Do you have lots of bad data living in Marketo? Is your Marketo rep nagging you to upgrade because your Marketo database is over the limit? A hot tub is not really too useful in the middle of the desert and neither is bad data in your database.
Bad data is bad news. Not only does it clog your database and hike up your costs and software subscriptions; it makes your marketing efforts ineffective.
In this post, I'll cover strategy and execution tips to help you get the dirt out of your data and to develop a six-step best practice data retention policy called the "Gold Nugget Data Methodology."
Gold Nugget Data Methodology
How do you systematically reduce the bad data in your systems? The Gold Nugget Data Methodology.
The Gold Nugget Data Methodology is quite literally like panning for gold; leads pass through different filters, slowly sifting out the dirt while leaving shiny nuggets of clean data remaining at the end. I've seen this process reduce database sizes by 10-40% to help boost performance while driving down software costs.
Part strategy, part execution, the Gold Nugget Methodology is a long-term strategy--it’s a comprehensive approach to database cleansing. Don’t think this is an overnight process (Although, there are a few quick wins in the process).
At each step, you’ll identify the criteria to review and define processes for performing the purging tasks. Once you have your custom Gold Nugget plan in place, you can confidently maintain your database throughout the year.
Note that these steps do not have to come in the exact order. However, they tend to get harder the further down the path that you go.
For the strategy and policy behind the the Gold Nugget process, read Gold Nugget Data Policy. As always, make sure to document your policies to share with others.
Let's dig in.
Give this Stuff a Home: A Data Cleanup Program
You are going to be doing a lot of data work so avoid using lists in the Lead Database--keeping your lists there will result in a lot of toggling back and forth between the Marketing Activities and Lead Database sections of Marketo.
Tip: Create a global data clean-up program that houses all of the data campaigns and lists. This will streamline ongoing management as all your cleanup campaigns will live in one place.
In the below screenshot, you'll notice that Smartlists live separately from the cleanup campaigns themselves. This process makes it easier to spot check potential records to delete.
Step 1 - Get Rid of Duplicates
Duplicates are great with babies but not with your database. Marketo is not the ideal tool for removing duplicates but Marketo does provide a great way to easily assess duplicates. The first step is to identify the duplicates by performing a quick assessment.
In your Master Data Retention program, create a Smartlist that identifies multiple records that share the same email address. You won't get all the complex logic to identify duplicates with different email addresses but this list will provide a quick snapshot. Once you get the count, dig into the data to see where the data might be coming from.
To perform the actual deduplication at scale, you'll need a third-party service like Marketo Easymerge or a piece of software like DupeBlocker or RingLead . Make sure to read Josh Hill's Deduping Leads in Marketo for other deduplication considerations.
How Often
- Perform a one-time deduplication to start.
- Run an ongoing service to reduce duplicates
- Rinse and repeat #1 every 3 to 6 months to catch duplicates that get through your ongoing service.
Other Resources
Best Practices for Managing Possible Duplicate Leads
De-Duplication Tools? Not Cloudingo or Ringlead
Deduping- How to Clean Up Your Lead Database.pdf
Step 2 - Eliminate People without Email Addresses
Marketo records without email addresses are virtually useless as they can't be mailed. They also increase the possibility for future duplicates. This step eliminates these records systematically once you have the strategy set.
Policy Example: Any Lead record without an email address is deleted after 30 days from both Salesforce and Marketo. Contact records are deleted from Marketo but not Salesforce.
Considerations
- Treat Lead and Contact Records differently? If using Salesforce, Lead records can generally be deleted since they are not marketable and don’t have revenue associated with them. Contacts require a bit more analysis. For example, an accounts payable Contact record might have an address for billing but not an email address. You probably don’t want to delete those folks.
- Reporting history is lost or not available. If you have a trade show list that doesn’t have email addresses, implementing this policy will delete that intelligence. Of course, I’d argue to push your trade show vendor for email addresses but that is an argument for another day.
- Leads can creep back into Marketo by default. If you delete out of Marketo but keep in Salesforce, set a flag in Salesforce so these records don’t flow back to Marketo unless an email address becomes available.
- Delete in Salesforce? Marketo? Both? The reason you may keep records in Salesforce but delete them in Marketo is to reduce risk of deleting records that are important to Sales but not to Marketing. For example, you probably don't want to delete a record that a Sales representative is actively calling even though it contains only an phone number.
Yes, Sales should always include an email address when entering a new record and Oscar presenters should always read the right results---"should" doesn't always happen.
Other Salesforce /Marketo Sync Considerations: Marketo and Salesforce: To Sync or Not to Sync 
How Often
- Perform a one-time delete to start.
- Rinse and repeat monthly in either an automated or manual process.
Step 3 - Purge Recently Deleted Salesforce Records
By default, records that are deleted in Salesforce are NOT deleted in Marketo. As a quick win, setup a campaign that runs regularly that clears out your deleted Salesforce records in Marketo.
How Often
- Perform a one-time delete to start.
- Rinse and repeat weekly with an automated batch campaign. I'd recommend to keeping it weekly or monthly as a backup process just in case some deletions happen in Salesforce that you want to get back.
Step 4. Delete the Disqualified
This step seems easy but it’s not. If Sales or Marketing has disqualified a record, it might be time to delete it. I say “might” because sometimes a Sales representative disqualifies a record when it should really be recycled. Or, it’s possible that your Disqualified lead lifecycle process is not quite ready for prime time.
Do you trust Disqualified data inputted by Sales reps? If the answer is "No," you'll need to work on a consistent lead lifecycle process with your Sales team.
This step involves identifying Disqualified records and deleting them. Of course, you need to have a consistent process in place to make sure your Disqualified records are actually bad. A process where a Sales rep says a record is bad when it's really not will lead to good data getting deleted.
Recommendation: Get conservative here. If you don't know, wait until the Inactivity step 6 to delete.
How Often
- Perform a one-time delete to start. You’ll want to develop some custom criteria so you aren’t deleting Recycled leads miscategorized as Disqualified.
- Rinse and repeat quarterly/yearly once you have the Sales process down. Run deletions based on Disqualified Reasons. For example, a Spam record might get deleted more frequently than a No Current Budget record.
Other Resources
Deleting leads from Marketo that have already been deleted from SFDC
Step 5. Remove the Bounces
If someone has bounced email, these should be easy candidates to delete, right? It’s not so simple. You might want to keep these around to maintain some reporting intelligence. You might also want to mail a few last times to mine data from those returned emails through services such as SiftRock and LeadGnome (Yes, these capabilities exist). Additionally, you might have a bounce that indicates a spam bounce rather than a no longer there bounce so you need to be careful to distinguish between the two.
How Often
- Perform a one-time delete to start based on Email Invalid is TRUE if someone has an email address. There are another couple of pieces of criteria to customize.
- Rinse and repeat quarterly/yearly once you have defined your retention duration.
Other Resources
Managing a Large List of Hard Bounces
#6 Delete the Inactive – The Big Sweep
How long should someone live in your database without activity before you delete? If you set you other five filters as conservative, this inactive criteria can serves as the final sweep. We’ve seen companies use very complex criteria to define this last step since its the end of the line for some data.
- 15 months of inactivity
- Salesforce Leads only
- No active opportunities
- Not part of a certain lifecycle stage
- And more
Tip: Marketo limits inactivity filters to 90 days. Create a custom Last Marketo Activity Date that populates every time a link is clicked, page is visited, etc. Use that date in future to determine inactivity.
Other Considerations
- Send a Wake the Dead email before deleting with a few Wake the Dead emails.
Wake the Dead Example Copy. “Hey, we love you but is it time to say goodbye? If you want to keep receiving great content, click here, Or we’ll remove you from our database.”
How Often
- Perform a one-time delete to start once you nail down the criteria
- Rinse and repeat quarterly.
Some Bonus Tips
- Before you delete, gain intel from those bounced with one last email. Sign up for an Email Mining service like LeadGnome of SiftRock. The trial will cost you zero and it takes 15 minutes to setup.
- Pass on delete intelligence to Salesforce for records you are deleting from Marketo only. Before you delete from Marketo, just add a comment to the comments field or log an activity in Salesforce (Example: Record was deleted in Marketo because there is no valid email address). This will prevent the future “why didn’t this person receive an email?”
Wrap-up
Keeping bad data around is not a strategy for long-term success. The Gold Nugget Data Strategy is a methodology that brings clarity to your data retention policies.
Once adopted, expect to see a database reduction of 10-40%+ which will boost your performance and decrease your data costs. Good luck mining.
Have any tips to add? Make sure to comment below.
Need any help? Give me a shout.
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