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How Virtual Reality Can Enhance Customer Experience Centers in a Post-COVID World

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How Virtual Reality Can Enhance Customer Experience Centers in a Post-COVID World


Due to the challenging times the world is facing right now, we are seeing more and more companies look to alternative means to showcase and demonstrate products. 

One of the things that have been on the rise of late has been the Customer Experience Center or CEC for short. Brands are looking for creative ways to connect with their customers, and utilizing modern technology to showcase a company’s vision has tremendous benefits.

CEC’s have proven in recent years to be one of the most effective ways for brands to showcase their latest products and identify with customers in unique ways like never before.

Benefits of the CEC

One of the more effective ways of showcasing complex enterprise offerings or products is through a live and interactive demonstration. This is the core value of the CEC; being able to provide prospective customers with the “try it before you buy it” approach. This is especially true for cutting edge technology, many times in which executives and enterprise decision-makers haven’t even tried as of yet.

With virtual reality specifically, a vast majority of C-level executives haven’t used the medium in an enterprise training setting. A small sampling has tried it for gaming but just haven’t had the opportunity to gauge the effectiveness of VR for improved learning efficacy.

Enter the CEC VR demo, a chance to see first hand the power of using VR as a medium for knowledge enhancement. It is one thing to have an online demo or case study, but it an entirely different ball of wax to put on a VR headset and try a simulation first hand.

Not only does the CEC provide a chance for brands to show off their latest technology, but they also showcase the brand’s leadership towards innovation and product excellence. This also gives a chance for brands to consolidate sales offerings in one convenient location without having multiple channels for difficult to showcase products. Many times due to cost it just isn’t feasible and this leads to longer sales cycles which slow profits — being able to consolidate is a huge win and should bolster overall win ratios.

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Drawbacks of the CEC

While the CEC can be a unique way to showcase and demonstrate product offerings, it does not come without some disadvantages. Specifically, during the current global pandemic, there may be issues having groups of people close together for product demos. Due to COVID-19, there is a lot more pressure on companies to reduce social interactions, and this may directly relate to the CEC mantra. In fact, due to the Coronavirus pandemic, a lot of CEC’s were forced to shut down as they typically had large groups visit them at a time.

Another thing to be aware of is the initial cost to put a CEC together. They take up considerable real estate, and the cost is generally high as many times they feature cutting edge technology and require specific hardware and support staff to maintain and utilize effectively.

How Current Drawbacks Are Being Addressed

In order to optimize the effectiveness of CEC’s many groups are starting to include them in their HQ’s where dedicated employee resources can be tasked without the need for extended travel costs and upskilling. This way the center is managed effectively and workers have the flexibility and freedom to continue their day to day activities outside of the CEC.

In the wake of COVID-19, many centers have had to reduce the number of people they can allow inside at one time. Space has been created between showcases and demonstrations and careful considerations are being taken to sanitize high traffic areas. By doing this and following social distancing protocols CEC’s can still function in a safe and effective way.

Immersive Technology & Hands-On Demos

With the advent of immersive technology, we have seen a number of CEC’s start to adopt using both VR and AR use cases inside their centers in order to have interactive demos of product offerings in order to bolster sales. Many times products can be complex and having a first-person demo of the product or simulation where the user can “try” the product even in a virtual environment can be extremely helpful for a buyer.

Virtual Reality specifically can place the user in an exact replica environment and allow the customer or prospective buyer to simulate the function of any product or even service. Augmented Reality, on the other hand, can be used with real-world props to highlight features and showcase overlays to enhance product knowledge to provide a more informed consumer which leads to bolstered sales. In the example above a box of Samsung headphones can be scanned with a smartphone to produce an augmented view on a mobile device and allow the user to see product reviews, technical specifications, and sharing options for getting a 3rd party opinion if needed.

All of these are tools that will enhance enterprise sales of which is the main role of the CEC anyway. With CEC’s using modern technology businesses that are adopting will see an uptick in sales as a more informed consumer is one that buys more. Overall the CEC revolution will continue to grow and we will see more and more brands look to enhance their sales and product offerings using immersive technology.



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TECHNOLOGY

How Machine Learning Can Prevent Credit Card Fraud

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How Machine Learning Can Prevent Credit Card Fraud


Machine learning can reduce false positive and quickly detect credit card fraud.

Using traditional methods to detect instances of credit card fraud slows down the process of resolving such issues. The application of machine learning in banking promises to find quicker and accurate solutions for all kinds of financial institutions.

The advent of digitization in banking has introduced several cybersecurity-related issues in such finance-based organizations. For example, reported financial fraud had increased by 104% in the first quarter of 2020, compared to Q1 2019. One of the main types of financial fraud is credit card fraud, which involves hackers accessing an individual’s card and illegally using it. While card-owners can call their banks and freeze the card on encountering this issue, preventing the possibility of such fraud must be the priority for banks and credit card companies. A guaranteed way to address this problem is by using machine learning for fraud detection. This application of machine learning in banking is yet another example of AI being used for finance-related purposes in organizations.

Scanning Purchase Patterns to Detect Fraud

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AI-based fraud detection systems closely scan the credit card statements of users continually. AI’s advanced pattern and anomaly recognition come into play by detecting buying patterns that cannot be found with human eyes. For example, if a user doesn’t particularly shop a lot online and suddenly—as per their card statement—starts doing so with some frequency, the system raises a red flag. Similarly, if a user suddenly begins making purchases from stores that are miles away from where they stay, a flag is raised. There are several such parameters. Additionally, the tools based on machine learning in banking can differentiate between a one-off purchase and multiple purchases following the same pattern. In this way, false positives are avoided in credit card fraud detection. Once the system raises red flags after scanning real-time purchases, credit card companies or banks can deploy their investigation team to look into the matter and take the appropriate action before any significant activity takes place with the card.

Using Machine Learning for Fraud Detection

The Denmark-based Danske Bank employs AI for credit card fraud detection. After implementing the technology for the purpose, the bank reduced the number of false positives in fraud detection by 60% and reported a 50% increase in the number of detected cases of fraud.

Banks and credit card companies walk a thin line during credit card fraud detection. They do not wish to incorrectly flag every transaction as fraudulent. At the same time, every case of credit card fraud must be caught immediately. Machine learning in banking is necessary to achieve both these objectives seamlessly.  



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