How to Personalize Every Email: It’s Easier than You Might Think
Retail marketers have tons of customer data. They know what products shoppers are viewing onsite, their sizes and styles, their favorite colors. They know when emails are opened, what device they’re on, what engages them. So why are they still sending the same email to everyone on their lists?
Data Rich, Insights Poor
Even though you have a ton of data, many marketers still struggle to gain any actionable insights from it. And understanding what your customers want is the only way to deliver the personal communications they expect.
You have unprecedented access to your customers. They are constantly connected, which gives you more opportunities to reach them. But you can’t abuse that access. Customers need to feel like they are in control. They don’t want to be “marketed to”. Instead, they want to connect with brands and engage in personal experiences. And you can deliver these personal experiences by using your data strategically.
If you aren’t using your customer data to inform product recommendations, you end up recommending the same products to everyone, and you could end up with something like this:
This email didn't help me discover new products. It didn't entice me to visit the website to shop. It is confusing. I’m not sure how a rocking chair, printer, motor oil and mattress can be recommended just for me and it is rather insulting that this retailer would label these products like they were chosen for me specifically when they have nothing to do with anything I have previously browsed or purchased from this company.
Don’t Fake It – Automate It
Shoppers are savvy. They can spot a fake from a mile away. Stop guessing and hand-curating your product recommendations and save yourself the time and effort by automating the process. Listrak’s Recommender automates the creation of targeted and personalized content and recommended products based on each shopper’s online behavior and purchase history. Personal messaging goes way beyond the last click, but the last click is an extremely important and telling part of the conversation. That’s why your product recommendations have to be based on both purchase and browse history.
“We were seeking a solution that would not only help customers discover new products but would give us control over what products were being recommended. With Listrak, we could quickly tweak the business rules and view what products would be displayed to each customer in advance. We were confident that we were promoting the right products to the right shoppers.”
~ Marketing director for a popular luxury retailer
Personalized messages come in many forms but the inclusion of product recommendations based on browse and purchase history will turn any message into highly targeted, one to one message. We’ve seen retailers increase their overall email revenue nearly 10% by including personalized recommendations. Here’s how you can personalize every message you send.
One to Many
Broadcast messages are still the bread and butter of most email programs. Retailers continue to blast the same message out to everyone on their lists for the simple fact that they make money. But, simply by using your customer data, you can not only personalize these messages to each subscriber, but you can also automate the creation of the message, saving valuable time and resources.
A Recurring Automated Campaign is very similar to a broadcast message as it is sent to the entire list at a pre-determined time, such as every Saturday at 9:00 am. The difference is that instead of the messages being designed and coded, a template is used and images are automatically populated based on each shopper’s browse and purchase history, or other business rules like new merchandise or top selling products in the same categories that were previously browsed or purchased. This allows retailers to reach their entire audience with targeted and relevant messages. This level of personalization enhances the shopping experience while allowing retailers to retain the benefits of broadcast messages. It is important to note that while these messages have about the same performance metrics as broadcast messages for opens, conversion rates and revenue per email, the unsubscribe rate is typically much lower because of the increased level of personalization.
We’ve seen personal product recommendations account for 70% of the revenue generated from these campaigns.
One to Few
Recurring Automated Messages can be even more targeted by layering on segmentation and customer profiling data. Instead of sending the emails to everyone, try sending them to shoppers that have visited your site but haven’t purchased within the past 30 days. Or to shoppers who are actively engaging in your emails by opening and clicking but haven’t purchased in 60 days. Or to customers who purchased merchandise in specific product categories. You have the data – and by setting up a few filters you can easily create highly personalized messages.
The more targeted your audience, the more relevant your campaigns will be.
One to One
Let’s face it, triggered messages, such as welcome, browse abandonment and post purchase loyalty, while personal to some degree as shoppers only receive them when certain actions are taken, can feel like auto-responses. Especially to shoppers who engage and shop often.
Personalize these messages by including product recommendations based on purchase and browse history. Below are two examples of browse abandonment messages. The one on the left is direct and can effectively re-engage shoppers but it isn’t personal at all. The first time a shopper abandons the site and receives this, that shopper could respond to the request to fill out the survey and explain why they didn’t make a purchase. However, that doesn’t get the shopper back onsite shopping – it just reminds them what they didn’t like in the first place. Also, if the shopper returns to the site a few weeks later to browse again, it would be a mistake to resend this message – especially if the survey was already filled out once.
The example on the right, on the other hand, is personalized to each individual site shopper. The first message highlights the item that was browsed and shows additional merchandise in the same category. The second part of the series takes a different approach by recommending products the shopper will love and includes the browsed merchandise but doesn’t call it out specifically. It looks like it could be a coincidence – but it’s not. If a shopper returns to the site in several weeks to browse again, the browse abandonment message received will look completely different as it will contain new product images.
We’ve seen the addition of personalized product recommendations lead to a nearly 30% increase in revenue when added to a Welcome Series while Browse Abandonment campaigns can account for as much as 10% of total email revenue when done well. If you’re already running a cart abandonment series, adding browse abandonment will greatly impact your bottom line as you can expect 64% of total revenue from your abandonment messages to come from the shopping cart remarketing campaign and 36% to come from browse abandonment.
Best Practices for Recommendations
Ready to add personal product recommendations to your emails? Here are some tips to keep in mind:
- Ensure that recommended products haven’t been previously purchased or recommended to customers within a certain timeframe. This will help customers discover new merchandise.
- Set parameters regarding inventory considerations, such as a certain number of products in stock, so you don’t accidently recommend merchandise that will sell out quickly.
- Keep product recommendations around the same price point as the merchandise that was browsed onsite. And don’t recommend sale merchandise to someone who was browsing full priced items.
- In Recurring Automated Campaigns, recommend full price merchandise and leave your sale items and promotions to your broadcast messages.
- Test to find out the right number of products to recommend in each type of message. Recurring Automated Campaigns can support a large number of images while Browse Abandonment typically do better with six or fewer.