CRM implementations are meant for improving business bottom line through improved relationship with customers, prospects. However customer database management is crucial to the efficient utilization of CRM tools. Customer data issues can lead poor ROI and therefore must be addressed immediately to reduce the chances of poor quality data. Let us discuss some CRM data management best practices.
Many companies, as they grow and branch out, use separate customer masters in their individual legacy systems. This creates redundancies and duplicates. Therefore it is advisable to create a single customer master that will act as a single customer data repository to establish greater control on customer data.
Use third party validation software along with the CRM system to check the customer datasheet for wrong or missing addresses and to capture apposite information. Similarly, make way for third party integration with sources like Hoovers, Jigsaw, etc to obtain the demographic information and understand the social behavior of the customer. This will make the database fit to be used for customer analytics.
Establish standardized processes for building customer database and restrict the database creation authority to specific groups within the organization. Create specific roles for managing CRM database (creation and verification) and ensuring that accurate customer data are fed into the downstream applications and IT database for further sales and marketing follow-up. This would ascertain stern control on customer creation and lesser duplicates and discrepancy in the database.
Run a match-merge program on regular basis to identify the duplicates in database and maintain data hygiene. For example, entities associated to duplicates can be examined in relation to the existing record after the merge process and duplicates can be removed. Also purge old data to maintain active information. Last but not the least; establish a workflow to keep your data current and to maintain data quality. Establish data cleansing techniques and rectify data at the source itself.
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