Deal teams are increasingly turning to data in order to make better decisions across the deal pipeline. But as data usage grows, so do data quality issues that can impact the dealmaking process. The key to improving data-driven performance in Salesforce organizations? Salesforce data cleansing.
Keep reading as we discuss the impact of poor-quality data on the deal process and how you can use data cleansing best practices to improve your Salesforce data.
What is Salesforce data cleansing?
Salesforce data cleansing is the process of identifying and removing errors in your Salesforce database. The objective is to create a complete and high-quality dataset that dealmakers can use to make better data-driven decisions.
All organizations collect data. But what creates errors and poor-quality data?
Bad Salesforce data can be a result of:
- Irrelevant or outdated information
- Missing data
- Errors due to manual data collection
- Typos and misspellings
- Low or inconsistent CRM usage
- Inconsistent formatting
The risks of bad Salesforce data
Dirty data is more than just an inconvenience.
A Data Quality Market Survey from Gartner estimated that poor-quality data cost organizations an average of $15 million a year.
When dealmakers face fragmented, incorrect, or even absent records, they’re forced to spend valuable time validating or searching for the information they need. Not only does this hinder productivity, but it can negatively impact their ability to progress and win deals.
Some of the risks associated with poor-quality data include:
- Poor decision-making: Dealmakers rely on CRM data to make decisions throughout the deal process—from qualifying deal-thesis fit to negotiating deals. When dealmakers have to make these critical decisions based on outdated or incorrect data, efficiency takes a hit. Dealmakers are left making suboptimal decisions that can lose them deals or leave money on the table.
- Limited collaboration: Complex deals often rely on collaborative pipelines to get them across the finish line. When data isn’t readily available, it becomes challenging for teams to seamlessly exchange information, which can cause delays in the process.
- Missed business opportunities: Data provides the context dealmakers need to evaluate and prioritize deals. Inaccurate data can cause dealmakers to prioritize the wrong leads or even spend time pursuing the wrong contacts entirely, ultimately preventing them from focusing on deals that are more likely to close.
- Low CRM adoption: CRM adoption is already a challenge for many teams, with 73% of sales managers stating that record creation and upkeep take up too much of their team’s time.* Poor-quality data can further perpetuate the issue. When dealmakers can’t pull valuable insights from their CRM data, they’re less likely to use and maintain that information. Over time, this causes additional deterioration in data and further decline in CRM usage.
- Poor founder experience: Without the right contact data, dealmakers are unable to engage prospects effectively. When dealmakers get their wires crossed, they risk sounding ill-informed which negatively impacts the founder experience. Even the smallest errors can put carefully built relationships and deals in jeopardy.
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How to start the Salesforce data cleansing process
There are many ways in which organizations can find themselves saddled with bad data. Which, unfortunately, means there’s no one simple solution for cleaning it up.
However, there are four methods to go about the Salesforce data cleansing process that can address most instances of dirty data in your CRM.
Data deduping
The deduplication of data refers to the removal of any duplicate records. For example, you might have the same contact in your Salesforce database multiple times. This means one team member might be working from one contact record while another is working from a different record entirely. This can create challenges in the deal process as team members are using different data to inform their outreach and deal decisions.
When deduping data, information is usually merged into one record and any duplicates are subsequently deleted.
In Salesforce, teams can set matching rules—or a set of criteria—to help identify when there are duplicate records within the database. This proactively prevents new records from being created when there is an existing match in the system, prompting dealmakers to simply update existing records.
Data appending
Data appending is the process of adding additional information to your existing Salesforce dataset. This can be entirely new data fields or simply filling in the blanks where information is missing.
With data appending, the goal is to create a more robust dataset with additional attributes and data points. This provides additional context and insights for dealmakers to promote efficient dealmaking.
Data can be appended from internal sources, for example adding contact information that has been sitting in someone’s inbox. Alternatively, when there are a significant number of data points that need to be appended, firms will often lean on CRM data enrichment tools to enhance their existing dataset.
Data standardization
Standardizing your data is the process of taking all the existing data points in your Salesforce CRM and ensuring that they are formatted and labeled consistently.
Standardized data helps dealmakers navigate data better and allows computers and other systems to make use of this data as well. For example, you may need to pull Salesforce CRM data into your analytics software to review team performance. If fields aren’t labeled correctly or the data is unreadable, you could miss critical information in your reporting.
The standardization of data doesn’t just impact internal teams. It can also impact the experience of prospective founders and portfolio companies.
Consider basic contact records. Say some contacts have their first and last names separated out into different fields. Meanwhile, others have their full names in one data field. This makes it challenging to use that data effectively in personalized email communications, and risks contacts getting emails or automated communication with errors or formatting issues. In a similar situation, if all contact information—such as email and phone number—are stored together in one field, it can cause errors with your outbound CRM platform that can impact deliverability altogether.
Data validation
Finally, data validation is when you audit your existing data and verify it for accuracy. This ensures that dealmakers are using information that is up-to-date and actually useful in the deal process.
For example, data validation can help identify when a key contact has moved on from an organization so teams can determine a new relevant contact.
CRMs like Salesforce have features to help deal teams proactively validate data, such as Validation Rules that can be implemented to validate data before the record is created. However, regular data validation is still crucial due to the rapidly changing nature of information in a firm's collective network.
How to improve your Salesforce data hygiene
Here are a few ways to prioritize data hygiene in Salesforce so dealmakers can spend less time struggling with data management and more time building relationships that close deals.
Create standards for data entry
While data can always be standardized post-entry, it’s much more efficient to have information inputted correctly the first time. Setting consistent standards helps dealmakers and other data users easily understand how data should be inputted into your Salesforce CRM.
Salesforce tools such as Data Integrity and Validation Rules allow Salesforce admins to set standards and requirements before records can be created. By catching these errors at the onset, it minimizes the need to retroactively fix inconsistencies within your database.
Commit to regular data audits
Data cleansing initiatives can be tedious and time-consuming, which can feel like you’re pulling resources away from deal-related activities. However, regular data audits are essential for boosting productivity and helping dealmakers be more efficient in their deal sourcing efforts.
Teams should commit to cleaning their database approximately every six months. With regular data audits and investing in data cleanliness, the process of data management can become less arduous over time.
Find ways to minimize human error
A human touch is always going to be critical in a successful deal pipeline. However, when it comes to data, automations and technology are much more effective at reducing the likelihood of errors.
For example, CRM automation tools such as Affinity for Salesforce scan inboxes and calendars for deal activity and input it directly into your Salesforce database. When implemented correctly, these types of automations can update Salesforce data in real time and minimize typos and formatting issues. Automations can transform data quality, improving the availability and usability of data. And by eliminating manual upkeep, you can save dealmakers up to 200 hours a year—giving them more time to focus on driving deals.
Invest in the right tools
The easier it is to record and access the right data, the more likely it is that your team will make it a priority.
Salesforce already has many features and functionality to boost data hygiene. However, there are many third-party apps and data cleansing tools that can further streamline the process.
Some tools that can improve data hygiene in your workflow include:
- Browser extensions, such as Affinity for Salesforce, that improve access to data.
- Salesforce data enrichment providers that can expand your existing dataset.
- Automation tools that update data in real time to reduce manual record upkeep.
Many of these tools can easily be implemented through the Salesforce AppExchange.
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Once you’ve cleansed your Salesforce data, maintain your data quality—and add to it
Quality data is essential for quality dealmaking. The more data that dealmakers have access to, the better they can prioritize opportunities through strategies like key account planning, which is why data cleansing and Salesforce data enrichment go hand-in-hand.
Affinity for Salesforce takes record creation beyond contact records. Affinity automates the creation and updating of Salesforce contact information—and enriches records with a complete history of interactions and activities, so dealmakers can uncover warm leads and confidently prioritize the right opportunities. The result? Deals that close up to 25% faster.
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Salesforce data cleansing FAQs
What is the data cleansing process in Salesforce?
The data cleansing process in Salesforce is when you fix errors and discrepancies in your database, such as deleting duplicate data or adding missing data points. This is a vital component of data management that improves your overall data quality and provides dealmakers with a richer dataset that can be used to efficiently prioritize deal flow.
What are the four main ways to cleanse Salesforce data?
The four main ways to cleanse your data in Salesforce are deduping duplicate data, appending additional data, standardizing data formatting, and validating the data for accuracy.
When you regularly engage in these different forms of data cleansing, you will have better-quality data that dealmakers can use to close more deals.
How can you prevent bad data from getting into Salesforce?
There are many ways to prevent poor-quality data from getting into Salesforce. However, one of the best solutions is to implement automations and software that minimize errors and keep data updated in real-time. Affinity for Salesforce’s automated activity capture eliminates manual data entry by analyzing your team’s emails and meetings to create, update, and enrich records.
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* Data from Affinity’s 2024 survey of 250+ business leaders across investment banking, media and communications, real estate, professional services, healthcare, financial services, manufacturing, and enterprise technology.
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