Dirty data result in productivity and revenue loss, while increasing reputation and operating risks. That’s why it is so important to keep your enterprise data clean.
Keeping your data clean is an ongoing process. Even though your database will never be 100% clean, there are a couple of strategies you can apply to improve data quality.
Since dirty data cost companies a lot of money every year, it is critical to understand their origin, how they affect your business and how to deal with them.
Dirty data result in wasted resources, lost productivity, failed communication—both internal and external—and wasted marketing spending. In the US, it is estimated that 27% of revenue is wasted on inaccurate or incomplete customer and prospect data.
Experian reports that on average, companies across the globe feel that 26% of their data is dirty. This contributes to enormous loss. In fact, it costs the average business 15% to 25% of revenue, and the US economy over $3 trillion annually.
"Dirty data costs the average business 15% to 25% of revenue, and the US economy over $3 trillion annually"
Clean data have an impact on delivering relevant marketing campaigns to what people really care about. As we live in an era where the Spotify experience has become the new normal, hyper-personalization will set you apart from the pack.
WHAT CAN YOU DO AS AN ORGANIZATION TO MAKE THINGS BETTER?
First of all, updating your CRM with detailed information on your prospect will get you in the right direction. It’s a matter of combining updated, accurate information. The more details you have about an enterprise (applications they use, buying decision unit, their stage in the buying journey, etc..), the better you will be able to segment and present them with the relevant information they need.
Another topic is to treat auto replies after email campaigns properly. This is a huge source of information telling you if a person has left the organization, if someone has changed roles, if their email address has changed and so on. It also enables you to capture details about new colleagues in their department.
With Drift Email, an Email Reply Management solution, you are able to automate this process.
A big danger of using dirty data is that you will lose traction with the buyer if you are not relevant. Data normalization is also a point of attention in this process of improving data quality.
At a basic level, data normalization is the process of creating relativity and context within your marketing database by grouping similar values into one common value.
Consequently, the purpose is to get valuable insights about your buyer (e.g. who are your most successful sales people, what are the products with most traction, etc.). Being able to see trends through reporting and dashboards will provide you with insights to start improving your sales and marketing strategy.
Above all, database hygiene is not the responsibility of one person or entity. It should be a joint effort where everyone touching customer data (demand gen marketers, sales staff, etc) should have the discipline to care about clean, updated and accurate data.
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