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

New Post has been published on https://thedigitalinsider.com/four-ways-ai-helps-emerging-e-commerce-platforms-compete-with-major-game-distributors/

Four 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 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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How to Identify Deepfake Videos Like a Fact-Checker

New Post has been published on https://thedigitalinsider.com/how-to-identify-deepfake-videos-like-a-fact-checker/

How to Identify Deepfake Videos Like a Fact-Checker

Deepfakes are synthetic media where an individual replaces a person’s likeness with someone else’s. They’re becoming more common online, often spreading misinformation around the world. While some may seem harmless, others can have malicious intent, making it important for individuals to discern the truth from digitally crafted false content.

Unfortunately, not everyone can access state-of-the-art software to identify deepfake videos. Here’s a look at how fact-checkers examine a video to determine its legitimacy and how you can use their strategies for yourself.

1. Examine the Context

Scrutinizing the context in which the video is presented is vital. This means looking at the background story, the setting and whether the video’s events align with what you know to be true. Deepfakes often slip here, presenting content that doesn’t hold up against real-world facts or timelines upon closer inspection.

One example involves a deepfake of Ukrainian President Volodymyr Zelensky. In March 2022, a deepfake video surfaced on social media where Zelensky appeared to be urging Ukrainian troops to lay down their arms and surrender to Russian forces. 

Upon closer examination, several contextual clues highlighted the video’s inauthenticity. The Ukrainian government’s official channels and Zelensky himself didn’t share this message. Also, the timing and circumstances didn’t align with known facts about Ukraine’s stance and military strategy. The video’s creation aimed to demoralize Ukrainian resistance and spread confusion among the international community supporting Ukraine.

2. Check the Source

When you come across a video online, check for its source. Understanding where a video comes from is crucial because hackers could use it against you to deploy a cyberattack. Recently, 75% of cybersecurity professionals reported a spike in cyberattacks, with 85% noting the use of generative AI by malicious individuals. 

This ties back to the rise of deepfake videos, and professionals are increasingly dealing with security incidents that AI-generated content is fueling. Verify the source by looking for where the video originated. A video originating from a dubious source could be part of a larger cyberattack strategy. 

Trusted sources are less likely to spread deepfake videos, making them a safer bet for reliable information. Always cross-check videos with reputable news outlets or official websites to ensure what you’re viewing is genuine.

3. Look for Inconsistencies in Facial Expressions

One of the telltale signs of a deepfake is the presence of inconsistencies in facial expressions. While deepfake technology has advanced, it often struggles with accurately mimicking the subtle and complex movements that occur naturally when a person talks or expresses emotions. You can spot these by looking out for the following inconsistencies:

Unnatural blinking: Humans blink in a regular, natural pattern. However, deepfakes may either under-represent blinking or overdo it. For instance, a deepfake could show a person talking for an extended period without blinking or blinking too rapidly.

Lip sync errors: When someone speaks in a video, their lip movement may be off. Watch closely to see if the lips match the audio. In some deepfakes, the mismatch is subtle but detectable when looking closely.

Facial expressions and emotions: Genuine human emotions are complex and reflected through facial movements. Deepfakes often fail to capture this, leading to stiff, exaggerated or not fully aligned expressions. For example, a deepfake video might show a person smiling or frowning with less nuance, or the emotional reaction may not match the context of the conversation.

4. Analyze the Audio

Audio can also give you clues into whether a video is real or fake. Deepfake technology attempts to mimic voices, but discrepancies often give them away. For instance, pay attention to the voice’s quality and characteristics. Deepfakes can sound robotic or flat in their speech, or they may lack the emotional inflections an actual human would exhibit naturally.

Background noise and sound quality can also provide clues. A sudden change could suggest that parts of the audio were altered or spliced together. Authentic videos typically remain consistent throughout the entirety.

5. Investigate Lighting and Shadows

Lighting and shadows play a large part in revealing a video’s authenticity. Deepfake technology often struggles with accurately replicating how light interacts with real-world objects, including people. Paying close attention to lighting and shadows can help you spot various items that indicate whether it’s a deepfake.

In authentic videos, the subject’s lighting and surroundings should be consistent. Deepfake videos may display irregularities, such as the face being lit differently from the background. If the video’s direction or source of light doesn’t make sense, it could be a sign of manipulation.

Secondly, shadows should behave according to the light sources in the scene. In deepfakes, shadows can appear at wrong angles or fail to correspond with other objects. Anomalies in shadow size, direction, and the presence or absence of expected shadows give you an overall idea.

6. Check for Emotional Manipulation

Deepfakes do more than create convincing falsehoods — people often design them to manipulate emotions and provoke reactions. A key aspect of identifying such content is to assess whether it aims to trigger an emotional response that could cloud rational judgment.

For instance, consider the incident where an AI-generated image of a bomb at the Pentagon circulated on Twitter X. Despite being completely fabricated, the image’s alarming nature caused it to go viral and trigger widespread panic. As a result, a $500 billion loss in the stock market occurred.

Deepfake videos can stir the same amount of panic, especially when AI is involved. While evaluating these videos, ask yourself:

Is the content trying to evoke a strong emotional response, such as fear, anger or shock? Authentic news sources aim to inform, not incite.

Does the content align with current events or known facts? Emotional manipulation often relies on disconnecting the audience from rational analysis.

Are reputable sources reporting the same story? The absence of corroboration from trusted news outlets can indicate the fabrication of emotionally charged content. 

7. Leverage Deepfake Detection Tools

As deepfakes become more sophisticated, relying solely on human observation to identify them can be challenging. Fortunately, deepfake detection tools that use advanced technology to distinguish between real and fake are available. 

These tools can analyze videos for inconsistencies and anomalies that may not be visible to the naked eye. They leverage AI and machine learning by utilizing speech watermarking as one method. These technologies are trained to recognize the watermark’s placement to determine if the audio was tampered with.

Microsoft developed a tool called Video Authenticator, which provides a confidence score indicating the likelihood of a deepfake. Similarly, startups and academic institutions continually develop and refine technologies to keep pace with evolving deepfakes.

Detecting Deepfakes Successfully

Technology has a light and dark side and is constantly evolving, so it’s important to be skeptical of what you see online. When you encounter a suspected deepfake, use your senses and the tools available. Additionally, always verify where it originated. As long as you stay on top of the latest deepfake news, your diligence will be key in preserving the truth in the age of fake media.