Wish you had a crystal ball you could use to predict exactly what your customers want so you could make the perfect game to satisfy their every desire?
An actual crystal ball doesn’t exist; however, we have the tools and techniques to help you with predicting customer behavior.
Customer behavior describes how and why customers make decisions and engage in other behaviors that impact their purchasing activity. Game publishers can study the massive amounts of data that players generate while playing games, along with demographic information, purchasing patterns, and interaction with marketing channels to gain insight into what customers will do in the future.
Additionally, publishers can use artificial intelligence tools to predict how players will respond to changes to games and new product offerings. This helps publishers create better gaming experiences and engage, retain, and monetize players more effectively.
The primary goal of predicting player behavior is to determine what players are likely to do in the future based on what they have done in the past. Within this framework, you can establish more specific goals based on your needs.
Players’ past purchases tend to predict what they will buy in the future. Predicting what players want to buy and when and how they want to buy it can help you create and market products that players are more likely to buy. Additionally, you can identify pain points and develop products and features that alleviate them.
User acquisition is expensive, and only a fraction of the users you acquire will ever spend money in your game. Predicting customer behavior can help you identify which players are the most likely to generate revenue and specifically target them with your marketing campaigns.
Predicting player behavior isn’t just about predicting what players will like. It also involves identifying aspects of your game that could cause players frustration so that you can eliminate pain points before players churn because of them.
Use your predictions to develop gameplay mechanics, new content, and in-app purchases that will increase player engagement in your games. You can also use the insights you gain to personalize how each player experiences the game and identify graphics and visual effects that are the most likely to resonate with players.
Use AI tools to create games that adapt to the skill levels and preferences of players. Create or alter NPC behaviors to match how players are likely to interact with NPCs in your game to build a more realistic and immersive world.
Predicting when and how players will respond to marketing messages helps you better target your marketing campaigns. You can also use predictive models to determine which marketing channels will be the most popular with customers in different demographics.
Use your insights to guide players to in-game content that they are likely to purchase. Promote your other games to players whose behavior patterns make them a good match.
The process of predicting customer behavior involves a series of steps. Publishers must repeat this process iteratively to account for changes in their customer base and the market.
First, determine what you want your predictive models to predict. A good place to start is by examining your key performance indicators and other metrics.
Examples include predicting:
Ultimately, what you want your models to predict will depend on specific business goals, such as improving retention or increasing in-app purchases.
Once you know what you want to predict, you need to build the infrastructure required to manage the data your models will use for predicting customer behavior. This involves constructing long-term data collections and data warehouse infrastructure to support passive information absorption.
If you already have some infrastructure in place, review it to ensure it is capable of delivering the relevant data you need. Modern data architectures can more efficiently and reliably integrate and cleanse data for predictive modeling.
The next step is to develop your predictive models. Start by choosing the best statistical techniques for your use cases. Then, build customer profiles that segment players based on traits they share, such as purchase history, where they live, demographics, and the product channels they use. Finally, build your models using the software tool of your choice.
The final step is to train your models using the data you have and then test them to see how well they are performing. Make adjustments based on your tests before launching and monitor and adjust iteratively after you go live.
Predicting customer behavior is a valuable tool for any game publisher. However, building and deploying effective predictive models can be resource-intensive. Sonamine can provide the tools, resources, and expertise you need or supplement your in-house team to help you reach your goals. Contact us today to get started.
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