The ability to sell to customers from all around the world, 24/7 is one of the greatest pros of online commerce. At the same time, global expansion brings a myriad of challenges merchants have to meet and obstacles they must overcome to successfully spread their wings. Firstly, they must thoroughly understand each market they want to enter. Secondly, they need to recognize diverse market-specific threats for their business and apply appropriate prevention measures. Finally, they have to keep up to date with each market they choose to operate on and adapt to the changing landscape in real time. Seems hard? Not necessarily, once you engage Artificial Intelligence and fuel it with human ingenuity.
Understand your target markets using hard data
For today’s ecommerce, cross-border expansion means much more than just accepting payments from foreign customers and shipping to multiple countries. It means adjusting the overall shopping experience to the specific preferences of customers from various markets. It means learning about their shopping habits and preferred payment methods. It means figuring out how they interact with online content and delivering content they can truly enjoy. It finally means discerning typical online behaviour from possible fraud attempts to avoid false positives, as well as false negatives. In other words, cross-border expansion requires superb Business Intelligence.
The multiplicity of variables one should take into account when going cross-border is overwhelming. However, business analyses do not necessarily have to cost the earth and take weeks. Thanks to advanced Machine Learning and numerous data enrichment methods, most expansion-related questions can be answered in a blink of an eye, with great accuracy. There are insurmountable amounts of data available online. Every single user, consciously and unconsciously, provides plenty of information about themselves and their home market. The challenge is to find the right data sources (know where to search and how to get them), select appropriate analytical methods and find interdependencies between apparently irrelevant variables.
This can be done by integrating with an advanced Business Intelligence solution like Nethone. It gathers over 3000 data points whenever someone enters the website or launches the mobile app integrated with the system. The number might be, of course, extended, if necessary. High quality data, however, is just one side of the coin. The other one is creation of customized Machine Learning models able to turn these myriads of data into accurate conclusions. This is where human ingenuity steps in. In order to create models that really do the job, one needs truly savvy data scientists and programmers who not only rock in their fields of expertise but also really understand business. Nethone team, for instance, consists of people whose experience has spanned a number of roles in diverse companies ranging from startups to corporations and each analytical project we carry out is preceded by extensive consultations with the merchant. Cross-border merchants need in-depth market research. It should be conducted through the use of right data and advanced Machine Learning. Those can be obtained and applied by the people who know how to use both to full extent. Data-based Insights significantly ease understanding of new markets and power up effective cross-border ecommerce.
Data-based insights make it possible to not only smoothly go cross-border but also to outperform local competition. Companies strongly rooted in one market oftentimes overestimate their experience-based insights and underestimate data-based ones, especially when those diverge from their well-established vision of the business landscape. A data-driven newcomer might, therefore, really surprise them.
Identify threats and take prevention measures
Once the expanding company learns as much as possible about its new target markets in positive sense, it is the right moment to take a closer look at threats, namely at market-specific types of fraud.
Operations carried out by fraudsters active on various markets are different. Although these differences are oftentimes very subtle, they might imply serious vulnerabilities. Anti-fraud rules and manual reviewing methods relatively effective on one market might be completely useless on another. Moreover, the accuracy of rule-based systems leaves much to be desired. This results in, so called, false positives – dropping of “healthy” users erroneously identified as fraudsters. For example, defining the use of VPN as a potential designate of a fraud attempt might make sense as long as the merchant does not target Chinese market where connecting via VPN is very popular among legitimate users. How to overcome this obstacle? Once again, Artificial Intelligence brings the answer. Instead of creating static anti-fraud rules and applying labour intensive manual reviewing, merchants can nowadays prevent fraud automatically, in real time – by engaging anti-fraud Machine Learning models. Those, if properly configured, can identify even the most sophisticated fraud attempts without interfering with “healthy” traffic”, whatever the market. In effect, there is no need to employ new risk managers to secure the bottom line during the cross-border expansion. The job can be done by the company’s hitherto risk team supported by AI.
It is possible thanks to thousands of parameters being taken into account during each transaction verification and multiple supporting technologies engaged in the data gathering process. It must be emphasized that leveraging Machine Learning in many cases excludes the use of static rules. For their maximum effectiveness, fine-tuned models must learn as they go and their performance should not be curbed by static rules. Sure, it is the merchant who makes the final decision about allowing or dropping a transaction but they should allow the machine to provide as much insight as possible. Each red flag, therefore, should be followed by rich evidential data. Nethone considers every transaction as “healthy”, unless it can prove a fraud attempt providing rich evidence to make the risk executive well informed about the transactions flow. In effect, fraud prevention does not lock sales anymore and stops being an Enigma. AI significantly boosts effectiveness of risk teams and helps cross- border merchants operate more cost efficiently.
Adapt to the changing landscape in real time
The world of online commerce is in flux and the pace of changes is astounding. Merchants selling their products cross-border, targeting multiple markets and fighting for diversified groups of customers, must always stay on the cutting edge. They must react to ongoing changes in real time and adapt their business to meet new challenges. Be it understanding of the rapidly evolving market or keeping one step ahead of fraudsters, the multiplicity of variables one needs to include in their ongoing analyses is overwhelming. Manual handling becomes counterproductive and even hardly possible. Happily, the essential feature of AI comes to the rescue.
Machine Learning models automatically adapt to the changing business landscape, recognize regularities and anomalies, become more and more effective with each new analysis being carried out. Moreover, through the use of Machine Learning, merchants can accurately predict trends and business indicators. Nethone users leverage the solution to anticipate LTV, MRR, Churn rate, expansion, contraction, and many more. In other words, merchants can go beyond adapting to the business landscape and actually form the landscape their competitors must cope with.
Don’t miss your chance
Artificial Intelligence conquers various fields of life and business these days. Autonomous cars are driven by machine taught algorithms, AI-based systems recognize particular individuals in pictures and intelligent chatbots provide excellent online customer service. What a waste would it be to miss the close to infinite range of opportunities that AI brings to online commerce? What a shame would it be not to use it to expand cross-border?
This article has been originally published in PCM eMagazine Vol. 2-9, issued by Payments & Cards Network.