Harvesting First-Party Data Using Multi-Topic Newsletters

There has been a lot of interest and discussion in the OI-verse about the value of newsletters as an email communication channel. Advantages range from relationship-building to providing more in-depth and relevant content. In parallel, there has also been a lot of discussion on the importance of first-party data in a “cookieless” world.

We all understand that data is the lifeblood of marketing in today’s connected world. But we often struggle when it comes time to collect more data from our customers, in part because of a lack of trust on their part.

The Benefits of Tracking in the Absence of Trust

People are increasingly sceptical when marketers ask questions about who they are and what they prefer. Questionnaires and preference centers gain little traction because of this. Our industry’s reputational goodwill has been progressively eroded to the point that privacy has become one of the biggest challenges that marketers face.

We all recognize the individual’s right to privacy but that at the same time we need to better understand who our customers are, what they want, and where they stand in their journey, to make our emails and our content more relevant, less spammy and more valuable.

One way of getting some of that information is to pay closer attention to how our customers behave when we mail them by implementing a link-tracking strategy. What our customers click on tells us a lot about them. By better understanding their interests, we can become more relevant.

What Could You Do with More Data?

With the disappearance of third-party cookies, what was taken for granted now needs to be replaced. That’s where click tracking comes in. Imagine what you could do with interest data. The possibilities are numerous.

Personalization – you could use dynamic content blocks to customize issues of future newsletters to make them richer and more relevant.

Targeting – you could use the data to reach subscribers with email campaigns that focus on the topic of interest, with more in-depth content, promotional offers, or sponsored content by another brand.

Detecting intent – although interest is not a true proxy for purchase intent, it is a strong sign which can aid in building audiences for promotion of related lines of business.

Building customer personas – The more you know, the more precise your personas will be. Seems obvious, but how often is it done effectively?

How to Capture Newsletter Click Data

Here are a few ways you can leverage click data and append it to your CRM database.

1 - Start with the links themselves
You can give them meaning by embedding or appending topic information to the links. If your platform allows it, you can create a link naming convention that gives structure and meaning to the links.

Alternatively, you can also use a query string to add UTM values to the links using utm_content or even utm_term to assign a category to the main content links. This may be optional depending on which method you chose when appending to CRM.

2 - Create Category Fields in the Database

You’ll need a place to store the interest data in the database. That’s the easy part. Just create a series of fields in the CRM in which to write the click values assuming you aren’t limited in the number of available fields.

3 - Set Up a Process to Write the Clicks to the CRM

There are several ways in which you can capture the click data and write it to the database. Again, the process will differ from one platform to another.

Blind submits: A simple, yet effective way to write click data directly to the database is to add a blind form submit to the “Read More” buttons in your newsletter. The form action then pushes a value to the appropriate field. This is the most basic technique, and it assumes that a single link click is indicative of interest in a given topic. This may not always be the case.

Automations: Another way to achieve this step is to use an automation (assuming that your platform allows them) which is triggered by a link click. The automation writes the appropriate value in the appropriation database field. The advantage of a program is that you can add some logic to quantify the click and update the value with each successive interaction. This assumes that your platform allows if/then logic in automations.

Scoring models: a more sophisticated approach is to use a scoring model (typically used for lead scoring) to compile engagement over time and assign a score to quantify the interest in a topic or category. Such an approach enables you to not only increase the score with each click, but also to decrement the score based on the age of the click. This is by far the most elegant way to classify interest, but also the most complex. Some platforms don’t include scoring models, some charge extra for them, and some cap the number of models you can run simultaneously.

Appending of historical data: while the previous approaches are “going-forward” techniques, you can also use past behaviour to capture interest. By exporting past engagement data (usually through an API) and you can classify it by topic categories. You then import the resulting .csv files for each topic category as a contact list. You than use this contact list to filter your audience or as a personalization tool.

It's Not Rocket Science

As you can see, though it can get a little technical, it’s not that complicated. You just need to determine which method best suits your platform and your needs. While it can represent some extra work, remember that you don’t need to capture every link click. Only those for the most significant topics and those that you would actually use to promote products, services, lines of business, etc. The rest is just noise.