Data Is the differentiator for Customer Experience

Customer data, while frequently abundant, is not always reliable. in their Winter 2016 Compendium on Customer Experience: Creating Value through transforming customer journeys. McKinsey & Co. point out that a staggering 25 percent of customers will defect after just one bad experience. Bad experiences can be fomented by bad data, bad products, and bad interactions. But the one thing that costs the least to remediate, is often the data.

Just 20% of organizations publish data provenance and data lineage. Most don’t say they have no plans to start, that’s according to a 2020 O’Reilly survey. Without an understanding of where data comes from, what hope is there of improving its quality?

Annual data decay rates, may be as high as 30% across companies’ customer data each year, a figure that escalates to 70% for B2B companies. This decline in data integrity intensifies over time, posing a critical challenge for businesses aiming to provide exceptional customer experiences.

In this data turmoil, the significance of Customer Master Data Management (CMDM) systems cannot be overstated. These systems act as custodians of customer data, ensuring its accuracy, completeness, and currency. Within this realm, customer consent, self-service consumer data verification, and curation play pivotal roles. CMDM systems empower businesses to navigate the intricate landscape of customer data, addressing issues like missing or incomplete data, duplicates, inaccuracies, and expired data.

At the core of this challenge lies the concept of data quality, a term with profound implications for businesses. Data quality is not just a technical concern; it is the foundation upon which successful customer experiences are built. Dependable data forms the basis for businesses to segment and target customers for marketing, a strategy employed by two-thirds of companies. The importance of data quality initiatives can be distilled into three key pillars: data quality, data governance, and leadership support. Companies with higher data quality invariably exhibit traits such as leadership awareness of data issues, a robust governance structure, and data management being the responsibility of a cross-functional team.

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