Have you ever gone to use data from your CRM, maybe in marketing automation, only to find that inconsistencies in the data are causing issues and embarrassing errors that could harm your company's reputation? Everyone has.
Standardized data, often referred to offhand as data normalization, is a critical piece of the puzzle when it comes to ensuring CRM data quality. Standardizing data improves data quality and fix many common data issues on its own.
A lack of standardization results in low-quality data. Companies lose 12% of their potential revenue, on average, due to bad data.
This causes many issues, including:
For these reasons, data standardization in your CRM system is absolutely critical for teams across your organization, including marketing operations, sales operations, and revenue operations teams. Most business processes can be improved with improvements to data quality.
In this article, we’ll break down a simple step-by-step process that companies can follow to put them on the path toward standardized CRM data.
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:
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.
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 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:
With these issues out of the way, you’ll have a clear path toward standardizing your data.
Part of improving your CRM data quality and data standardization is having a firm understanding of how that low-quality data is making its way into your CRM system. Knowing your CRM data collection methods and the complications that surround those methods can help you to take the appropriate steps to limit further data quality and standardization issues.
Consider your business requirements when collecting data. What data do you absolutely need to collect. What data is less important for your business processes?
Some common considerations when it comes to data collection methods include:
When you rely on your customers to supply their information (as most companies do) you are going to have unavoidable data issues. Consider what information you are asking your customers to provide and what data it may be best to collect in another way, such as through a data enrichment service.
Make sure form fields have proper validation in place so that you are sending clean, standardized data into your CRM.
Just like customers, your employees can make mistakes when inputting data into forms too, and just like those customer-facing data forms you need to evaluate what you are asking your teams to input and whether or not the forms have proper validation in place for each field.
Additionally, employee training on the importance and best practices of data quality and the impact of bad data on your business can go a long way toward reducing internally-caused customer data issues.
Importing data from another platform can often lead to data issues and standardization. You may import a lot of redundant data because another platform uses different field titles to refer to the same data.
Say you were importing a third-party list into Pipedrive. If you don’t have Pipedrive deduplication processes in place, you are likely to create duplicate records. Knowing the issues that are common with imports from each third-party platform is important for reducing data issues.
Or maybe you have data from a recent event that you appeared at. There can be all sorts of issues with outside data — data in all caps, titles that don’t match conventions, picklist values that are unverified, etc. You can almost always count on outside data lists differing from your own data in a variety of different ways.
Your CRM data collection methods are the gears that are responsible for feeding new data into your CRM. Most data errors and issues will start there. Taking steps to rectify those issues before they take place can limit a lot of standardization problems.
Of course, integrations are all around great for businesses. They ensure that our critical software solutions are talking to each other and sharing data where available. However, integrations are another common source of data issues.
One software may categorize data uniquely, use different fields to describe the same information. Salesforce and HubSpot integration is a common need for companies that are looking to connect and sync their sales and marketing operations. However, it can lead to a number of common problems including the creation of duplicate contacts and companies, complicated account hierarchies that lead to errors, and issues with sync timing.
Integrations simply add too much value to avoid, but you can be certain that nearly any integration between two separate systems will cause some unintended data consequences that you’ll have to figure out how to deal with.
To standardize your data, you have to have your standards defined. These standards are defined by a set of rules for each field.
For instance, some of the different standards that are commonly set for CRM field data include:
These are just a few examples of the different types of standards that you can set for your CRM data. The standards that you choose will depend on how you plan on using that data. For instance, a sales team with an auto-dialing solution might need phone numbers standardized to a specific format to work with their software.
If you use multiple systems, they all need to speak the same language to communicate with each other. That way you can ensure that you have data consistency across your platforms and create and aggregate reports from different sources.
How you standardize your data will depend on your business requirements. But with the right processes in place, you can always have clean data to improve your marketing, sales, and support campaigns.
With standards in place, you can begin the process of fixing standardization issues throughout your customer database. For smaller companies, using Excel functions and VLOOKUP might be powerful enough to fix most of your standardization issues. Larger companies with more CRM records may need to invest in a third-party solution to manage data at scale on a continuous basis.
Implement proper form validation and data cleaning processes to ensure that data is standardized and clean when it hits your CRM. The best way to keep a customer database clean is to make sure that bad data never makes its way into it in the first place!
Try to keep your expectations in check. In large databases, it is impossible to avoid quality and standardization issues at some level. There will always be errors that you’ll need to fix manually. But by making standardization and proper data collection a priority, you’ll greatly reduce the amount of time that you have to spend dealing with these issues.
Insycle is a modern customer data management solution that makes it easy for companies to clean and standardize data in popular CRM platforms like HubSpot and Salesforce. Insycle makes data transformation easy.
Insycle empowers you to subtly change your role. Instead of spending time chasing down and fixing bad data, you can take on a more strategic role to define standards and take a big-picture approach to customer data management. Insycle does all of the heaving lifting.
You won’t have to settle for a standard “on paper” that can be hard to follow and maintain. Through Insycle, your standards are enforced and processed automatically. This helps to create alignment between teams, who are free to focus on high-impact activities instead of spending their time fixing data errors and engaging in data entry. With Insycle, you’ll never have to go through a crazy “data cleaning sprint” again. Your data will always be clean and consistent.
Using Insycle, you can:
Insycle allows teams to fix data quality issues in bulk and automate critical data maintenance processes. Without Insycle, the true costs of bad data are hard to gauge while hampering execution across the organization.