The High Cost of Poor Customer Data Quality: How It Impacts Your Business

Have you paid attention to the quality of customer data in your enterprise? The price you pay for poor data quality is high, and it might be affecting your bottom line.

Poor-quality customer data can be a serious roadblock hampering operational efficiency, business performance, and customer engagement. Increased operational overheads due to inefficient processes, revenue loss on account of customer attrition, and lost cross-selling opportunities are some of the adverse outcomes of poor-quality customer data. 

Data quality at the source level and the reliability of those data sources are the root causes of low-quality customer data. The solution lies in adopting a modern approach to identity which goes beyond traditional data sources and leverages phone-centric data, attributes, and characteristics. A white paper titled Inaccurate Data Is Damaging Your Business and Your Bottom Linerecently published by Prove, cites the reasons for customer data quality issues and the approach to mitigate them.

Enterprises Pay a Heavy Price for Poor-Quality Customer Data

According to a report by Dun & Bradstreet, 42% of the 500 companies that were surveyed admitted that they had struggled with inaccurate data. Successful digital onboarding and servicing of customers depend on efficiently managed processes and seamless customer experience. Poor data quality may lead to a tedious and confusing onboarding process. Poor data prevents seamless customer verification and creates the need for additional validation. The eventual outcome is high application abandonment and, therefore, loss of revenue. Companies also risk business reputation on account of customer dissatisfaction and potential compliance issues.

A detailed study conducted by RingLead points to the key reasons for and the impact of bad data. Here are some of the major findings:

  • Data quality issues affect large and small businesses. Small businesses can lose up to 6% of their annual revenue to poor-quality data.
  • 21% of the companies admitted to reputational damage caused by inaccurate data.

There are several root causes of poor data quality. Incomplete information provided by a customer, fragmented information across multiple data sources, and failure to keep the data current are some of the main reasons.

Addressing Poor Data Quality With Phone-Centric Identity

A consumer’s phone number is the new national identifier. Modern platforms for data sourcing leverage the high accuracy provided by various data signals linked to a consumer’s phone number for identifying and validating the customer with a high level of confidence. Phone characteristics and attributes such as line tenure, line behavior, event history, and event velocity correlate with identity risk. The utility of a user’s phone number and its linkage to several other authoritative data sources, both public and private, have made it a vital cog in the identification and verification of a consumer’s identity. 

Verified and trusted phone data is one of the richest sources of information available about a consumer. The credibility and quality that phone data provides can massively transform business processes and customer experience, making them more efficient and engaging.

Download Prove’s white paper titled Inaccurate Data Is Damaging Your Business and Your Bottom Line here.

This article was originally published on Prove, and it has been syndicated and republished here with approval from Prove.