A Strategy for Acquiring Accurate Internal Data

Retailers have always relied on business intelligence to strengthen marketing efforts. This intelligence can be anything from age and gender to geographical location and income level. All of this data helps retailers identify their target audience and produce marketing materials that are relevant to prospects and customers.

While external data plays a significant role in customer intelligence, internal data is equally important, especially when looking at customer contact data. This data often informs decisions around external data purchases and is also used to make important business decisions.

Customer contact information can supply a great deal of intelligence to retailers by providing geographical information and household details. Additionally, with information collected through so many different channels, contact data plays a critical role in identifying unique accounts to prevent duplicate records.

Unfortunately customer contact information is often riddled with errors. A recent Experian QAS study showed that 91 percent of retail respondents don't completely trust their contact data in terms of it being completely clean, accurate and current. To improve contact data quality for business intelligence, retailers should review the current state of their data to more clearly understand how better information quality can be achieved.

Current State of Retail Contact Data
Contact data touches every business process and department. Therefore, bad data can lead to poor brand perception, wasted budget and diminished potential revenue. It's important to review your current data quality to see how much inaccurate data exists and which business processes it's affecting.

While every organization is different, there are some similarities across businesses. Experian QAS just conducted a global research study to gain insight into how retailers feel about their current contact data quality. The study revealed that 84 percent of retailers believe at least one negative consequence has occurred during the last two years as a result of data accuracy issues. Common consequences included lost customers and negative brand perception.