The Hyperscience Customer Experience

By: Hyperscience
Published: August 14, 2020

We sat down with our Customer Experience team, led by VP Jon-Marc Patton, to discuss our method for supporting and partnering with our clients to reach their goals and achieve continued success. Check out our conversation below.

On the Hyperscience approach to CX versus other vendors:

A lot of customers come to Hyperscience because they’re looking to replace their legacy systems or OCR tools, and sometimes they haven’t talked to the vendors in years. So having a dedicated Implementation Manager and Customer Success Manager on day one is huge. 

By taking a consultative approach and providing strategic assistance, we make sure that we’re providing real value to our customers’ business. We also connect our customers directly with our Product team to provide feedback and make improvements that matter to our users. When we were building our document organization feature, for example, we visited one customer mailroom and did in-person user testing with them to gather feedback on what we had built. A few months later, we returned to show them the new version, and they were shocked! They had never had a vendor allow them to test something before, let alone see their feedback incorporated – it was a rewarding moment.

We also emphasize providing customers with the know-how to onboard their own use cases once the initial implementation is complete, giving our clients autonomy to further optimize their workflows themselves. 

On supporting clients to become self-sufficient:

Becoming self-sufficient is more than learning how to use the software; it’s also about learning how to choose what you should automate with Hyperscience and how you can re-engineer existing or outdated workflows to drive additional value. 

When we implement Hyperscience we shadow our customers’ current processes to understand the entire end-to-end flow of a document coming into their organization all the way through what data needs to be extracted and how it is used downstream. This informs how we recommend configuring the application, and it also helps our customers think about why they’re doing things a certain way today and whether they need to do them that way in the future. We often encounter cumbersome processes or workarounds  that have been cobbled together by a company over the years in absence of technology like Hyperscience. It’s rewarding to point out the possibilities with Hyperscience and open their eyes to other workflows or processes that suddenly become redundant. These workflows are often deeply connected, so ripping out one part can create an opportunity to change the entire workflow for the better. That’s part of what we aim to help our customers do: think critically about their operations and how they can leverage automation to improve their business processes and drive operational efficiencies. 

On human-in-the-loop functionality being critical to high accuracy and automation: 

Most of the time, customers start out with a vision of 100% accuracy and automation, which unfortunately no product can deliver on today. What’s more, we actually think it’s the wrong way to think about the problem. No human is 100% accurate all the time, but by automating as much of the process as possible and using cutting-edge Machine Learning techniques that improve over time, you can unlock operational efficiencies and reduce error rates beyond what’s possible with human data keyers.

In our mind, humans will always be part of the solution. So while ML is automating an increasing number of tasks, it’s crucial that automation is followed up by tight integration with human-in-the-loop supervision. 

If you choose to prioritize 100% automation, you sacrifice accuracy or reliability and end up pushing a number of errors downstream, which typically results in a lot of expensive, time-consuming manual validation and rekeying. A lack of reliability also prevents adjacent steps in the process from being automated, since you need to throw people at the problem to review the outputs. By automating tasks and also automating the identification of tasks that need human intervention, we provide higher quality data, which prevents more costly errors from making their way downstream.

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