Analytics & StatisticsAutomation

Email Marketing Automation: Data Types and the Importance of it’s Quality

men discussing sitting with laptops whiteboard post its
men discussing sitting with laptops whiteboard post its

You’ve got lots of great ideas about automated email marketing flows. You have clear goals and great content. But sometimes, even with all that in place, you just can’t get the results you want. Have you looked into the quality of your database? Even the best automation ideas cannot bring the desired results if data is a mess. Knowing and understanding your data is essential, so let’s take a look at different types of data and the importance of data quality.

Email Automation Relies on Your Data

Data quality is actually the foundation of email marketing – all your strategies and tactics will depend on that. Do you want to add personalization to your email content? You need to have good-quality data behind it. You’re building an automated flow, and you want to add custom triggers. Again, it will require good quality data for automation to work as desired.

That said, many businesses recognize the need to improve the quality of data. In B2B, for example, the recognition of the importance of data quality has been continually growing. According to Statista, in 2018, 89% of their B2B survey respondents indicated that data quality is important to their business, and that’s an increase of 9% compared to 2017.

Different Types of Customer Data

Your email marketing automation will rely on different types of data, such as primary/basic contact data, interaction or behavioral data, and attitudinal data. If you bring all these different types of data together and make it available for your email marketing automation, your email campaigns will definitely benefit from that.

Primary or Basic Customer Data

Primary data is often referred to as contact details. These are the primary or “basic” fields such as email addresses, names, or phone numbers. This type of data can be developed further depending on whether you operate in the B2C or B2B. For example, B2B data would include company names and job titles into their basic data fields. While B2C could add social media handles into their contact details view.

This information is fundamental to your email marketing campaigns or automated flows.  If you don’t have an email address, you won’t send your email campaign to this contact. If you have an incorrect email address, you’ll get a bounce, and again, your recipient will not see your marketing campaign. From the B2B side, your goal isn’t to reach any person in the targeted company but to get your campaigns seen by the right person.

This type of data can be enriched with additional profiling information. It could include demographic data, such as gender, or even contain contact interests. This information can help you create more unique automated workflows, meaningful personalization tactics, and even targeted A/B testing plans.

Engagement or Behavioural Data

This type of data refers to customer engagement or interactions with your company. It is critical when building lead nurture or buyer journey flows and automation based on your email recipient interactions with your emails and landing pages. It includes email opens, clicks or forwards, even reading time, but it can extend to landing page views, your lead magnet downloads, demo requests, and much more.

With this data, you can achieve the next-level targeting, personalization, and really effective automation flows.

Attitudinal Data

Attitudinal data refers to your customers’ attitudes towards your brand and/or your products. How can you get to that level of detail? There are ways of collecting this information. For example, if you ask for customer reviews, product rankings, and other types of feedback, that could be a great source of attitudinal data. 

Attitudinal data is another layer of information that could enhance your email marketing automation. For instance, positive reviewers can inspire you to create meaningful segments of your “brand advocates”. In contrast, negative reviewers could prompt you to think about win-back campaigns.

How to Approach Customer Data Quality?

Once you know what kind of data you have, it’s important to assess its quality. Data quality will be reflected in your email campaign results. You should look at these five main aspects of data quality: completeness, accuracy, uniformity, consistency, and uniqueness.

  • Completeness. How complete is your database? Do you have a lot of missing information and blank fields? If that’s the case, please don’t fill these missing fields with fake details, incorrect or placeholder data. It will lead you to many data accuracy issues.
  • Accuracy. Check if your data is up-to-date and accurate. Inaccurate data will not help your email automation and campaigns. Inaccurate data can give you, for example, wrong personalisation results or lead your email recipients to incorrect email journey paths.
  • Uniformity. Uniformity refers to data values following the same standard. For example, U.S. or USA, or capitalisation, for instance, John Smith, JOHN SMITH, john smith).
  • Consistency. Consistency refers to records being consistent throughout different systems. For example, your Email Service Provider (ESP), your e-commerce platform, your CRM, etc. You should aim to maintain a high level of data consistency between different systems and platforms.
  • Uniqueness. Data uniqueness relates to data duplication. Duplicates in databases definitely cause a lot of headaches! It can result in errors in your marketing flows and also skew reporting results and insights.

If you’d like to learn more about different aspects of data quality, here’s a handy whitepaper from RingLead.

Next-Level Email Marketing Automation Depends on Your Data

To sum up, if you want to achieve greatness in your email automation, you must have the right foundations. And data is definitely one of the key ingredients to successful email marketing. Go beyond using basic customer information and integrate behavioral and even attitudinal data into your ESP customer journeys. Make sure you maintain data quality: uniqueness, correctness, completeness, accuracy, and consistency.