It’s no secret that customers value personalized product recommendations, both online as they shop and in email campaigns. And, according to a Harris Poll survey conducted earlier this year, nearly 70% of online shoppers said they appreciate when the recommendations contain merchandise they previously viewed on a retailer’s site.

Personalized product recommendations offer a number of benefits to both shoppers and retailers, including:

• Increased product discovery
• Increased loyalty and happy customers
• Increased average order value
• Increased items per order
• Increased time on site
• Increased revenue

With all of these increases, you might be wondering if there is a downside to automated product recommendations. There are some metrics that will decrease, including:

• Decreased bounce rate
• Decreased IT costs / time to design and manually curate recommendations

Browse and Abandon Email Campaigns

Browse and Abandon emails have come a long way. The first attempt at these campaigns were considered to be creepy by many recipients, as the messages contained the picture of the merchandise that was browsed with language like “come back and have another look” or “thanks for your interest in this merchandise”. Shoppers were a bit put off by these campaigns as they were unexpected somewhat alarming. They felt as though big brother was watching and the experience wasn’t great.

Marketers were quick to adjust their browse and abandon email campaigns and turn them into useful communications for their customers. From a simple “continue shopping” email, like in the example from Jambu, to the “interested in console tables” that include both browsed merchandise and related recommendations, like in the example from Hayneedle, these updated browse and abandon campaigns put the customer first and make a great impression.

For more examples and information, read Donna Fulmer’s blog “What’s working in browse and abandon campaigns”.

Browse and Abandon email campaigns can be similar to shopping cart abandonment remarketing series where shoppers are sent two or three – or more – messages designed to engage customers and take them back to the retailer’s website.  For example, Cultures for Health sends a five message browse and abandon nurture campaign. The first, third and fifth messages recommend products that the recipient’s recently viewed on-site. The second and fourth messages recommend products based on the “purchased this / purchased that” algorithm. This personalized campaign increased email revenue 60%.

Messages 1, 3 and 5

Message 1 - sent 12 hours after abandonment
Subject line  “Get started making healthy cultured foods today!”
Recommended Products: Based on recent browse history

Message 3 - sent 2 days after message two
Subject line “Start culturing today!”
Recommended Products: Based on recent browse history

Message 5 - sent 2 days after message 4
Subject line “Are you still interested in making cultured food?”
Recommended Products: Based on recent browse history

Messages 2 and 4

Message 2 - sent 2 days after message one
Subject line “Check out these products selected just for you!”
Recommended Products: Purchased this / purchased that

Message 4 - sent 2 days after message 3
Subject line “We have some great suggestions just for you!”
Recommended Products: Purchased this / purchased that

The Huge Impact of using Browse Behavior

It is common for less than 50% of a retailer’s list to be made up of customers who purchased at least once. Retailers can’t rely on only using purchase history data to inform product recommendations as they’ll be missing a huge segment of their audience who is still in market. That’s why the ability to incorporate browse history into email messages and online recommendations is so powerful.

According to a Harris Poll report from the January 2015, 80% of shoppers find it useful when retailers send emails containing product recommendations based on their past purchases and 71% like it when the emails contain recommendations based on products they browsed but didn’t purchase online.

Personalized emails have higher click and conversion rates and average order values. When personalized product recommendations are used in shopping cart remarketing campaigns, revenue can increase 19.1% on average. And emails that contain products that the recipient previously browsed can have up to 420% increase in clicks. But those aren’t the only benefits. Personalized emails also build customer loyalty by helping customers discover new merchandise that meets their specific criteria and needs.

How it Works

Listrak’s functionality makes it easy to automate this personalized content. You can build merchandising blocks with individual subscriber browse history, using up to the 25 most recent products browsed. And you can set specific parameters for inventory considerations to ensure you aren’t recommending products that are low in stock or have been discontinued.

You aren’t limited to only including previously browsed merchandise. You can easily set up merchandising blocks using recipes such as “viewed this / purchased that” or similar behaviors. Combining purchase intent (what the customer viewed) with the wisdom of the crowd (what other customers purchased after viewing or purchasing the same product) has shown increases in conversion rates as high as 59%.

The personalized content can be used in both online and email recommendations, ensuring your content is customized and consistent across channels.

Segmenting by Browse Behavior

Listrak is transforming the way retailers deliver highly targeted email messages to their customers by making it easy for retailers to segment their customers based on browse behavior. This means retail marketers can now capitalize on their shoppers’ expressed purchase intent shown by their most recent browsing behavior and their past purchases to deliver the right message that will move them along the path towards their next purchase.

These segments can be combined with email data, such as last open or click date or subscribe date, as well as purchase history data, such as last purchase date, AOV, most purchased brand and last item purchased, and preference center data, such as zip code, gender or birthday, to get even more granular.

Sample Segments

Can be combined to create highly-targeted groups

Online behavior:

• Last online browse date
• Products most recently browsed (up to 25)
• Category, department or brand most recently browsed
• Category, department or brand most frequently browsed

Transactional:

• Recency: first or last purchase date; projected next purchase date
• Frequency: total number of purchases
• Monetary: total amount of spend; AOV
• Categories, departments and brands purchased
• Products purchased
• Gender

Email:

• Subscribe date
• Last open, read or click date
• Last send date
• Email client

Preference Center:

• Gender
• Zip code
• Region / Country
• Birthday
• Memberships
• Brand / category preference
• Cadence (daily, weekly, monthly)