Why Data Cleaning In Marketing is so Important
- Datameer, Inc.
- May 18, 2022
Our previous article emphasized the importance of high-quality data on your journey to adopting machine learning within your organization. This article will focus on an all-important process required to achieve high-quality data – A process known as “Data Cleaning” in Marketing.
Before we jump to wordy definitions of this term, let’s look at some practical marketing scenarios.
Let’s say, for example…
You gathered customer data from a survey to create your mailing list.
While vetting your list, some users erroneously filled in their email as “ %g8mail.com ” instead of “gmail.com.”
You were creating a report on inbound lead by demography and noticed spelling inconsistencies within the country category.
You spot the word “United States” spelled as “ United S ” or abbreviated to “u.s” in others.
This can quickly lead to incoherency in your reporting process, hence the need for Data Cleaning .
What is Data Cleaning?
Experts at Tableau define Data Cleaning as fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
Data Cleaning And The Implication of Dirty Data
For the CMOs and executives, the insights, analytics, and Machine learning systems are all but fancy if they cannot inform the business and yield profitability.
One major cause for this could be a lousy data strategy. A bad data strategy is often synonymous with “dirty data” within an organization.
Experian, a data broker and one of the world’s leading global information companies, reports that, on average, companies feel that 26% of their data is dirty.
Ed Downs, the marketing manager at core logic, cites an interesting statistic in his article on dirty data – “bad data costs the average business around 15% to 25% of revenue, and the US economy over $3 trillion annually.”
These are huge numbers, and organizations that rely on such data are likely to incur losses due to ill-informed data-driven decision-making .
We have seen the implications of insufficient data. Let’s now discuss avoiding this “messy data state” by citing a real-life example.
Fighting Bad Data with Good Data Cleaning Practices: A Live Project
As a freelance BI Analyst, I have had the opportunity to work with different marketing agencies on various projects.
This section will share my experience from an actual data cleaning project I was a part of.
Our client, a music company, based in the US, had over 6 million rows of disparate customer contact information.
They sought the help of our marketing agency to help them:
- Clean the dataset
- Deploy a searchable data warehouse solution .
- Perform segmentation analysis
- Conduct email marketing using the clean dataset
Project Solution: The Cleansing Process
From duplicate records to alphanumeric phone numbers, we were able to identify potentially bad data and how best to deal with them.
Here’s what our step by step cleansing process looked like:
- Analyzing the datasets: Using tools like Open refine and excel, we assessed our data quality.
- Categorizing Data issues
- Filtering and eliminating duplicate records
- Identifying outliers within our dataset
- Dealing with null and erroneous values
- Discarding irrelevant and inconsistent data.
- And finally, setting parameters to maintain quality subsequently.
These were some of the steps we took to successfully achieve a clean and accurate database at a high level.
As a result of the data cleaning process, we delivered on high-level processes like segmentation analysis and email marketing.
After sharing my experiences, I hope that you better understand the importance of data cleaning and how critical it is to achieve a genuinely effective data-driven marketing strategy.
Key Takeaways from Project (low-lights)
Speed: The data cleaning process took up to a month and could have been faster.
Key-man Risk : There was a key-man risk because I was the only one on the team with technical expertise in SQL, Excel, and the data cleaning tool.
Datameer: The Speedy No-code Transformation Tool
The playing field has changed forever with cloud DW technologies like Snowflake and no-code transformation tools such as Datameer.
If I were to take on a similar project today, those low lights would probably be non-existent.
With a tool like Datameer , Collaboration and speed are now at our fingertips!
Every member within a marketing team, whether technical or non-technical, can now transform, clean, and participate in the data wrangling process.
Integrate Datameer with your Snowflake environment today and see how effortless data transformation can be.