New research has indicated that poor quality data is having a major negative impact on B2B lead generation, with around a considerable proportion of all leads coming from ‘dirty data’.
The survey, carried out by demand generation software platform Integrate, analysed over 775,000 leads from B2B marketers. The findings revealed that on average, 40% of leads generated for SMEs and their media companies were affected by poor quality data. Another source cited in the research, B2B research and advisory firm SiriusDecisions, previously found that around 60% of companies had an overall data health that could be classed as ‘unreliable’ and that 25% of the average marketer’s database contains inaccurate information.
What is ‘dirty data’?
Amongst the biggest problems with data uncovered in the research were:
- Missing fields – a lead missing a vital piece of information, such as an up-to-date job title or email address
- Duplicate data – the same contact information entered multiple times
- Invalid values/ranges – a lead which doesn’t meet the specified criteria (i.e. a contact based overseas when only UK-based contacts are required)
- Failed email validation – email addresses that are found to be inactive
- Failed address validation – mistakes causing problems with validating physical addresses of contacts
- Invalid formatting – a lead that is formatted in the same consistent way as others in a marketer’s database, meaning that it can often be overlooked
The most prevalent data errors were found to be duplicate data, followed by missing fields and leads not meeting certain set criteria.
The dangers of poor quality data
The biggest problem bad data presents in terms of lead generation and sales is the amount of time and resources wasted. Research by SiliconANGLE revealed that on average, as many as 75% of companies were wasting 14% of revenue on bad data. Sales teams waste valuable time chasing up unqualified leads based on poor, inaccurate or out of date information, when they should be focusing their energies on the most likely to be profitable prospects.
Poor quality data can also lead to poor business decisions and inaccurate sales forecasting, which can seriously affect the future and stability of your organisation, as well as providing poor levels of customer service. It can play havoc with your marketing budget, and lead to low click-through and response rates for any leads you do manage to generate.
Craig Rosenberg, the Chief Analyst of research and advisory service TOPO Inc, has a great analogy for understanding the importance of data cleansing and appending:
“Bad marketing data is the equivalent of putting the wrong kind of gas into your race car. Organizations are building significant marketing technology infrastructures today; yet, they continue to neglect the very foundation that supports this infrastructure— clean data. Data hygiene isn’t sexy, but it has to be part of the process used to support your marketing infrastructure. Basically, with bad data you’re leaving money on the table.”