Data Unpredictability Holds Organizations Back: How To Regain Control

An effective CRM is crucial for organizations to thrive. However, many companies find it challenging to manage their CRM data effectively because the data is unpredictable.

Unpredictability in this context refers to the inconsistencies, inaccuracies, and discrepancies that can arise within a CRM database due to various factors, such as human error, lack of standardized processes, and system limitations.

The consequences of unpredictable CRM data can be far-reaching. Inaccurate customer information can lead to missed opportunities, decreased customer satisfaction, and even reputational damage. Inconsistent data can hinder the effectiveness of marketing campaigns, sales forecasting, and customer support efforts. Unpredictable data makes it challenging to build reliable automations and systems, as the foundation upon which these processes are built is unstable.

As organizations grow and their customer databases expand, the problems associated with unpredictable CRM data become increasingly difficult to manage. Attempting to solve these issues by simply allocating more manpower is not a sustainable solution. Manually sifting through vast amounts of data to identify and rectify inconsistencies is time-consuming, costly, and prone to human error. And as CRM databases continue to grow, the complexity of maintaining accurate and consistent data becomes exponentially more challenging.

When companies encounter individual problems with their customer data, they often become aware of the immediate consequences and seek to address the issue at hand. However, a piecemeal approach to data management is insufficient in the long run. To truly mitigate the risks associated with unpredictable CRM data, organizations must adopt a proactive, holistic approach. This involves identifying and controlling potential issues across the entire database before they escalate—with severe consequences.

Unpredictability in CRM Data Causes Negative Consequences and Endless Manual Effort

The unpredictability of CRM data can have far-reaching consequences for organizations, affecting various aspects of their operations. Some of the most significant impacts include:

• Inaccurate reporting and analytics: Inconsistent or incorrect data can lead to flawed reports and analysis, resulting in misguided decision-making.
• Ineffective marketing campaigns: Inaccurate customer segmentation and targeting based on unreliable data can lead to poorly performing marketing campaigns.
• Missed sales opportunities: Incomplete or outdated customer information can cause sales teams to miss out on potential opportunities or pursue leads with a misleading or partial understanding of the prospect.
• Poor customer experience: Inconsistencies in customer data can lead to confusion and frustration for both customers and employees.
• Compliance and legal risks: Inaccurate or inconsistent data can expose organizations to compliance issues and legal risks, particularly in industries with strict data regulations.

Addressing these issues through manual efforts is an overwhelming task for any organization. It often involves countless hours of combing through CRM user interfaces or spreadsheets, identifying and correcting problems one by one. This manual approach is not only time-consuming, but also highly susceptible to human error, meaning that unpredictability is never truly eliminated. Further, the process of exporting data, making corrections, and reimporting it back into the CRM can introduce a host of new issues, compounding the problem.

These problems cannot be solved by simply allocating more manpower, especially as CRM databases continue to grow. The sheer volume of data makes it impractical.

Many people assume that a CRM handles data a certain way. But once they start exploring the issues in their database, they realize things do not always function as expected.

Many CRM systems conduct various data updates—such as bulk updates, merging duplicate records, and associating records—in ways that can lead to unpredictable outcomes. Without control over the details in these instances, organizations are left with inconsistent and unreliable data, hindering their ability to effectively utilize their CRM for reporting, segmentation, analysis, and automation.

For example, when merging duplicate records in HubSpot, the system typically pulls data from the most recently updated record for the majority of fields. However, this may not always be the data that the company wants to keep for that field. In some cases, the desired data might be associated with an earlier record.

These seemingly small discrepancies can have significant ripple effects throughout the organization. Even if not immediately apparent, data issues can plague the database and cause problems across every department.

By understanding the pervasive nature of unpredictable CRM data and its potential consequences, organizations can begin to appreciate the importance of a comprehensive, proactive approach to data management.

How Control Eases Headaches and Allows Companies To Employ Automation Throughout Their Operations

Gaining true control over your CRM data is the key to alleviating the headaches caused by unpredictability and unlocking the full potential of automation. By establishing a foundation of consistent, reliable data, companies can streamline their operations, improve decision-making, and drive growth.

When organizations have control over their CRM data, they can ensure that the data is accurate, consistent, and structured in a way that supports their business objectives. This level of control enables companies to confidently employ automation across various functions, such as:

• Marketing automation: With reliable customer data, marketing teams can create targeted, personalized campaigns that drive better engagement and conversion rates.
• Sales automation: Accurate and up-to-date customer information allows sales teams to automate lead scoring, prioritization, and assignment, ensuring that valuable opportunities are not missed.
• Customer support automation: Consistent customer data enables automated support processes, such as chatbots and assignment and ticket escalation automation, improving response times and customer satisfaction.
• Reporting and analytics automation: With controlled and structured data, organizations can automate the generation of reports and insights, providing decision-makers with consistently accurate information.

By leveraging automation across these areas, organizations can significantly reduce the time and effort required for manual data management tasks. Teams no longer need to spend hours digging through CRM data and fixing issues by hand to ensure smooth operations. Instead, they can trust the data and the automations built upon it, freeing up valuable resources to focus on more strategic initiatives.

But how to do this?

Enter Insycle: the complete customer data management platform.

By leveraging Insycle’s deep data maintenance features, organizations can establish a strong foundation of controlled, reliable CRM data. Then they can unlock the full potential of automation, streamline their operations, and drive better business outcomes.

Let’s look at a few ways Insycle can help companies achieve this.

Example 1: Data Retention When Merging Duplicates

Every popular CRM system has specific ways of handling duplicates, but very few provide the user with direct control over how data is retained when merging records.

In HubSpot, most fields default to keeping the value from the most recently updated record when merging. The idea here is that the most recently updated record will contain the most up-to-date information.

However, in practice, this is not always the case. If you have five duplicate records, it might not be the most recently updated record that has the most up-to-date information for each specific field. Perhaps an earlier record actually has the correct address, phone number, email, or other critical information. In that case, HubSpot’s default merging processes would result in the loss of important data.

With Insycle, you can define rules for choosing the resulting master record after the merge. Insycle uses a process of elimination to go down the list of rules that you set until only one record remains, which then becomes the post-merge master record.

Insycle also gives you unparalleled control over data retention by allowing you to set rules for merging and retaining data on a field-by-field basis, for every field in your database.

For example, in the example below, we are instructing Insycle to:

• keep the email from the master record
• keep the contact owner for the record with the latest activity date
• combine and append all membership notes
• keep the lead source from the record that became a lead first
• keep the pipeline enrollment from any record in the sales pipeline

With this kind of control, you can avoid the downsides of using default CRM merging processes and put yourself in a position to reliably build systems and deploy automation with confidence.

Example 2: Ensuring Accurate, Automatically Associated Records

In account-based marketing (ABM) and sales, ensuring that all related records in your database are linked to one another is critical for developing a full picture of each account and providing a stellar service.

Many CRM systems offer features to automatically link records based on shared fields between the two record types. For example, HubSpot’s automatic associations allow you to automatically associate contacts to companies using the company domain name from the company’s website URL field and the contact’s email address field.

While these are excellent features to have, each CRM takes a different approach. And in the case of HubSpot, the fact that automatic associations are only available for contact-to-company association can be an issue. After all, if you want your ABM teams to have a full picture of the account, other record types, such as deals, also need to be associated.

With Insycle, you can associate any record type in your CRM database including contacts, companies, deals, and custom objects.

Insycle allows you to use any field in your database as a potential matching field, including secondary related fields, such as matching the company’s phone number field with both the company’s phone number field and the mobile phone number field (for cases where an employee provides the company number instead of their own cell phone number). This flexibility ensures that you are able to associate more records within your database.

Insycle also allows you to analyze existing associations to determine which associations are missing. For example, a contact is associated to a company, and the company is associated with a deal. But the contact and deal are not associated. In this scenario, you could instruct Insycle to find the missing link.

With Insycle, you can create multiple templates using different matching methodologies to find missing associations, and schedule them to run on a set schedule. That way, automatic bulk associations are always happening in your database.

Insycle gives you unparalleled control over the associations in your database.

Example 3: Avoiding Overwriting Critical Data When Importing

Insycle also allows you to avoid overwriting critical data when importing.

Just like with data retention and merging and automatic associations, CRMs often have their own rules regarding how different situations are handled when importing data. This can include the manner in which existing records are detected, and how the imported data should be used.

To control how the data in your CSV is used, Insycle offers four import modes for every field in your CSV:

• Update: This mode imports the CSV values into your CRM, overwriting existing CRM values while skipping empty values in your CSV.
• Fill: This mode imports the CSV values only when there is no existing value in the CRM, but does not overwrite existing values in your CRM.
• Overwrite: This mode imports the CSV values into your CRM, overwriting any existing values in your CRM.
• Append: This mode adds the CSV values to existing values in your CRM.

With these import modes, you have complete control over how the data in your CSV is used during the import process. Using these modes, you can prioritize some fields from your CSV data over the existing data in your CRM, or the opposite.

This gives you extremely effective control while importing data. Further, Insycle import templates can be shared among colleagues, who can be invited to Insycle free of charge so that you can ensure that you are setting standards for everyone to follow when importing new data into your CRM.

Embracing Control for CRM Data Management Success

The importance of effective CRM data management cannot be overstated. Unpredictable CRM data can lead to a host of negative consequences, from inaccurate reporting and ineffective marketing campaigns to missed sales opportunities and poor customer experiences. Attempting to address these issues through manual efforts is not only time-consuming and resource-intensive, but also unsustainable in the long run.

With Insycle, organizations can achieve a level of data predictability and reliability that was previously unattainable. This newfound control provides peace of mind and the knowledge that the data being used for critical business functions is accurate, consistent, and trustworthy. Then companies can execute strategic initiatives with confidence, free from the uncertainties and risks associated with unpredictable data.

We invite you to learn more about how Insycle can help your organization take control of its CRM data. Visit our website to discover how our platform can empower you to overcome the challenges of unpredictable data and drive success through effective data management.