Every email marketer’s dream is to see higher engagement numbers, more people interacting with their emails. Gone are the days when batch-and-blast strategy made this dream a reality. Now the only way to reach subscribers is through relevant, timely, personalized messages.
At WhatCounts, we call this Smart Marketing, and it’s the marriage of right data, automation and personalization.
Part of collecting and using the right data is formatting it relationally because the possibilities for deep segmentation and personalization are endless. You can connect lists with relational tables, and from there use your information to send targeted messages to segments of users.
Additionally, you can use relational data inside email messages to send hyper-relevant content to customers.
First comes relational lists.
When data is stored relationally, you can create one-to-many relational lists from the related tables. This means all the data available in all the tables for a single subscriber is at your fingertips. This is a powerful tool for email marketers because you can use these relational lists to create segmentations.
Relational data tables imported as lists don’t even need to have an email address field, and they can include entirely customized fields. The only caveat is one of the fields in the table needs to match a field on a mailing list or a field on a different relational data table.
All the table needs is a field that can be associated with a field on an existing mailing list to connect the two.
Next comes deep segmentation.
Once you’ve added a relational table as a list, you can use the fields from both tables to create segmentations.
Say you want to send an email to customers who worked with a specific sales representative who isn’t working with your organization anymore. Sending a proactive message to these customers is a good idea, and you can produce a segmentation to do it using data in a relational format uploaded as a list.
You would choose to segment by the field in the table marked with the representative’s name.
Another example would be reaching out to customers who bought a product or service from you before, but haven’t purchased in a while.
Using a purchase history relational table, we can see the information about what the subscriber has bought. This includes details such as what the product was, what day the subscriber purchased it, what category the product falls under, and how much it cost.
Because the information is accessible in this relational table, you can easily set up a segmentation of subscribers who haven’t ordered a product in over 60 days over the price of $50. Sending these subscribers a coupon to shop again is a good way to create repeat purchasers.
Use relational data in email templates.
The factor most motivating subscribers to open your emails is whether you can answer the question, “What’s in it for me?”
With so many marketing messages flying into subscribers’ inboxes, yours has got to provide readers with an immediate benefit for opening your email. Subscribers want to know they’re going to receive something valuable and relevant to them if they open your email.
Relational data makes it possible for you to correlate information about subscribers in order to send them timely messages that matter.
It’s always exciting to receive a coupon from your favorite restaurant. But imagine receiving a coupon for your favorite meal at your favorite restaurant for the time of day you go most often.
Relational data can be pulled into email content to create these relevant messages. Once you’ve set up your relational list and segmentation conditions, you can pull the appropriate fields into your content.
This kind of valuable, personalized content is what makes subscribers more likely to open your emails, read your messages, and evangelize your brand.
This blog post was taken from our free eBook: Fresh Ideas for Engaging Subscribers with a Relational Database. Get it now!