Customer experience (CX) is a key focus for firms’ marketing and product functions, but it is inherently challenging to track and map customer behaviour across user interfaces and through the choices those interfaces present. For firms seeking to achieve greater synchronicity between clients and service providers, new emerging technologies offer solutions, but managing the change process when adopting new technology brings its own set of challenges.
First, change managers must consider how to integrate new capabilities onto their marketing technology (MarTech) stacks, overlaid upon older legacy systems. Within the MarTech industry, there are some 7000+ options of systems that may have been adopted as part of a firm’s legacy portfolio. These systems are not necessarily integrated, nor do they exist in a single organisational silo. A fragmented set of systems that has not been engineered to create a coherent picture of the customer drives up cost of ownership without increasing effectiveness, and that’s the best-case scenario. At worst, the fragmented system is costing companies revenue through missed sales opportunities.
Consequently, the data that these platforms collect, process, and use to create outputs is a spaghetti nest so complex that is hard to steer towards strategic marketing goals, which is especially problematic because data reveals the real story beneath the cost of fragmentation. While analysis of ‘big data –data sets either large in scale or massively complex—has long been discussed in the markets, fragmented system architecture undercuts the capacity to run meaningful analytics on the back of that data.
A coherent view of the customer
MarTech presents challenges beyond change management; firms cannot offer personalised service when basing their engagement with customers upon generic patterns of behaviour. Vasbourne Research found that 60 percent of businesses are operating without an optimised MarTech stack, leaving them struggling to deliver a consistent CX and thereby leaving money on the table.
In order to build a single, coherent view of the customer and develop services tailored to that customer’s needs, MarTech stacks need to be built upon a foundation that corrals and manages data. Cloud technology is a solid starting point when working to overcome the existing limitations of data siloes.
A cloud approach allows large data sets to be pulled from their systems and aggregated in such a way that they can be deployed to other platforms, while retaining a single point of storage, and reducing the risk of multiple copies of the data.
Cloud not only supports storage and deployment but creates new opportunities when scaling datasets. By deploying tools in the cloud, services can be accessed when and where they are needed, rather than requiring technology to be built on premise.
” In this technological era, creating a differentiated human experience and delivering consistently on ever-evolving customer expectations and brand engagement is paramount for companies to remain competitive. “
That support is increasingly important in the deployment of more sophisticated systems. Where huge data sets need to be consolidated into a picture, drawn in from multiple channels and points of contact with the customer, it is impossible to process without analytics. Artificial intelligence (AI) and machine learning (ML) tools excel in this application, as they can be trained to both spot patterns in data, such as behaviours which signals that a customer may move to another provider, and spot these behaviours even when the data patterns change.
When compared to historical, rules-based systems, there is an enormous difference in the potential of analytics underpinned by AI and ML, and technologically-advanced analytics add another dimension to the depth of service provided to customers.
For example, an AI system can be trained on historic data to identify the patterns of behaviour that may indicate a customer’s upcoming need for increased credit to support the purchase of a home. This approach is far more advanced than historic methods, wherein the service provider may have tracked a crudely developed pattern of behaviour based on previously observed, average patterns. Rather than offer a couple mortgages when they hit a certain age, the system might identify that a customer has adequate savings for deposits, is well-positioned in terms of income and size of family, and is properly motivated, say, because of an increase in rental payments. Data analysis that enables an offer of credit based on a customer’s actual circumstances is a deal maker.
Some other examples how leading financial businesses are leveraging these emerging technologies, such as Bank of America has deployed its bot, Erica, as a digital financial assistant for clients and over one million took up the service in the first three months. Equally, SunLife has successfully offered a virtual personal assistant to help insurance clients stay on top of their plans.
The human touch
MarTech’s potential weak point is an excessive reliance on science. When developing a comprehensive marketing strategy, technology is a tool, not a goal, and fully applying it to the CX requires both a deep understanding of that experience as well as a deep understanding of the customer.
The importance of that understanding is supported by the success of AI applications that are humanised through the use of assistant and bot, but automation of interaction alone does not bring the human and tech elements together. Rather, the data analysis and the user interface should not be treated as separate entities, but leveraged as a continuum, supported by cloud technologies and AI. That intertwined approach ensures that the understanding of customer goals is reflected clearly at the point of interaction, and this clarity enables businesses to move towards a true relationship, talking to and listening to customers on their own terms.
In this technological era, creating a differentiated human experience and delivering consistently on ever-evolving customer expectations and brand engagement is paramount for companies to remain competitive.
This article was first published by https://www.cioapplications.com
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