Effects of Missing B2B Data
Incomplete data reduces the value of customer data by reducing its usability and increasing the risk of arriving at inaccurate conclusions. What makes incomplete data particularly risky for use in sales and marketing campaigns is the difficulty associated with identification of individual data records that have data elements missing.
Data elements like a primary email ID or details of even one family member if missing, can adversely impact the results of even a well thought out and expensive marketing campaign. Visualize sending Birthday and New Year wishes to some family members and not the others-this is not helpful for any brand or business.
Data is often used to predict consumer behavior and make the relevant product or service offering. Incomplete data can result in flawed predictions of consumer behavior which in turn leads to inconsistent marketing decisions.
Using only Complete data
When companies detect incomplete data in a few data records from a large database, the knee jerk response is to exclude such data records and run marketing or predictive analysis using only those data records that have all the information. Working with only a portion of data, however accurate does not give professionals a realistic view of the market. This can lead to incorrect or partial understanding of market realities and consequent decisions that are sub-optimal or prone to failure.
Dangers of Including incomplete data
The second decision is to run a partial analysis with all records, complete or not. While statistical and marketing tools throw up professional reports, such analysis based on incomplete data might not be reliable. Without gender, age and seasonal data for instance, pretty graphs and seemingly convincing numbers can be risky to drive expensive marketing campaigns on.
Several database managers argue that there are only a small percentage of missing records in their database. However, they fail to understand that this small database error can have a large adverse impact on business decisions owing to misleading analysis that incomplete data, however minor, produce.
Data Management companies have rich experience in detecting and qualifying missing data. Missing and incomplete data could result in heavy losses if left unaddressed in the long-term.