5 Reasons Why Your Marketing Effort Needs Clean Data

Marketing is a creative enterprise. But it is also a data-driven enterprise. And any data driven enterprise needs clean data to work effectively.

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Photo by Franki Chamaki on Unsplash

Data analysts and professionals recognize the importance of cleaning your data. But it might not be obvious to Marketing professionals.

Here are 5 reasons why your marketing effort needs clean data

1. Duplication

Customer and user duplication might be the single most important reason to strive for clean data.

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Duplicated Records

In the above example, it’s clear that both records belong to the same customer. But without properly cleaned data, any marketing software will read those as two unique records.

So if you are running an email marketing campaign on this dataset, Dr. Strange will get two emails when he should have only received one.

Costing you an email, annoyance to the user and possibly duplicated efforts.

Depending on the dataset, it’s not always trivial to eliminate duplicate records but you can get started by first cleaning up and standardizing the fields.

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Field Clean Up

Now it is super clear that these are duplicate records.

2. Accuracy

With dirty data, you can run into many errors because of data inaccuracy. Let’s say you have a list of customers and you need to email all customers who signed up after a certain date.

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Messed up dates

If your data has dates in multiple formats, like this example, then you might not be able to filter it properly, resulting in emails being sent to the wrong people.

Inaccurate data rears its head especially with emails. If your emails are not accurate, then they get bounced and never reach their target.

3. Segmentation

An important marketing activity is segmentation of your customers and users. With proper segmentation, you can understand your customers better and use their profile to target and attract new people.

Without clean data, it’s almost impossible to segment users properly. Let’s say you have 2 different lists of customers that you need to match up so you can segment based on money spent per year.

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As you can see, without properly cleaning and standardizing names, it would be almost impossible to match and segment customers, especially at scale.

4. Personalization

It is very helpful in marketing to personalize your messaging as much as possible. That involves tailoring the content to the customer.

Personalization can be as simple as addressing someone by their preferred title or getting their title or company name right.

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If you were sending an email campaign and wanted to include the prospect’s company name, it would be best to clean up the company names so the email doesn’t say things like,

Hey Natalie Portman, Hope things are good at Apple Incorporated!

The email would read much better if it said

Hey Natalie, Hope things are good at Apple!

5. Analysis

Ultimately, you want to ensure that your marketing dollars are generating a meaningful ROI (return on investment).

To do that you have to determine the cost of your campaigns and the revenue it generated.

Coming up with the revenue generated for your campaign is an exercise and topic onto itself. But without clean data, it is almost impossible.

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In the simple example above, people have paid in different currencies. Converting these three might be a trivial exercise but imagine 100,000 customers all with different amount spent. Just adding those would waste precious time.

And throw off your analysis.


Marketers need clean data to do their job properly. Dirty data causes duplication, accuracy, segmentation, personalization and analysis issues.

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Or email us at info@lovespreadsheets.com if you have any other dirty data problems that are affecting your marketing.

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