Scrabulous – an awesome product-market fit

February 7, 2008

Hey guys (if there are any of you still reading this blog)! Apologies for the very long blogging break but as promised in our New Year’s resolution, we’re back! Today I wanted to do something we rarely do here on this blog, talk about another product. My reason for doing so is because it makes a great case study and allows me show my support for this product.

You may be aware of the Facebook application created by two brothers in India taking an age old board game and putting it on the web. You may even be one of those people who spends half of his or her day flexing those vocab muscles conjuring up every permutation of words one can make from 7 letters. If you do, you know what I am talking about. To those who aren’t, I am referring to the Facebook application, Scrabulous. Could anyone have imagined creating an online scrabble game would have so much success?

Less than eight months old, Scrabulous today, has the 5th highest number of active users (655,781) of all Facebook applications and of that list, the highest ratio of active to total users (25%). It’s even one of the more popular applications among Facebook employees. Its growth has been quite staggering and even more impressive is its ability to engage users and keep users coming back. I myself am guilty of having 3-4 active games at any given time and taking increasingly frequent breaks during the day to put down my “bingo”. With Hasbro’s recent demand to shut down the company, users have flocked together to show unprecedented support to keep the application up. The Facebook group “Save Scrabulous” has over 55,0000 users and over 8500 people have signed a petition asking Hasbro retract their cease and desist letter. A few fans even created a humorous and satirical music video as their way to show their love and support for the application:

To achieve this type of a large, passionate and loyal fan base is the dream of any product developer or marketer.

What’s interesting to note, as many of you are probably already aware, is that Scrabulous is not the first online version of the game. Far from it in fact. A quick search on Google for “online scrabble” will result in a plethora of online and downloadable versions of this classic word game. These have existed for years but none have found as many and passionate a user base as Scrabulous. This begs the question, why?

The answer lies at the crux of what makes a successful product. Marc Andreessen wrote a great bit on this a little while ago that I urge you to read. The key to making a successful product does not mean having a perfect product or an extremely stellar team (not to detract anything from what the creators of Scrabulous have done). What matters most is achieving a harmonious product-market fit. This means shaping your product to fit the current trends, needs and demands of the market. A lot can even be attributed to timing; being at the right place at the right time.

In Scrabulous’s case, the product was nothing revolutionary. On the contrary, it was a plain and simple online adaptation of the board game that had been done numerous times before. It was also far from perfect. Those who remember early iterations of Scrabulous will recall how frustratingly slow and unreliable it was (and to some degree still is). The user interface was (and still is) cluttered with intrusive, to the point annoying, ads that detracted from the overall experience and had much room for improvement (and still does). But all this did not stop users from returning or the application from taking off. Scrabulous’s user base continued to sky rocket thanks to Facebook’s immensely viral platform. So why did users continue to swarm to this application?

The reason becomes clear when we examine the market a little closer. Facebook has been seeing an exponential increase in users. It opens up its platform to allow third party developers to create and distribute applications on the Facebook network. Millions of users, hungry for novel ways to interact and engage with their newly created network of friends, start experimenting and adding these new applications as they are being released. Some of the applications are extremely successful while some fail and get buried in the figurative Facebook dust. In comes Scrabulous, an application that allows users to play the classic board game online with their friends in an asynchronous, turn based manner. The game becomes an instant hit. People love the ease with which they can start and participate in games. It provides a new way to interact with friends and play the game with people who may not be accessible to play face to face. The game itself meshes with the typical Facebook user’s profile: high school, college or recent graduate, educated, smart, with a large network of similar friends. The game finds a core of users and spreads like wildfire, thanks also to Facebook’s ability to promote such applications virally via news feeds, user invitations, etc.

The market therefore, was a perfect fit for what the Agarwalla brothers created. It wasn’t that they built a brand new product that took social networking to the next level or changed the way people interacted. They simply saw an opportunity that was ripe for the plucking and took advantage of it. Kudos to their team and the success they have achieved and I wish them luck in their battle against Hasbro.


What Comes After Google?

October 4, 2007

Question: What Comes After Google?

Yahoo just released a new Search Assistant feature this week (TechCrunch) (Read/WriteWeb). Ask has been trying a new interface lift for a while (TechCrunch) (Read/WriteWeb). While these are all very nice incremental improvements to search, are they enough to supplant Google? Do they tackle the fundamental problem of information retrieval in a paradigm shifting way? The answer is probably not.

Now imagine several years into the future. Will you find information in the same way in the future as you do today? Again, probably not.

This may sound like an obvious “duh”-ism, but its ramification certainly is not. As unfathomable as it may seem, Google, as we know it today, will probably not be how we find most of our information in a few years. Since Youlicit is an information retrieval company, we had to ask ourselves, “If not Google, then what?”

What is the logic that dictates the evolution of information retrieval paradigms?

Evolution of Information Retrieval Paradigms

To answer this question, we first plotted the different paradigms of information retrieval on a timeline. If we can figure out what the axis of this graph represents, then we should be able to predict which new solutions will succeed and which will not by simply identifying the solutions that maximize the metric along this axis.

If you’re a start-up, this understanding can guide you in building a successful innovative product. If you’re a venture capitalist or technology evaluator, this insight gives you a criterion for determining which technologies to invest in and which ones will fade away as fads of the day.

Evolution of Information Retrieval Paradigms

After plotting them on a timeline, we then explored the three major paradigms of information retrieval:

  1. Manual Organization
  2. Algorithmic Search
  3. User-Generated Recommendations

Manual Organization

Information retrieval, during its infancy, started off as a very rigid and structured process. Those who remember Gopher or Jerry and David’s Guide to the World Wide Web (later known as Yahoo) know how attempts were made to organize sites into a pre-determined hierarchy. However, as the number of web sites exploded exponentially, manually organizing sites into structured directories became practically impossible:

“A universal ontology is difficult and expensive to construct and maintain when there involve hundreds of millions of users with diverse background. When used to organize Web objects, ontology faces two hard problems: unlike physical objects, digital content is seldom semantically pure to fit in a specific category; and it is difficult to predict the paths, through which a user would explore to discover a digital object.”
Clay Shirkey

Algorithmic Search

Too many sites to categorize? No problem. Algorithmic search to the rescue. Web search engines, such as Altavista and Google, arrived and allowed the web to grow in its chaotic unstructured way while still providing a level of organization in the form of keywords. Now instead of having to know the correct directory hierarchy, users only needed to know the keyword combination (and page number) for sites they were looking for.

Counter to intuition, search engines actually decrease the relevance of individual results as compared to those in a manually organized directory. A hand-picked set of results are always better than an algorithmically generated set of results. However, since search engines have a much greater coverage of the Web, the average relevance of search results from a given set of topics is usually better than the average relevance of directory results on the same set of topics.

The other improvement made by search was the replacement of directory hierarchies by keywords as the primary recall mechanism. While still not perfect, guessing and checking keywords took a lot less effort than guessing and checking hierarchies. Seach engines effectively decreased the recall effort.

User-Generated Recommendations

Recently, we’ve witnessed the niche adoption of tagging, voting, stumbling and other “user-generated relevance” as a means of finding information. Why? It’s because they improved something along either the average relevance dimension or the recall effort dimension.

Take and Digg for instance. In the scope of technology related content, the average relevance of results from these folksonomy sites is better than from search engines because these folksonomy sites have been able to increase coverage by effectively crowdsourcing an easier manual organization process.

StumbleUpon went the other route. Instead of improving average relevance, it decided to reduce the recall effort from guessing and checking keywords to a one-click no-thought “stumble.” In doing so, it did something ingenious: StumbleUpon removed the world’s most scarce resource from the information retrieval process… human thought.

Answer: The Solutions that Maximizes the “Search Metric”

As well as they’ve done, both kinds of user-generated recommendation services are plateauing well before crossing the chasm into the mainstream market. Why? We think it’s because they’ve only focused on a singular improvement, either average relevance or recall effort, but not both.

In order for an information retrieval solution to penetrate into the mass market, the solution has to take a dual approach. It has to concurrently maximize average relevance and minimize recall effort. It’s simply a matter of optimizing the Average Relevance / Recall Effort ratio, or as we like to call it, the “Search Metric.” The solution that does this best will probably gain the most mindshare and supplant algorithmic search as the primary mode of information retrieval. And that is what comes after Google.


Does this imply that algorithmic search will become extinct anytime soon? Absolutely not. It just means that more and more people will find larger percentages of their daily information through means other than search. Our bets are on online “word of mouth” or user-generated recommendations.

Evolution of Information Retrieval Paradigms

We’ve builit Youlicit with this assumption at the core. Youlicit is a “word of mouth” or recommendation engine (as opposed to a search engine). We’re trying to maximize the Search Metric by combining user-generated relevance with one-click no-thought recall. We want to improve the information retrieval landscape by enabling the average user to harness the wisdom of the crowds with very little effort. If you’re as obsessed with this problem as we are, we’d love nothing more than to hear from you!

— The Youlicit Team