A CRM administrator is responsible for maintaining a lot of things related to the company’s CRM data. So, when the overall health of the data is in jeopardy, the Admin can find himself in a position where he must act. Whether your data is at risk, or you’re trying to be proactive to prevent issues, a CRM data management plan is crucial for the longevity of your CRM success. Here are our top four tips for CRM data management best practices (With video clips!).
#1: Audit Your Data
Auditing your data is a crucial first step in any CRM data management plan. You won’t know how to move forward if you don’t have a clear picture of where your data stands in the first place.
Start by identifying your data assets. Which solutions (CRM, ERP, Marketing Automation, etc.) house customer data? Are they integrated or are they standalone? Focusing on your CRM is a great start, but you’ll maximize the benefit of your data audit by looking at the bigger picture.
Once you understand where your data assets reside, start to ask yourself deeper questions about the importance of those assets:
- Which pieces of data have the most commercial value? Email address? Social followers?
- How would I rank the importance of my assets on a numerical scale?
- What are my top three must-have assets?
With a preliminary outlook of your existing data in hand, it’s time to turn to team leaders to add context. Department heads, managers, and other leaders can explain how they’re using the data, who on their teams is accessing the data, and any consistent issues they’re noticing. Are email addresses often outdated? Are phone numbers wrong or missing altogether?
These team leaders also can help the CRM Admin understand who should have access to the system on a hierarchical basis. Who are the relevant team members? Does one department need access to emails and phone numbers, while another subset also needs access to invoices? Should permissions be set on an individual level? These are discussions you’ll have while auditing your CRM data.
#2 Clean Your Data
Once you have a baseline of information on where your crucial data resides and how team members are accessing it, you can begin the process of cleaning your data. Cleaning data is data management 101. It’s the most efficient way to rid the system of useless information and it helps pinpoint what your data management rules need to address (Which we’ll talk about next!). There are several ways to clean your data:
Filtering: Use the filtered search in your CRM to find the records that are missing the key data you identified during your audit. For example, searching for records whose email address equals ‘blank’ reveals how many are missing that contact detail. Further, filtering the list by record owner lets you assign the task of updating the records to the appropriate team member.
Bulk Edits: Bulk edits and segmentation rules let you update records missing generic or common information, like a state or country for example. You can use segmentation to find the records that fit a particular criterion beyond contact details alone, and then bulk edit the results. For example, you may segment deals filtered by a certain status and then bulk edit them to change all the statuses at once.
Exception Reports: Exception reports are a great way to find erroneous or incomplete records. Run them every month to keep your data on point.
Merging and Deleting: Before deleting or merging duplicate records, ask the owner of that record to verify which is the most accurate record. Use that record as your baseline to compare all other duplicates.
If your data is really out of control, consider purchasing a tool to help you clean it. One tool that comes to mind is our sister company, StarfishETL. Although StarfishETL is mostly known for its uses in CRM migration and integration, part of its playbook is cleaning data before and after the migration or integration process.
#3 Establish Data Management Rules
At this juncture of data management, you’ll need to do three major things:
- Standardize CRM naming conventions
- Create data entry and deduplication rules
- Document everything!
Here are a few best practices for CRM naming conventions.
- Avoid free text fields whenever possible, aka fields where people can manually enter data. A few weeks ago, I was a victim to this. I was looking for customers who use a product we sell. The company name starts with an acronym, and it’s overall an odd name because it’s all lowercase letters and there’s an apostrophe in the middle of the product name. So that right there makes it a candidate for errors. What I found was, the data the CRM returned to me was only half right because before we implemented the dropdown replacement, people had been entering their own descriptors of the product elsewhere. Their interpretations ranged all over the board from writing the name in all caps, to excluding the apostrophe, to writing only the first three letters of the product name. Free text fields increase the likelihood of data entry errors. Replace free text fields with drop down menus and multi-select fields wherever you can.
- When it comes to abbreviations, make sure you are selective about which words you will abbreviate and keep that consistent across the board. For example, will it be capital INC or lowercase inc. If you’re a writer or you’re familiar with style books, this is a very similar concept. Everyone has to adhere to the “style” you select.
- Spelling out numbers is a best practice to avoid errors and create consistency. Consider that as an option for your data management playbook. An example would be writing out Third street as opposed to writing the number 3
- Indefinite and definite articles can muddy the waters. One best practice to consider is to leave them out completely or move them to the end of the record name. For example, you could write “The Blues Brothers” as Blues Brothers or Blues Brothers The
- Best practice for punctuation? Avoid it. Putting punctuation in the wrong place will affect the quality of your data.
Data Entry and Deduplication Rules
In all likelihood, you have some duplicate data entries in your system. If you’re not sure if a record is a duplicate, ask the record owner. It is possible to have two companies with similar names, so you don’t want to accidentally delete something you shouldn’t. Best practice for dealing with duplicate data is to merge the records instead of deleting to make sure you’re not losing any valuable info. For example, maybe there’s a record with multiple email addresses because there are multiple points of contact for that individual. Merging the records together will salvage the other email addresses.
Admins can indicate required fields be entered before a record can be saved. So if you go back to your list of your most valuable data assets, determine if any of those are not currently required and discuss making those fields mandatory.
Workflow rules have pros and cons. A pro is that it would be less frustrating for the end users but a con is that sometimes the workflow could kick back an unexpected iteration of a piece of data entered in the CRM. A workflow rule can automatically update a field to match the preferred nomenclature. So for example, if someone enters U.S.A. but you want USA, the workflow could autocorrect that. So that’s where an unexpected iteration could occur if for some reason the workflow alters something it isn’t meant to.
Validation rules are a bit stricter than workflow rules. These kinds of rules will enforce data entry formats and pop up an error message if the user tries to save the data incorrectly. So, if someone enters a phone number with only 6 digits, the system physically won’t let them save it until it is corrected.
Once your data management rules are established, create documentation you can share with the team. The documentation should include the rules for naming conventions, data entry, deduplication protocols, etc. Think of it like a style guide for your database. Create a shared place where you can update in real time instead of sending out static document, so if things change you know everyone has the most up-to-date version of the data management plan.
#4 Train your Team
If you do all the work to create a data management plan, you want people to follow it, right? This is where the training comes in. Create a digital handbook for all your team members and run through it in a team training session. Use the opportunity to field questions and get everyone on the same page.
Incorporate your training into the onboarding of new employees as well. During orientation, they too should receive a copy of the handbook and be briefed on how to utilize it properly.
Last, but certainly not least, designate someone as the “enforcer” of these rules. An admin or a team leader can fill this role, doing spot checks and monitoring the data on a regular basis to make sure everything’s staying on track.