Digital Footprint AnalysisBuyer Data Trails Can Help You Keep Fraud at Bay

Digital Footprint Analysis: Once It’s on the Internet, It’s There Forever.

The idea that everything on the internet is permanent is a bit of an overexaggeration. But, there’s a kernel of truth to it.

Whenever you browse a website, upload something online, make a purchase, or login from a new location, you leave behind small amounts of data. Most of the time, this data trail goes unnoticed. But, a motivated individual, business, or government entity can potentially track your footsteps by closely examining your digital activities.

This process is known as digital footprint analysis. When performed by merchants, it can help identify security vulnerabilities, prevent fraud, and enhance user experiences.

What is a Digital Footprint?

Digital Footprint

[noun]/di • jə • dl • fo͝ot • print/

A digital footprint refers to the trail of data left behind by a user whenever they use a digital device to access an internet service like a website, social media app, or online store.

There are two types of digital footprints: active and passive.

An active digital footprint is generated deliberately. For example, users may intentionally post on social media, leave comments or reviews on websites, submit online forms, send and receive emails and texts, or share their data with third parties so that they receive personalized advertisements.

A passive digital footprint, on the other hand, is generated unintentionally. Users leave traces of data behind simply by being online. A user’s web browsing history, device information, IP address, sensor data, and advertisements viewed all comprise part of their passive digital footprint, as can personally identifying information (PII) like their social security number, medical records, and tax information.

How Merchants Can Use Digital Footprints

When used responsibly, digital footprints can help merchants collect valuable information about shoppers that can result in better experiences and fewer fraud risks. Specifically, active and passive digital footprints can help sellers:

What Comprises Your Digital Footprint?

As mentioned before, your digital footprint consists of both information you voluntarily divulge, as well as data that’s collected without your knowledge.

What Makes Up a Digital Footprint?

Device fingerprints, which are compiled using data about your device’s hardware, software, and system, form the bulk of your passive digital footprint. Specifically, your IP address, geolocation information, and browser data, as well as your operating system metadata, language settings, touchscreen gestures, mouse movements, and keystrokes can all be tracked.

Your browsing history makes up another part of your digital footprint. We’re talking about the websites and pages you visit, your login patterns, the items you buy online, and the apps you download and use. In addition to these datapoints are the messages and videos you willingly send and upload to social media platforms like Facebook, Instagram, or LinkedIn.

What Tools Can Help Me Analyze Users’ Digital Footprints?

So far, we’ve discussed what a digital footprint is and how you can use it to benefit your business. But how can you analyze the data in your possession?

Like robust fraud prevention, thorough digital footprint analysis requires the use of several complementary tools. These include:

Risks & Challenges

By now, it’s probably apparent that privacy risks are a significant concern. Data privacy laws like the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) restrict the information you can collect about your users, and may bar you from tracking them in real-time.

These regulations also govern the use of your customers’ data. While performing digital footprint analysis for the sake of fraud prevention may be defensible, using footprints for commercial purposes (like eCommerce personalization or ad targeting) may land you in hot water with regulators.

Besides, even fraud prevention is not a foolproof use case. Digital footprint analysis may lead to false positives, which occur when legitimate users are flagged as fraudulent. While it’s better to err on the side of caution, overly aggressive fraud detection strategies can create unnecessary friction for genuine buyers. As a result, you may alienate real customers at the expense of blocking fake ones.

The data collected from a user’s digital footprint isn’t always accurate, either. VPNs, emulators, privacy-focused browsers, IP address spoofing tools, and other evasion techniques can help fraudsters (and bona fide users alike) evade detection, which may make it difficult for you to understand the full extent of your users’ digital lives.

Future Trends in Digital Footprint Analysis

Increasingly sophisticated fraud tactics like clean fraud and SIM swapping will require even more sophisticated fraud detection techniques to combat.

Static digital footprints are a good starting point, but tomorrow’s fraud prevention strategies will need to incorporate machine learning tools that are capable of ingesting information in real-time. 

Digital fingerprinting-enabled behavioral biometrics are also likely to replace traditional data collection techniques, since the former is more thorough and difficult to evade.

Federated learning techniques, which allow AI models to be trained without accessing user data directly, may replace customer data platforms (CDPs) and analytics suites as the go-to approach for digital footprint analysis at scale. This privacy-preserving approach can help you use machine learning tools to root out fraud without running into the data use concerns that traditional footprint analysis techniques face.

An omnichannel approach to collecting digital data (and harnessing it for personalization and targeting) may also transition from an optional approach to a standard practice. Roughly 70% of online purchases today are already made on a mobile device, and it’s conceivable that tomorrow’s purchases could come from emerging channels like video-based shopping platforms or hardware devices like virtual reality (VR) headsets.

Digital Footprint Best Practices for eCommerce Merchants

From the vantage point of fraud protection, getting the most out of digital footprint analysis requires that you integrate it thoughtfully into your broader security strategy. To start, follow these best practices:

Digital footprints offer valuable behavioral context, but as mentioned before, they can’t be your only line of defense. True resilience comes from a layered security approach, which means that you should continue to incorporate traditional tools like Address Verification Service (AVS) and card verification value (CVV) checks, biometrics, multi-factor authentication (MFA), velocity checks, and 3-D Secure 2.0 for best effects.

Fraudsters move quickly, and your fraud detection tools will need to move even faster so that you can intercept suspicious activity before a fraudulent transaction goes through or an account takeover succeeds.

Solutions that leverage digital footprint data to conduct risk assessments in real time will play an important role in preventing fraud during high-risk moments in the buyer journey, such as account creation, logins, password resets, and checkout.

Remember that much of a user’s digital footprint is passively generated, so full disclosure and transparency is mandatory if you want to maintain your customers’ trust.

As a baseline, your privacy policy must be regularly updated and readily accessible. It should also clearly state what data you collect, how you collect it, and why you need it. If you’re using digital footprint data for personalization and ad targeting, you should seek explicit consent first.

Fraud evolves constantly, so a "set it and forget it" approach to fraud detection is ineffective and prone to failure. Instead, you should regularly review and refine your models based on the latest known fraud trends. Analyze your users' digital footprint data often, and deploy machine learning fraud models so that you can automatically incorporate the latest information into your systems.

Also be sure to establish feedback loops. Note confirmed fraud incidents as well as false positives so that you can pinpoint weaknesses in your fraud logic, optimize for detection accuracy, and reduce friction for genuine customers.

When it comes to fighting fraud, prevention is only half the battle. At Chargebacks911®, our dual-layered fraud prevention and representment solutions help you combat chargebacks before and after they occur.

By simultaneously thwarting the majority of fraudulent chargebacks prior to checkout and actively challenging the ones that make it through, you can manage representment costs, reduce chargeback fees, and keep your chargeback ratio in check. Curious to learn more? Reach out for a no-obligation ROI analysis today.