Enhancing data quality can greatly elevate the customer experience. Research highlights the significance of personalized interactions with customers:

• 66% of consumers expect brands to recognize their individual needs and preferences.
• 52% desire that all promotional offers from a brand be tailored to them.
• 54% of shoppers indicate they are inclined to browse products in physical stores and purchase them online (or the reverse), prompting 53% of brands to invest in Omni channel approaches to accommodate this behaviour. The foundation of effective, personalized customer experiences lies in accurate and enriched data; its absence can lead to customer disengagement.

Data Enrichment vs. Data Cleansing

The process of data enrichment is primarily concerned with augmenting your A1 database with additional information, while data cleansing is dedicated to eliminating inaccurate, irrelevant, or outdated data entries. Both practices are critical for sustaining a healthy and effective database; however, data cleansing is usually conducted first to allow for the integration of new, supplementary data through enrichment. In addition, data enrichment can greatly enhance the customer experience. Research highlights the importance of personalized interactions:

66% of customers wish for brands to comprehend their specific needs, 52% prefer personalized offers, and 54% are likely to browse items in-store before making online purchases, with 53% of brands focusing on Omni channel strategies to meet these expectations. Having accurate and enriched data is essential for developing targeted, personalized customer experiences, as a lack of such data can lead to customer attrition. Furthermore, data enrichment can contribute to cost savings. A well-defined data enrichment strategy allows organizations to concentrate on retaining critical data, such as customer contact information and transaction histories, while less pertinent data can be eliminated or moved to more economical long-term storage. Additionally, enrichment aids in identifying and removing duplicate data, which can further lower overall costs.