Your CRM Attribution Is Unreliable — Here’s How to Improve Marketing Measurement

As privacy laws tighten and the industry moves towards a future free of third-party cookies, marketers are increasingly reliant on first-party data to help them make smart decisions. Unlike third-party data that is collected and aggregated from multiple external sources, first-party is data that you collect directly from your audience or customers. 

Most of this first-party data is likely housed in your customer relationship management software, or CRM. However, many marketing teams are struggling to adapt to this shift, quickly realizing that their first-party data isn’t up to the challenge: their CRMs are full of inaccuracies, incomplete records, duplicates, and more that make it difficult to use said data. 

The stakes are high, as they need accurate data to drive smart decisions and make the right investments in an increasingly challenging and rapidly-changing digital landscape. Now, more than ever, marketers need a clear data governance strategy and a well-configured CRM system–deeply integrated with the rest of their marketing technology stack–to help them navigate new challenges and capture opportunities in their markets. 

In this blog post, we’ll take a closer look at common challenges of CRM data and offer concrete steps to improve the quality of your first-party data to drive better marketing decisions.

What is CRM Attribution?

CRM attribution refers to the process of understanding which marketing touchpoints contribute to conversions. This is key to shaping future marketing strategies by helping you pinpoint which interactions are actually influencing customer behavior and driving conversions. 

The Role of CRM in Marketing Attribution

Your CRM is in many ways the backbone of your marketing strategy. It’s a repository of customer interactions that chronicle a customer’s journey from their first contact with your business to becoming a paying customer and beyond. 

However, the challenge is making sure that the data being input into the system paints an accurate picture of your customer’s journey. 

Incorrect or incomplete data can lead to faulty attributions, where credit is misassigned to various touchpoints, skewing ROI calculations and making it difficult to confidently make strategic decisions.

Common Challenges of CRM Data

Let’s first take a look at some common challenges marketing teams face with their CRM data. 

Data Silos and Fragmentation

Data silos are a significant challenge, particularly in larger organizations. When different teams use separate processes or disconnected systems, it results in fragmented customer profiles and inconsistent data, making accurate attribution nearly impossible. 

For example, if the sales team primarily uses a CRM system and the marketing team uses a separate marketing automation software or account-based marketing (ABM) platform that is not synced properly with the CRM, their data might not align, leading to incomplete customer profiles. 

To overcome this, it is essential for marketing and sales teams to establish a single source of truth that aligns all teams around the same dataset. This unified approach ensures everyone has access to the same customer information, enabling more accurate and consistent attribution.

Incomplete, Inaccurate, or Outdated CRM Data

Data silos also lead to a closely related problem: incomplete, inaccurate, and outdated data. CRM systems are only as good as the data they contain. When different teams operate in isolation, the information collected often remains fragmented and inconsistent. This can result in incomplete entries, inaccurate information, and outdated records, which significantly impact the accuracy of attribution and marketing analysis.

While siloed teams are often a major contributing factor to this issue, there are many other causes of “dirty” CRM data, including human error, data decay, duplicate entries, lack of standardization, and more. 

Inconsistencies in How Data is Entered 

Inconsistencies in how data is entered (e.g., formatting, completeness) can also make it difficult to retrieve and analyze data effectively. For example, if some users enter dates in a MM/DD/YYYY format, and another team uses a DD/MM/YYYY format, this can make it extremely difficult to analyze the data accurately.

Even differences in how empty fields are handled can make it difficult to effectively analyze results. For example, some users might leave a field blank while others enter “N/A” or “Unknown.” This inconsistency makes it challenging to filter and segment data accurately, which can lead to unreliable insights and decision-making.

Establishing and enforcing data entry standards – down to the small but important details – is essential for maintaining data integrity.

Poor User Adoption 

If the CRM is not user-friendly or if employees do not understand the benefits of using the system, user adoption may be low. This can lead to underutilization of the CRM and contribute to poor data collection practices. 

For example, if sales representatives find the CRM interface cumbersome, they might skip entering important customer details or delay updating records. These user adoption issues will  prevent the CRM from being a reliable and usable source of customer information.

Over-Reliance on Manual Processes 

Another common issue with maintaining data quality and integrity is relying too heavily on manual processes for entering or updating data. Manual entry is often extremely inefficient and prone to error, leading to low-quality data and wasted time for your teams.

For example, if team members need to manually input data from event sign-ups into the CRM, they might enter incorrect email addresses, accidentally duplicate attendee names, or miss important details. These errors can lead to inaccurate reporting and ineffective campaigns. Manual entry can also slow down campaign execution, as team members must spend extra time correcting mistakes and reconciling records instead of focusing on more strategic activities.

6 Strategies to Improve CRM Data Quality

Improving the quality of CRM data is essential for reliable marketing measurement. These are a few strategies that can help you get a handle on your first-party data so you can drive the right revenue-generating decisions

1. Regularly Audit and Cleanse Your Data 

Implement routines for regular data cleansing to correct inaccuracies, remove duplicates, and fill in missing entries. For example, schedule quarterly audits where data stewards review CRM records to identify and merge duplicate entries, correct any inaccuracies, and fill in missing information. This helps to catch errors early and maintain high data quality.

2. Standardize Data Entry Guidelines 

Standardizing data input processes across the organization can also reduce the risk of errors.  For example, establish clear data entry guidelines, such as using a consistent date format (MM/DD/YYYY), making critical fields like contact information mandatory, and providing predefined dropdown options for common entries like customer types. 

3. Implement Data Enrichment and Integration

Enriching CRM data with additional external data and integrating it with other systems like marketing automation tools and social media platforms can provide your marketing team with a more complete view of the customer and enhance the accuracy of attribution models.

For example, most email marketing and marketing automation platforms, like HubSpot or Marketo, integrate natively with major CRMs, allowing the system to capture lead data and information about customer interactions on the website or with content assets.

When someone fills out a contact form on your website or landing page, that data should feed directly into the CRM, automatically generating a customer profile. This automation ensures that the information entered into the system matches the data provided by the prospective customer, ensuring accurate and timely data.

4. Leverage Advanced Analytics and Machine Learning

Advanced analytics and machine learning can be applied to sift through vast amounts of data and identify patterns in a fraction of the time that it would take a human analyst to review and analyze the same dataset. These technologies, informed by human expertise, can help you automate extremely time-consuming tasks and optimize marketing efforts by predicting customer behavior and improving attribution accuracy.

For example, machine learning algorithms can analyze historical data to identify which marketing touchpoints are most effective at different stages of the customer journey so you can allocate your marketing budget more efficiently, focusing on the channels and tactics that are most likely to drive conversions. Predictive analytics is another powerful tool that can analyze patterns in your CRM data and predict future customer actions, such as which leads are most likely to convert or which customers are at risk of churning. 

5. Clarify Data Ownership and Governance

Clarifying ownership of CRM data means assigning specific responsibilities to team members or departments to ensure that data entry, updates, and maintenance are consistently managed. This can be achieved by:

• Assigning Data Stewards: Designate data stewards within each department who are responsible for overseeing the accuracy and completeness of data. For example, the sales manager might be responsible for ensuring that all contact details and sales activities are accurately recorded in the CRM.

• Creating a Data Governance Team: Establish a cross-functional team that includes representatives from sales, marketing, customer service, and IT. This team can set data standards, monitor data quality, and address any issues that arise. For example, the data governance team might implement a policy requiring that all customer interactions are logged within 24 hours.

6. Prioritize User Training

Ensuring that all CRM system users are properly trained and understand the importance of data accuracy and quality can lead to better data management and entry practices across the board. Provide regular training sessions for employees on the importance of data quality and how to properly enter and maintain data in the CRM.

For example, offer quarterly workshops to refresh employees on data entry best practices and introduce any new tools or processes. While it takes time, investing in a culture of data quality and integrity is worth the time and effort.

Take Your Marketing Measurement & Attribution to the Next Level 

Accurate CRM data and effective attribution are within reach. By implementing these strategies, marketers can not only trust their data more but also derive actionable insights that drive successful strategies and campaigns. 

Silverback Strategies can help improve the quality of your CRM data for more accurate marketing measurements and attributions. We’ll make sure that your marketing investments are effective and efficient so you can focus on what you do best: running your business.

Contact us today to learn more about what we can do for your business.