But in the digital world, it just might, especially when Netflix is the one listening.
In case you missed it, The Wall Street Journal published an article last week sharing insight into behaviors of Netflix users toward certain shows.
The results are fascinating. While its safe to assume Netflix has dirty laundry on just how quickly we binged through Unbreakable Kimmy Schimdt this summer, based on data shared this week, they also know at which point we fell in love with the show, or when the relationship fizzled out.
New data suggests Netflix knows not only how many people stick with a show, but also at which episode they decide to commit. Perhaps what is most fascinating, is that they weren’t able to find a single pattern to adopt across shows.
Interestingly enough, our data approach at Dragon Army follows a very similar structure to what Netflix is uncovering. Patterns in behavior aren’t just unique to users, they’re unique to each game we create. Just as Netflix changed the paradigm for TV viewership, mobile gaming is also distinguishing itself from console game design, and we’re able to do it through data.
Almost exclusively, what we design here at Dragon Army is free-to-play. Which means, unlike designing for a console game, revenue comes not from the purchase of the game, but from the ongoing upgrades and purchases within the game as play progresses.
We can’t risk losing a player on day one. For Netflix, replace in-app purchases with continued subscription revenue and you have the same challenge.
What makes adapting new revenue models even more interesting lies is how it is applied. While Netflix Chief Content Officer Ted Sarandos was quoted stating that these statistics do not influence creative decisions, in game design, our world is built on them.
Player Data & Defend the Dam
In early tests, players of our most recent title, Defend the Dam, showed that those who made it past level seven they held a much stronger chance continuing on to complete the game. However, those that had more trouble in the earlier levels were much less likely to return.
We were then able to go back in and understand what actions the “advanced” players were taking in levels one through six and compare those actions to the population who abandoned.
We found “advanced” players were spending more time in the earliest levels garnering achievement points, rather than completing a level and moving on to the next. This would in turn, unlock opportunities to make later levels both easier and more fun.
Instead of asking users to defeat 10 waves of creatures with every level, we decreased that number, introducing 10 waves for the first time only in level seven. By decreasing the waves of creatures encountered in the early levels, players who wanted to play by moving from one level to the next felt accomplished sooner, thus encouraging them to stick around for longer.
Furthermore, when the next set levels were set to be released this fall, we had a known influencer group worth targeting with fresh content. Understanding these actions allows us to invest our time in analyzing the micro actions players are taking within the experience.
While this practice of data analysis is not new to the game design community, it is a fresh school of thought for us as marketers. As we think of building the apps, experiences and content of tomorrow, uncovering patterns in behavior is becoming less of a nice to understand, and more of a precursor to building differentiated success.