Why Clean CRM Data is Essential for ABM

The success of your ABM efforts depends on high-quality data.

Account-Based Marketing (ABM) is a popular strategy for building engagement with specific accounts. However, as with most marketing strategies today, the success of your ABM efforts depends on high-quality data.

B2B marketers deal with massive amounts of data from internal and external sources. Often, CRMs are a central component of a B2B marketer’s data initiative. Most of this data is often dirty and incomplete, creating the need for marketers to clean it before they can gather any insights from these datasets.

Let’s go over the meaning of clean data and its role in ABM programs.

What is Clean Data and Why is it Important for ABM Programs

Clean data is correct, consistent, and usable. 

Customer data is ever-changing, therefore, at a high risk of quickly turning into dirty data. Some of the reasons CRM data can become dirty include:

• Poor data collection
• Customers changing contact information
• Customer’s changing roles in the same company or leaving the company entirely

Clean data is important for ABM for the following reasons:

1. Better personalization

One of the reasons why ABM is popular in B2B marketing is because it allows marketers to create better relationships with key participants in the B2B journey. We already talked about the importance of personalizing B2B messaging in this post.

 "Since any B2B buying journey consists of different individuals with varying personalities, interests, whims, and inclinations, then B2B marketers must use messaging targeted to each of these personas to make sales and keep the user engaged with the product and company after the sale."

Data is a key part of personalization; therefore, getting it straight will help you avoid damning mistakes like getting the prospect's name wrong or contacting the wrong person with the wrong messaging.

2. Improved Decision-making

Account-based marketing is a data-driven initiative. This means that marketers must have the right data to make the best data-driven decisions that help them achieve the goals they set with their ABM programs.

With the right decision-making tools supported by quality data, marketing teams can be more productive and deliver ROI with their marketing initiatives.

3. Faster Sales Cycle

One of the reasons account-based marketing is popular among B2B marketers is that it allows for shortening the sales cycle. 

 "Marketers can nurture various stakeholders in the B2B decision-making process simultaneously."

If the marketer is working with quality data, then the nurturing process is coordinated and targeted, thus increasing its success.

The Consequences of Using Dirty Data in ABM

Dirty data is every marketer's nightmare. 

It can cause you embarrassment when you present misleading data or keep contacting the wrong people with your offers. Not only that, it can have far-reaching consequences for the overall business if you don't catch dirty data and fix it sooner.

Here are some of the consequences of using dirty data for your ABM programs:

1. Low-Quality Leads

Dirty data makes it hard to identify high-quality leads. Therefore, you end up spending your marketing efforts on the wrong leads who will not convert instead of finding and engaging with high-quality leads who are more aligned with your products and services.

2. Poor lead scoring

In addition to earning poor leads from dirty data, you are also more likely to have the wrong scoring information. For instance, if you have duplicate records that you don’t notice, you will be scoring the same lead twice, which makes it difficult to deliver a coordinated sales and marketing experience.

3. Poor personalization

As more customers become pickier, the importance of personalization grows. If you rely on dirty data to make personalization decisions, you end up sending the wrong messages to the wrong audiences, resulting in poor engagement, lost businesses, and wasted resources. Some common personalization mishaps that happen due to dirty data include addressing prospects by the wrong name or job title.

4. Negative brand perception

Nothing hurts a business more than a negative perception by customers. Suppose you contact a B2B prospect addressing them by the wrong name and role. You send the message that your brand does not care to research its prospects before reaching out to them. You lose the chance to make that first impression, and in the worst case, that prospect never buys from you.

How to Clean Your Data So That It’s Ready for ABM

It’s not always possible to collect clean data. 

In most cases, any business dealing with data will find incomplete sources, duplicate entries, typos, corrupt information, and conflicting entries. This is why data cleaning is always important before you proceed to data analysis.

You can clean your CRM data manually through data wrangling tools or use a computer program to automate the data cleaning process. Before you start cleaning your data, you must first understand what your goals with the data are.

Clean data for ABM could mean having the right attribution or contact information.

Let’s cover the steps involved in cleaning data to prepare it for ABM.

1. Identify the errors existing in the data. These could be typos, missing lead sources, missing contact information, or duplicates. Kudoz can help you with identifying data with opportunities without a lead source and missing contacts on an opportunity.
2. Remove duplicate or irrelevant entries from the dataset
3. Fix structural errors such as typos, mislabeled categories, duplicate categories, etc.
4. Remove unwanted outliers that are irrelevant to the analysis or are erroneous
5. Handle missing data. If you are already using Kudoz, you will always be alerted when you have missing lead sources on an Opportunity or contact roles not associated with your CRM Opportunity data. You can fix this directly by inputting the missing value. Alternatively, you can drop any entries with missing values.
6. Validate the data. The final step in data cleaning is ensuring that your data makes sense, follows the appropriate rules, gives insight, and is of good quality.

Tips for Keeping Your Data Clean Over Time

Not many data teams are thrilled by the prospects of cleaning data – for them, the most enjoyable activity is the analysis where they get to see the story data tells.

But let's face it, a lot of the data businesses have, needs tons of cleaning before it can tell any meaningful story, especially to marketers who want to engage with their B2B customers.

Fortunately, it is possible to keep your data clean over time, by implementing best practices such as:

1. Create a Data Quality Plan

A data quality plan will vary from one organization to the next, but it should contain the Key Performance Indicators for your organization. Before creating the plan, identify the common errors that arise in your data, and establish systems to stop these errors.

2. Check Data at the Point of Entry

Data collection is a major source of poor-quality data. Therefore, the best way to ensure consistently clean data is to improve your collection processes to collect clean data.

If missing lead sources or contact information are a major problem, you can use a tool like Kudoz to identify missing data.

Kudoz works seamlessly with Salesforce CRM to detect missing lead sources and contact roles on your Opportunities. It takes this to the next level by scheduling tasks and prioritizing them so that you know when the data you collect doesn’t meet your KPIs.

3. Validate Your Data

Successful ABM programs rely on having the right information for a lead, contact and opportunity. Validation tools can help you with verifying that the information you have is correct and up to date. 

You should also validate new data that comes into your CRM database to check for errors. Kudoz can help you pinpoint missing lead sources and contacts any time you collect new data.

4. Audit Your Data Regularly

While it is possible to maintain a clean database, errors always creep in and when left alone long enough, you could end up where you started with dirty data. Dirty data could also arise from failing to remove outdated data from your database. 

Such outdated data might include:

• Customers who have opted out of your email list

• Emails that have bounced

To keep your data clean, schedule regular data audits to ensure that you are still using quality data to inform your marketing strategy – don’t wait until you’ve made costly decisions to fix dirty data.

5. Build a Data Culture

 "While it’s your marketing team that is responsible for creating ABM strategies, data quality should be a culture within your business."

All employees should be aware of your company’s data standards to ensure that your database remains clean and organized.

The Benefits of Having a Clean Well-Organized Database for ABM

Today’s businesses are data-driven. However, most of these businesses are still facing the same challenge of dirty data. Dirty data is almost inevitable when dealing with massive amounts of data, but that doesn’t mean that your marketing activities must be driven by poor-quality data.

Data cleaning is an important activity that allows your marketers to work with the right data when conducting account-based marketing initiatives. 

"When the marketing team works with clean data, the business benefits by shortening the sales cycle, attracting high-value accounts, and increasing engagement with customers." 

Since data cleaning is an ongoing process, tools like Kudoz can make your work easier by helping you address data quality issues such as missing lead sources or missing contact information as soon as your Salesforce CRM collects customer data.

Learn more about how Kudoz supports your data-driven marketing efforts.