Anna Kayfitz
Anna Kayfitz 9 January 2018

How to identify a data quality Issue in your Marketing Automation Tool

Bad data can effect your marketing performance, analysis and the bottom-line. In order to prevent that you must identify data quality issues prior to them becoming an issue. Here are five ways to identify an issue with your data quality and solutions.

Data is one of the most important part of any marketing platform. Without accurate, complete, consistent and current data marketers will have a hard time emailing different segments, improving marketing performance, optimizing campaigns and analysing past performances. But how do you identify data quality issues inside your own Marketing Automation Tool?

The first step is to talk to your sales and marketing teams. Chances are they are already struggling with data problems. The second step is to run analysis on your data to identify if you are due for a cleaning. And the third stay is to stop bad data from happening in the first place.

Here are some data quality issues that you should look out for and some ways of fixing them:

  1. Inability to segment. Think of all the different creative and messages that you want to tailor to your prospects and clients. For example, do you want to deploy an email to all Vis of Marketing only to find out that titles are not standardized and include thousands of variations.

    The solution: Standardize your data into different buckets. In the example of VP of Marketing, any titles that are VP of Digital Marketing, VP of Product Development or VP of Marketing would all be classified into VP of Marketing.
  2. Bounce Rate is on the rise. You should monitor your email marketing bounce rate as it is a key indicator of the quality of your data. Analysing the source of high bounce rates will help you eliminate bad data providers while, looking at the date create will help identifying outdated leads and the need to refresh your leads.

    The solution: Either perform email validation prior to deploying a new list or clean out historical emails by nurturing them.
  3. Duplicates. While most marketing automation tools will not allow to duplicate email address, you may uncover different emails for the same person. It is not easy to find out if you have duplicate issues. One way is to perform a simple test and check what percentage of your leads have personal domains such as Gmail, Yahoo and so on. If the percentage is greater than 5%, you should look at de duping especially if you are sending different offers to different segments.

    Solution: De dupe your data using automatic de duplication tools or hiring a data cleaning company to do it for you.
  4. Incomplete Data – You may be sitting on MQLs that you cannot pass to sales due to a few missing fields that are required to reach the desired score.

    Solution: Data appending using third party data providers can help fill in missing data such as industry, address, phone number, revenue information and number of Employees. Alternatively, you can use dynamic forms to fill in your missing data.
  5. Missing Deadlines. The final indicator that there are data quality issues are missed deadlines. It could be that your team is so busy dealing with issues with campaign naming that they cannot find the right campaign to attach to the latest emails. There could also be too many custom lists to identify a key segment or they are too busy trying to figure out why the latest email did not perform well. Their daily tasks take longer and longer time to complete.

    Solution: Audit your data and processes inside your marketing automation tool to identify ways to streamline activities and make work more efficient for your marketing team.

Do not wait until issues prevent you from doing your campaigns, sending sales MQLs and segmenting properly. Conduct data cleaning on regular basis to prevent these issues.

Once you have identified data quality issues and have fixed historical data, it is now time to implement preventative strategies. Here are some techniques you should utilize -  

Check your forms: Forms are under your control and usually the easiest thing to fix. You should ensure that formatting is the same across all your website forms. Using drop downs, toggles and switches over free form text fields would ensure all data is standardized. Dynamic forms can also fill in missing data by asking a prospect or customer to fill in additional information.

List uploads: If multiple people are uploading data to the CRM prior to standardizing fields, checking for duplicates and not ensuring all data is complete, you will have bad data going forward. A way to fix it is have proper training and ensure that only one or two people are responsible for manual data uploads and that they follow a strict process.

Monitor the source of bad data: Finally, you may have other sources that contribute to bad data. It could be third party tools that are generating automatic leads, your sales team can be adding bad leads that are then introduced to your marketing automation tool and so on. Therefore, monitoring data quality by source is going to ensure that you stop bad data as it happens.

Poor data quality can be stopped at the source and historical data can be cleaned. The key to making sure that it gets done is to identify the issue, clean the data and make process changes to prevent it from happening in the future. 

Mathew SDC
Mathew SDC

Thanks for sharing such insightful article Anna. We have recently implemented a marketing automation tool like Aritic PinPoint and your article helped me to understand more about data quality issues.

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