Trust matters- Three strategies to build trust with your consumers
Consumer experience is more important than price. In a survey, American consumers indicate that consumer experience weighs in at 75% of their purchase option choice.1 Consumers value a fast, convenient shopping experience1, and in my opinion, eCommerce merchants who can deliver those experiences gain market share. As merchants have created defenses, fraudsters have increased the intensity and sophistication of attacks. This calls for a layered data insight approach to prevent, detect or mitigate fraud while maintaining a good consumer experience. A good consumer experience helps build trust.
Today’s consumers have high expectations. To meet those expectations, brands need to prioritize convenience when designing consumer interactions. Convenience should not come at the expense of accepting more fraud because fraud is expensive. The LexisNexis? Risk Solutions 2019 True Cost of Fraud Study Retail Edition estimates the total amount of losses a merchant incurs per $1 of fraudulent transaction is as much as $3.13. The cost of fraud has increased by 6.5% since 2018's study.
Based on internal analysis done by LexisNexis Risk Solutions in collaboration with merchants, consumer experience improves the conversion rate which brings more revenue. Everything lost to fraud takes away from money that could be invested in improving consumer experience. However, you need to maintain the balance between the two. Making better decisions on what is fraudulent and what isn’t is critical to the consumer experience. Consumers don’t want to be treated like a criminal. To make accurate decisions, merchants need to follow a layered approach which is focused on contextual data, machine learning insights and a passive form of behavioral biometrics.
Make data your secret weapon: not just more data but the right data.
Most merchants have two big challenges: either they do not have the right data or do not have the data in a usable format. Evaluate the data you have, and collect the right data. Once you have the right data, work with vendors or other merchants with the right expertise to enhance the data and provide relevant insights. One way to collect more relevant data is by participating in consortium models and collaborating with trusted partners. Incorporating additional data helps you complete the 360-degree view of your consumer’s identity and provide a better consumer experience . Your new consumer could be someone else’s best consumer . It also helps you pro-actively block fraudsters who have already committed fraud on other websites.
Leverage Machine powered insights.
There is a need to combine human discernment and scalability of machines to eliminate human bias and create scalable repeatable processes. These machine learning models work great when we provide feedback into the models – the output is only as good as the input. If you have history on users’ behavior, the model can detect patterns seen before. But what happens to zero-day patterns? There are two possibilities, your zero-day patterns have been seen elsewhere at other merchants or they are completely new. Leveraging consortium-based machine learning models can reduce the uncertainties associated with behavior new to you but have been seen before globally. Anomaly detection models can be insightful in detecting zero-day new behavior/attacks, but it will always be a challenge to detect new patterns no matter how good the anomaly detection tool. Therefore, you need to make sure you make the most of the local and global data by using consortium-based machine learning.
Unleash the power of passive behavioral biometrics
Behavioral Biometrics is the field of study related to the measure of uniquely identifying and measurable patterns in human activity. This is in contrast to physical biometrics, which involves innate human characteristics such as fingerprints or iris patterns.3 Behavioral biometric tools can help build trust relating to good consumers by building strong user scores over time that increases confidence in specific good behavior. This can reduce false positives by modeling behavior on a per user basis. It’s important to note you need to look at the entire consumer journey to help better differentiate suspicious and good behavior, resulting in more accuracy. Combining the way a user interacts with their device and information relating to the trustworthiness, integrity and authenticity of that device can form a compelling way to detect high-risk scenarios accurately.
To find your data insight gaps and to ultimately improve your consumers’ experience, ask yourself the following questions:
· How actionable is the data I am currently receiving?
· How do I benchmark my fraud prevention compared to the industry?
· Are my other actions improving my consumer experience?
· What data am I missing that could be used to identify anomalies or good consumers?
3 https://whatis.techtarget.com/definition/behavioral-biometrics