Did you know over the last year, around 63% of marketers increased their investment in data-driven marketing.
The reason behind this has a lot to do with how leveraging consumer data can help generate useful insights on consumer engagement for understanding target audiences much better.
However, for marketers to gain value with the collected information, the data must be viewed & correlated at some granular level and this could be challenging for marketers considering how 67% of businesses depend on CRM data for growth yet 94% of B2B companies speculate database inaccuracy.
So, are businesses confident enough regarding the health & quality of their database? This further leads us to the million-dollar question, should businesses be adapting more efficient ways to manage marketing data? Well, the answer is yes.
Data hygiene is not a fresh topic for those that remain active in the marketing sector. But, there’s a good reason behind this topic as a recurrent theme for businesses everywhere – the reason being: how data quality serves as one of the biggest challenges for businesses today.
Take a look at some of these astounding statistics to further understand the situation:
- 62% of organizations depend on marketing along with prospect data that are inaccurate by up to 40%.
- An average of 25% B2B database remains inaccurate.
- Around 64% of successful data-driven marketers state improving data quality is an extremely challenging obstacle in achieving success.
5 Ways to Manage Marketing Data Cleanly & Seamlessly
Perhaps, the numbers on data are nothing new and you may already be aware of the problem dirty data poses. But, statistics alone are not enough to explain the substantial impact proper data hygiene can bring forward.
Knowing how to manage it efficiently shows great importance as well. With that said, here are 5 ways to manage data more confidently.
1. Developing a Plan for Data Quality
First things first, it’s essential to understand the place that gives birth to the majority of data errors to detect the issue and nip it in the bud. In short, identifying it at its primary stage and working upon a plan to manage it.
In any plan, the inclusion of metrics can serve great importance as ideally, data quality has to be summarizable within a scale of 1-100. Yes, we understand how different data can contain different data quality, however, the presence of an overall number can assist organizations in measuring constant improvement.
In the end, it’s all about hatching a clear and precise set of actions to kick off the plan while also making sure that these actions are regularly updated to match any data quality changes.
2. Validating your Data Accuracy
Given how 94% of businesses are suspicious of inaccurate customer & prospect data, validating your data accuracy in real-time is extremely crucial. Fortunately, today there are highly efficient tools that can aid businesses in cleaning data.
After all, effective marketing can occur when high-quality data & tools are utilized for seamlessly merging various data sets.
Now, you can always validate your data accuracy online without the use of appropriate tools, however, this could take tons of manual work – something that most marketers don’t have the luxury for.
3. Managing those Data Duplicates
The issue of duplicate data cannot be stressed enough – It is extremely costly and harmful for any long-term marketing plan as it provides inaccurate reports.
Plus, the fact that several email list building services would normally charge based on your listings, further implies that you’re exhausting your finances since you’ll likely pay twice as much.
While removing duplicate entries is the main goal, the work does not end here, so, make sure to consider the following procedures as well:
- Standardizing: Confirming the exact type of data is found in each column.
- Normalizing: Ensuring each data is recorded on a consistent note.
- Merging: This combines relevant parts of datasets for creating new files as data is spread over multiple datasets.
- Aggregating: Sorting data & expressing it through a summarized form.
- Filtering: Focusing on datasets that include only the desired information.
It’s more than common for duplicates to sneak their way into new practices, hence, it’s vital to detect & remove them before your business suffers damages that are beyond repair.
4. Standardizing Contact Data Right at Its Primary Stage
Listen up, this may sound too much but you can’t really maintain healthy/good data quality hygiene while letting unhealthy data seep into your CRM as well.
Makes sense? If not, here’s another perspective on it: before data cleaning even begins, it’s crucial to check critical data at the entry point. This ensures your information is standardized as it enters the database, hence, making it easier to sniff out duplicates.
To ensure ease of operation, it’s important to remain vocal with your team and walk them through creating an SOP or standard operating procedure. Following the SOP helps ensure your team allows only quality data in your CRM right from the start.
- Scaling: Transforming the data in order to make it fit within a particular scale like 0-100.
- Removing: Removing any duplicate & outlier data points for preventing any bad fit in the linear regression.
5. Append your Data
You’ve got your data set aside for each record in the database such as their first name, email, last name, and even a business address. But what if you’re able to gather more, say their phone number, title, annual revenue, or even the location?
In any database, not having the complete picture (i.e. comprehensive data) for each of the records is called a “white space”. One solution to this is, of course, data appending. Today, there are software companies that can capture information directly from first-party sites.
Those tools can essentially help clean and compile data for you. As such, it offers a more complete information profile for business intelligence & analytics. Having accurate & complete data is something that can allow your team to operate confidently and make good business decisions.
Having clean data is only the tip of the iceberg when it comes to putting an effort on comprehensive data management. However, its importance should never be underestimated as the lack of it could be fatal for any business.
It’s no surprise how data-driven marketing has become a fundamental priority as marketers work towards understanding their audiences much better. But with the increasing volume of data at hand, it can hinder the ability to gain meaningful insights. Hence, leveraging clean data management is quite important today.