How improving data quality in retail can fix failed deliveries, boosting brand loyalty and sales (Sponsored)

loqate-GBG

Brand, logistics and sales are all impacted by data quality. When your data isn’t correct, it results in delivery failures, leading to poor customer experiences. This in turn impacts sales because a disgruntled customer won’t buy from you again, and may even write you a bad review. The problem is surprisingly common; with 62% of customers reporting a failed or late delivery within the past year – and most customers blame the retailer, whether or not they were at fault.

Yet if you’re a startup, you want to build ongoing relationships with loyal customers who may even go on to become brand ambassadors. Keeping up to date and accurate data – such as address and email – is vital to maintaining those relationships.

Working closely with the independent research company Loudhouse, Loqate, a GBG solution released the report Fixing Failed Deliveries: Improving Data Quality in Retail, surveying over 300 retailers and over 2,000 consumers across the UK, US and Germany, outlining common problems in data collection, and the consequences of poor data in the retail sector.

According to the report, one out of 20 orders doesn’t get delivered on the first attempt, and 65% of retailers said that failed or late deliveries are a significant cost to their business. These costs involved with failed deliveries can be avoided by ensuring accurate information.

The value of accurate data is illustrated by the 1-10-100 rule, a concept developed by George Labovitz and Yu Sang Chang in 1992 to quantify the hidden costs of poor quality data. In the 1-10-100 model, $1 is the amount it costs to verify data in the first place, also known as prevention. The continued use of incorrect data, known as remediation, ends up costing businesses $10, and over time, if businesses fail to clean and update their data, the amount rises dramatically to $100.

Bad data leads to poor decisions, communication and efficiency, which can have serious impacts on your business. So, instead of overestimating the quality of your data, take a moment to consider the real consequences poor data may leave you faced with. Rather than paying a hefty sum trying to clean data further down the line, tackle the issue from the very beginning by validating at the point of capture.