104"


I copy-and-pasted the above text into our application and hit “Save”. Finally, I chose the keyword “mcommons” and added a default response.

Try it! Text MCOMMONS and your address to 30644 and we’ll reply with our closest office.

Now imagine you’re a retail business and you want to build a store locator. Easy! Just upload a spreadsheet of your store addresses in one column and your contact information in the other. We automatically take care of the rest. Want to advertise local events, rallies, or house parties. No problem!

It’s a “Starbucks Locator” for the rest of us!

Locations can be as accurate as a home address or as general as a city, depending on the information your users share with you. For example, if someone texts in a zip code, we can figure out their city and state. If they fill out their mailing address a web form, we’ll add it to their profile. And of course you can manually edit any profile or import your own CRM data and we’ll use that instead. Once we stored everyone’s location information, the next step was to find everyone’s legislators. We went through every profile and updated their profile with their congressional district.

Now that the data is in place, we can start doing some really cool things:

Advanced Group Targeting:
Armed with latitude, longitude and congressional district, we were able to really improve your ability to create groups of users:

• Reach out to people only in a certain state (e.g. only text people in California)
• Advertise events near a particular city (e.g. text everyone within 25 miles of New York City)
• Target specific legislators (e.g. text everyone in the NY-01 congressional district)

And while we were at it, we also created some useful new group functionality for our advanced users:

• Create groups that contain other groups
• Exclude people from your groups based on search criteria
• Finer grained control of AND and OR logic

For example, you can target a group that includes everyone in California but except Los Angeles and excludes an uploaded list of your major donors.

Targeted Legislative Call Campaigns
Our customers have run some amazing legislative call campaigns over the past couple years, really helping to affect change in policy. But with the addition of location information, we can now take these call campaigns to a whole new level!

When we first introduced mConnect, all calls were routed to the capitol switchboard and users asked to be connected to their representative. Now that we know everyone’s representative, we can automatically route the call to the correct destination! This eliminates the switchboard operators as a potential bottleneck and makes for a fantastic user experience. Not only that, but your organization can see in real-time exactly which legislators were called and how the call was made — from the web, Facebook, email, or text message.

To take it a step further, we realize that organizations don’t always want a shotgun approach; they would rather target specific legislators. With Advanced Targeting, you can now select particular senators to target. This enables your organization maximize your efforts and reach exactly the legislators that you need to.

Got some great ideas that you’d like to see incorporated into our platform? Feel free to leave a comment below. Ready to get started with mobile? Contact us or find us on Twitter @mobilecommons.

It didn’t quite work out the way we planned; we received no donations over the phone.

It did, however, produce a very interesting result–people who got the text message gave online with an increased response rate of 77%!

We suppressed a third of their list to see if the text had any effect on online giving, and it turned out to have a huge impact.

Download the Full Case Study (pdf)

We are excited about all the different ways people are pushing mobile fundraising and we hope that this integrated approach becomes another arrow in the mobile fundraising quiver.

Background
According to the Mobile Marketing Association’s best practices, text messaging providers are required to look for special opt-out keywords and automatically unsubscribe users who text them in.  The published list of opt-out words are STOP, END, CANCEL, UNSUBSCRIBE or QUIT.  Our customers are all required to include opt-out instructions (e.g. “reply STOP to unsubscribe”) in their first interaction with end-users and encouraged to include instructions on subsequent messaging whenever possible.

Perhaps due to the conversational nature of many of our customers’ programs, people often don’t realize that their messages are initially processed by an algorithm. As such, many people do not reply with carrier-recommended opt-out keywords, but rather ask to opt-out in plain English (”Please remove me from your list. Thanks!”).

Early on, we realized that for the best possible user experience, we needed a way for organizations to opt-out users who send in plain English responses.  The Mobile Commons platform provides an SMS Inbox that our customers can use to view all their incoming messages.  We added an “Opt Out” button to the Inbox, allowing our customers to read incoming messages and opt-out users as needed.

Over the past two years, we’ve accumulated a ton of data about users’ mobile behavior.  Specifically, by analyzing the list of manual opt-outs, we are able to determine the most common words and phrases that people use to opt-out that aren’t recommended by the carriers.

Experiment
We pulled 50,000 random, anonymous  text messages from our database from the past year that included both automated and manual opt-outs.  We then did a frequency analysis of the bodies of the messages to determine which words and phrases were the most common.

Results
As expected, STOP by itself is by far the most prevalent opt-out word, accounting for 80.4% of all opt-outs.  The word STOP followed by more text (SMS signatures or an entire phrase) was the second most popular, with 9.3% of all opt-outs.  In total, incoming messages starting with the word STOP represented 89.7% of all opt-outs!

The requisite carrier opt-out words rank as follows:

• STOP: 89.7%
• CANCEL: 1.0%
• END: 0.8%
• UNSUBSCRIBE: 0.7%
• QUIT: 0.1%

92% is obviously a huge percentage and almost all opt-outs can be captured just by checking for these words at the beginning of a message!

This still leaves about 8% of all opt-out requests going unprocessed.  For systems like ours that process millions of text messages, 8% represents a huge number of people who would like to stop receiving text messages but are confused about the exact procedure to follow.  At Mobile Commons, we strongly believe that mobile is a highly valued communication medium and we should go the extra mile to make the best possible end-user experience.  Keeping mobile communication spam-free and nuisance-free is critical to our success!

As with all good algorithm design, we began by analyzing the thousands of messages in our database.  This would allow us to write regression tests using real-life messages.  We’d like to share some of our findings with you.

Much to our surprise, the word or phrase that started the majority of the non-standard opt-outs was PLEASE, as in “please stop”, “please remove me”, or “please only send emails. texts cost money.”  It’s nice to know people out there are still polite!

The following table represents the percentages of the most common words that are not part of the Mobile Marketing Association required list but resulted in an opt-out:

• PLEASE: 21.7%
• STOP2: 8.3%
• REMOVE: 7.9%
• F*CK: 4.6%
• TAKE ME OFF: 2.4%
• DO NOT: 2.9%

Just to be clear, we do not mean to imply that every message beginning with a “please” should be an opt-out.  In fact, the majority of them are not opt-outs. It means that of the opt-out messages that do not use standard opt-out words, 21.7% of them began with the word “please”.

The word STOP2 was somewhat surprising, but after further investigation, we realized it was a very common response to the often used phrase: “Rply STOP 2 opt-out.”

Conclusion:
Over the past year, we’ve worked very hard on our message processing algorithms.  The algorithms often go unnoticed by end users and customers - after all, it’s pretty tough to see an algorithm =)

We have a number of goals with our message processing:

• Correctly identify the incoming messages, whether they be opt-ins, opt-outs, help requests, replies, phone call requests, search queries, donation, and more
• Automate as much as possible, reducing the amount of extra work and manual intervention our customers need to do to keep their campaigns running smoothly & their lists clean
• Keep the number of false positives as low as possible (accidentally opting-out a user is a terrible thing to do!)

Our engineers have done a great job of all three, including processing opt-outs.  We automatically catch an extremely high percentage of them correctly with almost no false positives.  This makes for a great user experience:  a “Please take me off your list” text message will be followed by an immediate “You have been removed. Thanks for your support” response (as opposed to the “Unknown keyword. Try again.” response you often find on shared short code services).

Of course, our job is never complete. We continue to update our algorithms as we learn more and more about user behavior and our natural language processing improves.  We’ll keep you updated on our blog!

Maybe you’ve seen them before.  Every single text message you get from one of your friends ends with “<3 Stacee” or “Hugs&Kisses”.  It’s called a text messages signature. They are just like email signatures; users setup their phones to automatically insert a message at the end of every text message.  This can be your name, a short phrase, or anything else you like, and are very common on newer phone models.

For example, I could setup my phone so that every message I send ends with “-=Ben=-” so people know it’s from me: “I’ll meet you at the coffee shop at 3pm -=Ben=-”  If you’ve seen these before, you know exactly what I’m talking about.  If you haven’t you probably think it’s crazy that someone would fill up part of their precious 160 characters with a signature!

Over the past year, we’ve processed millions of text messages.  We’ve seen all sorts of different SMS signatures and unexpected user behavior and we want to share some of our data.  Specifically, we wanted to understand how prevalent are SMS signatures and what can do to improve our text message processing algorithms.

Experiment Setup

First we chose a single keyword that we knew was actively promoted.  We then extracted 6,000 unique text messages start started with keyword in the past year.  Next we removed all duplicate messages so we only had a single message for each phone.  Then we removed multiple different messages for each giving phone; it turns out that people change their signatures more than you think (some change them weekly)!  Finally, we manually went through the list and discounted any extraneous messages that were obviously manually typed and clearly not a signature.  Ambiguous messages were also removed from the list.  This left 5,242 unique text messages to analyze.

(To protect the privacy of our customers and users, we are unable to publish the list of signatures, but we are able to share aggregate results with you.)

Results

• 647 users out of 5,242 had SMS signatures, or 12.34% of users
• The average signature length is 11 characters
• The longest signature was a whopping 91 characters!
• 513 signatures, or 79% of them, had punctuation or other non-alphanumeric characters in them
• 13.8% of all signatures contain a heart <3 and 7.7% contain a smile =)

We were also curious if SMS signatures varied by demographic.  We repeated our analysis for a known audience that was significantly older than the average.  We do not know (nor could we share) the exact ages of users, so let’s just define the term ‘older’ to mean exclusively adults, as opposed to teenagers or college students.  In this case, only 18 out of 414, or 4.3% of adults use SMS signatures, compared with 12.3% above.

Challenges

By now you have probably guessed why this is such a significant topic for Mobile Commons.  SMS signatures might be fun when texting your friends, but they post a significant technological challenge for companies like ours.  Our products are built around recognizing keywords for opting in and search terms for querying data while on the go.  How can we reliably parse keywords and queries when messages have seemingly random text added to them?!

This is best illustrated with an example:  Consider FishPhone, an SMS service that lets you text FISH and the name of a fish to 30644 and get back the health and environmental impact of that species.  For example, “fish halibut” tells you the environmental impact of halibut.  But what happens if the text message reads “fish halibut peace,<3,and :)”?  Or “fish atlantic salmon xoxobaby girlxoxo”?  How can we tell what you’re searching for? It gets even harder when you allow multi-word keywords (e.g. You can text WHERE IS and a bottle water brand to 69866 to find out how far away your water’s source is).

This is a great example of a real world problem.  You can write the best algorithms in the world for parsing text messages, but until you start processing live user data, you have no idea what you’re likely to encounter or how wrong your assumptions were.  Unfortunately I can’t reveal  our algorithms (that’s part of our secret sauce), but I can say that they have evolved tremendously over the past couple years.

Conclusion

In message processing systems that only rely on a single keyword, SMS signatures aren’t really a problem; your system can just grab the first word.  When you start allowing multi-word keywords (e.g. text “do something” to 30644), keywords with search terms, or data collection (text your email address and zip code), signatures become quite difficult to deal with.  We’re quite proud of the natural language processing algorithms we’ve written over the past two years to elegantly handle these sorts of real-world challenges.  We constantly tweak our algorithms and run regression tests to ensure we have the most sophisticated and accurate text messaging processing product available!

Dane Grams of HRC gave a presentation at the DMA 2009 Washington Nonprofit Conference session titled “New Media Channels — Adding Text Messaging, Social Networks and Media to the Marketing Mix” last Friday. Also on the panel were Scott Goodstein from the Obama campaign and Grace Markarian of the Humane Society (HSUS). We’ve embedded the full 8-minute video below.

One of the main points that Dane addresses is that organizations have causes that matter and can run highly effective campaigns even though they don’t have Obama as their figurehead. At Mobile Commons, we’ve seen proof of this again and again as we see people use our tools to spread causes and ideas they care about (and now we’ve made that even easier by tying into Facebook and Twitter). Dane and Grace have both had great successes with Mobile Commons and Convio eCRM integration and will continue to because they’ve embraced mobile as an ongoing part of their communications and are constantly looking for ways to engage more deeply with their constituents.

Thanks to Tad at Convio for posting the great video.

Twitter has had phenomenal growth over the past year and organizations have been very quick to embrace Twitter as part of their media strategy. In addition to mobile, many of our customers also use Twitter as a way of communicating. To integrate with Twitter, click the ‘Setup’ tab in your Mobile Commons account and enter your Twitter login information. Once you do this, a new “Send to Twitter” checkbox will appear as an option whenever you schedule a broadcast. If you check the box, your message will simultaneously be posted to Twitter as well as to your mobile list!

The popularity of Facebook goes without saying. We’ve had Facebook apps available in beta for a while and they officially launched this week! We’ve gotten great feedback as customers have used our Facebook apps to collect mobile donations, trigger calls to the White House, and get supporters to join their mobile lists. Facebook integration really is incredible and deserves it’s own blog post which is coming soon. If you have any questions or would like advice on getting started with Facebook, please don’t hesitate to contact us. We’ll be happy to help!

These new features are part of a larger trend we’re seeing based on connecting mobile technology to the web. Many of our tools have had web components for a long time: Mobile donations and phone calls can be triggered by filling out a web form. mData databases can be queried from a Flash widget. mCast text-to-screen apps can shown anywhere on the web. We’ve been working hard to expand beyond static sites and really connect you with the social graph. With our Facebook and Twitter integration, we continue to lead the way helping organizations spread their ideas, extend their reach, and accomplish their goals!

Jeff, our intern from this past summer, wanted to hack away on an open-source project over his winter break.  He created a Java library for the Mobile Commons API.  It’s released under the MIT License, so you can feel free to use it in your own applications.  Libraries like this one really lower the barrier-to-entry for developers looking to integrate SMS and voice into their Java applications.

Are you interested in contributing to the open-source community?  If you have used our API in the past and want to release your code, let us know.  Just email us at developers@mcommons.com and we’ll showcase you and your work on our site!

We get tons of questions about mobile giving, how it works, and what is required to get started.  We’ve quickly realized that there is a lack of useful information currently available on the web for organizations trying to learn.  A Google search for “mobile giving” currently returns links to the Mobile Giving Foundation, a year-old article on Mobile Active, and a 6-month-old article from about.com.

We couldn’t find very many useful sites with case studies, new campaigns, strategy tips, or other information about mobile giving.  We (and other vendors) certainly have lots information on our own company web sites, but that’s probably not the best place to showcase all the exciting things happening around mobile giving.

So today we’re proud to announce that Mobile Commons is sponsoring a new blog called Mobile Giving Insider.  The site provides useful information about mobile giving, resources for nonprofits (e.g. vendors, technical documentation, etc), and a detailed FAQ.  Additionally, it will be a blog with original reporting, highlights about exciting new campaigns that non-profits are launching, and informative case studies. We really hope this becomes a little community where lots of people can contribute, cross-post, and add your own original content.

The site is now up and running at http://mobilegivinginsider.com and you can subscribe to the RSS feed at http://feeds.feedburner.com/MobileGivingInsider.

Have any interesting news or case studies that you’d like to share or promote? Please email the editors at tips@mobilegivinginsider.com.