Who should receive bespoke LiveOps experiences - and who is better served by scalable systems? The answer lies in disciplined segmentation, predictive player signals, and knowing when stability matters more than constant reclassification. G5 Games' LiveOps Game Designer, Polina Ponomareva, explores how to balance precision and efficiency without losing control of cost or player experience.

 

Which player segments deserve to have bespoke events versus being served through scalable, automated systems?

First of all, new players are the ones who need bespoke events. This is one of the most important segments, because the flow of first days shapes the player's journey: whether the player understands the game, whether they stay, and whether they will ever pay.

Personalized LiveOps onboarding usually has a very high value in terms of the game flow.

Moreover, the core audience deserves bespoke content. These are the most engaged and loyal players, and very often the most paying ones as well. It’s critical to keep the core audience engaged. Our goal is to increase their stickiness, payments and fun.

The rest of the audience can be handled through scalable, automated systems, and that’s great! For example, for mid- and low-engaged players, personalized LiveOps could be not that effective and rational. Automated events with well-thought design and balance, progression-based flow usually work perfectly well. It is just important to always keep an eye on them. 

Bespoke LiveOps should be used where timing and specific engagement really matter, while scalable systems work great with volume and consistency.

What do you think works better: segment stability versus constant re-segmentation?

I believe it should always be about balance. Constantly changing segments is inefficient and can easily become confusing for players. If segments change too often, players start facing difficulty, which is not fun, but frustrating, too much or not enough rewards, monetization pressure – and that feels bad. Stable segments also make LiveOps much easier to design, test, and work with.

At the same time, it’s crucial to review your segmentation from time to time. You need to ask yourself:

Does it still reflect player needs?

Does it still drive the metrics?

Does it still support the design goals?

These questions help you understand whether it’s time for re-segmentation or not. LiveOps should be dynamic, while using stable segments as a base.

This way, segmentation will be fair for players, while still being responsive enough for LiveOps.

Which player signals do you consider most predictive of future value rather than past spend?

One of the most important signals are related to engagement, not monetization. Past spend explains what already happened, but future value is explained by session length, win rate etc.

For example, recent activity – how often a player comes back, what is the session length, amount of matches / grinds played.

Another example is depth of engagement – which features a player uses, how fast they go through an event and does they win it. Players who are engaged in events and dive into systems probably will pay later, even if they haven’t done it yet.

Also it’s event behavior. Joining events early, progressing, and finishing them is often a signal of the engaged player.

Payments still matters, but mostly as a second signal.

Recent payments, reaction to offers, or willingness to pay in general say more about future value than total lifetime spend.

Future value comes from habit, engagement, and intent – not from how much a player paid in the past.

How can you reuse insights from different projects without losing each game’s unique player behavior?

If the projects are similar, it is possible to find helpful insights in data from other projects, then tune them for your project. Player behavior is always game-specific, but the patterns behind it could be repeated.

Once you detect spikes and downs in other projects, you look for the reason for it. Those reasons are usually events, changes, features or updates and by analyzing them, you can come to a conclusion which one could also work great in your own project. Before bringing new features, segmentations or other things for other projects, it’s always better to check whether the expected result actually answers the metrics, the game flow, and the way players behave in this specific game.

So the reuse is never about copying solutions. It’s about taking concepts and basis, testing them, and adapting them to the reality of the game.

How refining a segment can materially improve engagement or monetization outcomes?

Refining a segment can make an important influence on both engagement and monetization. When you create segments you define the groups of players and their sizes. Then you refine them, to make them rational in sizes and cohorts types. It’s also important to check your segments from time to time to iterate the borders of the segment, because you need to stay up to date with the current state of the players, so you’ll be able to give them the most relevant content. 

To give the players the best experience and relatable content we should always use data, so the prices and content will work best for the players, they’ll get items they want and the events which are challenging and engaging for this particular group. 

Well-thought segments help to create more relevant LiveOps, which drives retention and revenue. So, refining a segment cuts the costs and creates a more specific segment with better targeting.  

How do you prevent LiveOps segmentation from drifting toward over-personalisation that becomes operationally expensive?

First of all, it’s important to define player groups based on meaningful patterns without getting into tiny details. The segments should be rational and cover the main goals of the feature.

Then you should understand clearly what you want to achieve with segmentation. What are the goals, and how will this help you reach them? Over-personalization can work, but only in specific cases and for specific groups.

Automated systems help to avoid overcomplicating operations, because some groups can be insignificantly small or there is no need to operate the group separately from another bigger group. So, usually you check the impact of the group, its size and significance for your metrics or its value and compare with other groups and your goals. It helps to determine the state of the specific group and its importance. If it is acceptable, you can make a bigger one out of the few small ones to reduce the costs of production and tune the flow without losing anything.   

This way you still deliver the right content, without going to over-personalization. 

How do you understand that segmentation works?

Metrics! Define what the KPIs and success metrics are: higher event participation, better retention, ARPDAU, or speed of the players. Then you monitor the segment’s response – for example, are players converting more often or staying active longer?

The best way is of course to test the segmentation. If different variations of segmentation are tested against each other, or against a baseline, then you’ll be able to see if it actually improves outcomes.