Why Clean Data is Your Organization’s Biggest Asset
Data is the most essential resource for business growth in 2022.
The internet says so. But you already knew that because over the last decade, you’ve been been increasingly collecting and relying on data to strategize, make decisions, connect with customers, and measure success.
What you also know is that data lies.
Bad data costs U.S companies three trillion dollars per year, according to IBM. A study by Gartner has found that most organizations surveyed estimate they lose $14.2 million dollars annually.
As a marketer, dirty data can not only lead to poor decisions, but it can also damage your relationships with your customers and your brand’s reputation.
Salesforce, Pardot, and other tools in your marketing suite enable you to collect, analyze, and use data in powerful ways. While an internet search produces pages of data cleaning products available to help you, data integrity is your responsibility and requires human eyes and analysis.
Common Causes of Dirty Data for Marketers
Incorrect Form Data
Forms are essential for lead generation and data collection. Seventy-four percent of companies use web forms for lead generation, with 49 percent saying their online forms are their highest converting lead generation tool. While forms are a highly effective way to collect data from your customers, they’re a big contributor to dirty data. Because users can:
a. Intentionally fill out forms incorrectly to shield their identity or for ease
b. Accidentally enter the wrong information
c. Misspell words or use shorthand
These mistakes are problematic when you’re using form data to segment lists, personalize content, populate merge fields, or customize offers.
Duplicate Data
Even the most vigilant of marketers wind up with duplicate profiles in their CRMs when it comes to the raw data. You are going to have to merge duplicate customer data, making decisions about what data to keep and discard. It could be two or more users inputting different data on the same customer, customers filling out multiple forms with a different email addresses or other unique identifiers, or mistakes in data entry causing duplicates from imported data.
Regardless of the cause, an important step in ensuring clean data is merging or resolving duplicate profiles. Salesforce and Pardot both have built-in tools for finding and managing duplicates, but the administrators create the rules for handling them and that takes analysis and strategy. These data cleansing tools will be the first step improving data quality.
Inconsistent Field Data
If your fields don’t exactly match across tools, you risk creating dirty data. For example, if you have a forms tool that has a ‘Name’ field, but your CRM has ‘First Name’ and ‘Last Name’ as two separate fields, it creates syncing problems. This goes for drop down lists you create that correspond exactly across tools, such as country or product interest.
Inaccurate Activity Data
Because operating systems and email service providers (ESP) handle incoming emails differently, an email open does not always mean it was opened by a human. For example, when Apple released iOS 15, Apple Mail users’ images were pre-downloaded by their ESP regardless of whether or not they opened their emails. This means tracking pixels, invisible images used to detect email opens, would download automatically. Your data would show an email open when in fact there was not.
Location data and tracking cookies can also be hidden or misrepresented based on server location or user preferences. Marketers need to know what factors can skew activity data and factor that into their decision making, reporting, and communicating processes.
Dirty Data Consequences
Damaged Customer Relationships
Your customers’ personalized marketing journeys depend entirely on the data fueling them. If your data isn’t right, you will fail to give your customer the right experience.
The most obvious example, which will also be obvious to your customers, is populating merge fields in your copy with the wrong information. If an email shows up with the wrong name or product interest in your customer’s inbox, you’ve immediately damaged your credibility and your relationship with your customer. Bad data can also send customers down the wrong product journey or serve them with the wrong content.
Sloppy communication with your customers undermines your ability to build trust and create relationships with them. It also distracts from your offer, so while personalization can drastically improve conversion rates, dirty data can drastically decrease them.
Disengaged Audience
When you send customers offers that don’t apply them, you risk annoying them. Dirty data can lead you to create and send offers that wastes your time and theirs. Audiences know organizations track their activity. The more they’ve engaged with you, the more they expect to receive relevant offers. The more information your customer is willing to give you, the less they will tolerate non-targeted offers.
Flawed Decision Making
Measuring marketing campaign performance and using it to make better decisions is critical to continuously improving and staying competitive. However, if you’re using dirty data to measure performance, it won’t help you improve. Use metrics with less outside variables to measure performance, such as email link clicks vs. email opens.
If you’re making strategic decisions based on demographic information, such as location, gender, language, or income, cross check your data for accuracy. These often get entered wrong into forms and can be inaccurate data if you’re using other methods to track them, like using IP addresses to determine locations.
How to Clean Your Data
Create Clean Data Criteria
Based on your organization’s goals and how you want to use your data, decide what data qualifies as clean. From there, you can archive data that doesn’t meet your criteria and set up parameters to keep dirty data from entering your database.
Perform a Data Audit
Search your data for errors, duplicates, and junk, correcting or archiving them as you go, so you can ensure accurate reporting and form fields. Again, there are tools that find and delete duplicates in your database in minutes, but you have to set the parameters for what you want keep or how to merge records. Plenty of step-by-step guides and resource are available to help you perform a data audit. It can be a tedious and time-consuming process, but is well worth the effort to have successful data management.
Standardize Data Entry Across Marketing Tools
Field names, drop down menu options, and required fields should be the same across platforms. Use as many drop down menus with listed options as you can to keep your data consistent, organized and easy to sort. Through this best practice, you will be able to minimize the amount of missing data you receive.
Create a Standard Data Entry Process
Know all the ways data can enter your marketing tools. Create a standard process for data entry, accounting for the clean data criteria you set. Automate as much of the process as you can.
Check and Clean Your Data Frequently
Data cleaning isn’t a once-and-done job. Create a formal audit process and perform it every quarter or six months to evaluate and archive data that doesn’t meet your criteria. For data that comes in through forms, perform daily or weekly data checks to ensure it’s quality enough to enter your database. It’s important to have a set data cleaning process in place to improve data quality.
Your Data Cleaning Takeaway
Cleaning data might seem like a menial task, but it’s critical to your organization’s success. Without reliable, accurate data, your investment in your marketing tech stack is wasted. Make clean data a high priority and give the task to someone highly competent because it’s your organization’s most essential resource for business growth.
