Over the past 12 years, computer and video game distribution strategies have undergone a seismic shift. Sales of digital games exceeded those of physical copies for the first time in 2013, and the trend was further accelerated by the 2020 lockdowns. In Italy, for example, the first week of confinement led digital game downloads to skyrocket by 174.9%.
Looking ahead, the market is poised to continue growing, with Statista projecting it will grow at a CAGR of 5.76% between now and 2027, eventually reaching a market volume of $25.4 billion by the end of that year.
Despite this, competition remains fierce. The digital games market is dominated by only a handful of platforms, and with 94% of spending taking place digitally, that leaves very little room for new entrants. Established players — such as Steam and Epic Games Store in the PC sphere — take advantage of this to impose hefty fees on publishers.
For these major entities, integrating AI into their operations is second-nature. However, for smaller, emerging platforms, AI could be a game-changer — one that allows them to challenge the incumbent oligopoly.
While replicating successful AI implementations requires careful consideration of platform-specific characteristics and operational contexts, here are four ways in which AI can help fledgling e-commerce companies compete with digital distribution giants.
#1: Enhancing fraud detection
On gaming platforms, fraud happens at a much larger scale — and more recurrently — than in other e-commerce verticals. Given its capacity to process and analyze vast amounts of transaction data, AI’s algorithms can promptly identify suspicious patterns or anomalies.
By scouring through extensive transaction databases, machine learning algorithms can adapt and recognize fraudulent operations, ranging from uncommon user behaviors to irregular payment schemes and purchases from atypical geographic regions.
In traditional rule-based systems, some of these indicators might go unnoticed, hindering a company’s ability to detect fraud and exposing it to potential financial losses.
At our company, by implementing AI-powered software — developed by a third party — we have prevented approximately 95% of fraudulent transactions. We also work hand-in-hand with technology. Once an operation is flagged as dubious, our manager personally reviews it. Digital game keys are not released to the buyer until the purchase has been manually approved by our manager.
#2: Streamlining Customer Support Queries
In e-commerce, AI-powered chatbots are one of the most common applications of artificial intelligence.
Since there are many solutions in the market already, chatbots are relatively easy to implement, even without historical data. Because they can learn from user interactions, chatbots yield results practically right away, and help companies reduce their need for customer support staff.
Additionally, they free up time for the existing customer support agents.
In our experience, most queries received — around 70% — are pretty simple and repetitive. Examples include:
- Is the game available for purchase?
- When can I receive the game key?
- How do I activate my license key?
- What is the status of my order?
In 80% of these cases, our AI bots have been quite successful at helping our users without needing to transfer them to a live operator. Thus, we can say that our bots cover roughly 56% of our incoming support requests, liberating valuable resources that were previously poured into support staff so that we can use them elsewhere in the company to enhance our growth.
#3: Identifying UX conversion-driving patterns
A common dilemma e-commerce-oriented business owners face is identifying those factors that successfully drive conversion and those that don’t.
This is another area where AI can help, by collecting user data that pinpoints recurring behavioral patterns that either lead or deter conversions. Based on this data, companies can make UX-centered adjustments to their website.
Additionally, AI can create customer segments that boost the effectiveness of marketing efforts. Since it can create user profiles across various dimensions, AI can uncover connections and group look-alike segments that might not be obvious through manual reviews. For example, customers who purchase GTA 5 may also be interested in games from a different genre that, in principle, bears no relation to GTA 5.
To facilitate this, we have implemented a third-party AI personalization solution from Retail Rocket. By leveraging historical customer purchase data, this tool helps us accomplish several tasks, such as providing personalized product recommendations — both on our website and through email — and identifying relationships between products, enabling us to suggest complementary purchases.
Additionally, we can also time our customers’ next potential purchase. This also improves our timing for marketing messages. All in all, we can proudly say that these efforts have bolstered our sales via marketing channels by approximately 15%.
#4: Forecasting sales
Given the time-sensitive nature of the gaming industry — for instance, Steam imposes constraints on how many keys publishers can generate — effective forecasting is key.
Here, we have implemented a straightforward AI model that is based on two primary methods: time series forecasting and regression analysis.
By detecting patterns, the former helps us predict future sales figures and adapt to seasonality, which is an important factor in the gaming field. On the other hand, the latter assists our team in establishing relationships between sales data and other variables — demographics, pricing, product categories, and more.
Since there are wide divergences in these parameters — for example, there are sports games released annually, such as those by EA Sports, and other strategy games that span across decades — getting these critical factors right is of paramount importance for accurate forecasting.
We first started with this in the spring of 2024, so, as of now, our results are similar to what we were achieving without AI. However, we expect that as we further calibrate and refine our model, and accumulate more historical data, our accuracy will significantly improve over time.
Final thoughts
In some fields, such as gaming, AI can become a democratizing factor — one that enables emerging, high-potential platforms to compete with established behemoths.
Having said this, to fully realize its potential, it is not so much about simply integrating AI for the sake of it, but about doing it right.
For smaller companies that cannot afford to maintain an in-house team of AI specialists, a viable solution is to utilize existing third-party software. Some of these ready-made solutions can be used by regular developers, even if they are not specialized in AI.
My suggestion is that you do not transition all of your workload right away to AI. Instead, take a gradual approach. For example, ask AI to handle 10% of user queries, or to dynamically price 10% of your products.
Last but not least, maintain the human touch. Having people review the quality of AI’s support can be very beneficial. As AI proves its worth, you can expand its scope within your organization.
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