NETWORK EFFECTS AND THE PLATFORM

Since launching last May, Tastebud has received about 10,000 app installs. And, overall and per user engagement metrics (recommendations, requests, song plays, comments, etc) have generally been trending up. But the most important metric, retention, is below where it needs to be. Tastebud has a “leaky bucket” problem. No matter how many new users we acquire, Tastebud will never achieve significant growth if existing users continue to churn out of the app at a high rate.

That’s not to say that Tastebud has no loyal users. It most definitely does. There are people who joined the app in its earliest days and have continued to use it, on a near-daily basis (often multiple times per day), to post recommendations or to discover what others are recommending. There just aren’t enough of these people. Over the past three months, Tastebud has averaged roughly 1,250 monthly active users, who have, on average, launched the app about 2.5 times per month (about 20% of users launch Tastebud 3+ times per month).

So, why exactly is Tastebud having difficulty retaining users? One explanation is that we’re experiencing the “chicken-and-egg” and “ghost town” problems, (more on these below) that commonly occur with network-effects products, products that become more valuable as more people use them (the telephone system being the classic example). A critical mass of users must be reached in order for the network effects to take hold and sufficient value to be realized. In the case of Tastebud, which has yet to reach critical mass, what many new users are experiencing is the following: (1) a lack of friends or other trusted/recognizable sources on the app when they first sign on; (2) inconsistent volume of new and relevant recommendations or requests to keep them engaged; and (3) minimal feedback on the content that is being created. Tastebud won’t be able to keep users until it has enough users.

To overcome the chicken-and-egg challenge of achieving critical mass, Chris Dixon recommends building tools into network-effects products that allow for a “single-player mode”. This provides the product with standalone or intrinsic value that enables it to generate initial user traction even before the network effects take hold. Dixon refers to this as a “come for the tool, stay for the network” strategy and offers up Delicious (my bookmarks) and Instagram (filters for my photos) as examples. Pinterest, which initially attracted many users as a personal scrapbooking tool before evolving into a more fully formed social networking platform, is another example.

Though we didn’t explicitly build Tastebud’s share functionality to help it overcome the chicken-and-egg problem, it certainly enables the app to function in single-player mode. Users can quickly search for items they want to recommend and then easily create a nicely packaged recommendation that can be fully shared out to Facebook, Twitter, Pinterest, and phone contacts (SMS). The ‘share message’ contains a large image of the entertainment item and a link to a web-based view of the full recommendation. This enables friends and followers to have a high quality Tastebud experience even though they aren’t on the app.

Example of recommendation made on Tastebud shared out to Twitter

The Platform Business Model

When we refer to Tastebud as a network-effects product, what we’re really saying is that it’s a platform; specifically a two-sided platform that enables the creation and consumption of entertainment recommendations.

Sangeet Paul Choudary an entrepreneur and advisor at 500Startups, is an expert on platform strategy and network economics. Explaining how platforms differ from traditional business models, Choudary says:

Traditionally, Pipes has been the dominant model of business. Firms create stuff, push them out and sell them to customers. Value is produced upstream and consumed downstream. There is a linear flow, much like water flowing through a pipe. Unlike Pipes, Platforms do not just create and push stuff out. They allow users to create and consume value. At the technology layer, external developers can extend platform functionality using APIs. At the business layer, users (producers) can create value on the platform for other users (consumers) to consume.

These are the defining characteristics of a platform:



1. A platform provides the infrastructure and tools for producers to produce and consumers to consume.

2. A platform creates the rules-of-play and conditions for interactions between producers and consumers to occur

3. Value is created and network effects are realized when there are enough producers and consumers with overlapping intent for interactions to spark off between them.

4. The goal of the platform is to scale its ability to enable more and better interactions.

5. The platform must manage quality and relevance (often through editorial, algorithmic and/or social curation) to ensure the interactions continue.

Mutual Baiting and Ghost Town Challenges

Choudary also refers to the chicken-and-egg challenge described above as the “Mutual Baiting” problem. For a two-sided business to work, both producers and consumers need to be on the platform. However, producers won’t come to the platform without consumers and vice versa. Consumers act as a bait to get the producers to come in and vice versa.

This problem is particularly challenging when producers and consumers are two entirely distinct groups (e.g. a two-sided network such as Uber). In the case of Tastebud, many users fill both roles; that of making recommendations and viewing (and providing feedback on) recommendations made by others.

The “Ghost Town” problem is a related challenge encountered by platforms, which often don’t have any standalone value. In its earliest days, users visiting the platform find nothing to consume and, therefore, no value in the platform. Producers, in turn, don’t contribute unless they see some consumer interest. A vicious cycle ensues and the ghost town remains a ghost town.

In addition to the “single-player-mode” tactic described above, there are several “seeding” strategies that a platform can use when attempting to overcome the Mutual Baiting and Ghost Town challenges.

Its important to note that attempting to acquire both producers and consumers at the same time can be extraordinarily difficult. It’s typically best to focus efforts on one side of the platform or the other. Once the first side has started to build it will act as bait to begin attracting the side side.

On a recent episode of This Week in Startups, Casey Winters, Product Growth Lead at Pinterest, describes (beginning at 6:57 mark) the strategy that helped launch GrubHub while he ran marketing at the restaurant food delivery service:

Do you go and get the restaurants first? Or, do you go and get the consumers first to attract the restaurants? GrubHub actually went and got all the delivery menus for restaurants first. Put them all up online. And that was great for SEO. And that got people to come visit GrubHub. And then we went to restaurants and said, “Hey there’s all of these people that are using GrubHub to find which places to order from. Would you like to appear on the site higher than a lot of these restaurants and get more orders? And then later on, GrubHub said let’s start with restaurants [presumably, focusing on consumers first didn’t work so well]. Let’s go send sales people to a market. Let’s sign up 50 restaurants with a pay-for-performance/cancel-at-any-time contract and then once we have these restaurants in, let’s say, SOMA we can go out and bid [on Google AdWords] on “SOMA Chinese delivery”, “SOMA Thai delivery” and get customers that way and start ranking organically.

Reddit, the successful link-sharing news site, has become a classic example of a business that ignited its massive growth by seeding the supply-side of the platform. Co-founders Steve Huffman and Alexis Ohanian were so embarrassed by the barren state of Reddit when it first launched, that they decided to populate the site with link submissions using hundreds of fake accounts. To new potential users, it appeared as if Reddit had a robust community of contributors that filled the site with great content to consume. The two founders kept up the fake account creation for several months until there were enough users on both sides of the platform to sustain the Reddit community on its own.

With Tastebud, we’ve been testing two different approaches to seeding the platform. The first is the “fake it till you make it” tactic, albeit on a much smaller scale, that Reddit utilized. Shortly after Tastebud launched we created several fake user accounts and have used them to post both recommendations and requests for recommendations, to follow other users, and to provide feedback on other users’ posts. This tactic has been largely ineffective as the number of fake accounts we created has been too small to generate any noticeable difference in the level of overall user activity. Even at their peak (when user activity was in a trough two months after launch), fake accounts represented no more than 5.0% of total weekly active users and currently represent less than 1.0%. In addition, we’ve used these fake accounts to create activity on both the supply and demand sides of the platform, rather than focusing on trying to attract either producers or consumers.

To be completely honest, despite the fact that it’s a fairly common growth tactic, using fake accounts is just something that I haven’t been able to get fully comfortable with; especially when Tastebud is designed to be a platform built on trust. Therefore, we’re ending the use of fake accounts and reallocating those efforts toward a different seeding strategy; that of having the platform itself create the product. For Tastebud, this is a more viable and effective strategy and we’ve approached it by launching a series of themed or topic-based “house” accounts. The initial set of accounts has been designed to work across multiple entertainment categories and have fairly broad consumer appeal. The “Feeling Nostalgic” account, for example, offers throwback recommendations of classic movies, songs, TV shows and novels primarily from the 80s and 90s. “Brain Binge” recommends items that will enlighten as well as entertain, such as documentary films, biographies and educational podcasts. “Celeb Picks” offer fun recommendations from celebrities that we’ve curated from magazines, social media accounts, etc.

Tastebud “house” accounts feature recommendations curated around specific themes

We’ll also be testing more narrowly focused themed accounts such as “ComicConned” (sci-fi, fantasy, superhero and nerd content), “Hip Hop” (music, movies and TV grounded in hip hop culture) and “Horror” (movies, books, and TV shows in the hugely popular horror genre). We’ll expand or prune themed accounts based on the level of consumer interest and activity they generate.

Reverse Network Effects and Curation

As critical mass is reached and the platform begins to scale, a new problem will likely emerge if the overall quality of the platform isn’t managed properly. Typically, the earliest adopters of a platform are sophisticated users who are adept at creating value. As new, less-sophisticated users come on board, their output may begin to dilute the platform’s value, which, in turn, lowers engagement and ultimately drives consumers away.

Content-based platforms such as Tastebud will fall victim to these so-called “reverse network effects” unless they maintain a high signal-to-noise ratio, where the amount of relevant content exposed to consumers is significantly greater than the amount of irrelevant content. Platforms can manage content relevancy through any combination of the three broad forms of curation:

1. Editorial Curation (expert generated): The personal tastes and opinion of human “experts” determines what content is presented to users. This manual form of curation is particularly effective in the early days of a platform before it scales. Editorial curation helps establish patterns that can then be automated and scaled.

2. Social Curation (user generated): Users provide feedback in the form of data via tools (voting, rating, favoriting, etc.) regarding the quality of the content. The aggregated user data is then used to sort and rank content and determine its relevance.

3. Algorithmic Curation (software generated): Software is used to automate the selection of what content should be presented to consumers based on a specific set of rules. Algorithmic curation is highly scalable but is often criticized for its inability to capture subtleties such as creativity, emotion and context in the way that human curation can. Personalization, which can be active or passive, falls within algorithmic curation. With active personalization, the user is selecting the rules that will govern which content is displayed to him/her. Passive personalization uses data gathered behind-the-scenes about the user — such as prior behavior or location — to present content that will likely align with his/her preferences and interests.

Example of filtering feed on Books

Tastebud currently offers active personalization in the form of a filtering tool that allows users to select which of the six entertainment categories will display in their feed as well as whether to show recommendations only, requests only, or both. In addition, the themed accounts described above offer users a form of editorial curation. Tastebud “editors” identify and recommend to users the most interesting entertainment items that fit within the given theme. There’s also currently a leaderboard of the app’s 50 top “tastemakers”, generated by a combination of social and algorithmic curation. This gives new users what is essentially a “suggested users” list of people they should follow when they first join Tastebud.

In the future, we plan to leverage social curation to provide Tastebud users with “Most Popular” and “Trending” lists. We’ll be able to feature, for example, the 25 most popular books on Tastebud (since launch, past year, past month) based on total recommendations, total favorites/saves, or a combination of both. Or, we might present users with the Top 10 songs that are trending over the past week based on recommendations or audio plays.

Its also likely we’ll at least test algorithmic curation to provide users with some form of passive personalization. This, however, will be a bit of a balancing act. While, we want to ensure that users of Tastebud always come away from the app with a recommendation of something they’ll love, we also believe that the element of surprise and serendipity are critical to providing a fantastic overall experience. Our hope is that every now and then, users of our app will be exposed to something unexpectedly great that expands their tastes in movies, music, TV or books, thereby popping any filter bubble that begins to take shape.