Getting Data Quality Right in Salesforce Before You Do Attribution

The days of guessing what to do with your data are over. Connect the dots, and you'll maximize attribution success!

Salesforce is a powerful marketing and CRM tool through which B2B marketers can collect data about their marketing campaigns and customers. Such data tracks the customer's journey from the first touchpoint with an organization.

For this data to be useful; however, marketers use attribution to quantify the role of each advertising impression on a customer's purchase or conversion decision. Attribution gives marketers visibility into what campaigns are generating the desired outcome, whether a subscription to a newsletter or a purchase from the business.

Marketers have different attribution models to choose from when working with data, but the most important thing they need is quality data.

The "Garbage In, Garbage Out" principle applies to any data-driven marketing activities and decisions. If marketers work with bad quality data, they make poor decisions. Therefore, you must ensure that you are working with high-quality Salesforce data before working on your attribution models.

How Poor Quality Data Hurts Your Attribution

Marketers today must keep track of multiple campaigns and marketing channels. In addition to keeping track of existing marketing channels, they have to explore additional marketing techniques to incorporate into their marketing mix.

Data is a critical component that marketers track to determine what strategies work and perform well.

But for them to have an accurate picture of their marketing results, they must have reliable and high-quality data.

  High-quality data is:

• Reliable
• Relevant
• Accurate
• Available/accessible
• Complete
• Auditable (leaves an audit trail from the time it was created)

Poor quality data comes about from shortcomings in data collection and data analytics. 

Most poor data quality issues arise from:

• Data collection, especially in tracking web activity, can be flawed as it follows not the person but the device activity. This means that the data does not account for other people's possibilities to access and use the same device for their online activity.
• It's also common today for one person to use multiple devices (sometimes within the same customer journey. For example, they could use their phone to research a product, but eventually buy it using their laptop, or vice versa.)
• Marketers cannot collect offline data, meaning they miss essential insights about users' behavior.
• Duplicate data arises when you do not incorporate data validation into your CRM tool.
• Data decay: when data decays, it ceases to be relevant to your business. For example, when a customer changes their email address, the old address becomes a decayed record. Thus you must remove it from your records to work with relevant data.

What happens when you rely on poor-quality data? 

A marketing attribution model is only as accurate as the data used. Using poor quality data for your attribution models has the following consequences:

• Wasted marketing spend. Poor data leads you to make an inaccurate budget allocation to marketing activities that might not generate the best leads or any leads at all.
• Inaccurate targeting. When you rely on poor-quality data, you have the wrong information about the best audiences for your products. You end up marketing your business to the wrong audiences.
• Lost customers. Poor quality data results in flawed communication that does not resonate with your organization’s ideal customer.
• Reduced productivity. Time is a valuable asset for your business. But you lose a lot of it each time you make decisions based on poor quality data. Your time wastes valuable time following the wrong leads or projects, thus wasting valuable company resources.
• Poor customer experience. When your CRM contains poor quality data about your existing customers, it becomes impossible to create meaningful relationships with them. You fail to address their needs resulting in a poor customer experience. Your business also loses existing customers due to misaligned communication.

How to Ensure Data Quality When Using Salesforce

Marketers rely on attribution models primarily to understand the impact of each marketing method. Without this data, they cannot accurately measure their activities or improve their marketing campaigns to reflect what customers respond to.

According to Salesforce, businesses lose about $700 billion a year or 30% of the average company’s revenue to poor data. 

In addition, a lot of time and resources are wasted researching incomplete data and fixing errors in data.

Salesforce Administrators face several data quality challenges when using Salesforce:

• Incomplete contacts: about 90% of the average Salesforce user’s data consists of incomplete contacts.
• Missing lead sources
• Duplicate data (about 25%)

 While businesses want to be data-driven, the role of data management is left solely to the IT team. Data cleaning is considered a dirty job no one wants to take on. Data cleaning is time-consuming, but it is essential for any data-driven business that wants to make data-driven decisions with a positive ROI.

It is critical for your business to rely on the correct data, especially about your customers, how they find your business, and the communication they respond to during their customer journey.

So how can you ensure that your business makes decisions based on high-quality data? Here are a few suggestions.

Create a Data Management Process

While most organizations recognize the importance of data, most of them struggle to manage their data. A data management plan is key to unlocking data quality and higher ROI for small and large businesses. 

To create a data management process that works for your organization:

• Identify your objectives and how they influence the type of data you want to collect. Clarity on your business goals makes choosing the right data collection tool and processes easier.
• Build your data processes, including your data sources, data collection methods and schedule, the data storage process, and the people involved in analyzing data
• Find the right technology that is aligned with your business goals and data processes.
• Set your organization’s data governance standards to ensure uniformity throughout the business

Streamline Your Collection Processes

Most data quality errors arise during the data collection phase. If you are already dealing with bad data, analyze the root causes of these issues to address them in your new data collection standards.

Identify the type of information your company needs and set up data collection systems to ensure the data you need is complete.

Eliminate manual data collection processes as these are more likely to raise errors such as typos, which affect the accuracy of your data.

Automate

Numerous data quality errors arise from manual data processes such as data cleaning.

Data cleaning takes time - which is why most people do not like cleaning data.

However, by automating data quality checks, you can save your marketers and data management teams the time it takes to identify missing data or data quality issues.

Kudoz automatically alerts your Salesforce admin about the data quality issues in your data. It will create a notification when your data has a missing lead source or when a contact is missing from your data.

Once an alert is generated, it is automatically added to the task manager as part of the Salesforce admin’s tasks. With the Kudoz tool, you can now complete incomplete data and remove duplicate fields to make your data reliable.

With Kudoz integrated with your Salesforce CRM, you can focus on getting the right results from your attribution model since:

• You will always know the source of every opportunity
• Each opportunity is linked with a valid contact

Integrate Your Data

Your CRM should also serve as the single source of truth for all customer interactions, from sales calls to social media interactions to website downloads. Integrate your CRM with other business solutions in your company.

This ensures that data flows from one system to another and that the data is consistent throughout your systems.

To work, you must have a standard data policy across the organization and educate your employees on data management best practices to ensure that your organization works with reliable data.

Monitor Your Data

 Data quality is not a one-time activity that you do and forget all about; it involves constant monitoring of your data to maintain and improve its quality.

• Integrate your Salesforce CRM with Kudoz to help with regular monitoring to check for incomplete and duplicate data.
• Create regular reports to monitor data quality
• Monitor your sales process closely
• Monitor your data regularly to eliminate decayed data such as contact information

Don’t Let Bad Data Stop You

Data-driven attribution is only successful if your business uses quality data. 

Unfortunately, companies end up with poor quality data due to disjointed data collection processes, poor data management practices, and heavy reliance on manual processes.

Since most people dislike data cleaning, implementing solutions that simplify the process ensures that your business powers its ROI with data-driven decisions throughout all its departments.

Kudoz simplifies your data-related activities when using Salesforce CRM by identifying missing contacts and missing lead sources so that you can start your analysis with clean data.