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For a long time, the games industry didn’t have the means to build two-way relationships with players. Like the film industry, the relationship with the customer only travelled in one direction and ended when the purchase had been made.
Developers would design the games they thought players would want to play, the games were marketed to create demand and then released to the paying public, with developers’ attentions immediately turning to the next game. If a player liked a particular game, then it was highly likely that they’d purchase subsequent releases in that franchise. However, there was nothing you could really do about those players that didn’t like the game, and besides, they’d already paid their money.
In today’s Free-to-Play gaming economy, rising acquisition costs, poor retention rates and disappointingly low levels of profitability have created an environment where the player now holds all the cards and demands an inspiring experience.
With no upfront investment, players have no commitment to persevere with a game that doesn’t engage them from the get-go. This puts the onus firmly on the developer to understand player behaviours and use that data to create gaming experiences which maximize player satisfaction.
The evolution of analytics
Analytics 1.0 – This was focused solely on game performance, dashboard reporting of what had happened in the game but without providing the clarity that would enable developers to know where any issues may lie, or how to solve them.
Analytics 2.0 – This phase was about changing the game at the design level. Developers could see where the problem may lay, but could only implement broad-brush and one-size-fits-all changes to the game.
Analytics 3.0 – With the most current approach, publishers can give users the ability to change the game for each player. Big Data capabilities — capturing large numbers of data points powered by incredibly fast database technology — enables game designers to personalize the gaming experience to individual players within player segments, based on player engagement and playing style.
The personalization paradigm shift
The emergence of connected games has put the games industry in the unique position of being able to access a broad range of behavioural information about every single player. As a result, game personalization provides a paradigm shift by empowering the industry to completely re-assess its relationship with players. F2P developers increasingly understand that maximizing engagement and revenue is not only about building great games, it also requires them to pro-actively manage every player’s experience in-game.
Because every game has a wide spectrum of players with different abilities and different playing styles, Analytics 3.0 enables developers to adjust the game for segments of users. They can identify each user’s playing style and engage with them live as they play.
By looking at various factors such as competence, momentum, rewards and intensity of gameplay, you can nurture novice players and challenge expert ones. This is critical in the early stages of the game when churn is at its highest.
Dollars in the data
Soaring acquisition costs coupled with plateauing lifetime values has meant that many publishers and developers are finding that their games are no longer viable. In fact, it is generally accepted that the percentage of games that are profitable is in low single digits, so where does the industry go from here?
The best way forward is to focus on growing your spender base and their engagement, rather than pouring more and more money into acquisition.
By diving deeper into the vast amounts of event data generated by F2P games, it’s possible to undertake highly accurate behavioral forecasting. To support optimized decision making, offers made and changes to game balancing can be tested against modelled predictions for retention and changes to spending patterns — which, in turn, translates into lifetime value.
Because Analytics 3.0 provides direct access to highly granular event level data, publishers and developers can perform detailed analysis of their player base by their acquisition channel to ensure they’re focusing their resources on bringing on-board players who are ready to engage with the game.
Finding the balance
Game personalization is all about finding the right balance. Typically what happens is that a developer, using Analytics 2.0 technology, identifies that there’s a retention headache on a particular level of the game, causing players to fall out of the game after a specific number of attempts.
The challenge for this developer lies in knowing how to adjust the balance of the game so that the majority of players benefit. For example, lowering the difficulty setting might be great news for the novice players who were struggling to complete the level, but it’s not so great for the experienced players who now find the game too easy and therefore leave.
Game personalization is about recognizing that players are not the same and harnessing your data to create gaming experiences which are unique to each player.
Putting the needs of your customers first might seem obvious to many sectors, but in the games industry, it’s only now becoming a real focus. With a small proportion of all F2P games making money, there is clearly an imperative to focus more closely on players to unlock the dollars contained within the rich data they leave behind.
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Game analytics 3.0: This time, it’s personal
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