The Cost of Bad Data (+ How to Fix It)
Your marketing and sales team decides to run a campaign, personalizes each email and start to send them out.
While a few emails are delivered successfully (yay!), a few bounce back and others are ignored (nay!).
Before you lash out at anyone (or anything!), you must know that it could be the fault of bad data.
But, your team collected the information from the prospects. How can that be wrong?
You are right! Information collected from the prospects isn’t usually wrong when you are contacting them initially. But over time, people change jobs, email addresses, profiles, etc.
What is the cost of bad data? My campaigns are giving brilliant results. Why do I worry?
A study by DiscoverOrg found that sales and marketing departments lose approximately 550 hours and as much as $32,000 per sales rep from using bad data.
It is not only about losing enormous amounts of money every year, it is also wasting the time of each rep in chasing down the non-responders. They could have been utilizing that time in a lead generation and engage hot leads instead.
We don’t have to mention the lost morale of the sales team when they aren’t able to close deals.
What is the core problem? Bad data!
1. Sales and marketing are friends!
It is the combined efforts of marketing and sales that boost sales. While both of them will have their independent systems of following up leads, it is essential that both parties have access to one single database.
Both the teams should be able to edit and update this database.
2. Identify and merge duplicate contacts
Do you remember the times when the same brand emailed or called you twice for the same offer? They might be different people but the purpose is the same.
So unprofessional (and annoying)!
Go through your leads and merge the duplicates. You can always automate the process of finding duplicates.
3. Remove inactive leads
You have the database of people who aren’t opening any emails or messages for a long time.
Are they adding any value to your brand? No.
If they haven’t taken any action in the past six months, consider removing them from your database.
4. Have written standards
It is important that data is entered in a standardized format. Also, ensure that all entries are free of spelling mistakes.
5. Establish a regular schedule of data cleansing and augmentation
At regular intervals, take the time and effort to go through your database in detail. Ensure to build processes and written guidelines around it.
This reminds me of Jonathan Burg’s statement,
“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”
Are you a victim of bad data? Which tools or process did you use to update information and fix bad data?