4 Ways AI Helps Emerging E-Commerce Platforms Compete with Major Game Distributors

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 primary time in 2013, and the trend was further accelerated by the 2020 lockdowns. In Italy, for instance, the primary week of confinement led digital game downloads to skyrocket by 174.9%.

Looking ahead, the market is poised to proceed growing, with Statista projecting it should grow at a CAGR of 5.76% between now and 2027, eventually reaching a market volume of $25.4 billion by the tip of that yr. 

Despite this, competition stays fierce. The digital games market is dominated by only a handful of platforms, and with 94% of spending happening digitally, that leaves little or no room for brand spanking new entrants. Established players — resembling Steam and Epic Games Store within the PC sphere — make the most of this to impose hefty fees on publishers. 

For these major entities, integrating AI into their operations is second-nature. Nevertheless, for smaller, emerging platforms, AI could possibly be a game-changer — one that permits them to challenge the incumbent oligopoly. 

While replicating successful AI implementations requires careful consideration of platform-specific characteristics and operational contexts, listed below are 4 ways by which AI will help fledgling e-commerce corporations 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 capability to process and analyze vast amounts of transaction data, AI’s algorithms can promptly discover suspicious patterns or anomalies. 

By scouring through extensive transaction databases, machine learning algorithms can adapt and recognize fraudulent operations, starting from unusual user behaviors to irregular payment schemes and purchases from atypical geographic regions. 

In traditional rule-based systems, a few of these indicators might go unnoticed, hindering an organization’s ability to detect fraud and exposing it to potential financial losses. 

At our company, by implementing AI-powered software — developed by a 3rd party — now we have prevented roughly 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 will not be released to the customer until the acquisition has been manually approved by our manager. 

#2: Streamlining Customer Support Queries

In e-commerce, AI-powered chatbots are one of the crucial common applications of artificial intelligence. 

Since there are numerous solutions out there already, chatbots are relatively easy to implement, even without historical data. Because they will learn from user interactions, chatbots yield results practically instantly, and help corporations reduce their need for customer support staff. 

Moreover, they unencumber time for the prevailing customer support agents. 

In our experience, most queries received — around 70% — are pretty easy and repetitive. Examples include:

• Is the sport available for purchase?
• When can I receive the sport key?
• How do I activate my license key? 
• What’s the status of my order?

In 80% of those cases, our AI bots have been quite successful at helping our users while not having to transfer them to a live operator. Thus, we will say that our bots cover roughly 56% of our incoming support requests, liberating invaluable resources that were previously poured into support staff in order that we will use them elsewhere in the corporate to reinforce our growth. 

#3: Identifying UX conversion-driving patterns

A standard dilemma e-commerce-oriented business owners face is identifying those aspects that successfully drive conversion and those who don’t. 

That is one other area where AI will help, by collecting user data that pinpoints recurring behavioral patterns that either lead or deter conversions. Based on this data, corporations could make UX-centered adjustments to their website. 

Moreover, AI can create customer segments that boost the effectiveness of selling efforts. Since it will possibly create user profiles across various dimensions, AI can uncover connections and group look-alike segments that may not be obvious through manual reviews. For instance, customers who purchase GTA 5 may be eager about games from a special genre that, in principle, bears no relation to GTA 5. 

To facilitate this, now 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, resembling providing personalized product recommendations — each on our website and thru email — and identifying relationships between products, enabling us to suggest complementary purchases. 

Moreover, we may time our customers’ next potential purchase. This also improves our timing for marketing messages. All in all, we will proudly say that these efforts have bolstered our sales via marketing channels by roughly 15%. 

#4: Forecasting sales

Given the time-sensitive nature of the gaming industry — as an example, Steam imposes constraints on what number of keys publishers can generate — effective forecasting is essential. 

Here, now we have implemented an easy AI model that is predicated on two primary methods: time series forecasting and regression evaluation. 

By detecting patterns, the previous helps us predict future sales figures and adapt to seasonality, which is a very important think about the gaming field. Alternatively, 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 instance, there are sports games released annually, resembling those by EA Sports, and other strategy games that span across many years — getting these critical aspects right is of paramount importance for accurate forecasting. 

We first began with this within the spring of 2024, so, as of now, our results are much like what we were achieving without AI. Nevertheless, 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, resembling gaming, AI can develop into a democratizing factor — one that allows emerging, high-potential platforms to compete with established behemoths. 

Having said this, to completely realize its potential, it isn’t a lot about simply integrating AI for the sake of it, but about doing it right. 

For smaller corporations that can’t afford to take care of an in-house team of AI specialists, a viable solution is to utilize existing third-party software. A few of these ready-made solutions will be utilized by regular developers, even in the event that they will not be specialized in AI. 

My suggestion is that you just don’t transition your entire workload instantly to AI. As a substitute, take a gradual approach. For instance, 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 standard of AI’s support will be very useful. As AI proves its price, you’ll be able to expand its scope inside your organization.