Actionable analytics always start with clean data. If you read our post about using Marketo's Performance Insights tools, you know clean data is especially crucial for making decisions about the health of your marketing efforts as a whole.
Data quality is like a clean house: It’s a bit of a moving target, and the job is never done. But if you have systems in place, cleaning gets a lot easier. And sometimes, it can even be fun.
That’s what I’ve learned about housekeeping from the January Cure, a smart annual email-nurture challenge from Apartment Therapy. With one task each day, they helped me start the new year with gleaming baseboards, fresh flowers, and the courage I needed to stare down mystery boxes in the back of the closet.
For the sake of your revenue cycle analytics, here’s a database audit version of Apartment Therapy’s challenge: Treat yourself to a DIY data quality cleanse with the following 20 assignments. If you’d like, bite them off once a day for a month. And don’t worry—the goal isn’t to fix everything at once. Even if some of these become longer-term projects, taking a good look at your data to identify any challenges will put you on track for analytics you can trust.
Week 1: Keep Your Email List Clean
- Monday: Review your Blocklist. Make sure any new competitors are included.
- Tuesday: Make a smart list of leads to delete because they have missing or invalid emails (create a protocol for deleting records if you don’t yet have one).
- Wednesday: Make a honeypot to catch spam. Make sure you also avoid spam traps.
- Thursday: Schedule a coffee date with your CRM administrator to chat about how they handle duplicate records, how many currently exist, and what they need from you to keep that number low, because duplicates prevent you from accurately tracking the lead lifecycle.
- Friday: Put a regular reminder on your calendar to delete records you don’t need.
Week 2: Keep Your Program Data Useful
- Monday: Confirm that all programs have a period cost. If period costs are not being entered consistently, decide on two internal communication steps you can take to change that.
- Tuesday: Review your program tags. Are they meaningful and being used consistently?
- Wednesday: Archive any programs no longer in use.
- Thursday: Check channel statuses. Update statuses, and then delete any that shouldn’t exist.
- Friday: Take 5 minutes to delete any test programs you’ve made and no longer need.
Week 3: Update Record Data
- Monday: Create a program to standardize fields for function and seniority based on title.
- Tuesday: If you are not yet using a vendor to enrich your account data, brainstorm your questions and then spend 10 minutes researching vendors. Schedule time to do more later.
- Wednesday: Review your forms. Do they use consistent fields and picklist values? Identify picklist values you would like to standardize across forms.
- Thursday: Confirm that all records have an acquisition program and date. If not, update.
- Friday: Create a program to standardize state and country data in Marketo and in your CRM. While you are at it, standardize city names like “NYC” and “Philly” as well.
Week 4: Stress Test Your Strategy
- Monday: How accurate is your lead source data? If you are tracking UTMs, do you always do so consistently? Jot down one step you could take this week to improve.
- Tuesday: Do a quick thumb-o-meter with your sales team. How much do they trust that MQLs are really qualified? Ask for any insights they’ve had into your lead scoring accuracy.
- Wednesday: Can you easily report on how quickly MQLs are accepted by sales? If not, figure out if the challenge is in data collection, data structure, or process adoption.
- Thursday: Take a critical look at your team’s KPIs with a trusted team member. What burning questions aren’t being answered because you don’t yet have the data? Brainstorm database changes that could make those questions easier to answer.
- Friday: Document the improvements you’ve made so far and any challenge areas you’ve uncovered. Do something nice for anyone who helped you get this far—yourself included!
Data quality is an ongoing process, but your database is already cleaner than it was when you started. Staying on top of the little things—and identifying any major gaps—can help you and your bosses sleep easy, knowing that the analytics driving decisions are closely aligned with reality.