The Ultimate Guide to Salesforce Data Cleansing

Salesforce is a widely used customer relationship management (CRM) tool that helps businesses of all sizes nurture leads, monitor sales data, and track all interactions with their customers. 

Over 150,000 companies rely on Salesforce to help power their sales and marketing efforts, storing crucial data points like contact information for customers and leads. 

But, Salesforce users can run into challenges when they overlook the importance of data quality and don’t have the proper data cleansing procedures in place. Depending on the severity of data quality issues, Salesforce users could end up with costly consequences like inaccurate sales forecasts or investing their valuable resources in the wrong customers. 

Thus, the importance of having clean Salesforce data cannot be overstated. It is what allows the sales and marketing teams to make informed decisions, gain better planning capabilities, and more accurately target and personalize interactions with customers for better conversions. In short, clean Salesforce data paves the way for positive sales growth.

Throughout this guide, we’ll provide you with some helpful tips for Salesforce data cleansing – including the general steps of the process and why business performance hinges on getting it done correctly. 

Signs of Dirty Salesforce Data

Dirty, or unclean, data is simply any data that is faulty — whether it contains duplicate entries, inaccuracies, inconsistencies, or missing data. 

Any organization can end up with data quality issues in Salesforce, even at no fault of their own. For instance, contacts may change jobs, change phone numbers, get married and change their name, or fill out multiple contact forms, all of which could leave their current contact information saved in Salesforce outdated and inaccurate. 

Of course, there is an element of human error that can cause dirty Salesforce data. If a sales rep is adding a new contact to the database, they may transcribe two digits of their phone number or misspell their name or job title, which could make their contact information difficult or even impossible to track down in the future. 

These data quality issues can materialize in various ways in your sales and marketing performance, hinting at a possible dirty data problem. For instance, maybe you recognize your email bounce rates are increasing, or your sales forecasts are becoming less accurate with each passing period. You may experience a lower ROI on your marketing spend due to inefficient customer targeting, or have customers reporting duplicate emails or phone calls, detracting from their overall experience with your company. 

However dirty data issues in Salesforce arrive, it is an inevitable problem that nearly every business will deal with. In fact, some studies show that data decay, or the rate of data quality deterioration, is 2.1% for B2B businesses, or 22.5% annually. This means if you have 10,000 contacts saved in Salesforce, by the end of the year, 2,250 of them could be outdated or have data inaccuracies!

Best Practices for Salesforce Data Cleansing 

Though dirty data issues are common, there are ways to rectify ongoing data quality problems and support better sales performance with effective data cleaning. Before we dive into the exact steps for Salesforce data cleansing, here are some general tips and recommendations to keep in mind throughout the process. 

• Create a data cleansing strategy: Set clear objectives for your cleansing efforts, and set the scope of the project to ensure you have the proper resources and tools 
• Have the right tools and processes in place: Invest in dedicated data cleansing tools and programs to help automate the process and avoid doing it fully manually 
• Set a regular schedule for data cleaning: Data cleaning is not a one-time task, it must be done on a regular basis to uphold data integrity; establish a regular cadence for ongoing data cleansing, like every month, quarter, etc.
• Create formatting standards: Establish formatting standards for database consistency; educate and inform anyone who has edit access to the database of these standards
• Identify common causes of dirty data: You can fix existing data quality issues with thorough cleansing; however, you need to identify and address what is causing these issues for your cleaning efforts to be sustainable

Step 1: Initial Assessment

To begin Salesforce data cleansing, you need to make an initial assessment of your situation. This may involve taking a high-level view of the role Salesforce plays for the organization, which can guide the rest of the cleaning process. This is important context that can ensure you’re involving the proper stakeholders throughout, and achieving a standard of data quality that is necessary to get sufficient value out of your CRM. 

Further, performing an initial audit will give you a better idea of the types of data quality issues you’re dealing with, which can impact the types of tools and programs required for data cleaning.  

Step 2: Document Data Entry Points

Next, you should examine and document the data entry points to Salesforce. This is a crucial step that will help you determine where your data quality issues are stemming from, and provide you with some insights into what a possible solution might be to prevent future data quality problems. 

So, consider how data is input into Salesforce for your organization, which might include: 

• Manual input from a sales representative
• Data collection from a user-completed web form
• Integration from other software

After assessing your data entry points and pairing them with the common data quality issues you assessed in the first step, you may start to notice patterns about where the majority of dirty data stems from. In other words, is the cause manual data entry errors, system integration issues, or legacy data import problems?

Additionally, by documenting the various data entry points, you may gain some further insights into how data flows within your organization. This might mean you realize more employees have edit access to Salesforce than you previously thought or integration between Salesforce and another program is not working as intended. 

Based on your assessment during this step, you are better equipped to continue with your Salesforce data cleaning. You may even decide to make strategic changes regarding your organization’s data management or governance policies to improve data quality going forward. 

{{ad-01="/blog-component-assets"}}

Step 3: Resolve Data Quality Issues

Now, you can start to address and resolve your data quality issues in Salesforce. Though this can technically be done manually by poring through each individual data point, this would be highly inefficient and may lead to further inaccuracies. For this reason, it’s recommended that organizations leverage the appropriate data cleaning tools that are suitable for their unique data quality issues, as we mentioned above.

At a basic level, the following are some of the key dirty data issues that are addressed at this stage. 

Duplicate Data

Duplicate data in Salesforce means you have records that are unnecessarily stored more than once. This means you might have two records for one contact, lead, or account, which can lead to double the time and resources being spent on just one contact that could have been devoted to two. 

Simply put, you will need to identify which data entries are duplicated and work to remove the duplicate entries. Salesforce does have a built-in data duplication management feature that can be set with various matching rules to prevent this from occurring. However, in the larger scheme of your overall database cleansing efforts, you likely require a more advanced and comprehensive solution that is custom-tailored to your business objectives and can resolve any existing issues, not just prevent future duplications.  

Inconsistent Formatting

Formatting inconsistencies are one of the most common sources of dirty data in Salesforce. This often occurs when you’re importing data from other sources, or through varying practices among your sales team when inputting data manually. 

Maybe you want to devote your marketing efforts to your top markets in the country, which you’ll determine based on the number of contacts you have in specific states. If certain contacts were entered using varying naming conventions for state names, like FL, Fla., or Florida, you may overlook one of your biggest markets because you only filtered for contacts located in “FL”. 

This type of formatting inconsistency can be applied across many different fields within Salesforce and has serious implications for your marketing performance and even the bottom line of the business. 

This is why it’s important to set formatting standards for database consistency and efficiency, which may require group input and collaboration. Data that is currently stored in Salesforce and does not adhere to these standards should be updated accordingly, and any new entries should abide by these standards. 

Missing or Inaccurate Data

Missing or inaccurate data in Salesforce can cause you to lose out on certain opportunities and calculate inaccurate sales forecasts. Again, how data gets imported from external sources or inaccuracies and inconsistencies with manual data entry can lead to missing Salesforce data. 

Similar to what we discussed above about excluding contacts located in Fla. or Florida, missing data fields can have a similar negative effect on your sales and marketing efforts. What’s more, you may face further issues with billing or invoicing if you don’t have the correct billing address for a customer, which causes even more issues for your business. 

To handle these issues, you will need a way to validate all your existing data for accuracy and enrich your records with third-party sources where you have inaccuracies or missing data. Of course, you can reach out to customers individually to verify their data or provide you with missing information, though this can be extremely tedious and time-consuming, especially if you have thousands of contacts saved in Salesforce. 

So, using a more advanced strategy and dedicated data enrichment tools can be a much more effective and efficient way to accomplish Salesforce data cleansing. 

Step 4: Ongoing Data Quality Maintenance

As we’ve mentioned various times, Salesforce data cleansing is not a one-off event but should be continued over time to retain the integrity of the database. As time passes and you continue to collect new data, there is always a possibility for dirty data issues to arise. 

Organizations should establish a regular schedule for ongoing data quality monitoring and cleaning. This can be at an interval that makes sense for you, like every month or quarter. The idea is that the more frequently you cleanse Salesforce data, the less work you should have each subsequent period. 

Over time, you will continue to refine your data quality and work to uncover the most common sources of dirty data. You will be able to strategize the best courses of action to prevent future quality issues, like limiting who can alter the database and setting up clear formatting standards. 

Salesforce data cleaning can be quite a task, depending on the severity of your data quality issues and the resources available to you. Either way, data cleansing isn’t something you can put off – at a certain point, you will notice it impacting your marketing effectiveness, your customers’ experience, and even your bottom line. For these reasons, organizations of all sizes find it best to entrust their Salesforce data cleansing to a trained team of data experts, like Assivo. 

Common Salesforce Data Cleansing Pitfalls

After reading through these helpful tips on how to effectively complete Salesforce data cleansing, it's also important to know what not to do. 

Here is a quick look at some of the pitfalls and mistakes you’ll want to avoid while cleaning that could compromise the health of your database: 

• Data loss: If you don’t have a data backup plan in place, you could unintentionally lose or delete valuable data during the cleansing process
• Overlooking data relationships: Make sure you understand how your Salesforce data might be connected to other external databases or tools, and how altering or deleting certain data from your CRM could impact your other resources
• Not investigating dirty data causes: You should look into the root cause of data quality issues to keep the results of your cleaning work more sustainable and prevent future problems
• Unrealistic expectations: Organizations may be overly optimistic about the speed and scope of their Salesforce data cleansing efforts, so it’s important to be realistic about the timeframe and capacity for data cleaning to avoid frustration
• Lack of documentation: Without detailed documentation throughout the data cleansing process, there could be a lack of transparency or accountability about any data that was deleted or altered

Wrapping Up: Salesforce Data Cleaning with Assivo

Salesforce data cleaning is an essential task that ensures sales and marketing teams are using a CRM that can provide accurate insights and drive performance. Without adequate data cleansing, dirty data issues could plague the CRM and lead to inaccurate sales forecasts, ineffective marketing campaigns, and frustrated customers. 

The sales cleansing process should be done thoroughly to properly eradicate existing data quality issues and help mitigate future dirty data problems. While Salesforce has a few native tools to assist with small-scale data cleansing projects, organizations that require a larger cleansing effort with more strategic objectives should seek out experienced providers who can custom-tailor a solution to fit their needs. 

The data cleansing team at Assivo is well-versed in the major CRM platforms, including Salesforce. We take the time to understand the unique needs of your business and the role Salesforce plays for your organization, using our time-tested processes to restore the integrity and accuracy of your database. 

To get started, schedule a free consultation today to meet with our team and see how a custom data cleansing solution can support your business.