Audit and Take Stock of Your Data

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To understand how serious your CRM data standardization issues are, you have to take a look at what you have in your database.

This audit should look at standardization issues, but should also look at other data-related issues that might be gumming up your processes as well. Raw data is almost certainly rife with errors. Any human input data is. In general, you want to make sure that your CRM data is:

Correct
Clean
Complete
Properly Formatted (standardized)
Verified
There are multiple ways that you can audit CRM data. You could audit it by-hand. In larger companies, this will likely be a process that will require attention from multiple different people. Additionally, you can use advanced Excel functions to isolate problematic data. Both of these will be time consuming and are likely to miss some of your data errors. Companies with larger databases will likely need to program a custom solution or find a suitable third-party tool, as Excel can only run smoothly with so much data at any given time.

The first time that you conduct a CRM data audit will always be the most painful. Once you have fully audited and cleansed your data, you can implement a data maintenance process that will make it less cumbersome in the future.

Ideally, no customer data that does not meet that criteria would ever find its way into your CRM systems. However, any company that collects a wealth of customer data will have some issues and errors that are present.

Related articles

Declutter Your CRM By Purging Low-Quality Data Automatically

Sharing CRM Data: Why Exporting is Painful and How to Automate It

Salesforce Duplicate Management: How to Automate Salesforce Deduplication

2. Remove Clutter From Your Database
Before we can begin standardizing data so that it can be used throughout the customer lifecycle, we want to make sure that we are doing so from a solid starting point.

While some data problems create a chicken-and-egg scenario (data that would be better off standardized first, then cleaned), a good general rule of thumb is that clean data will be easier to work with and evaluate. Those case-by-case scenarios can be identified and prepared for in the auditing stage.

We recently published a guide to data cleaning. In that article, we outlined telephoner au danemark some of the common types of CRM data issues that companies experience. These serve as a good starting point for keeping track of the different issues that you’ll need to fix during the data cleaning process before you can begin working toward standardizing your data:

Duplicate data. Duplicate data in HubSpot and other platforms is a big problem because it breaks the single customer view that your marketing, sales, support, and success teams rely on to evaluate their engagements with prospects and customers. It splits the context of those interactions between two or more records. It also leads to more of the embarrassing mistakes that harm a company’s reputation — such as emailing or mailing marketing messaging twice to the same customer or prospect.
Irrelevant data. Unnecessary data that takes up vital storage space within your CRM.
Redundant data. Data contained in two fields (or across multiple records) that are trying to convey the same thing. For instance, “Location” and “City” may convey the same data in two separate fields, taking up space and leading to confusion when your team goes to use the data.
Inaccurate data. Standardizing your data doesn’t do you much good if you are standardizing inaccurate data. Make sure that you have data verification or enrichment plans in place to ensure that the data that you are collecting is accurate.
Low quality data. Data that is non-personalized or generally low quality. This can include organizational emails like [email protected], [email protected], or free email accounts for B2B companies. Another example would be emails that have bounced when previously mailed.
With these issues out of the way, you’ll have a clear path toward standardizing your data.