How to Achieve Growth and Manage Risk With Real-Time Customer Insights
Organizations are using new tech to create a dynamic, contextual understanding of their customers.
Today, organizations are operating in a particularly volatile environment. The Russia-Ukraine war, the global pandemic, the climate crisis, rising inflation, and surging interest rates continue to have a huge impact on global populations, businesses, and economies.
To ensure success in this ever-changing landscape, organizations need to be agile, using all available information to anticipate and quickly respond to new risks and emerging opportunities at both a strategic and operational level.
A volatile environment requires a dynamic response
To stay one step ahead, organizations need timely information on their customers, their internal operations, and the external environment that they can trust and action. This will allow them to rapidly extract insights and disseminate these through the organization to inform decision-making.
But too often these insights fall short or are derived too late due to challenges in capturing and analyzing data in a timely manner.
Organizations must recognize that when so many things are changing so rapidly, they need to invest in people and systems that will help make sense of that change and respond to it. Organizations need data and analytics.
Gareth Herschel, Gartner Research VP
Progress demands a shift in culture and technology
When it comes to being more dynamic in their decision-making, organizational, technical, and cultural factors often hold organizations back. Major stumbling blocks include:
Many large companies continue to operate in a siloed environment with separate departments managing different parts of the customer journey. This ultimately drives different sets of decisions and can lead to the creation of different datasets for the same customer. Consequently, Know-Your-Customer (KYC) teams might analyze a certain set of data when considering the risk of doing business with a given customer; marketing teams might base outreach on a different set of information; and sales team members might pull another data view together to support a client meeting. This results in inconsistent decision-making, higher costs, and fragmented customer experiences.
Data quality issues and lack of 360° view of customers
Dynamic decisions need to be based on a foundation of trusted data. But many organizations face issues with data quality – both in terms of accuracy and completeness. Deriving insights and making decisions based on this inaccurate data is fraught with risk. And the inability to effectively combine internal and external data sources means that many organizations lack a true 360-degree view of their customers, vendors and business networks, despite heavy investment in programs designed to achieve this fuller view.
Only 14% of organizations have achieved a 360-degree view of their customers.
Legacy technology and lack of real-time capability
Many large organizations are encumbered by legacy systems and technology that were not designed to operate as part of a real-time architecture. In order to move to more dynamic decision-making, investments must be made to modernize architecture and enable real-time data flows and analytics.
Culture and business processes
The culture of many organizations is still rooted in a mindset of ad-hoc and periodic analyses. For example, many KYC teams still operate with a periodic review cycle to manage risk. Marketing teams continue to conduct ad-hoc campaigns. Sales teams continue to perform regular, scheduled reviews with their clients. Analytics teams run monthly batch jobs.
A shift in mindset is needed to move to an “always on” mode, where teams are perpetually on the lookout for relevant market changes or client circumstances to prompt timely action and relevant engagement.
The combination of these factors leads to slow, suboptimal decision-making, exposure to unnecessary risk, missed revenue opportunities, higher costs, and a poor experience for customers.
So, what can organizations do to change?
5 steps to trusted, real-time insights
Bring together disparate internal and external data sources to build a contextual view
Organizations must adopt new approaches to combine internal and external data to build a holistic view of their customers. This should include high-frequency data feeds that provide timely insights into customer circumstances such as payments, digital interactions, and news data.
Advanced techniques such as Entity Resolution provide a step change in capability over traditional matching technologies by allowing more data to be connected, which leads to a richer, more accurate and trusted 360-degree view.
Graph capabilities also allow the generation of additional context: e.g. understanding family and social groups, corporate hierarchies, and supply chains. This enriched, contextual view can then form the basis for all customer-focused decision-making throughout the entire customer management lifecycle
Invest in modern, real-time architecture
To be able to react to emerging risks and opportunities at speed, organizations must invest in modern platforms and move away from legacy systems that are holding back transformation. Ultimately, companies will achieve significant cost reduction through a simpler and more cost-efficient IT estate.
99% of companies report a significant data and technology barrier to executing their data-centric transformation.
Embed analytical models to drive relevant and timely customer engagement
Once a contextual view of customers is generated and the right architecture is in place, organizations can extract timely insights from their data to inform decision-making and appropriate engagement.
This could involve deploying risk-based models to support perpetual-KYC, triggering reviews based on changes in a customer’s profile or network. For example, where a company has been acquired by an organization whose UBO (Ultimate Beneficiary Owner) appears on a third-party sanctions list or new relevant negative media that may impact a company’s reputation.
It could also involve deploying models to highlight opportunities for relationship managers. For example, recommending financing options to commercial clients based on changes in their supply chain and predicted working capital requirements.
These models can also drive direct engagement with customers: e.g. personalizing a customer’s online experience to show them relevant products and services, or routing customers to particular service channels based on predicted lifetime value.
Integrate with existing end user and customer-facing systems
In order to maximize the value from models and ensure seamless adoption, the outputs need to be integrated within existing systems.
This might mean pushing outputs of KYC risk models into an existing case management or workflow tool, or integrating the outputs of product propensity models into CRM systems, marketing automation or digital experience platforms.
Companies should adopt platforms that have flexible APIs to enable this interconnectivity and can drive the dissemination of timely insights to various systems and end users across the organization.
Ensure tight controls around the use of data
When driving decisions across multiple domains, the need for controls around data processing are more important than ever.
For example, the use of certain data attributes from a credit bureau may be permissible to use for credit risk and fraud, but not for sales and marketing.
What’s more, certain users might only be allowed to see certain data. It would make sense that a customer service team should not have access to suspicious activity reports from the financial crime team. But for global organizations, these restrictions might vary by country. This is why understanding these controls and having the right technology to implement them are paramount.
Organizations also need to work with a single platform that can dynamically provide different views from a single copy of data, otherwise costs will rise with many copies of data needing to be stored and maintained to fit different security and data permission profiles.
Leveraging the power of real-time customer insights
Building a connected enterprise that utilizes new technology to transform siloed data, which may be mis-mapped, wrongly classified, or simply bad, into “trusted data” that can then be conjoined with larger/deeper sector data to see the bigger picture is the key to driving better decision-making across the entire enterprise. In today’s world, where customers are increasingly empowered, connected, and eager for seamless experiences, insight-led companies can be better equipped to understand their customers’ needs as well as the health of their businesses.