The layers of CX innovation | Andy Mura | Engati CX

Andy Mura, Head of Marketing at the ExB Group, joined us on Engati CX to talk about cognitive computing and the layers of CX innovation.

Andy has been working in tech for almost ten years as a consultant, initiator, mentor, founder/co-founder, or CMO.

He is now the Head of Marketing at ExB, the Cognitive Process Automation (CPA) platform for insurance companies in the DACH region who want to have a single end-to-end input management solution for data extraction and intelligent document processing automation.

Layers of CX innovation- a summary

This section will contain a summary of our interview with Andy.

Could you tell us a bit about your experience with cognitive computing and its genesis in data processing?

ExB works with Cognitive Process Automation. You can think of cognitive process as an extension of your own brain.

It uses AI, Machine Learning and Natural Language Processing to process data with computers instead of a human brain. It’s really hard for people to recognize patterns and extract useful data from documents and reports.

Cognitive processing helps make sure that you get all this data in a faster manner.

There are different processes:

The first step is data validation. A cognitive system validates the database on what inputs you have. It makes sure that the data makes sense.

Then you have aggregation . It sorts the data to find patterns.

It also involves classification and extraction to find the entities and the relationships between them. Then, it tries to find cause and effect relationships between the entities.

The system can be highly adaptive if you implement the latest deep learning technology. The old, legacy systems are rule-based and only process information a certain way. But new systems are more intelligent and adapt based on the documents that you feed into the system and don’t rely on rules.

Cognitive systems can also be interactive. They could process information that comes in through chatbots and craft personalized messages based on the information extracted.

How will data and digital transformation improve customer experiences?

Due to the COVID-19 situation, digital transformation has gone from being a buzzword to becoming a necessity.

There are 2 keywords when it comes to digital transformation- speed and personalization.

Data allows businesses to create better customer experience with speed and customization.

Customization arises from all the data that we can collect.

Speed comes from automating all the manual processes that are monotonous. All the processes which are repetitive can be automated to provide a much better customer experience.

How can companies use AI-driven NLP to enhance customer experience and drive retention?

We often talk about customer intimacy. Our economy switched from a one touchpoint economy to a subscription based economy in many areas.

This applies not only to B2C products, but also B2B products and services.

You can even have a car as a service. You pay monthly or you give it back when you don’t want it.

Even the aviation industry is talking about fleet as a service.

Everything has switched from one touchpoint to a continuous relationship with customers. Companies need to create a bond and set the stage for a long term relationship.

So it’s essential for businesses to keep improving and personalizing their service to match the individual customer’s needs. To do this, businesses need to adopt AI and technology that collects as much data as possible.

The world is very noisy. There’s too much data and too much content out there. Giving customers too many options would result in the paradox of choice and leave them confused.

Companies need to use AI to curate a collection of options uniquely suited to the individual customer’s needs. Not the demographic. Not the segment. But the individual customer.

How can Cognitive Computing in conjunction with big data and algorithms that comprehend customer needs pose an advantage in economic decision making?

Computers are really good at categorizing information, providing the right data and highlighting the insights. But you need humans to use that data and those insights to create an executable strategy.

Humans need to work together with computers. Now, you can create all the rules you want and use the data to perform relevant tasks. You can then take insights that were selected for you and convert them into actions.

Do you like the insights? Read the complete article here.