While the primary theme of my blog is sales intelligence, you cannot have sales intelligence if your databases are rife with duplicate records, invalid emails, missing or incorrect firmographics, and non-standardized values. These errors wreak havoc on marketing and sales. I came across a 2016 Integrate report that had a series of quotes on the subject which addressed the impact of bad data quality across multiple marketing activities:
The issue of data quality continues to be one of the biggest roadblocks to effectively analyzing the prospect and customer journey. It also dramatically increases the costs of analytics projects and negatively impacts performance.
– Sameer Khan, Sr. Product Marketing Manager, IBM Customer Analytics
Dirty data is the silent killer of marketing campaigns. It makes you look bad, depresses the impact of great content and offers, and can put your brand, reputation and domain at risk (or worse).
– Matt Heinz, President, Heinz Marketing
Data is the oil of any marketing engine, and in order to create perpetual demand generation, data accuracy needs to be a top priority. Marketers must be ruthless and deliberate about data quality and standardization at point of entry.
– Jonathan Burg, Sr. Director, Marketing+Customer Acquisition, Apperian
And if you still aren’t convinced that data quality can choke your initiatives, their research found that 40% of B2B records have some form of data quality issue with duplicate data representing 15% of your marketing database. Invalid Values and Ranges (10%) and Missing Fields (8%) were also common problems.
So if forty percent of your marketing data is faulty, then a significant percentage of your Marketing Qualified Leads passed to sales will contain errors including contacts not at companies (not evaluated by Integrate but contacts decay at a 25% rate per annum), and bad firmographics resulting in wasted time, incorrect routing, inaccurate lead qualification, and poor messaging. Furthermore, initiatives such as account based marketing (ABM), account based sales development (ABSD), and predictive analytics will stall if they are fueled by bad data.