It’s not news to us (nor likely to you) that email marketing is one of the top drivers of customer engagement, yielding the highest potential ROI of all marketing channels. Marketing departments are often building entire budgets around email marketing — but are they optimizing their email efforts to improve deliverability?

According to 250ok, delivery to the inbox for North America was around 88% in 2019, but the DMA reports that some industries saw much lower deliverability. While trends in deliverability and engagement are trending upward as a whole, the industry average doesn’t mean much if your organization’s mail isn’t making the cut.

Nearly all major mailbox providers (MBPs) utilize machine learning to determine how they should filter inbound mail. Their primary goal is to ensure their users are happy, by delivering “wanted” mail to the inbox and keeping everything else out — whether it’s never delivered or filtered to spam. These filters monitor user engagement and sender reputation in real time to determine how mail is routed on their networks.

As a sender, you’ll need to follow some core best practices if you want to be sure your mail reaches the inbox.

One of the questions we hear the most often from marketers when we talk about the importance of MessageGears’ direct connection to your database is this: If connecting directly to the database is so essential and transformative, why don’t your competitors do it too? After all, our most common competitors are massive organizations that would seem to have plenty of money and resources available to be able to do pretty much whatever they want. From the outside, it can be hard to understand why they wouldn’t simply copy MessageGears if there was value in doing so.

So, why don’t they? To really answer that question, we need to take a look at the history of email service providers, and why they’re structured the way they are.

Every marketer has faced challenges when creating what they hope to be successful, personalized cross-channel messaging campaigns. For those working in highly regulated industries like banks and other financial institutions, those challenges can often seem impossible to overcome, especially when data access is a primary factor.

The many and changing regulations that govern banks apply fully to their marketing departments. And while marketers have been able to work around those regulations to create amazing cross-channel campaigns, many still struggle with getting access to the data they need and using it to send highly personalized marketing and transactional messages.

But banks have a lot going for them when it comes to marketing. Not only do they have a lot of first-party data on their customers, but the right addition to their martech stack could give banks the data connection they seek, while maintaining the highest level of security.

I previously wrote about how we recently rolled out MessageGears Engage, our brand new product that offers secure and scalable access to customer contextual data (or any other internal data) in an easily consumable format. This enables personalization in real time, empowering marketers to deliver an unprecedented level of brand engagement and up-to-date content.

That marketer-focused overview gave a good high-level look at the product, but I’d like to take a deeper dive into the struggles that modern enterprises face, and how Engage can be an incredibly useful tool to help organizations overcome data hurdles.

To get marketing personalization right, it’s essential that you have access to information about the consumer. The brand’s knowledge about the consumer informs to a great extent the messages they send, and determines how relevant those messages can be. For most senders, though, information access stops at the point when they hit Send, so the messages have no way to keep up with what’s often rapidly changing data. Because most companies are using old data, the problem only gets worse. There are tools that try to solve this problem, helping marketers personalize messages at the time a consumer engages with them, extending the how long they can be relevant.

The problem we consistently see across large enterprises that want to send relevant messaging is that the data that is available is not always the data they can use in their messages. Time and again, I.T. resources do their best to make data as usable as possible, but oftentimes the real world doesn’t allow for easy and “real” use of that data.