I recently had a series of conversations about how the public perceives two brands that I find intriguing: Bob’s Red Mill, a natural foods producer based in Oregon, and Hyperloop, a platform for rapid long distance transportation that is being implemented by a number of organizations all over the world. I took some notes—and created this post.
To me, the common thread between Bob’s Red Mill and Hyperloop is that they both let the people behind them represent them. It makes their value propositions credible in a way that clever writing and a huge creative budget can’t.
Due to a misunderstanding, at the last minute before takeoff an airline refused to allow a pair of special-needs passengers to fly. This upset the passengers deeply and stranded them at an unfamiliar airport.
No one should have been surprised that intense criticism of the airline spread rapidly via social media, portraying them as bad-guys even though the incident was (arguably) a one-time mistake by an isolated group of employees.
This wound up being a good thing, because:
The airline discovered this issue, apologized to the would-be passengers and their families, refunded their money, offered them additional free flights, and came up with a new process to keep the problem from recurring. All-in-all, the airline—our hometown favorite here in Seattle, Alaska Airlines—took a regrettable mistake, and did everything possible (considering it was after the fact) to make it right with those affected. In this way Alaska Airlines also earned positive PR by showing they’re the kind of company that owns up to their mistakes and jumps on an opportunity to do the right thing when they can.
This post isn’t about Alaska Airlines—it’s about the other guys
I’m pleased to see more and more stories about companies turning customer complaints into positive publicity. But this post is for the other guys, anyone who isn’t sure they have the right attitude, either individually or organizationally, to handle all customer criticism in a positive way.
I discovered an interesting video recently while helping a client demonstrate how users of a SharePoint document management system can share information about the documents they are managing. The video is by Michael Gannotti, a technology specialist at Microsoft, and it apparently shows how Microsoft uses SharePoint 2010’s social media features in-house. The video covers other SharePoint 2010 features as well, but I found 2 segments particularly relevant.
Social Media features in SharePoint (from timestamp 6 minutes 49 seconds to 15 minutes 50 seconds):
people search — users can find people who are experts on the subjects they’re researching;
publishing — via wikis, FAQS, and blogs;
user home pages — users can fill out their own profiles, add types of content, see their friend and group feeds;
viewing other users’ pages — users can find out more about co-workers and their work;
adding meta-information — tagging, liking, and adding notes or ratings to alert others about the relevance of content to oneself, to a project, or to a topic; and,
publishing (blogging) options — users can post to SharePoint either via a rich web-based text authoring environment or direct from a Word document.
Using One Note For Sharing (from timestamp 17 minutes 34 seconds to 18 minutes 34 seconds):
A recent US Republican Party social media experiment misfired not because of poor moderation, as some critics have assumed, but because site managers failed to recruit and motivate the right community. This post explores ways to create an open, uncensored forum that can more constructively represent both loyal followers and potential converts who were (presumably) the intended targets of the site.
Saying they want to “give the American people a megaphone to speak out,” last week GOP Congressional leaders announced a new web site, AmericaSpeakingOut.com, an open “town meeting” where everyone has an “[o]pportunity to change the way Congress works by proposing ideas for a new policy agenda.”
Earlier this week I participated in an exciting brainstorming session for an online fundraising event. My client is an education-focused non-profit. They want a homegrown solution that is customized to their needs and frees them from 3rd party fees. And it has to be ready for launch at a live breakfast event next month. (Yes, it is a short time frame – viva la technology!)
We arrived at what we think is a new framework for online fundraising, a hybrid between the time-honored “this thermometer shows progress towards our goal” and the scavenger contest vibe of mobile social crowdsourcing apps – like Foursquare and Gowalla – which is along the lines of “you just added your tenth new venue to our database – here’s a virtual prize to reward you and keep you going.”
First we’ll offer supporters an opportunity equivalent to “sponsoring a table” at a physical event. But instead of offering companies or individuals the opportunity to purchase enough tickets to fill one (or more) tables in a hotel conference room, we’ll offer supporters the opportunity to form a team and sponsor one or more students, at $1500 a student. In this way people can participate whether or not they can contribute $1500 up front.
The people forming teams, hereinafter “team captains,” are really pledging to assemble a personal fundraising network big enough to sponsor some number of students at the rate of $1500/student. Of course a team’s captain could opt to cover their target contribution entirely by themselves if that’s how they roll. But we expect to be able to find many more captains who think they can achieve $1500 (or some multiple thereof) by combining their own personal generosity with outreach to generous friends.
Here’s where it gets interesting from a social media standpoint:
We’ll provide each team captain with a unique link to their team’s own donation page. (From a technical standpoint, it’s really the same donation page dynamically rendered as the donation page for a particular team depending on the link followed – more about this below.) Captains can distribute the team’s link to their friends, and their friends can disribute it to their friends, and so on. And when I say “distribute” I mean via email, blog, Facebook, Twitter, etc., accompanied by the non-profit’s own call to action or whatever each captain thinks will get the job done.
When people click on the team link they arrive on the team donation page and are presented with encouragement to contribute to the team’s success. On one side they’ll see what the team has commited to, and how far along the team has come (our take on the classic thermometer-of-progress graphic), and a leaderboard that compares the commitments and progress of the various teams.
Suggested contribution amounts will be in increments of $10, in an effort to attract small donors (as Obama’s campaign recently demonstrated) as well as higher-rollers. A key feature is that each donation increment is tied to a symbolic (“virtual”) milestone towards the $1500 goal. For example, $10-40 could buy a virtual sponsored student virtual school supplies; $50-100 could buy virtual text books or a backpack; $150-250 could buy a virtual chair or a desk; etc.
Once someone makes a contribution on the team page they are rewarded not only by a thank you message, and by seeing that they have moved the team towards its goal, they also see graphics depicting the symbolic milestones – virtual books, supplies, etc. – their contribution equated to.
Once someone makes a contribution they are also given the opportunity to invite their friends to join the team (via quick links to email, Facebook, and Twitter).
Before people make a contribution on the team page they are also asked to enter into the team donation form the name of the person who sent them the team page link (hereafter “the referrer”). This may have been someone besides the team captain, it may even be someone the captain doesn’t know. Each referrer’s name is recorded, along with the donations attributed to them, and the organization, the team captain, and the referrers themselves can all be kept informed of the impact of each referrer. In this fashion referrers can bask in the knowledge that they were successful in recruiting contributors; captains will know who to thank (or who to give a pep talk to); and the organization can get intelligence about which team captains and team members were the most effective recruiters – handy for subsequent campaigns. In addition, referrers who ran particularly successful “campaigns” can be debriefed in order to discover, and potentially duplicate, their secrets.
As alluded to above, all of the team donation pages required for multiple online fundraising events can be created with relatively few tweaks to the current web site, using the current online donation form and the existing donor database. Basically all that is required is:
a single new dynamic web page that mashes-up (embeds) the current donation form;
a few additional visible and hidden form fields (adding team ID, referrer name);
a couple of new database tables (or a new database if needed for security) to associate donor and amount with team and referrer; and
a handful of simple but colorful icon-like graphics.
Its a relatively simple matter to query the new data tables in order to compute what is needed to dynamically display the team leaderboard and the graphics showing milestones available or acquired through a donation.
Version one of this framework will have to be kept simple – some of the above features may not make the cut. Future editions will have additional features, like enabling people to sign themselves up as team captains. To keep it simple we’re keeping this an administrator-only process for this event. Summary reports about referrers will be generated by a human administrator behind the scenes now, but later this can be automatically emailed or displayed via an online dashboard. Yet another feature addition might be a referrer leaderboard that encourages friendly competition between individual referrers.
Looking forward to your feedback! Is this being done already by anyone? Are there any low-hanging fruits we could add to this framework?
Panda Security recently released (in beta form) what it claims is the first cloud-based anti-virus / anti-malware solution for Windows PCs. Not only does it sound like a clever tool for data loss prevention, but it demonstrates another way in which information service providers can aggregate individual user data to develop classifications or benchmarks valuable to every user, a mechanism I’ve explored in previous blog posts.
In essence, every computer using Panda’s Cloud Antivirus is networked together through Panda’s server to form a “collective intelligence” for malware detection and prevention. Here’s how it works: users download and install Panda’s software – it’s a small application known as an “agent” because the heavy lifting is done on Panda’s server. These agents send reports back to the Panda server containing information about new files (and, I presume, related computer activity which might indicate the presence of malware). When the server receives reports about previously unknown files which resemble, according to the logic of the classification engine, files already known to be malware, these new files are classified as threats without waiting for manual review by human security experts.
For example, imagine a new virus is released onto the net by its creators. People surfing the net, opening emails, and inserting digital media start downloading this new file, which can’t be identified as a virus by traditional anti-virus software because it hasn’t been placed in any virus definitions list yet. Computers on which the Panda agent has been installed begin sending reports about the new file back to the Panda server. After some number of reports about the file are received by Panda’s server, the server is able to determine that the new file should be treated as a virus. At this point all computers in the Panda customer network are preemptively warned about the virus, even though it has only just appeared.
Utilizing Panda’s proprietary cloud computing technology called Collective Intelligence, Panda Cloud Antivirus harnesses the knowledge of Panda’s global community of millions of users to automatically identify and classify new malware strains in almost real-time. Each new file received by Collective Intelligence is automatically classified in under six minutes. Collective Intelligence servers automatically receive and classify over 50,000 new samples every day. In addition, Panda’s Collective Intelligence system correlates malware information data collected from each PC to continually improve protection for the community of users.
Because Panda’s solution is cloud-based and free to consumers, it will reside on a large number of different computers and networks worldwide. This is how Panda’s cloud solution is able to fill a dual role as both sampling and classification engine for virus activity. On the one hand Panda serves as manager of a communal knowledge pool that benefits all consumers participating in the free service. On the other hand, Panda can sell the malware detection knowledge it gains to corporate customers – wherein lies the revenue model that pays for the free service.
I have friends working in two unrelated startups, one concerning business financial data and the other Enterprise application deployment ROI, that both work along similar lines (although neither are free to consumers). Both startups offer a combination of analytics for each customer’s data plus access to benchmarks established by anonymously aggregating data across customers.
Panda’s cloud analytics, aggregation and classification mechanism is also analogous to the non-boolean document categorization software for eDiscovery discussed in previous posts in this blog, whereby unreviewed documents can be automatically (and thus inexpensively) classified for responsiveness and privilege:
Deeper, even more powerful extensions of this principle are also possible. I anticipate that we will soon see software which will automatically classify all of an organization’s documents as they are created or received, including documents residing on employees laptop and mobile devices. Using Panda-like classification logic, new documents will be classified accurately whether or not they are of an exact match with anything previously known to the classification system. This will substantially improve implementation speed and accuracy for search, access control and collaboration, document deletion and preservation, end point protection, storage tiering, and all other IT, legal and business information management policies.