In today's fast-paced, data-driven world, it's crucial to have up-to-date information and take into account the human factor when it comes to improving services. However, human input can sometimes introduce inconsistencies in algorithms.
That's why data cleaning is essential, enabling one to determine what to include, what to exclude, and where to focus efforts.
When it comes to optimizing modern support services, various elements come into consideration, such as Customer service, Artificial intelligence (AI), Customer relationship management (CRM), Proactive support, and Experienced customer service.
In this blog post, we'll explore how to utilize AI to drive service improvements, making support processes more efficient and enhancing the overall customer experience. Let's dive in!
The data can help ease that out but we wanted to look at that in detail together as a group because AI is the expert in the data. How to develop those models and interpret the information that comes out of them? Then we need an AI company like Ascendo to be the expert on that to help point in the right direction.
Effective internal change management and product feedback from service data are crucial in driving product efficiency and reducing costs within the medical device industry. AI systems can improve customer service, as well as provide valuable insights for device improvement and customer understanding of equipment maintenance.
Why Do We Need an AI System?
Service is a huge cost driver for medical device organizations across the globe. So it's a requirement, it's necessary. It provides a lot of value for customers as well. There's huge attention on how to capitalize on that value while also reducing the cost. And that's true for the companies themselves, but also customers and there's a lot of customers that do automated self-service on these pieces of equipment, and they want to be able to manage how to do that themselves.
The question is, can they do it at the level that an organization does? Do they have that kind of knowledge and expertise within their group? The only way to do that is to provide them with the right data, right tools, and the right information to be able to service that equipment at the right level.
And here, large organizations are very interested in Artificial Intelligence-based solutions for service because it is a way to improve efficiency for them, as well as for customers, and to create more value over time as those insights and that information can be feedback to improve devices. And also to help customers understand how to maintain their equipment better. Which also enhances customer experience. So Artificial intelligence is being used all over every industry.
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In organizations, many areas can benefit the most from AI because of the massive amount of data and information that is running through a large organization every single day. For any organization, what comes to mind first and foremost is always returning on investment, and what is the potential financial benefit?
If we look at some areas that can be potentially removed because the data shows that it is not important or valued, over time, it would save the organization a million dollars and that's every year, that's an annuity over time. It's not just about finance though. Service is all about customer experience, so organizations have to be able to prove that whatever change they make, first of all, it doesn't degrade the customer experience in any way or another quality and compliance.
When you have access to service record data and can look at trends and patterns and see that certain devices are failing more frequently than others, for some reason, then that becomes a huge indicator of a customer experience issue or we might need to pull the device out and then replace it, or somehow determine which devices are functioning at the right level and not. We can also avoid field actions, and narrow the scope of a field action if we could figure it out.
We need to understand the human element also. Customer use patterns could be anything, that the way that they're using the equipment is somehow different in one region or another. It could be that the service process is not ideal in one country with one set of tools. There are so many variables and unfortunately, the tools to be able to assess all of them have been limited for organizations. So they have to embark on very large projects, to look at that information to try to narrow down the scope of field action or to figure out the root cause or put together, these are ways that we could speed up all of those various categories. In this way, we can drive service improvements significantly on our own.
Data-oriented ventures need to be in a position with present information and additionally cited a little bit about humans. Into data, human beings enter data, there will be some stage of algorithm inconsistencies and any must component into that stage of records cleaning to see which ones to take and which ones to omit and the place to emphasize, and all of that.
It required shut collaboration between the AI group and then the scientific team. To apprehend the procedure and the tools and what's wished to be capable of telling, if we're seeing something a later was once associated with the reality that the PM wasn't done, or used to be unrelated? Now, the question is, once it's out there and they have real information about what's happening, how do we utilize that to then feed back into our service processes? Data and design requirements for the future to improve, and here is the tool that Ascendo developing and putting together that could benefit organizations in that effort.
We can say that a sustaining engineering piece is huge for a lot of companies. It's a Strain on R&D, resources, and investment, and it's necessary. But if there's a way to sort of provide better data around, the top opportunities, we should choose it. Because sometimes it gets hard, there are many items, and as a service organization, if you want to see improvements in the devices that are out there but R&D and sustaining engineering rightfully ask well, which ones are we going to go after?
Better data help determine which issues require sustaining engineering resources, process improvements within the service, or device replacements. Using an AI tool like Ascendo provides real-time customer feedback, accelerating product improvements. Customer service, artificial intelligence, CRM, proactive support, and experienced customer service enhance both existing and new products. They also address firefighting scenarios. Support outsourcing can be utilized strategically.