Leveling the (Local) Playing Field: How Smart Technology Can Help Merchants Compete in 2013

[A version of this post first appeared on VentureBeat, read it here.]

There has been a lot of talk lately about how the best business technology, historically only available to big corporations, has become increasingly available to small businesses. Jeremy Levine, a venture capitalist at Bessemer Venture Partners, pointed out in an interview with the WSJ that the “innovations in software UI, online distribution and application development will drive massive new software offerings for small businesses”. Let us to take a step back and understand the technology tools that can benefit the largest group of people who have been hardest hit by large enterprises and new technologies: the tens of millions of local merchants all over the world. 

A few weeks ago, Angelica’s, my favorite small café a few blocks from Union Square where I would go at least twice a week for lunch switched from making the best sandwiches ever (made to order and made with love), to selling only pre-made, wrapped sandwiches. Why? The old model executed the old way took too long and limited revenue. But lost in the new way is the quality of the food and the personal experience that made Angelica’s special. The good news is that there is a new generation of technology tools that allows local businesses to get the efficiencies they need to compete without sacrificing all that makes them unique.

Purchase Intent, Discovery, and Selection 

For local businesses, discoverability is key – both offline and online. If Angelica’s café were bordering Union Square, it wouldn’t have difficulty attracting the throngs of pedestrians – a big, friendly sign would probably do the trick.  Most local businesses are not that easily discoverable. They need to leverage the digital equivalent of the big, friendly sign: making sure they are easy to find online. These days a need sparked in the offline world will most likely still end up being solved by an online assistant – ‘Hey Siri, where can I get a massage nearby?’ A quick search on Google Maps, Yelp, and Foursquare or specialty sites like OpenTable, TripAdvisor and Citysearch are frequently consulted to find a local business. It’s crucial for our café to be on the digital map and listed everywhere with up-to-date information, including price list and menu data. Companies like Infogroup, Yext, Constant Contact and our company, Locu, can help local businesses manage and distribute their information online.

Beyond the several third party sites, a local business’s online presence matters. Does the business appeal reputable? Website builder tools like WordPress or Squarespace have become more and more versatile and easy-to-use and have started to offer vertical-specific functionality. A great web presence will drive a lot of online visits (that may translate into offline visits) via search and can be complemented by online advertising, either directly using Google’s Adwords or Facebook Ads, or online marketing facilitators such as Yodle, ReachLocal, or ThriveHive.

Purchase intent is also increasingly triggered by social media- it probably hasn’t been too long since you’ve heard someone say “One of my friends was posting about this amazing restaurant on Facebook the other day” or “I really want to buy those pants I recently saw pinned on Pinterest” or “I just got this deal from Groupon today, this could be a great place to visit this weekend”.

Making live updates to Angelica’s web presence is not the kind of thing a busy local merchant has time to do, but it’s so important. Posting daily-changing menu online, instead of just on their menu board, with photos of specials to allows their customers to easily find and share the information. This is where technologies like Locu’s help the most: we can automate and optimize a merchant’s web presence based on the customer, time of day, and other contextual cues that help drive attention and intent to Angelica, without requiring Angelica to become a guru in the art of optimizing her web presence.

Facilitating Transactions

Once the decision where to buy has been made, it’s all about the transaction. Whether booking a yoga class on SpaFinder, making an appointment with a hair stylist on StyleSeat, scheduling a car repair via YourMechanic, finding a photographer on Thumbtack or making a reservation at a restaurant with OpenTable, more and more transactions are initiated and facilitated by smart technology and increasingly on mobile devices. Plenty of companies, including GrubHub, Seamless and Eat24, facilitate take-out and deliveries for restaurants. 

The point-of-sale (POS) market has been undergoing a huge transformation, with many of the new solutions operating on non-proprietary tablet devices, such as Apple iPads, reducing the hardware cost and increasing usability. It’s never been easier for a local business to set up a reliable POS system and leverage its data to gain insights into its sales and identify areas to improve profitability. The newer POS systems like Square’s Register, Groupon’s Breadcrumb and Instore come with a set of intuitive analytics, and startups like CoPilot Labs can offer additional insights based on POS data (even from the old-school POS systems).

One specific example of how Angelica could benefit from all this would be to allow customers to pre-order sandwiches. It would not only reduce the waiting time but also help Angelica sell more sandwiches during the busy lunch hour.

Customer Engagement & Loyalty

Chris Luo of FiveStars points out in a recent blog post that “loyalty has often been the last marketing tool deployed, and the methods used to drive it have been rudimentary.”  While there have been a number of startups including Belly, Punchcard and Cardify, that provide digital loyalty programs in all shapes and forms, I still see a lot of businesses hand out physical punch cards. Digital loyalty systems can help merchants provide customized, relevant promotions, in turn increasing engagement, and ultimately driving word-of-mouth.

Email marketing remains one of the most effective ways of engaging customers and tools like Mailchimp made it really easy for business owners to stay in touch with their clientele. An email newsletter can help build a long-lasting relationship between merchants and their customers and trigger purchase intent – how about an exclusive special to bring someone back? I’d happily give my email address to Angelica to stay in the loop. 

What’s Next: Integration

It’s easy for local merchants to be overwhelmed by the seeming laundry list of technologies they should adopt to make their lives easier.   In fact, there’s a wide open market in integrating the many tools and signals a merchant has access to so that they can start to get insights from multiple channels. 

Wouldn’t it be nice for a local business to know at the point-of-sale where a certain customer has been and how frequently they have made a purchase? Some of the new POS solutions have taken an integrated approach, looking to offer a seamless end-to-end solution. As more and more data surfaces at each interaction point between merchant and consumers, and becomes available through powerful, but highly usable analytics tools, local merchants, like our café, will be able to gain actionable insights to increase revenues. 

Marc Andreessen, famous entrepreneur turned venture capitalist, referencing the classic ‘Walmart versus local retailer’ example in an interview with TechCrunch, points out “Walmart’s advantage in logistics and in pricing and in data analytics was just so great that they could kill small retailers at will.“ But perhaps not anymore, he adds, “there is an opportunity here for a shift of the balance of power for big businesses to small businesses”. We at Locu could not agree more. The world is a much more interesting, vibrant and beautiful place because of local businesses, like Angelica’s café.  To a great 2013 and a very real trend which will finally level the playing field for local merchants.

[Thanks to Sarah Dekin, Adam Marcus, Marc Piette and Carrie Stalder for reading and providing helpful comments to drafts of this.]

Few people are aware of the huge technology achievement that has happened here at Locu this year – our engineering team has been killing it in 2012, building the world’s largest real-time repository of local business offerings data and I think they deserve the nomination for the 2012 Crunchies “Best Technology Achievement”. Here’s why!

[This blog post first appeared on blog.locu.com]

About two years ago, a few of us were using every spare minute outside class to work on a demo for Linked Data Ventures, an MIT seminar taught by Sir Tim Berners-Lee. We shared a passion for structured data and local businesses and dreamt of a world where there would be a real-time local business offerings API to enable rich, local search (“where can I find a grilled turkey sandwich with avocado somewhere nearby for less than $10”). While the data to power that query has been available somewhere (whether buried in a PDF file on the merchant’s website or on a chalkboard outside the restaurant), it’s only now coming alive thanks to Locu. Locu started with the crazy idea to ‘structure the world’s information’ and we’ve come a long way – one menu at a time.

Here are the 7 reasons why I believe Locu’s technology deserves the nomination:

(1) 25 million times better local search. – We’ve indexed more than 25 million menu items in over 600,000 structured price lists (mostly for restaurants, our first vertical, but increasingly for spas, hair salons, nail studios, dry cleaners, gyms, etc.) in 2012. Thanks to Locu’s smart recrawl technology, the data stays fresh.

(2) “This can’t be done” or can it? – Creating a real-time, structured repository of local business offerings data from the long-tail web has been one of the last remaining challenges in deep web search. Even just restaurant menu data had been a huge challenge: “We’ve been working on menus for more than two years now,” says Shashi Seth, Yahoo’s senior vice president of search and marketplaces, in an interview with the Huffington Post last year. His conclusion: “This can’t be done by people.”

(3) Beyond conventional (crowdwork) wisdom. – Yes, Locu’s patent-pending technology stack including distributed crawlers, document analysis, and machine learning classifiers, is unique and impressive. But the “dot on the i” for me is the human computation layer, featuring a hierarchy of trained crowd workers, the largest and most likely only one of its kind. Read about theLocu workflow and crowd task decomposition here on our blog for more insights into the inner workings of the system.

(4) Helping local businesses. – Locu is the local merchant’s friend. Our data technology is also a backbone to our popular merchant dashboard, atlocu.com, where we provide a free service that lets business owners easily manage their offerings data in real-time on the web, mobile and print.

(5) Creating jobs. – While our goal is to help local businesses be more successful, which will lead to growth and job creation, we have already trained thousands of crowd workers as part of the human computation technology described above, and offer a stable income to workers around the world – for example, read one crowd worker’s touching story here.

(6) Benefiting an entire ecosystem. – Locu’s data is already viewed by millions of people each month, across restaurant and merchant websites as well as the hundreds of sites and apps built with Locu’s API, like OpenTable,TimeOut ChicagoCityVoter and Maluuba.

(7) You’ve probably used it. – Whether you’ve been browsing the web or mobile site of one of the thousands of local businesses already using Locu or looking up a menu just before making a dinner reservation, you’ve probably come across a ‘powered by Locu’ logo. Want to see our data in more places?Vote now and help spread the word!

If you have a minute, and if you agree with me that the above achievement is truly impressive, please go to crunchies2012.techcrunch.com right now and vote for Locu in the “Best Technology Achievement” and please share with your friends, colleagues via Facebook and Twitter. Thank you!

Thoughts on Series A

Today, we announced our second round: a $4m Series A financing. A big milestone in moving one step closer to our goal of enabling the next generation of web and mobile applications, by providing rich, structured data sets. While it’s great to have more capital to take Locu to the next level, I am even more excited about the group of people that have joined to support us in our endeavor. A few thoughts on the past few weeks of fundraising craziness and the why-we-chose-who-we-did further down.

We took our first VC meetings in February. There was a lot of excitement about our mission to “structure the world’s information” and the early traction we have in the local data space. Several firms brought us back for follow-up meetings with other members of their firm or network, cumulating in Monday partners meetings. About a month later, we had a number of term sheets in front of us. The following weeks, while investors were busy doing due diligence on us, I was equally busy doing due diligence on them.  The discussions with the founders of portfolio companies, board members and members of the ecosystem not only proved very helpful in the vetting process but are also enriching from a general networking perspective.

Here’s the list of our new Series A investors and a few reasons why I am extremely excited by our new backers:

Lead investor: General Catalyst. Founded in 2000, General Catalyst has been quickly rising to become one of the best VC firms in the country, with investments in ITA Software (Google), Kayak, AirBnB,  and many others. Most importantly, all partners are successful former entrepreneurs. Larry Bohn, who will be joining Locu’s board, led two software companies to an IPO as an entrepreneur and has an impressive portfolio as an investor that includes companies like Demandware, Hubspot, GoodData, BigCommerce – right at the intersection of the data space and e-Commerce & SMB technology. A perfect fit for Locu.

LowerCase Capital. LowerCase Capital is Chris Sacca’s investment firm. After speaking to a friend and founder of one of Chris’ portfolio companies about a year ago, I knew I wanted Chris to be involved with Locu. Chris’ bio is truly impressive and he is one of the nicest, thoughtful investors you’ll ever meet – every interaction that we had since our first met a few weeks ago has been extremely value-add. #Impressed.

Lightbank.  I was very curious to meet Eric and Brad, Groupon co-founders and founding partners at Lightbank, so flew to Chicago and took a meeting with them. Unlike most pitches, Eric cut straight to the chase and we spend the better part of the hour discussing market entry strategies and operational challenges. I was impressed by the energy of the team and what they have created in the last two years. Local is our first vertical and few people understand it better than them; Lightbank’s portfolio includes some great companies like Beachmint, Zaarly, OnSwipe and our MIT friends from E-La-Carte.

SV Angel. The firm led by Ron Conway and David Lee, is arguably one of Silicon Valley’s most prestigious early-stage investors. We didn’t get a chance to work with them during our seed, so we are more than excited to have them on board now.

I also want to use this opportunity to thank the whole team for the hard work that got us here. I am excited for the journey ahead.

Happy Easter 2012.

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10 hours into the Locu Easter Hackathon @ 9pm 
 

I haven’t posted in a while. We have had an amazing run with Locu since raising our first round last summer, building out the technology and product, signing up first customers.

Most importantly, I am proud of the whole team (#LocuMafia) and excited for what Locu will become over the next couple of months (and years).

Raising Money Isn’t About Raising Money

This blog post was first published on OnStartups.com – one of the best blogs for entrepreneurs – check it out if you haven’t seen it yet.

Our company has raised more than $600k in seed money over the summer. In a matter of a few months we transformed from a group of students working on what one notable Silicon Valley investor called a “class project” in early April 2011 to an eight-person company, with a unique technology, a clear value proposition and strong customer interest for our product. I’d like to share some highlights and lessons learned from our journey.

Back in April, we had been bootstrapping for about half a year, building our first prototype and some interesting back-end technology and learning a lot about our market. We also realized we had been working well together as a team and were all ready to commit full-time.describe the image

Testing the waters

One of our early inflection points was a team trip to the Bay area for a week in late May. We had been selected to present at a startup showcase and had also set up a few meetings with investors while out there. The four of us shared a double room at the cheapest hotel we could find, the Ramada Silicon Valley in Sunnyvale. It worked out perfectly: free WiFi, a good enough breakfast buffet and an In-N-Out down the street.

We went to almost all pitches that week as the whole team. While this is not sustainable for the whole fundraising process, I highly recommend it early on. It helped us grow stronger as a team and develop a common lens for feedback. Also, rather than insisting that our approach was the right one, we explored all possible directions to make sure we were not missing the bigger picture. After our daily debrief by the hotel pool, we would prepare for the next day. One night, Marek, one of my co-founders, built a prototype to test an idea that had come up during the day and that has now become an integral part of our product.

Learning

That brings me to learning. Looking back, these early meetings were invaluable. One thing that became clear really fast was that investors were much more interested in learning about the menu acquisition and data curation technologies we had been building than about our recommendation application. We had stumbled upon a potential solution to a big problem the local search industry had been battling with for years. Going forward, we built our pitch more around the technology and how it could enable a data platform for local business data and had much more success.

Network

A lot of people have asked me how many investors I spoke to and met with in order to close the round. You might have seen the Anatomy of a Seed deck by Brendan Baker who analyzes AppMakrs $1m seed round, involving 173 people and taking 130 rejections to get to 14 commitments. Our round was a bit smaller and we were fortunate to hit a few super nodes early on, but I still ended up talking to around 100 people in the process.

While I prefer meetings that lead to investments, there is thing to be said about the ones that don’t. Raising money is about building a network. A lot of people might be interested or intrigued by your idea but not end up investing for one reason or another. However, they might end up introducing you to potential business partners, clients or other investors. I talked to one potential investor and even though he did not end up investing, a month later, he emailed me and introduced me to a potential client.

Filter

At an early stage, it is important to surround your startup with people that can support you and extend you network in the areas you most need it. For us, we were targeting investors with backgrounds in data platforms, local, small business marketing, and the restaurant industry. AngelList turned out to be invaluable in the process of filtering. As Scott Kirsner from the Boston Globe recently put it, they are a true matchmaker between investors and startups.

A few words on due diligence: You probably expect your investors to do due diligence before investing. You should do the same. In a world of LinkedIn and AngelList, it is relatively easy to find people in your (extended) network that have worked with or can vouch for an investor. Even if it means delaying the closing of your round, don’t take money from investors you don’t think have the best interest of your business in mind or from whom you get a bad vibe.

Looking back, raising money was much more than just getting money in the bank. The process helped us to grow as a team, significantly refine our product and business model and most important of all, bring on investors on board that understand our technology, support our (ambitious) vision and will help us build a better company.

So you want to be an entrepreneur, eh?

This blog post is for people recently admitted into a MBA program who want to start a company post graduation. While there are many great blogs out there to help entrepreneurs and early-stage start-ups (for example, Dharmesh Shah’s blog OnStartups is an amazing resource), I hope to fill a gap where I saw one when I started at MIT two years ago.

This is not so much about entrepreneurship as it is about making the most out of graduate school to hit the ground running come graduation.

This post is by no means a guaranteed recipe for success. A note on the term ‘success’: I am proud of the progress we have made with Locu and while there are clear metrics that will show whether our start-up will be a success, there’s a word to be said about personal success. To me, this term is tightly linked to happiness (for an expert opinion on the topic, please consult my friend Jeff’s blog on happiness). And, while often stressed, I am happy.

About two years ago, I came to Boston to start my MBA at MIT’s Sloan School of Management. I had no idea about what would be the best approach to find a great idea and team to start a company (I later learned that team is often much more important than idea). Here’s an attempt to capture some of the key take-aways that in retrospect made my time at MIT worthwhile or – if unaware of them before – I would have found useful knowing when I started.

1. Find your niche 

If you are at a place like MIT, there’s a high risk you will be overwhelmed (like me) at first by the sheer quantity of ideas and projects available at your fingertips. Cross-campus lab classes (a good selection can be found here), conferences, meet-ups. There’s probably an entrepreneurship event almost every night. While introducing yourself  with “Hi, my name is Rene, I am interested in entrepreneurship” might not be a bad approach in the first few months, you should quickly find an area that excites you and try to learn as much about it as you can. I have heard similar advice from Ric Fulop, a Sloan graduate, when he came to speak to us in one of our classes. He had no background in batteries. He started reading and doing a lot of research, before reaching out to the top researchers in the field at MIT. He ended up starting A123 Systems with one of them, Dr. Yet-Ming Chiang, and the company went public last year.

I regarded my first semester at MIT as one big brainstorming session. At the time, there was no MIT Ideastorm yet, a great format Adam, Morgan and Slava came up with and which I hope will survive for generations to come. Going into my second semester, I knew I was most interested in emerging web technologies, such as linked data, semantic web technologies and big data, tackling some of the big remaining problems such as trust, identity, and data freshness. By then, I was spending a good third of my time on “the other side of campus” – at MIT CSAIL and the MIT Media Lab. Having been a passionate hacker in my earlier years, I was eager to catch up with the latest advances in machine learning and building my first django app.

2. Build your brand

During my second semester, my personal tagline evolved to “Rene – MBA / hacker – interested in semantic web, big data” or something like that. One thing led to the next and in May I was sitting across from Sir Tim Berners-Lee himself, pitching him the idea for a cross-campus, hands-on seminar to be called Linked Data Ventures (LDV). I don’t think Tim would have gone for a beer with me to brainstorm about start-up ideas but I got to know a lot of great grad students at that time who would. For a lesson on personal branding, look at what Miro and Tom have done with the MBA Show and how they are leveraging it for their startup.

3. Connect with experts in your field

A month later, I was in San Francisco, at the annual Semantic Technologies conference, learning, mingling with industry experts and pitching them to come to MIT that fall for LDV. I also interviewed some of them for a series on semantic technologies I wrote for the MIT Entrepreneurship Review. I started going to meet-ups and got to know a bunch of interesting people who helped me connect the dots between what I had read and researched.

On a more general note, being in grad school is an excellent excuse to meet with anyone in the world and I highly recommend it (as long as you do your homework and prepare for the meeting). Networking is a term that is often overused, but getting to know people who share some interest or passion with you, is a great thing.

4. Team, team, team

By the start of the third semester, I had been working on two fairly serious start-up projects and various other (not so serious) ones through some of the classes offered at MIT. I was excited about LDV and had recruited a few friends and people I wanted to work with to take the seminar with me. Our first team meeting was at the Muddy Charles and after five (or six) pitchers of Sam Adams, we knew this could be more than just a “class project”.

We were off to a slow start. It took us almost four weeks to find a name for our project, Goodplates, and we hadn’t written a line of code with only three weeks to go to demo day. However, we had spend a lot of time researching the needs of our potential users and had come up with a  unique approach to making trusted recommendations on a dish-level. On demo day, our app was fully functional (luckily all of us were able to code and not afraid to get our hands dirty with 4store and SPARQL) and we earned some praise from Sir Tim Berners-Lee, but more importantly, we realized we can work well together as a team and decided to move forward with the project.

5. Surviving

There will be a point in your journey where it’s all about surviving.  For a group of people to decide to work on a project full-time instead of staying in school or taking a high-paid job somewhere else, it often takes more than a good idea. It requires coming up with a plan to either raise money or make money – enough money to pay the bills and make it to your next milestone.

While many of our MBA classmates had already have accepted a job at that point and will be training for marathons, building up their alcohol tolerance at BHP, flying to Indonesia for fun, or perfecting the art of BBQing pork ribs, we had decided to go for it and were working hard to get our start-up off the ground. There’s a ton of  things that can catch you off-guard or go wrong (hey, it’s part of the fun) – things which contributed heavily to my sleep deprivation during the last weeks of my MBA. Buy me a beer at the Muddy sometime and I can tell you about some of them. You will also be faced with uneasy decisions such as : Should you go to Silicon Valley to meet investors or on a week-long sailing trip in the Carribean with your MBA classmates?  I can tell you the weather is nice in Palo Alto in May.

6. Escape Velocity

Bill Aulet first introduced me to the term “escape velocity”. He was referring to how the curriculum and special programs and events such as the MIT 100k competition (that reminds me to make a shout-out to my friends Ani, David, and Lindsay from Sanergy) are all aimed at giving entrepreneurs the best possible shot at succeeding. Bill and his team at the MIT Entrepreneurship Center supported us along the way and even gave us free office space right after graduation for the summer, while we were busy fine-tuning our pitch (Goodplates is now Locu), figuring out our business model, raising money, and building a kick-ass product. It couldn’t have been better. Not only was our office right next to the Dean’s office with a free supply of espresso, we also had a river view, which certainly helped in recruiting our first employees.

I plan to post more regularly going forward, for example, on how we raised money (spoiler: AngelList is great!).  I hope this post is useful. If not, at the very least I had fun writing and reflecting on two great years at MIT!

Kudos, Karma, Kommunity: Impact of Social Design Elements on Online User Engagement

Why do some online communities succeed while others fail? One of today’s biggest challenges is figuring out online user engagement. Rather than comparing average website visitor numbers, the really important question is how many visitors become regulars or even addicts – and can’t live without your service. Understanding and improving user engagement is key – not only does it enable viral growth of a community, but it also helps to better reap valuable monetization benefits from more effective online advertisement.

This blog post is based on a paper I wrote in Sarah Szalavitz‘s class on Social Design at the MIT Media Lab.  I start off by taking a quick look at the fundamentals of communities, decision making, and game mechanics. In order to compare engagement levels on community websites, I will create a metric based on Quantcast traffic frequency data. I will then look at the engagement levels on 22 different online communities and derive some key differences. Furthermore, I will conduct an in-depth analysis of two Q&A sites, Stack Overflow and Answers.com, using a simple game mechanics framework.

If you are interested in reading the whole paper, feel free to ping me.

How do Communities Work?

Group Size

In order to better understand how user engagement can effectively work in online communities, one first has to understand the basic mechanics of communities. Christopher Allen, wrote a comprehensive blog post in 2009 illustrating the various types of communities in which we co-exist:

  • Support Circle (3 to 5 people): consists of people one is closest to and would “seek advice, support, or help from in times of severe emotional or financial stress”
  • Sympathy Circle (7 to 20 people): this circle is “often made up of kin, but usually includes some peers as well”
  • Trust Circle (40 to 200 people): there is “some type of intimate connection” to people within this circle
  • Emotional Circle (around 300 people): often also referred to as “weak ties”
  • Familiar Strangers (>1,000 people): people we have very infrequent interactions or random encounters with

I will come back to the concepts of weak and strong ties and social network size in the section on how to influence people. An interesting experiment was published in the paper “Social Network Size in Humans” where Granovetter tried to estimate the average social network size in an experiment based on the exchange of Christmas cards. It turned out that the maximum network size averaged 153.5 individuals. Granovetter identified the following determining factors:

  • Passive factors (for example physical distance, work colleagues, overseas)
  • Active factors (emotional closeness, genetic relatedness)

The study suggests the existence of cognitive constraints on network size.

Individual’s Involvement in Groups

“In online communities, […] participation inequality power rule is very apt”, closely tied to the 90-9-1 principle: “1% of people create content, 9% edit or modify that content, and 90% view the content without contributing.” is what Wikipedia has to say about the 1% rule.

Exhibit 1: Community Participation Rule of Thumb

Source: Christopher Allen, Life with Alacrity Blog

Michael Wu has done empirical research (10 years of user contribution data, 200+ online communities) that shows that the rule is a great rule of thumb, but depends on the setup and type of community.

How to Influence People?

There has been a tremendous amount of research into how people make decisions and how these decisions can be influenced (I suggest the following books: How We Decide, Jonah Lehrer, Houghton Mifflin, 2009,  Why We Buy, Paco Underhill, Simon & Schuster, 2000,  Your Brain is (Almost) Perfect, Read Montague, Plume, 2007, Predictably Irrational, Dan Ariely, Harper Perennial, 2010).

What is most relevant to this paper is the question of how people can be influenced to engage in an online community, with a particular focus on the effect of the social design component.

With respect to this discussion, there seem to be two schools of thought: (1) Influencing through powerful hubs, frequently called Influencers or Mavens, and (2) Influencing through the masses, in a decentralized way.

Exhibit 3: Influencers versus Masses

Source: Fast Company Article, Is the Tipping Point Toast

Clive Thomson has recently asked in Fast Company: Is the Tipping Point toast?- challenging the theory people like Malcolm Gladwell and Ed Keller are supporting, namely that key influencers are critical to spreading news, influencing community. Central to their argument is a study done by Milgram (famously known as the Milgram small world experiment) which, apart from showing support for the 6-degrees of separation theory, also shows that key people (three friends, in the experiment) are responsible for the success of a letter delivery in a majority of the cases, leading Gladwell & Co. to conclude that these super-connectors are critical to the system.

Duncan Watts, on the other hand, repeated the small world experiment on a larger scale in an online-based experiment and found that super-connectors are not that important – only around 5% of traffic went through them. He concludes that not people but ideas matter: “If society is ready to embrace a trend, almost anyone can start one–and if it isn’t, then almost no one can”.

In Thomson’s article, Watt also compares trends to forest fires: “There are thousands a year, but only a few become roaring monsters. That’s because in those rare situations, the landscape was ripe: sparse rain, dry woods, badly equipped fire departments. If these conditions exist, any old match will do. “

More support comes from Paul Adams, the former social research lead in the UX team at Google who now works at Facebook, with a great presentation titled The Real Life Social Network.  Adams says that “the role of influentials is over-estimated” and “whether someone can be influenced is as important as the strength of the influencer”. Whether someone can be influenced depends on “what their social network looks like” and “what they have experienced before”.

How do these influencers compare to the super-users identified in the previous section? It is not entirely clear whether these two terms are interchangeable. While super-users are characterized by their high level of activity, influencers can be identified by analyzing the activity within the social graph i.e. who is talking to whom in a given community. Influencers are those that shape opinions and influence others through communication. Therefore, they may or may not be super-users at the same time.

Understanding Game Mechanics

A lot of research has been conducted into social games, gamification and how businesses can use it. A great source for an initial overview is provided on Gamification.org, a wiki for game-related knowledge.  The site lists the following game features as the key components when building a game:

  • “Activity feed” (to show players what is going on)
  • “Avatars” (unique representations of players)
  • “Easter Eggs” (the fun stuff: include intentional hidden messages, in-joke)
  • Instances (unique experiences outside the normal experience
  • Leaderboards (track performance, compare to others – dangerous – don’t scare of newbees, tell stories – social leaderboards “you and your friends” – missions built in)
  • Notifiers (feedback to users about progress, performance in game)
  • User Profile (all data about one’s activity)

Amy Jo Jim provides a different way to think about gamification: In her presentation she points out four particular elements that build on each other:

  • Collecting
  • Points
  • Feedback
  • Exchanges Customization

In a different talk, she makes a very important distinction with respect to points. They can take three different forms and functions:

  • Experience Points (earned directly for user’s actions)
  • Skill Points (interacting with the system)
  • Influence Points (assigned by other people)

Furthermore, to go into a little more detail, Gamification.org lists 24 different game mechanics, ranging from A like Achievement to V like Virality. The authors of the site have also identified 3 attributes:

  • Type (Progression, Feedback, Behavioral)
  • Boosts / Benefits (Engagement, Loyalty, Time spent, Influence, Fun, SEO, UGC, Virality)
  • Personality Types (Explorers, Achievers, Socializers, Killers)

Obviously, the title of this paper has engagement in it, and this type is by far the most important benefit and brings with it most of the other benefit i.e. if Engagement is high, loyalty, virality, fun will typically be pretty high as well.

Looking at the similarities between the different frameworks proposed in recent literature as well as identifying the key factors that I believe will be most relevant to user engagement in the context of online communities, I have developed the following simplified framework for gamification:

Exhibit 4: Gamification Framework

Later on in this paper, when looking at the case studies of StackOverflow and Answers.com, I will use this framework to point out the key differences between the two sites.

Measuring Engagement

As recently pointed out by Rick Webb in a talk in the Social Design lab at the MIT Media Lab, “engagement is very hard to measure”, there is currently no well-defined industry standard.

Current Metrics

Here are some examples of the metrics, Google Analytics, the leader in tracking web site traffic and providing analytics, provides:

  • Average page views per visitor
  • Time spent on site
  • Total time spent per user
  • Recency and Frequency (including frequency of visits, time since last visit)
  • Length and depth of visit
  • Bounce Rate

These metrics can be helpful; however in today’s world of Flash and Ajax, they might not provide good information value.

In particular areas, such as online publishing, there are a few other useful internal and external social metrics that can help measuring engagement, as suggested by John Byrne, BusinessWeek’s online editor, in an interview with Eric Ulken:

  • Internal Metrics
    • Number of Comments
    • Return commenters
    • Number of times emailed
    • External Metrics
      • Tweets/retweets
      • Diggs / Delicious saves
      • Inbound links from blogs

EROC Engagement Ratio of Online Communities

Not only are there no clear metrics for online user engagement, also a lot of the internal analytics data is proprietary, which makes it difficult to compare online communities.

Quantcast, apart from providing website traffic rank data, also offers a break-down of traffic into three different user groups: addicts, regulars and passer-by.  According to the website, addicts are the “hardcore segment of a site’s audience, who have 30 or more visits to that site in a month”, regulars are users that “frequent the site more than once per month but not as much as addicts” and passer-bys “have a single visit over the course of a month”.

In order to make the data easily comparable for different websites, I am constructing the following ratio:

EROC = [ 2 * Aa * Av + Ra * Rv ] / [ Pa * Pv ]

where

Aa = Addicts % of total audience

Av = Addicts % of total visits

Ra = Regulars % of total audience

Rv = Regulars % of total visits

Pa = Passer-By % of total audience

Pv = Passer-By % total visits

While somewhat arbitrary, EROC puts the following criteria into one simple number:

  • Addicts are more valuable then Regulars (using factor 2, can be adjusted based on monetization of addicts versus regulars and more general business model and industry)
  • Addicts and Regulars are more valuable than Passer-Bys
  • Passer-Bys should not only have a small percentage of audience but also a small percentage of visits

The higher EROC, the better the user engagement of a particular community website. However, it needs to be noted, that best practice EROCs can differ by industry, as some communities have higher interaction frequency than others by nature

Data and Analysis

While the data is not available for every website, I was able to collect it for 22 community websites, grouping companies in the following segments:

  • Social Networks I (defined by demographic focus)
    • Cafemom.com
    • Parents.com
    • Myyearbook.com
    • 4chan
    • Social Networks II (defined by interest focus)
      • Rottentomatoes
      • Seekingalpha.com
      • Stocktwits.com
      • Goodreads.com
      • Mapmyrun.com
      • Online Dating
        • Okcupid
        • eHarmony
        • Online Video Content
          • Hulu.com
          • DailyMotion.com
          • Metacafe.com
          • Online Q&A
            • Stack Overflow
            • Askeachother.com
            • Answers.com
            • Social Content Sharing
              • Tumblr
              • Wikia
              • Posterous.com

I have also included LinkedIn and Pandora as comparables.

The overall average for EROC is roughly around 1. A few sites stand out with very high EROCs: okcupid.com (2.97), Hulu (2.78), Tumblr (2.37). On the low end of the spectrum, there are metacafe.com (0.14) and answers.com (0.232).

A key take-away, as expected, is that there are severe differences in the EROC for the different categories. For example, online dating is 2.41 while for social networks II (interest focus) EROC is 0.67.

While certainly a small sample size, this could imply that online dating communities – based on the necessity to create profiles as well as people’s strong desire for interaction have a much higher percentage of users that are highly active. Within the online video content group, one can also see a difference which could be due to Hulu doing an amazing job in engaging users or just the fact that people watch television every day, but might only watch a movie once a month. Goodreads and rottentomatoes.com also have lower OCERs which again might be due to the fact that people consume at a much lower frequency.

In general, there might be different issues to consider when analyzing this type of data, such as user access via third party apps (e.g. in the case of Twitter, where a high percentage of users access the service via a third party app such as TweetDeck) or mobile app usage, which is not reflected in the data either.

Case study: Comparison of popular Q&A Sites

I decided to compare Stack Overflow and Answers.com as they are both in the same category of online Q&A, yet are very different from a social design perspective and also have very different engagement ratios:

Exhibit 5: Comparison of Stack Overflow and Answers.com

Stack Overflow

 

EROC = 1.288

Answers.com

EROC = 0.232

In the following sections, I will compare both online communities according to the gamification framework developed earlier.

I m a new user to both sites, so I hope I can bring an objective angle to the analysis, evaluating the sites from a first-time user’s perspective (which is an interesting perspective when trying to find out whether a site is able to easily engage new users).

Stack Overflow

I will briefly analyze the site along the key dimensions of my framework: User Identity, Actions, Social, Discovery, Feedback and Points.

User Identity

While there is a box for leaving a small comment about oneself, most people share very little information. Also the key information tags name, member for, seen, website, location and age are a small number compared to most other social networks. There is a very strong focus on stats, in particular reputation – the biggest number displayed on the page is the reputation score directly below the person’s avatar.

Also, the user profile not only features the raw score, but also indicates that the user has placed in the “top 10% this week” – providing a feedback / ranking  measure. What’s interesting here is the small time frame; it allows even new users to quickly rise up the ranks and ensures high frequency interaction with the website.

Further down on the profile page, the sites lists a user’s questions or questions he/she has been involved with. For each questions there are several stats: number of votes, number of answers and number of views.

Actions

Only way to build reputation is to ask and answer good questions (other people’s questions). There is a variety of actions that is tied to a minimum reputation score; i.e. new users have a limited set of actions and have to rise to a certain level to do certain actions (a commonly used game mechanic). A lot of these actions fall into the social category and will be discussed in the next section.

Here are some of the key actions:

  • Create posts (no reputation required)
  • Vote up
  • Flag posts
  • Comment everywhere
  • Vote down
  • Retag questions

Social

As pointed out before, a lot of these actions are of a social nature, in line with Stack Overflow’s vision: “you earn reputation from your peers, you the community’s trust – and will be granted additional privileges on Stack Overflow”. Similar to Wikipedia, Stack Overflow is a collaboration site – users can edit other users’ answers, all changes are tracked. Furthermore, there’s a chat platform where users can meet and group around particular topics (see Appendix).

One thing that’s interesting to note is that there is no integration with Facebook and Twitter for spreading messages or actions, something that is very popular with other social sites such as Foursquare. I believe it is a conscious choice by Stack Overflow, due to the particular use of the site by its users.

Discovery

There aren’t many surprise and or discovery elements, as far as I can tell (note: as a Stack Overflow novice, I might not have reached the level where these show up). One thing that could fall into this category though is the bounties. Users can also offer bounties for solving questions. They are effectively a certain fixed number reputation points which come out of their own account.

Feedback

The feedback mechanisms are very clear and direct by having other people voting questions and answers up or down.  In addition, the user that has asked a particular question can, after looking at all the answers, decided which one is most helpful and mark it as “accepted answer”. Furthermore, other users can suggest edits. Both of these actions are also rewarded with reputation points.

Points

When describing game mechanics earlier, I pointed out three types of points systems: experience points, skill points, and influence points. Stack Overflow is a perfect example of mastering the points system, tapping into all categories, yet putting a very strong focus on influence points. As pointed out earlier, the reputation score impacts what people can do on the site (see screenshot on the right); for example, the 5% of “talk in chat” translates into 20 reputation points.

Stack Overflow has a relatively simple leaderboard and a large collection of badges (>60 different ones ranging from A like Altruist to Y like Yearling) categorized into three main categories:

  • Bronze badge: “awarded for basic use” e.g. Commentator (someone who left 10 comments)
  • Silver badge: “awarded for long term goals” e.g. Epic (earned at least 200 reputation on 50 days)
  • Gold badge: “are rare”, “actively work towards these” e.g. Famous Question (someone who asked a question with 10,000 views)

For each badge, the number of people that currently hold that badge are shown. For example, there are currently more than 64,000 Commentators, more than 150 Epics and more than 6,000 Famous Question badges issued. Interesting to note, that while gold badges are supposed to be hardest to earn, some of them have a higher number than certain silver badges.

Answers.com (WikiAnswers)

As done for Stack Overflow, I will again briefly analyze the site along the key dimensions of my framework: User Identity, Actions, Social, Discovery, Feedback and Points. As before, all the relevant screenshots can be found in the appendix.

User Identity

A typical user profile has the username, gender, age and location in addition to some key stats:

  • Trust points
  • Contributions stats, including # of Answers, # of Edits, Organization, # of Questions, Community
  • Badges

I will talk in more detail about trust points and badges in the points section.

In general, I noted that most profiles have very little information. Also the navigation is poor; it took me a long time to find information about how to earn points, what the different stats mean and where the leaderboard is. Another difference to Stack Overflow is that most users- even the top users – have no profile pictures, giving the site a less personal touch.

Actions

In general the key actions are asking and answering questions. Answers.com overlays 14 different member roles, ranging from Bug Catcher to Wiki Influential Teen. For example, there is a Vandal Patrol with different tasks and positions; the group has a program coordinator by the user name of An8thing and one has to send an email to JoinVandalPatrol@WikiAnswers.com to join. In general, the Community Roles & Programs pages are overloaded and very difficult to make sense of as a first time user.

Social

Answers.com writes that “half the fun of participating in a community is the interaction with your fellow members”. Yet, apart from awarding Trust Points (see below), the options available for interaction are the message boards, discussion pages and the community forum. The various roles that are available are geared towards high-frequency users creating a divide between regulars and addicts that seems hard to bridge.

On top, Answers.com offers Facebook integration to “see your friends’ activity”.

Discovery

I wasn’t able to identify any particular discover elements. Due to the large amount of activities and roles available, users might feel overwhelmed and not very receptive for discovery elements anyway.

Feedback

Users can receive Trust Points (see below) from other users when they click on “recommend contributor” to reward that user for a good contribution. Compared to Stack Overflow’s sophisticated reputation system, Answers.com’s system seems much easier to game and not as effective, e.g. as trust points can currently not be substracted.

Points

Trust Points “enable you to vote for a WikiAnswers member whose contributions you think are worthwile and legitimate”. Trust Points can currently not be substracted.

In addition users collect points for answers, edits, questions and other interactions.

The leaderboard is much more complex than Stack Overflow’s. While it is intuitive to have an overall leaderboard, it is not really clear why leaderboards are necessary and useful for the particular categories (questions, answers, community).

Lessons Learnt from Social Design perspective

For a social design perspective, one of the big trends is the “shift from storytelling to storysharing”.  Sarah Szalavitz has been developing a strategic framework at 7Robot around the social design of systems using this shift “through choice optimization and behavioral economics”.

The above case studies of Stack Overflow and Answers.com have illustrated the huge impact of these techniques on user experience, which is directly translated into user engagement.  For example, Answers.com has very poor choice optimization (for example, it is still unclear to me which group I should join and which badge would be valuable to me) while Stack Overflow has done an excellent job in choice optimization (simple leaderboard, easy to understand system of badges, intelligent time horizons).

Getting game mechanics right matters! The case study of Stack Overflow versus Answers.com has shown how Social Design elements can help to successfully engage online communities. The proposed framework of breaking down the game into User Identity, Actions, Social, Discovery, Feedback and Points has proven helpful in understanding the key differences.

With respect to measuring user engagement effectively, the proposed EROC metric can help to more easily compare traffic frequency data across multiple communities, however falls short to take into consideration the various subtleties of different verticals and user preferences. More work has to be done in this field to provide better metrics to companies and analysts.

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