Data is the stuff that modern businesses are built on. It drives marketing and product development. It can reduce the costs of claims, increase sales, and drive customer satisfaction.
With each passing year, more data accumulates. According to IDC, the digital universe will reach 44 trillion gigabytes by 2020. That’s nearly as many digital bits as there are stars in the universe.
But data isn’t definitive. Consider your insurance agency’s marketing database. Prospects move, and their addresses change. Do they notify you? Employees enter a name or phone number incorrectly. Will you spot the mistake in the sea of data?
When information in your system is duplicated, outdated, incorrect, or incomplete, good data can turn bad quickly.
How much is bad data costing your insurance agency?
According to a report released by Software AG, bad data can cost organizations in a big way - up to 25% of their annual revenue in some cases. Marketing Profs has estimated that approximately 550 hours and $32,000 is lost each year for every sales representative using inaccurate prospect data. In some industries, companies waste upwards of $180,000 each year sending direct mail to recipients at the wrong address, and one major retailer lost more than $3M per year as a result of bad data hiding good data.
But what about the insurance industry? Surely such a risk-averse field wouldn’t stumble as a result of bad data.
We’d all like to believe we’re immune to these sorts of errors, but the fact is, even the insurance industry is impacted by poor data management. In a Data Driven Insurance survey conducted by West Monroe, two-thirds of respondents cited data quality and accuracy as the greatest challenge to advanced analytics, and 51% said inaccurate data was a major risk to marketing analytics.
If you want to integrate big data into your insurance agency’s processes, you need to get rid of bad data that’s hiding your good data.
Where does bad data come from?
Some bad data occurs naturally as information can quickly become outdated. However, 61% of companies surveyed in a report by Experian Data Quality cited human error as the most common cause of data inaccuracies. Typos, duplications, spelling errors, and mistakes in data entry can all lead to unreliable information.
What can I do about bad data in my insurance agency’s marketing database?
If human error can create bad data, human attention can correct it. Here are a few steps you can take to keep your marketing database clean and trustworthy.
What red flags have alerted you to unreliable data in your marketing database?