Video game recommenations Archives - Gameopedia

Category: Video game recommenations

Game Discoverability: Improving with Standardized Data and Emotional Resonance

Imagine being a passionate indie game developer, pouring your heart and soul into creating a unique gaming experience, only to find your game buried under thousands of titles, struggling to catch the attention of players. Or picture yourself as a gamer, eagerly searching for your next adventure, but getting lost in a sea of options, unable to find a game that truly resonates with you.

This is the reality of today’s gaming industry—a landscape overflowing with content, where game discoverability has become a daunting challenge. As the digital marketplace grows, so does the struggle to connect gamers with the right games. But what if the problem isn’t just the sheer number of titles, but the lack of standardized, high-quality data to guide discovery?


The Discoverability Dilemma in Gaming

In an op-ed on GamesIndustry.biz, industry expert Rob Fahey highlighted the fierce competition for consumer attention in the gaming world. The rise of digital distribution has resulted in a market flooded with a staggering number of titles, making it increasingly difficult for consumers to find games that match their interests and for developers to stand out. This challenge is multi-faceted and has several key components:

  1. Competing for Player Attention: With so many games vying for visibility, it’s challenging for quality titles to break through the noise and capture players’ attention. The overwhelming volume of new releases every week creates a situation where even the most innovative games can be easily overlooked.

  2. Marketing and PR Challenges: Many indie developers, who often operate with limited budgets, lack the marketing and public relations expertise needed to gain visibility in such a crowded marketplace. Unlike major studios, these developers cannot afford extensive marketing campaigns or media coverage, putting them at a distinct disadvantage when it comes to promoting their games.

  3. Algorithm Limitations: No algorithm, no matter how advanced, can fully solve the discoverability issue. Algorithms are constrained by the finite nature of consumer attention, the limited hours in a day, and the disposable income available to spend on games. Moreover, algorithms often prioritize popular games with high engagement, which can further marginalize niche titles or indie games.

These challenges paint a somewhat bleak picture of the future of game discoverability. However, there are more optimistic solutions that can be explored, particularly those that focus on improving how games are categorized and presented to players.

The Key to Solving Game Discoverability

The crux of the game discoverability problem lies not just in the overwhelming amount of content but in the lack of standardized, high-quality data. Fahey’s concerns are valid, but they overlook the potential solutions that can be found in improving how games are categorized and presented to players.

One of the most significant issues contributing to poor discoverability is the misclassification of games across various platforms. For instance, the indie game Spiritfarer by Thunder Lotus Games, known for its profound narrative and emotional impact, is often incorrectly categorized as a simulation game. This kind of mislabeling can alienate potential players who are looking for a different experience, leading to missed opportunities for both gamers and developers.

How Spiritfarer's genres are classified across different sources

The Importance of Accurate Game Classification

Accurate classification is critical for game discoverability. When games are categorized correctly, it becomes easier for players to find titles that match their preferences, whether they’re searching by genre, theme, mood, or gameplay style. Unfortunately, the current lack of standardized classification systems means that many games are being mislabeled or poorly categorized, which negatively impacts discoverability.

The root of this problem is the absence of a standardized framework or taxonomy for accurately describing games. Without consistent, high-quality data, discovery algorithms are bound to fail, unable to connect gamers with the experiences they’re truly seeking. This isn’t just a technical glitch—it’s a fundamental flaw in how game data is curated across the industry.

To address this issue, the gaming industry needs to adopt a more standardized approach to data collection and classification. By creating a universal taxonomy that defines genres, sub-genres, themes, and gameplay styles, the industry can improve the accuracy of game categorization. This will not only help players find games that match their interests but also increase the visibility of niche or indie titles that might otherwise be overlooked.

Leveraging Metadata for Enhanced Discoverability

Metadata plays a crucial role in improving game discoverability. Metadata refers to the data that provides information about other data—in this case, the attributes of a game. These attributes can include anything from the game’s genre, theme, and setting to more granular details like the mood it evokes, the pacing of its gameplay, and the complexity of its mechanics.

Platforms like Steam, PlayStation Network, and Xbox can benefit greatly from enhanced metadata. By incorporating more detailed metadata into their search and recommendation algorithms, these platforms can offer more personalized and accurate suggestions to players. For example, instead of simply recommending a game based on its popularity or recent release date, a platform could recommend games based on the specific themes or moods that a player has shown interest in.

The use of metadata can also help to surface hidden gems—games that may not have achieved mainstream success but have unique qualities that appeal to specific player demographics. By tagging games with more descriptive and nuanced metadata, platforms can ensure that these titles are more easily discoverable by the right audiences.

Learning from Netflix and Spotify: Models for Game Discovery

Other industries have successfully addressed similar challenges with content discoverability, and the gaming industry can learn valuable lessons from them. Platforms like Netflix and Spotify have mastered content discoverability, providing a blueprint that the gaming industry could follow.

Netflix’s Approach to Content Discoverability

Netflix excels at recommending content based on comprehensive metadata, keeping users engaged even when their preferred content isn’t available. Netflix’s recommendation engine leverages detailed metadata, including genre, sub-genre, themes, directors, and even visual style, to make personalized content suggestions. This approach helps users discover new content that aligns with their preferences, benefiting both the platform and the content creators.

How Netflix suggests similar content when they don't have exactly what you are looking for.

By adopting a similar approach, gaming platforms can improve their recommendation systems. Instead of simply showing players the most popular or trending games, platforms could use metadata to recommend games that match the player’s specific tastes, such as games with similar storytelling styles, atmospheres, or gameplay mechanics.

Spotify’s Mood-Based Recommendations

Spotify, meanwhile, has perfected the art of matching music to the listener’s emotional state, creating a personalized and resonant user experience. Spotify’s playlists are often curated based on mood, such as “Relaxed,” “Focused,” or “Energized,” and the platform uses algorithms to suggest songs that fit these moods. This approach has been incredibly successful in helping users discover new music that resonates with their current emotional state.

How Spotify presents different vibes

Imagine if gaming platforms could offer a similar experience, recommending games based on the player’s mood or desired emotional journey. For example, if you’re looking to unwind after a long day, a platform could suggest serene titles like Abzû rather than intense games like Call of Duty. Conversely, when you’re in the mood for something intellectually stimulating, the platform could guide you to thought-provoking games like Against the Storm.

Gameplay comparison

The Role of Emotional Resonance in Game Discoverability

Another critical aspect of game discoverability is understanding the emotional resonance that games have with players. Emotional resonance refers to the emotional impact that a game has on its players—the way it makes them feel during and after gameplay. Games that successfully tap into specific emotions, whether it’s excitement, relaxation, or nostalgia, often have a more profound and lasting impact on players.

By incorporating emotional resonance into the discoverability process, gaming platforms can create more personalized and engaging experiences for players. For example, a player who enjoys emotionally intense games might recommend titles that evoke similar feelings, even if those games differ in genre or gameplay style.

This approach not only enhances the discoverability of games that might otherwise be overlooked but also helps to build stronger connections between players and the games they discover. When players feel that a game resonates with their emotions or experiences, they are more likely to engage with the game and share it with others, further increasing its visibility.

The Future of Game Discoverability

By adopting standardized data frameworks and understanding the emotional needs of gamers, the gaming industry can significantly enhance game discoverability. This approach shifts the focus from mere popularity to emotional resonance, offering a new way to uncover hidden gems and ensuring that every game, from indie titles to mainstream blockbusters, finds its audience.

The future of game discoverability lies in the combination of standardized data, enhanced metadata, and a deep understanding of emotional resonance. By integrating these elements into the discoverability process, the gaming industry can create more personalized and effective discovery experiences for players. This not only benefits the players but also the developers, who can see their games reach a broader and more engaged audience.

Bridging the Discoverability Gap with Quality Data

At Gameopedia, we understand the scale of this challenge and the opportunity it presents. Our extensive metadata database, built on over 15 years of industry expertise, is designed to tackle these discoverability issues head-on. By providing a rich tapestry of game attributes and a standardized framework for interpreting them, we aim to revolutionize how games are found and enjoyed. Our commitment to quality data and a deep understanding of gaming’s nuances promises to bridge the gap between gamers and the experiences they seek, making significant strides in solving the discoverability puzzle.

As the gaming industry continues to evolve, the importance of standardized data and accurate game classification will only grow. By addressing these challenges now, the industry can ensure that players have access to the games they love and that developers can reach the audiences they deserve. With the right tools and approaches, we can create a future where every game finds its audience and every player discovers their next great adventure.

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Improving Video Game Recommendations: Addressing Challenges and Opportunities in E-Commerce

Have you ever browsed through what seemed like Steam’s entire catalog trying to find a game you might like? Have you spent more time on Netflix trying to decide what you want to watch as opposed to watching something? These are problems most of us have faced or are facing, and are a result of several factors, especially lack of personalization.

Personalizing your products for your customers is critical in today’s world. One can see an average increase of 20% in sales when using personalized experiences. Companies want their customers to be aware of how they are catering specifically to user needs. For example, if you’ve played a shooter game, you might be recommended to play another just because other people who liked the first game you played also enjoyed this. Your reasons for playing the former might differ from the others and thus, the latter might not be a good fit for you. The lack of good video game recommendations reduces trust in these companies to provide a good service experience. 

From the companies’ end, good customer experiences help in generating income, as well as differentiating themselves from the competition. An e-commerce company needs to focus on three things to thrive:

  • Increase the acquisition rate of new users
  • Increasing conversion rates of your users
  • Ensure that users don’t leave (reduce churn)

The Importance of Recommendations

Personalization has become a major factor in the success of e-retail companies. Whether it is addressing customers by name in communications or providing them with special offers based on their interests, online stores are increasingly focused on improving this.

Recommendations are the deepest level of personalization and are a necessary feature to be added to their portal. They are integral for both customers and the companies which cater to them for a multitude of reasons we will look at. For a customer, they provide the following benefits:

  • A significantly better user experience
  • A sense of being understood and seen
  • More personalized benefits and deals

For companies, the benefits are just as tangible if not more so:

  • Improved customer engagement
  • Significantly increased customer retention
  • Larger levels of web traffic
  • Better sales and revenue

Below are some examples of companies that thrive based on their recommendation systems.

 

Case Studies

Let’s take a look at one of the world’s most successful companies, Netflix. While Netflix started as a movie rental service, today, they stream movies and have over 200 million paying customers across the globe. A key part of this evolution is their personalized recommendation system. 

Understanding Video Game Recommendations: Netflix's Recommendations
Netflix's recommendation system suggests a variety of content you might enjoy.

Netflix’s recommendation systems have been developed over years by hundreds of engineers after analyzing millions of users. When a new subscriber joins, Netflix asks them to pick shows/movies they like, and as they watch more over time, the suggestions are powered by these as well as some additional factors like:

  • Viewer history
  • Viewer ratings for prior shows
  • Information like title, genre, category, and more
  • Other viewers with similar preferences and taste
  • Time an episode/movie lasts vs time duration of a viewer watching a show
  • The time of the day you’re watching
  • The device on which you’re logged in

Closer to home, we have Steam, which is a digital game distribution system, with more than 120 million monthly active users and a catalog of over 50,000 games. It is also home to a powerful video game recommendation system that helps gamers find games they will love.

They recommend games based on your played games, purchase history, store browsing history, and games that other players with tastes similar to yours love.

 

Understanding Video Game Recommendations: Netflix's Recommendations:Steam's Recommendations
Steam's recommendations are based on a variety of factors like games you've previously played, browsing history, and the like.

However, neither of these do a perfect job. Let’s look at why.

The Problems with Recommendations Today

We’ve looked at the importance of personalization and the role recommendations play in this. However, despite online stores realizing how vital a good quality recommendation is, they still haven’t perfected the art of suggesting the right products. Here are some of the common problems faced by customers while trying to find what they need.

Wrong recommendations: Thanks to imperfect algorithms or lack of high quality data, sites can often suggest irrelevant or incorrect recommendations. These reduce customer trust, engagement, and overall, is a waste of a good opportunity.

Impersonal communication: We all buy products and services for a variety of reasons. However, distributors still use generic and non-engaging messages most of the time while communicating with users. Messages such as “You might like Item X” without mentioning why you might like it can turn your customers off.

Choice overload: Too much choice can be a detriment to your customers. A recent consumer report discovered that more than half (54%) of consumers have stopped purchasing products from a brand or e-retailer website because choosing was too difficult, with 42% admitting to abandoning a planned purchase altogether because there was too much choice. These problems are a result of sub-optimal recommendation systems on websites.

 

Behavior Vs Motivation

The reason for inadequate online recommendations is that these mechanisms are primarily driven by behavior as opposed to motivation.

If several people play the same game, they might do so for different reasons. Let us take one of the most popular games which came out in June 2020, Valorant, as an example. Valorant is a 5v5 tactical first person shooter (FPS) where the characters you play as (agents) all have unique abilities. It has a monthly player base of at least 12 million throughout 2021, making it one of the most popular current FPS titles. Let’s analyze the different possible motivations that drive people to play Valorant:

Satisfying the urge to compete, dominate, and win: A large number of people play video games to compete against other skilled players and dominate the leaderboards for a sense of achievement. Valorant has this in spades with its highly competitive online multiplayer nature and detailed rank progression.

 

Strategizing for the win: Gamers enjoy certain games because they involve a great deal of planning and strategizing to be victorious. With its deeply tactical nature, Valorant satisfies this motivation.

To play with friends or meet people: A significant portion of players like games for their socialisation aspect. Whether it is being able to play with your buddies, meeting new like-minded strangers you can have fun with, or working as a team, Valorant fills these socialization shoes very well.

Current state of product recommendations
Nothing hits the mark like playing games with your squad.

For an adrenaline rush: Gamers often get motivated by the rush of adrenaline or dopamine they get as they play games that excite their senses, and this is what keeps them coming back to the game as well. Valorant certainly fits this criterion.

Aggression: Some people like playing video games for the violence and ferocity that come as a part of the game, especially shooters and hack & slash games. Valorant satisfies this urge.

The behaviour here in common is people playing Valorant. However, as you can see, their motivations may be completely different. For instance, in terms of story and lore, Valorant is found lacking compared to Overwatch, another popular competitive multiplayer title. Thus, people who play Overwatch because they like its lore and narrative aspects might not be as interested in Valorant.

How can you Improve Video Game Recommendations?

Gamer motivations are a culmination of their emotional and psychological makeup while also covering traits like values, personality, and life situations. To revolutionize video game recommendations, you will need to start by understanding the games you’re recommending, and why people play them. Next, look at your user base and try to understand each individual at a fundamental level. Finally, once you have an understanding of the games as well as your user, see why people play what they do, and use that to provide a video game recommendation. As a result of this, you will:

  • Provide fewer recommendations: This will keep you from overloading your customers with choice.
  • Give better recommendations: When you understand your users’ motivations, you can suggest games that are aligned with their motivations every time.
  • Personalized recommendations: Each of your recommendations will effectively communicate why a particular game is right for your user, as well as address their needs.

Apart from the above, you can improve e-retail personalization in general by:

  • Refine your search pages. You can use metadata to improve product descriptions and make it easier for your algorithms to match products to customer preferences and needs.
  • You can use referral bonuses to improve signups and good email marketing that conveys personalized deals and offers to your customers to increase retention.
  • Ensure your home page, product pages, and promotional offers are tailored to your customers’ needs based on data you’ve collected and their preferences. 
  • Intelligent machine learning algorithms combined with high quality data are your best friends. The next section will go into detail about recommendation models you can use in conjunction with them.

Recommendation Models

Below are the models most commonly used by e-commerce companies:

Popularity-based: These are products that are best-selling currently. For example, Among Us blew up in 2020 and was a game that popped up on Steam’s bestseller list. These also include games that have been popular for a long time, such as Counter-Strike: Global Offensive. It is meant primarily for new users on the website.

Quality based: The games which have a high number of positive reviews and ratings show up here based on this model and are recommended to users. However, this might not be the best method as peoples’ tastes can drastically differ, and a game might have ‘boosted’ reviews. Also, newer games might not have enough reviews to show up, despite possibly being something your user might love.

Content-based: This model recommends products based on their similarities with other products. It leverages the description and content of items and an understanding of the user’s consumption history. For example, Valorant is recommended to players who love Overwatch and Counter-Strike: Global Offensive, since it has similar characteristics to both these games.

Collaborative Filtering: In the newer, focused sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating).The system generates recommendations using only information about rating profiles for different users or items.

Of course, hybrid recommendation systems which use a mix of these models are your best bet to provide personalized recommendations to your customers. Going back to Netflix, they make recommendations by comparing the watching and searching habits of similar users (i.e., collaborative filtering) as well as by offering recommendations that share characteristics with content that a user has rated highly (content-based filtering). 

Metadata is crucial to fuel understanding of your products. This will help you organize your product database, as well as categorize it better. High quality and comprehensive metadata gives personalization algorithms more data to train on. If you want to know more about the importance of video game metadata and managing it, this blog might help you.

Conclusion

Personalizing recommendations is the best way for e-commerce companies to improve revenue as well as stand out among their competitors. When it comes to video games, understanding the motivations as to why people play the games they play is integral to making good suggestions. Gameopedia’s quality-checked and extensive metadata as well as our intelligent sentiment analysis tool can help with optimizing your content and website for better personalization and improving video game recommendations. Contact us to learn more about what we can do for you and your business.

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