Email marketing is still relevant and if you are not yourself already using it for your business, there are plenty of resources like these statistics out there to convince you of this fact. What has changed though compared to the past, is how email marketing is done, and as everything else nowadays is driven by data and analytics, the same is true also for email marketing. Even before we start thinking about analytics, we, first of all, need email marketing data, that will be used to calculate KPIs, create custom email marketing dashboards and reports for monitoring, and eventually, generate explanatory and predictive models that will help us to optimize our email marketing.
As we rely more and more on analytics for email marketing, it is becoming important to select the tools that we’ll use by also considering the email marketing data that we can have access to. In this post, we categorize the different email marketing solutions from the perspective of the data they expose to their users, and we try to present it, in a brief but hopefully helpful way for you.
Email Marketing Data versus Transactional Email Data
When it comes to email marketing there are two main categories of applications. Traditional Email Marketing apps like Mailchimp and Constant Contact and Transactional Email Services like Mandrill and SendGrid. Typically, there’s a difference between email marketing and transactional emails, even legally speaking, but both categories can be used for marketing regardless of the marketing strategy that will be executed. Some good definitions taken from a great post on the differences between the two from Hubspot, are the following:
- Email marketing: Any email that is sent to someone that includes, directly or indirectly, a commercial message. We say indirectly because for example a drip campaign for lead nurturing although it might not contain direct information about your product it is also considered marketing material.
- Transactional email: one-to-one emails send to your customers as a result of their actions. For example, when someone wants to reset her password, this is considered a transactional email. Strictly speaking, transactional emails shouldn’t contain any marketing material in them.
But things get blurry, especially if we look to the differences from a technical perspective. For example, it is not uncommon for someone to use a transactional email service for marketing reasons just because of the scale of the lists that has to deal with.
For the purpose of this post, we will consider both categories as valid tools for executing email marketing and we’ll focus mainly on the differences on the data we get access to. The type of the application that we’ll use might greatly affect the data that we’ll have access to and for this reason, it is important to understand the main differences between Email Marketing applications and Transactional Email Services.
From an application perspective, there’s a huge difference between transactional email services and email marketing applications. The first are mainly infrastructure components, where SMTP is provided as-a-Service behind an API that can be used to be integrated with your infrastructure. On the other hand, email marketing applications are complete applications and not just APIs and as a result, they come with a much richer data model behind them. This difference has a huge impact on the email marketing data that we can access on a transactional email service compared to a typical email marketing application, with the latter usually being much richer.
Someone might argue that as an infrastructure component, the data from a transactional email service has to be integrated with data coming from your own system, after all, such a system will be mainly used to build your own email marketing infrastructure which is true. With this approach, you will have to more or less reinvent the wheel in some cases but at the same time, you will have more flexibility to define exactly what you need and end up with a robust data model with complete control over it.
Data from Email Marketing Applications
In this category, we have applications like MailChimp and Constant Contact. Of course, each one might model the email marketing process a differently but there are many commons concepts found in most if not all of them. These applications can be grouped in the following sub-categories.
- Traditional Email Marketing Applications like MailChimp.
- CRM platforms with Email marketing Capabilities like HubSpot
- Customer Engagement platforms like Intercom
- General Marketing Applications like Marketo
The first and last category are quite similar in terms of data modeling, probably because the concept of a campaign is an important and common one in marketing in general. Additionally, the CRM and Customer Engagement tools are also similar in the way they represent their data. In a very brief summary, the main difference between these two sub-categories is the following:
Marketing applications are modeled around the concept of Campaign and the user is of secondary importance.
While Customer related platforms, even if they have email marketing capabilities, are modeled around the concept of the User.
If we think about it the above statements make complete sense, on one hand, we have marketing tools and when we do marketing what we are always doing is run a campaign, while on the other hand, we have tools built initially to model our relation with customers and thus it makes complete sense that everything is built with the user in the centre.
Although these differences might sound a bit un-important at this point, they actually make a big difference in terms of how much noise our data will have, for example, a CRM will most probably have additional data that is not that useful when we want to make an analysis for email marketing, or how robust a mechanism for pulling consistently data out of such a system can be. Also, there’s the case of remodeling, for example, if we pull out data from a CRM and we plan to use this as email marketing data, we might have to remodel the data in order to use it effectively.
In most cases, email marketing applications offer a data model which is much closer to the marketing world than CRM tools that are enhanced with marketing capabilities.
A typical data model from an email marketing application is built around the following entities:
- Campaign. Which is how you send mail to a list of recipients.
- Lists. A list of recipients that is included in a campaign, each recipient has at least an email address and an arbitrary number of metadata.
Actually, these two entities are enough to describe the foundations of a data model for email marketing. The rest of the stuff we can find on such applications are usually auxiliary entities around these two top-level ones and metadata that can help us to better manage the lists and the campaigns, for example, segments, tags, and reports.
A typical data model from a CRM or Customer Engagement application will include the following entities:
- Contact. Which represents a person or an entity from which we have information on how to get in contact with.
- Messages or Emails. Which are exchanged with the contacts?
Again these two are the minimum required entities that a CRM or a Customer Engagement application will have in order to provide some kind of email marketing functionality. Of course, in order to deliver that, it has to create a number of auxiliary entities. For example, segments are important here as they can create something similar to the list we had in the email marketing applications. These tools will have also a number of other entities that exist for their CRM functionalities.
At this point, it is interesting to mention a concept that is usually found in CRMs and which is borrowed as an “advanced” functionality by the email marketing applications. At the core of a good CRM, there’s always the concept of workflow, where its user can define a workflow that will be executed in order to get in contact with a customer. Workflows are also used in email marketing applications to compose and execute complex campaigns. In most mature applications either CRMs or Email Marketing Applications we encounter some kind of workflow concept.
The above are the core entities that we expect to find when pulling email marketing data from any of the application categories we mentioned and although not exhaustive you should anticipate that any data, from most of the tools out there, will include them.
Data from Transactional Email Services
In this category, things are much simpler. The platforms we find here are practically SMTP-as-a-service products where the SMTP protocol is exposed over a Web API for easy integration with your infrastructure and applications. We do not have any sub-categories and the data model is much simpler. The main data that we expect to be able to pull from such a service is the following:
- Lists: which is a way of organizing our recipients, although usually with less metadata compared to what we find in email marketing applications.
- Rejects: Mainly a blacklist of addresses
- Whitelists: addresses that you want to not be blacklisted if something goes wrong (e.g. a bounce event)
These are the most commonly found entities that model the data that we can pull from a transactional email service, there might also be some other, e.g. for creating and maintaining “templates” but these are more vendor specific entities.
Usually, from transactional email services, there are two options to pull data that can be used for analytical reasons.
- Activity logs that the service allows us to export, e.g. emails that were sent, to whom and when.
- Webhooks where we can subscribe for specific events and get notified when something happens, for example when a mail was bounced.
The truth is that with transactional email services we’ll have to do more work to get even the basic KPIs out of it compared to email marketing applications, as the latter usually have implemented at least some basic metrics out of the box. On the other hand this simplicity and access to raw data might be useful and more versatile in many situations, for example we can have access to the data faster and easier and by having the raw data we have more freedom to calculate anything we want compared to pre-computed metrics.
As we said at the beginning, email marketing is still relevant and a thriving ecosystem of different tools and rich data is a good indication of this. We tried to present the main categories of tools that exist out there from the perspective of email marketing data that each one provided to its user. The main conclusion is that like everything in life, there are compromises that we have to make depending on our needs and expectations. The simplicity of transactional email services and the raw data that they provide come with the price of having to implement almost everything from scratch while the email marketing tools and CRMs with their rich data models force you to a specific view of the world that might require a few changes and workarounds before you can get what you are looking for (if it’s not included there already).
This was a first attempt in presenting email marketing platforms and their data on a high level, in future posts we plan to delve deeper into the details of each one separately so if you have anything to suggest just let us know!
If you want an easy way to pull your email marketing data from your favorite marketing application, give Blendo a try! Reach out to us and we’ll show you around!