In today’s digitally-driven environment, being able to deliver on customer service that meets continually evolving end user expectations has become a key business priority.
By Lionel Bisschoff, founder and CEO of Learning Machines
In fact, companies that invest in customer experience initiatives have the potential to double their revenue within 36 months. One of the ways to accomplish this is through the application of modern event processing principles.
Being able to react to customer events in real time using advanced analytics and machine learning is vital to the success of service organisations. Building customer loyalty becomes a critical part of this. Research shows that 57% of customers spend more on brands or providers to which they are loyal. However, 77% of consumers have admitted that they now retract their loyalty more quickly than they did three years ago. It is therefore an organisational imperative not only to build loyalty but to ensure it remains in place if the company is to have consistent, long-term growth in these uncertain economic conditions.
The right combination of tools, technology, workflows, and architectures can result in service-driven businesses successfully implementing event-driven processes that are vital for real-time and smart decision-making. Ultimately, this drives the customer experience and helps increase loyalty.
Source of truth
This is where understanding the source of truth for the organisation becomes vital. When data is viewed as a statistic by-product of operations, as is the case for many organisations, the priority becomes to simply preserve it. But when data is viewed as dynamic in nature, as descriptions of events happening in the world, the priority shifts to acting on those events.
An intelligent agent can greatly facilitate this by automating much of the processes involved. Fortunately, companies themselves can be considered intelligent agents. However, the process up to now has been reliant on people collectively performing many functions with automation not factoring into the equation. But service organisations no longer have the luxury of not embedding at least some level of automation to enhance the customer service experience.
Value of data
After all, the value of data is only truly unlocked through its transformation. By capturing what is happening in the world (data) and finding out what happened (information), knowledge can be built on why events happened the way they did. But more importantly, companies can derive insights to predict what might happen in the future and make decisions on how they should best respond.
Throughout this data transformation process, the likes of descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics form key components in shifting data from a liability into an asset.
As such, event-processing can be of significant value to the service organisation. Event processing can often conveniently be thought of as an ‘Event-Enrich-Decide-Act’ pipeline. For instance, a client might inquire about a loan using Facebook (the event). The information can be enriched by determining the client’s contact details and confirming the potential loan amount. In turn, the company can then decide to go forward with the application and create a new lead from the customer. Finally, it acts by sending the client an SMS with the potential loan amount linked to the application.
Ultimately, this comes about by moving events (or data) effectively through an organisation. This is done using a variety of technologies and methodologies. But it requires an organisational will to embrace this change and a more advanced way of extracting value out of data.
Machine learning
In this regard, it is important to incorporate machine learning into the process to integrate all stages of data analysis and insights into the operational processes. As organisations are under pressure to effectively incorporate machine learning and data science into their operations, MLOps has emerged to standardise and streamline the machine learning lifecycle management at the organisation.
Being able to leverage the cloud in this regard is a key enabler as the solutions to conduct analysis are pre-integrated with machine learning. Furthermore, the focus is now on the business value achieved and not on the technology to do so leaving the service-driven organisation with the capacity to enhance the customer experience.