The journey from data to decision

September 28, 2023
Michelle Millar
min read

At TMM London earlier this year, Chris Benjaminsen, Founder of FRVR, dropped some bombshell facts at the Fireside Chat:

“Our rule is, you have three seconds to entertain a user, otherwise they’re gone… for every second where we’re not engaging the remaining users, we lose 8% of [them].”

Those numbers sound wild, but FRVR’s games have been played by a billion people, so you’d best believe they have the data to back that up.

But data alone is not a strategy; on the journey between data and decision are the crucial steps of information and insight. Let’s use this tantalising example to explore what that could look like.

Data vs Information vs Insight

Data is a collection of facts; information is data put into context. Context could mean knowing where this data sits in relation to historical data, or observing the external factors that could be influencing it… whatever it may be, the context fundamentally affects the information that the data imparts.

Information plus knowledge provides insight.
This is where we begin to enrich the information with our experience, to make assessments on probability, to draw on analogy and models of behaviour, and ultimately to create a narrative that generates understanding.

Understanding drives decisions. Once we have a theory of the why behind the data, only then are we able to suggest how we can use it, or change it.

By applying some hypothetical context and knowledge to the data of “three seconds to entertain a user”, let’s demonstrate just how much this could change our resulting decisions.

Theory 1: Attention Spans

One option might be to place this Three Second Rule in the context of ever-shortening attention spans. If we’re at the mercy of some biological or generational force causing attention spans to dwindle towards what can only be a pending singularity event, then we interpret that “3” as just one data point along a diminishing curve over time. To strategise effectively, we’ll need to know how fast it’s falling, and therefore how long our strategies will hold true if we base them on this current data.

Either way, the idea that we’re in a race against time itself puts us on the back foot. Applying the model that “it’s just kids these days” necessitates a reactive stance, with strategies that have their strengths in being short term, adaptive, and aimed towards getting the most out of every acquisition before their goldfish brains fixate on the next shiny thing. Fast conversion and early monetisation become our primary goals.

Theory 2: Attention State

In option two, we might apply a different context on user attention, borrowing from psychology the idea of “attention span” as being task-dependant i.e. varying based on our attention state: sustained, selective or divided. Modern technology keeps us in a state of divided attention, constantly flicking from one video to the next, or one app to the next. We could theorise that in this attention state, users take no more than 3 seconds to decide to stay on our content, or go next.

! That doesn’t mean our players are incapable of sustained attention states, just that it takes something truly engaging and interesting to stop the scroll.

Every executive has seen their fair share of books promising the secret to more focus time and reaching that optimal “flow state”. Applying this analogy to gaming, we might think about a First Time User Experience that snaps gamers out of their divided attention state, into that engaged, rewarding, and sustained focus mode that they’ll crave returning to.

Or, here’s a different bit of knowledge that we might apply to the exact same information to gain a different insight: TikTok may be the apex predator of divided attention, but close observation shows that Gen Z isn’t just using the platform to swipe themselves into a catatonic daze. Many users are leveraging divided attention states to build communities that discuss, recommend and curate long-form media, which later results in an asynchronous focused attention state aka buying and reading actual books. Using the #BookTok analogy, we might shift our strategies to an entirely new model where users sample and curate short game experiences, which convert to longer commitments later, when something they’ve sampled really resonates.

Theory 3: FOMO vs Friction

A third context to bring to the Three Second Rule might be the rise of opportunity cost, and the fall of friction.

When we are deciding how to spend our leisure time, we are subconsciously ranking that choice against every other choice we could make instead. When you get stuck scrolling through the Netflix menu for 20 minutes and choosing nothing, it’s because each movie has to convince you it’s going to be more interesting than every other movie available on the entire platform. The benefit has to outweigh the opportunity cost of choosing it. And the more options we have at our fingertips, the higher we weight that cost. It’s FOMO in its purest form.

Of course, that’s also dependent on how easily you can make another choice. It used to be that once you got to the local Blockbuster on a Saturday night, you had to find something to watch in that store, because you sure as hell weren’t going to drive to another one. But when thousands more options are a single swipe away? The path to “just quickly checking” those other options now has a very low coefficient of friction.

A cost-benefit analysis taking place within seconds based on subconscious assessment is something we’re familiar with from the world of web design. If this is the knowledge we bring to the Three Second Rule, then we have two dials to turn: we can focus on convincing the user very quickly that there is literally nothing more fun they can do right now than play our game, or we can make their choice to start playing our game as frictionless as possible. Or maybe both?

"Using data without context is like walking a tightrope while blindfolded; it looks impressive until you fall on your ass."

Conclusion: Data without context is…

This is obviously an over-simpification, and the ‘right’ answer will vary depending on many other contextual factors like game, genre, demographic, and budget. If you have an opinion, don’t be shy to tell us!

What should be clear though, is that using data without context is like walking a tightrope while blindfolded; it looks impressive until you fall on your ass.

So how do you avoid this? Start with a good data-driven strategy that includes:

  • Good quality data presented in the context of its history, its future, and its related metrics
  • Deep industry and domain knowledge in both presentation and interpretation
  • A diverse team that brings fresh ideas, knowledge and understanding to the table
  • The ability to test theories rapidly, thereby bringing in more data, more context, more knowledge, and ultimately better decisions.

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