As banks and credit unions pivot from managing the impact of the pandemic to reopening and repositioning business models to reflect a more digital economy, it is clear many of the changes in consumer behavior will be altered forever. From the way consumers shop for new financial services to the way they transact and interact, we are beginning to understand that consumers are expecting digital experiences to be central to all stages of their customer journey.
But digital capabilities and improved customer experiences don’t operate in a vacuum. In a digitally empowered world, financial institutions must leverage the power of big data, AI and machine learning to drive customer engagement and conversion. To accomplish this, many institutions are moving to the cloud, hiring data scientists and officers, and finding marketers who understand how to bridge the gap between the pace at which data is generated and the ability to create real-time engagement.
It is time for financial institutions to harness the power of data, AI and advanced analytics in more ways than simply evaluating risk and monitoring security. For instance, data and predictive analytics can be deployed in exciting ways to better understand consumers and provide advice and solutions in real time.
Working together, data and AI can optimize the customer journey across digital and non-digital touchpoints, creating more meaningful engagement and better results for both the consumer and the financial institution. This integration can also allow financial institutions to allocate resources more intelligently and expand financial inclusion to new markets with new services.
Customer Experience (CX) Matters
Improving the customer experience (CX) is the perennial business priority when we ask financial institutions about their top strategic objective. This is because how consumers feel about your brand impacts your ability to expand relationships and retain customers. The ability for a dissatisfied customer to leave is made easier with digital relationships. In fact, customers may leave, open a relationship at another competitor, and never completely close their account with your institution.
When the pandemic hit, consumers became much more familiar with what is possible with the combination of data, AI and advanced analytics. From receiving proactive recommendations on Spotify or Netflix, to being able to shop on Instacart, Amazon or virtually any other retailer using a smartphone, the consumer understands that they have the power to decide if their digital experience is comparable to the experience from digital leaders.
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Poor Data Quality Can Destroy Great Experiences
Despite understanding the power of data and analytics on the delivery of a great customer experience, the vast majority of financial institutions do not have faith in the data they are using or the way it is being used. The reasons for this lack of confidence is not due to the amount of data available, but instead due to legacy systems, the lack of adequate analytic talent, internal silos, data access and quality, and executive support of data initiatives.
In fact, 60% of financial institution executives surveyed by the Digital Banking Report said that the quality of data used by marketing and business intelligence areas was either unacceptable (22%) or acceptable, but requiring significant additional support (38%). Only 17% of organizations surveyed said the quality of data was acceptable (13%) or excellent (4%).
One of the most important uses of data and AI is for the facilitation of personalization. Building a personalized stream of targeted communication for customers requires an accurate, real-time, and universal understanding about the individual recipient. Consumers are increasingly turned off by generalized or non-personal engagement.
Because of outside digital engagement, customers no longer hope for hyper-personalization … they expect it.
If your understanding of the customer is incomplete, or worse yet incorrect, the impact will be an extremely negative CX.
The challenges of improving data quality can seem overwhelming and obscure the benefits of quality data processing, the costs of doing nothing are higher, especially when organizations consider the cost of missed engagement opportunities, mistaken personalization and loss of reputation.
Gartner reports that poor data quality is a primary reason for 40% of all business initiatives failing to achieve their targeted benefits – this includes digital transformation initiatives. With organizations using more applications than ever, and with customer expectations being higher than ever, there are more sources of poor, bad or dirty data than ever … leading to inaccurate decisions and mis-targeted communication.
For an organization to be data-driven, the responsibility for data quality has to be shared by all business teams. It also must be trusted by all users.
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Improving Data Quality
According to the Digital Banking Report, more organizations are investing in data and technologies to improve and enrich data quality to improve outcomes, save money and make better business decisions. But the numbers can be deceiving.
When organizations were asked about the change in investment in data and AI solutions compared to 2019, 31% of organizations said that their investment had increased by more than 50%. Another 11% indicated a 25%-50% increase, with 30% saying that their investment was up to 25%. Only 26% stated there was no change. Unfortunately, digging deeper into the numbers showed that most of the investment growth was against relatively insignificant prior investments.
Some ways to improve data quality include:
Leverage metrics. One of the fundamental ways to improve data quality is to measure the quality of data over time. This includes, but is not limited to, determining incomplete or redundant data entries, poorly formatted data, mismatched data and other quality metrics.
Engage your entire organization. As mentioned, it is everyone’s responsibility to manage the data process. From the manual inputting of information, to the uniform formatting and analysis of data, all departments and employees must be on the same page.
Monitor and manage missteps. There will be errors in the collection and deployment of data. When an error occurs, it must be brought to the forefront and corrected. The origin of the error must also be identified and corrected to improve use of data going forward.
Create consistency. All steps of the collection, input, storage, extraction, analytic and deployment process must be consistent across the organization. These well-documented processes helps to avoid many data quality issues.
Partner with leaders. Most financial institutions are not equipped to bring disparate data sources together, let alone perform the needed analytic steps to enable an excellent customer experience. Banks and credit unions of all sizes should consider partnering with third parties that specialize in building clean databases and creating actionable insights.
If your organization is structured around good, accurate and consistent data, you’ll have the ability to respond to market challenges and opportunities in an instant. This will provide a level of customer experience that differentiates your organization in the marketplace, and generates financial and reputational benefits not afforded other organizations.