Why a collaborative, data-driven strategy can boost customer experience
Thu 9 Nov 2017 | Ronald van Loon
Ahead of his keynote session at Big Data World, Frankfurt 2017, Ronald van Loon, Director at Adversitement, discusses the trends in customer experience which are driving demand for big data and artificial intelligence solutions
Due to the growing number of smartphones, tablets, laptops, IoT devices and sensors in today’s digital world, a point of sale is wherever a customer is located.
“Customers want responsive, seamless experiences. Therefore, there’s a corresponding widespread demand for responsive, intelligent artificial intelligence (AI) technologies,” says van Loon.
He points to technologies such as chatbots, face recognition and intelligent assistants as emerging systems which are able to provide fast, interactive virtual assistance and support to engage customers.
There’s an increase in the number of online customer interactions across multiple channels, says van Loon, which has made it critical for businesses to provide real-time, interactive, customised experiences across all channels and devices.
Improving CX strategy through AI
According to van Loon, AI and machine learning technologies can be utilised to support and improve critical business functions, allowing organisations to put customer experience (CX) at the forefront of their business strategies.
“Data is constantly changing, and the number of information from diverse sources like text, images, video and IoT devices, is generating real-time data.
“AI and machine learning capabilities enable businesses to gain unique insights into customer behaviours, preferences, and activities, enhancing their ability to provide an elevated customer experience.”
Van Loon highlights Natural Language Recognition as an example of a new innovation which can strengthen a business’s analytics foundation, and help organisations better understand the needs of their customers.
Organisations need to minimise information silos, update their legacy systems, and take steps towards digital transformation
“It has applications in customer service industries, among others, and automates the process for clients calling in and requesting service and support,” he explains.
Roadblocks to deployment
For van Loon, businesses need to create a real-time modern data and analytics infrastructure built on a foundation of digital capabilities, like AI and machine learning. However, he argues that certain hurdles hinder progress, such as not deploying the right teams, systems, and processes to support a data driven environment.
“Legacy systems and a silo approach to data and analytics are not optimal environments for supporting machine learning capabilities.
“Relying solely on historical data and an instinctive approach to data and analytics are no longer viable means for creating successful business strategies in an enterprise model. You have to rely on a data driven approach to decision making instead of instinct or past experiences.”
Organisations need to minimise information silos, update their legacy systems, and take steps towards digital transformation, argues van Loon. According to the expert, enterprises have to properly integrate their data from different sources, such as customers and suppliers, to give algorithms access to all pertinent data.
“They also need to adopt a hybrid cloud platform to process high volumes of data integrated from diverse sources,” he adds.
Enterprises must have a modern infrastructure that is able to collect data, as well as manage security, governance, data quality, processes, and storage. This enables organisations to gain insights, to look back at historical data, and predict and define actions live and in real-time.
Companies will be able to leverage AI to create unique, individual customer profiles for every single customer
“The right agile teams, people, processes, and technology must be implemented into your infrastructure to support a collaborative, data driven environment that facilitates information sharing across departments to improve business decision making capabilities,” van Loon continues.
Evaluating algorithms to identify and correct potential data bias, implementing machine learning training to increase algorithm learning outcomes, and ensuring that input is categorised correctly can further help companies overcome challenges related to bias, he suggests.
Future big data applications
Speaking on how the big data market will progress over the next five years, van Loon believes that prescriptive and predictive analytics are going to play an integral role for the future of enterprises.
“Right now, organisations are focusing on personalising the customer experience and, in the coming years, companies will be able to leverage AI to create unique, individual customer profiles for every single customer, all by leveraging big data within their enterprise.”
As video becomes one of the most popular sources of information van Loon adds that machine learning will play an increasingly important role in determining and identifying objects, images, and information within video content, and will continue to become more advanced in the future.
“Data contained within video will play a large part in shaping enterprise strategies in the future, as strategies, systems, and technologies evolve to support the massive increase of video from devices,” he concludes.
Ronald van Loon will be speaking at the forthcoming Big Data World, Frankfurt, 28th and 29th November 2017 at Messe Frankfurt. To hear from van Loon and other big data experts from around the world, register today for your FREE ticket.
Tags:analytics Big Data feature IoT
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