Is Predicting Hit Songs Futile?

I recently covered Columbia Professor Duncan Watts’ “cumulative advantage” experiment, in which similar groups of people started with the same selection of songs but ended up with different choices for which songs were hits. If people were just judging the songs on content, the groups’ choices for hits should have been similar. However, there was also a social factor: Except for a control group, each group’s members could see the popularity of songs within their group but not within other groups.

Professor Watts proposed that the divergent choices for hits were due to each group’s piling-on to whatever happened to be initially popular within that group. See the original post for details.

In my praising the experiment, I held back on some questions about the strongest claim in Professor Watts’ New York Times Magazine article. In essence, he claimed that predicting hits was futile due to the inherent randomness of social systems like the word-of-mouth that affects entertainment choices:

Because the long-run success of a song depends so sensitively on the decisions of a few early-arriving individuals, whose choices are subsequently amplified and eventually locked in by the cumulative-advantage process, and because the particular individuals who play this important role are chosen randomly and may make different decisions from one moment to the next, the resulting unpredictability is inherent to the nature of the market.

This effect was true of Professor Watts’ experiment, but is it realistic to have the early-arriving individuals “chosen randomly”? Isn’t there a relatively small percentage of people who act as tastemakers: people who are into new stuff first and whose knowledgable opinions influence others? If these people have non-random qualities, shouldn’t there be a lot more predictability?

The Limits of an Individual Influential

After some email back-and-forth with Professor Watts, I was surprised to find that the role of “influentials” is potentially a lot less than is commonly believed. In a draft of a paper due for publication later this year, Watts and collaborator Peter Dodds detailed their mathematical simulations of various scenarios involving influentials. The results were summarized in the Harvard Business Review’s Breakthrough Ideas for 2007:

Our work shows that the principal requirement for what we call “global cascades”—the widespread propagation of influence through networks—is the presence not of a few influentials but, rather, of a critical mass of easily influenced people, each of whom adopts, say, a look or a brand after being exposed to a single adopting neighbor. Regardless of how influential an individual is locally, he or she can exert global influence only if this critical mass is available to propagate a chain reaction.

To be fair, we found that in certain circumstances, highly influential people have a significantly greater chance of triggering a critical mass—and hence a global cascade—than ordinary people. Mostly, however, cascade size and frequency depend on the availability and connectedness of easily influenced people, not on the characteristics of the initiators—just as the size of a forest fire often has little to do with the spark that started it and lots to do with the state of the forest.

The researchers’ forthcoming paper makes a compelling case for these conclusions, exploring influentials’ role under many different scenarios. However, its various social-network models all start with the single “spark” of an individual discovering and communicating something. It does not consider a scenario where a large number of simultaneous and non-random sparks occur throughout the network. That is, if a single, random spark can cause a forest fire under the right conditions, how about a bunch of sparks purposely set at once, across that same forest?

The Potential of Coordinated Influence

The coordinated, multi-spark scenario matters because it is how certain social-marketing companies supposedly work: unleashing a small army of “on message” people to tell their friends about some great new thing. One might argue that a favorable newspaper review, radio airplay, or other one-to-many media do something similar, simultaneously “sparking” many consumers at once.

The key point: Instead of having a single line of sentiment that needs to propagate enough times to reach critical mass, the multi-spark scenario has many lines propagating, each of which could randomly run into other lines, thereby accelerating toward a critical mass.

Bringing this all back to the original New York Times Magazine article and its assertion that hits cannot be predicted, the multi-spark scenario is a way for hits to be predicted. In essence, it increases the prediction reliability by manipulating the system.

You may say this is unfair, like loading the dice, but it’s how entertainment marketing works. Companies spend marketing dollars in proportion to what they think will be popular, thereby making what they think will be popular more popular. Economically, the question is whether the cost of manipulating the word-of-mouth system is worth the increased probability of a hit.

Note that predicting a hit doesn’t mean being right all the time; it just means that across many attempts the gain is greater than the cost. Thus, even if you only went from a 3% hit rate to a 5% hit rate, predicting was worthwhile if it cost less than the benefit from those extra two percentage points.

So, I’m not ready to conclude that it’s futile for entertainment companies to predict hits. If the companies were merely acting as pure observers, then Professor Watts’ case would be strong enough for me. However, because entertainment companies’ predictions are often entangled with manipulating the systems being predicted, there may still be reason to try to pick winners.

Whether the benefits outweigh the costs...well, that’s another experiment to do.