We continue to live through a period of significant economic and political uncertainty and that is putting extra pressure on organisations to make more efficient and effective decisions. Acquiring a more sophisticated view of our customers behaviour and
the markets we serve is critical, if we are going to survive and thrive in these unpredictable times.
Customer retention is one key element of this challenge. People no longer expect, but demand a seamless end-to-end customer journey when they are dealing with organisations of all shapes and sizes, across all platforms. The secret to keeping up with that
demand lies in harnessing the potential of data-driven technology.
Offering a frictionless and fluid customer journey is vital for any business that is serious about keeping their customer’s happy in a fast-paced digitalised word. Indeed, recent research we commissioned has revealed that most customers (51%) are so fed-up
with slow sign-up processes that many will simply abandon their application altogether.
At the same time, 74% of businesses say that improving customer experience is a critical or high priority, but 28% admit they are not sure they offer a friction-free service to customers. And just 35% are using automation to help them make accurate decisions
about new customers, which can radically speed up processes by removing the need for human involvement.
The good news is that the solution is there to solve this problem. It all starts with the data.
By combining access to traditional and non-traditional credit data with complementary data science and machine learning tools, organisations can now access even smarter insights to understand their market and make faster, more accurate decisions, even if
a customer’s circumstances change during a time of financial uncertainty.
Thanks to different data sources providing a more granular and predictive insight into affordability and financial behaviour, organisations can now build a much more complete and accurate view of their customers than ever before.
Organisations need to access the widest possible data universe encompassing internal, external data sources, traditional, non-traditional, structured and unstructured, current and historic data.
They need to use data science, machine learning and on-demand analytics to reveal new and unique data attributes and patterns of consumer behaviour over time.
By doing all this, it’s possible to widen their prospect pools by identifying new, underserved markets and customer segments without changing overall risk appetite. It also removes the costs, risks and complexity of building an integrated in-house data and
82% businesses say that they know how important data, analytics and AI are to their prospects, while 71% plan to invest in advanced analytics and automation as a priority in the next 12 months.
It’s encouraging that so many businesses have identified investment new technology in the coming year, because it will enable them to identify the right customers and serve them far more effectively. Taking some of the pressure off in the process.