Gone are the days of people finding personalization intrusive and creepy. Today’s consumers appreciate — and even expect — personalization. Of more than 2,000 consumers surveyed by Formation.ai, 79% agreed that the more personalization tactics a brand uses, the more loyal they are to that brand. Today’s technology allows marketers to take it even further with deep personalization.

We all have our own individual preferences and patterns. Deep personalization allows for consumer experiences to acknowledge and benefit from that, ensuring they constantly improve over time as companies learn more about individuals. That happens through a combination of people’s complete history with a brand and any contextual clues from a specific moment.

For example, if I’m in the market for a new winter coat, a brand using deep personalization could predict this based on my website browsing behaviors. Real-time insights available can help brands drive deep personalization, informing marketing messages in the future. In addition to sending browse abandonment messages for winter coats — ideally the one I am predicted to purchase — that item can be featured in regular campaign emails, brought to the front of the mobile app and mobile messaging.

Adjusting to Deep Personalization

Consumers are increasingly aware of the data they provide to brands. While they don’t understand every process their data goes through, they do expect to exchange it for relevance. Keeping pace with consumer demands requires an investment in deep personalization.

Deep personalization is a transformation. Brands need to break down silos between marketing, sales, customer service, merchandising, and other channels to create a single experience. It can happen in bits and pieces, but ultimately, it requires full dedication to data-driven, AI-driven marketing.

Deep personalization requires data and content, and the tools to operationalize both of them within and across channels. Brands need to invest in consolidated consumer profiles, make consumer data easy to access across marketing and sales channels, and upgrade to advanced personalization. This is already happening in many industries with the increase of in-house data warehouses, data lakes, data science teams, and more. Companies are also increasingly seeking solutions that connect to these owned data assets in near real-time so that partner technologies are as much a part of this internal ecosystem as possible.

Where Does Deep Personalization Work Best?

Think about your entire customer journey. Deep personalization is best-suited for all of it as it enhances any experience driven by data.

Acquisition touchpoints can be personalized using data from your retained customer base and pre-acquisition engagements with your brand. You can personalize first conversion engagements based on acquisition source, collaborative algorithms, and more. Retention and loyalty engagements can be personalized using the data you have collected from the lifetime of engagement with the individual consumer.

When deploying deep personalization, there are four big things to consider:

Marketers can divide email metrics into two general categories:

1. Activity metrics: These measure subscriber activity on your emails, including number of emails delivered, opens, clicks to a landing page, unsubscribes, bounces and spam complaints. 

2. Objective metrics: These measure whether your campaign achieved its goal: total number of conversions, total revenue, revenue per email, revenue per subscriber, average order value, number of leads converted to buyers, and many more.

You need both sets of metrics to measure your email program performance accurately, but some are more important than others. That’s what makes them key performance indicators, or KPIs.

Those KPIs are what you get judged and rewarded on, so you need to use everything in your toolbox to improve your numbers. Real-time personalization can help you make measurable progress, and not just because it has a big “Wow!” factor. 

Real-time personalization is a tactic you can use strategically to drive business. It can take your customers over many of the hurdles between your email and “add to cart” on your landing page.

Below, we identified three essential KPIs for a successful retail email program and how real-time personalization can help you increase each metric.

1. KPI: Click rate  

This baseline activity metric shows what percentage of your subscribers clicked on a link in your email. It’s a standard engagement measure and one to track over time to watch for trends.  

How to increase it: Add a real-time poll that asks customers to answer a question relating to your brand, products, their preferences or even some fun, offbeat current event, and then displays the results right away so they can see how they compare to other shoppers. 

Why it works: Who can resist a fun poll question? It’s a low-commitment way to get a click without resorting to clickbait. Plus, the click can take customers to a landing page with product recommendations that match your customer’s interests. 

Bonus: You can use your customers’ answers to guide future targeting or campaign planning. Win-win!

2. KPI: Revenue per email

This metric gauges the amount of revenue earned per email delivered. You can use many means to increase RPE, such as persuading more prospects to start buying, nudging your occasional buyers to shop more often or induce your regular shoppers to spend more each time they buy. 

How to increase it: Add dynamic product recommendations based on items that match customer preferences or previous purchases in current inventory and which refresh every time the subscriber opens the email.

Why it works: Selling more products at full price is one of the best ways to boost RPE. It’s also a huge challenge, given many consumers have become conditioned to think of email as the bargain-basement channel. 

Suggesting available items (no sold-out disappoints to discourage a sale) that are closely personalized to customers’ preferences and behavior can help customers discover things they wouldn’t necessarily have found on the sale racks.

3. KPI: Browse to buy ratio

This objective metric measures the proportion of first-time purchasers. It’s usually reported as percentage of the total number of buyers in the campaign.  

How to increase it: Embed a live video to your promotional email to provide information that can help move your browser closer to a purchase. 

Why it works: People love video. Advances in technology and improved email client compatibility have made live video a more reliable email experience.

What about the open rate?

Many marketers will notice a conspicuous absence from this list of core KPIs. The open rate is most useful when you track it over time to see if it’s going up or down. As a barometer of subscriber engagement, the open rate is pretty good. As a core metric for campaign goals? Not so much, unless the sole objective for your campaign is to get an open. 

Plus, the open rate metric doesn’t really tell you what you want to know. It’s not a reliable metric because image-blocking can undercount opens. Also, the open rate measures only email activity, not revenue-based campaign goals.

The beauty of email marketing is that you can measure just about anything and use what you learn to improve your email program. And, the beauty of real-time personalization is that it can improve almost every KPI you have. 

What are your KPIs, and how could real-time personalization help you meet your goals in 2020?

New decade, new chance to track and smash your email marketing goals for 2020!

Where to begin? With this curated list of 9 email marketing statistics for 2020. Each one is tied to a strategy that can help you achieve just about any business goal. 

What’s your main objective this year? We have a stat for that – that is, we’ve tracked down research to help you form and clarify your plan for developing strategies and tactics that will help you achieve it. 

1. Advanced personalization generates $20 of added revenue for every $1 invested in it.

If this isn’t the statistic of the year for email marketers who need to make a case for tactical investments that show a clear return, we’ll eat our virtual hats. 

The data is from The Relevancy Group’s 2019 report, The Value of Personalization Optimization for Retailers, and is one of many eye-opening findings of the research.

If your list of 2020 marketing goals includes increasing revenue (and whose doesn’t?), get your own copy of the report or read our analysis of the findings on advanced personalization.

2. On average, marketers lose a full 34% of newly acquired subscribers within the first month.

It’s an old marketing maxim that your newest customers are your most engaged, but this stat shows that they grow cold quickly if you don’t nurture them. Not everybody is ready to buy all the time. But, you need to get them clicking quickly to keep their interest and show you offer them something valuable in exchange for their email addresses.

Try adding a live polling function to your welcome email and business-as-usual emails to get those pre-purchase clicks. This gives customers people a reason to engage even if they aren’t in the market to buy. And if you phrase your polling questions right, you can also collect important preference or attitude data that you can use to segment and target your customers even before you get behavior data.

3. Birthday emails can lift conversion rates by 60% over non-birthday emails with the same offer. 

Birthday messages aren’t just nice greetings to send your customers. They have a revenue payoff, too. If you already send birthday emails, add a dynamic feature to pique your customers’ attention, such as a scratch-off image revealing their special birthday deal.

4. BOPIS is big: 72% of Generation Z shoppers used Buy Online Pick Up In Store services within the last 30 days.

Who knew Gen Z could be the ones to save brick-and-mortar retail? While their parents and older siblings shop from the couch, consumers born in 1995/1996 like to visit the store – but not to poke around until they find what they want. 

This Package Concierge study found 58% of younger shoppers have used BOPIS at least once. The reasons vary, but many revolve around same-day gratification. They find what they want online, but they don’t want to wait or pay to have it shipped. 

Help your BOPIS shoppers of all ages find you by including personalized location information in each email, with a live map of the nearest location or the street address.

5. Consumers are interested in technologies that show whether a product is in stock (55%), help them compare prices or read reviews (49%), make it easier to find a product or its location (47%), or try an item before buying it (38%). 

All of those action capitalize on consumers’ interests in taking time and friction out of the buying process. 

Your email messages can take even more steps out of the process by adding dynamic content that uses real-time data to alert customers about low or sold-out inventory, to add your social feeds from review sources like BazaarVoice or show a map or store directions to bring them to your doorstep.

6. Emails using dynamic content based on real-time data generated an estimated $7 million of incremental revenue, or 17.4% more than purchase-based personalization.

Here’s another statistic from The Relevancy Group’s 2019 report, The Value of Personalization Optimization for Retailers, showing how personalization at the highest level beat out simple name personalization and even purchase-based personalization for revenue generation. 

Basic first-name personalization has pretty much lost its edge as a customer engagement device, and purchase-based data can be too limiting. You don’t need to scrap them because they do serve a purpose in showing your customers you know who they are and what their history is with your brand.

Rather, add a top layer of data-driven advanced personalization because it’s the most effective of the three methods and generates measurable revenue.

7. Adding videos to your email can boost open rates by 6%, increase click rates by 300% and reduce unsubscribes up 26%.

People love videos, as the stats show. Adding live video to email messages was thought to be too complex given the limitations imposed by different email clients and platforms, but a module like Liveclicker’s LiveVideo element can bypass those concerns.  

8. Both active and inactive subscribers order more often and spend more than nonsubscribers.

This study of email activity by the millions of subscribers who get emails from its platform because it showed that inactive subscribers aren’t always gone for good. 

The research found subscribers order at least 25% more often than non-subscribers, spend at least 6% more and are much more likely to shop again. Inactive subscribers are 26% more likely to make a follow-up purchase than non-subscribers, and their monetary value to the mailing list is about 32% of an active subscriber.

The findings support efforts to tune tuning up our acquisition efforts to attract new subscribers and to be judicious about handling inactives. Instead of just lopping them off your list, look for patterns that show website browsing and purchases even among subscribers whose records few or no email opens.

9. Even though 86% of consumers are concerned about data privacy, 72% of them will engage only with personalized marketing messages.

We’ve been hearing this mixed message for years, so it shouldn’t surprise you. But you can put this set of stats to work in your email messages in 2020 to build trust and serve up meaningful, not creepy, personalization.

Add links to a plain-language version of your privacy and data-management policies in every email. It’s not just a nice thing to do; it will help you comply with strict privacy and email requirements in laws such as the newly enacted California Consumer Privacy Act, which affects any marketer holding data, including email addresses, on California residents.

As you build trust, you can also add in the relevant personalization that dynamic content based on real-time data instead of potentially inappropriate or irrelevant numbers. Everybody wins in this scenario.

July has come and gone, August is here and before you know it September will be over, and Holiday commercials and emails will start dropping left and right. 

This is a busy time of the year for email marketers as they plan their holiday campaigns and end-of-year strategy. Spending countless hours preparing layouts, messaging, subject lines, etc. it’s easy for a very important part of the process to get lost in the chaos – email list hygiene.

Get all the inspiration you need to sleigh your holiday revenue goals. Download our lookbook: 9 Solutions (With Real-Life Examples!) to Retailers’ Biggest Holiday Email Headaches!

We’d like to focus on steps that are sometimes overlooked when planning holiday marketing campaigns. Take these measures to make sure your messages reach the inbox during the busiest time of the year. 

1. Start Growing Your List Early

If you’ve waited to grow your email lists for the holiday season, you’re already behind. If your goal is to drive the maximum sales at the end of the year, you should have started to grow your data months ago; you have some ground to catch up on! But avoid the temptation to cheat the system and buy email lists; nothing good comes out of a bought list. There could be hidden threats such as honeypots, spam traps, bots, screamers and much more in that data you just purchased.  (Read: 7 Reasons Why You Should Never Buy An Email List

2. Clean Your Data 

It’s imperative that you run your email data through email hygiene processes. Why?  Because email hygiene detects more harmful threats in your data, unlike verification. According to a report from The Relevancy Group, “ Real-time email verification is a necessary tactic to ensure that email addresses are valid, but it is an incomplete defense. To improve deliverability and suppress against multiple threats, marketers need to adopt verification and email hygiene.” 

Email verification is a great solution to detect bounces but it does little to guard against those threats such as spam traps, honeypots, moles, bots and much more that can tarnish your sender reputation and deliverability right as the holiday season ramps up.

3. Enrich Your Data

Today’s consumers expect personalized, relevant experiences. And to provide those, you need relevant data – usually lots of it. 

Research indicates 3% or more  of customer data becomes obsolete due to changing conditions. Customers move, get married, change names, and adapt to new lifestyles. 

Marketers are always in need of updated and accurate customer profile information. They can either obtain this information by searching for supplementary data or purchasing new data from other parties. The reliability of these options varies greatly, and it’s hard to guarantee that the information you have purchased is valid. If you need to enrich your data to fill in the missing or outdated consumer information, consider data appending

Data Appending is simply filling in the missing pieces of your data, completing the puzzle you’ve already started. As long as you have some sort of information such as an email, phone number, or name, there can be more added. This reveals the complete customer picture and essentially saves data that might otherwise have been lost or set aside.  

More About Data Appends and How They Affect Email Success

4.  Target Audience & List Segmentation 

Without segmentation, your engagement rates tend to suffer. Open rates usually decline, while unsubscribes tick up. It’s hard to keep emails relevant and engaging if you’re sending the same thing to everyone.

Segmenting your list based on demographics, behavioral history, and transactional history helps maximize with your content relevancy. According to Mailchimp, “segmenting your lists had open rates that were 14.37% higher, clicks that were 64.78% higher, and unsubscribe rates were 8.89% lower than people who don’t segment their lists”. 

List segmentation can drastically improve your email marketing campaigns. Your audience is not all the same, and you know that. So why target them all the same, if they’re completely different? 

If you want your email campaigns to be successful this holiday season, one of your first priorities should be email list hygiene. Clean, reliable data truly is one of the most important components of email marketing!

About the Author 

Jenna Devinney has been the Marketing Specialist at Webbula for 2 years. Her key responsibilities are social media, and content marketing, and has written dozens of articles in the data solutions space. Check out Webbula’s Intelligence report page to learn more about what Webbula has to offer. 

About Webbula 

Webbula is the undisputed industry leader in data quality technology. Our emailHygiene and Data Appending services mitigate delivery threats, enhance data lists for email campaigns and create actionable audiences for online ad serving. Webbula has a proven track record of helping our customers navigate hazardous data quality obstacles and increase their return on investment.

Our passion remains what it has been since day one, to provide the confidence and reliability that our customers have come to depend on. Webbula pioneered and perfected email hygiene, the ability to detect Spam Traps, and much more – all in pursuit of truth in data. We look forward to another decade of leading the way and of helping all of our customers succeed.

Machine learning is one of the most talked about “new” strategies and technologies in the industry today, but we often find that it is highly misunderstood — and many times, that’s our fault on the vendor side.

It’s our goal to clear up machine learning and decipher what it means for marketers and the amazing benefits it can provide on even a day-to-day basis.

Machine learning is simply a way to apply an algorithm on top of data to process and make decisions faster than what a single human can possibly accomplish.

Besides the misunderstanding of what it actually is, what keeps marketers from leveraging machine learning in email marketing? Here’s a few of the answers we hear across the country:

  1. Don’t have the right data to utilize.
  2. Data is “bad,” or in other words, it’s outdated, unclear, or flawed.
  3. No budget for machine learning technology to apply to tech stack.

If you’re experiencing any of these problems as well, you’re not alone!

One of the leading issues we hear marketers say about leveraging machine learning for email is that they have a data problem. No matter the size of the organization, small, medium or large, there’s a “data problem” in all of our businesses! We like to dig a little deeper and ask what the problem is. And we consistently hear there’s “bad” data, too much data, or not enough data to get started.  

Everyone — even Amazon — struggles with their data:

She’s a mom blogger, and a mother of 2 young boys. Apparently, she bought a toilet seat. And after completing her purchase, she started to get messages about buying more toilet seats or toilet seats that she may like! She bought a toilet seat out of necessity, not as a collector. So getting these types of messages from Amazon is not only annoying, but also just plain stupid.  

Others commented about their own woes with Amazon, getting messages about more burial urns, coffins, or even punch-down tools.

But it doesn’t have to be this way.

You can utilize machine learning for email without causing even more data woes, costing your budget an arm and a leg, or having to deal with infinitely complex tech stack integrations.

So where do you start?

It helps to think with the end in mind.

Machine learning makes the complex, simple. The above illustration shows all the factors and components that go into applying machine learning to marketing.

But what’s important about this illustration is not all the factors and components, rather it’s the fact that machine learning does all the work for you. Machine learning helps ingest, process, and make sense of all your data. Whereas before you would spend time pulling and organizing raw data and then uploading to your marketing technology, machine learning completely eliminates that step for you, so you can focus on what’s most important.

This is the model we use at Cordial to help illustrate all the work that goes on behind the scenes that translate into actionable tactics you can put into place to become an email all-star.

Knowing what’s possible, here’s three ways you can use machine learning technology in real-world, applicable ways for your business.

1. Using machine learning for testing & optimization

Testing and optimization is one of those topics that everyone talks about, but no one actually does.

Why don’t marketers test and optimize messages more frequently? It can be time-consuming, inconclusive, and or near impossible with the technology you use today.

Testing messages doesn’t usually produce enough revenue results to justify the amount of effort that is put into creating the new content and campaigns. It’s all about results at the end of the day for each one of us. So, we thought about this process that marketers go through when initially rolling out machine learning to optimize messages to yield statistical significance — or in other words — results!

At Cordial, we take testing and optimization seriously. Which is why we worked to make it as easy as possible to leverage machine learning across subject lines, content, hero images, and promotional offers to continuously optimize towards the desired goal in your campaigns. The machine learning algorithm utilizes a method of testing called Thompson Sampling, which is based on an algorithm called Multi-Armed Bandit Theory.

But you don’t need to remember any of that — all you need to know is that the algorithm works to automatically optimize a message based on the best performing combination of factors.

2. Using machine learning for triggers & automations

Using machine learning for testing and optimization also directly applies to triggered and automated messages. You may have triggered automations like your welcome series, abandoned cart, or upsell series that haven’t been touched in months.

Because of the automated nature of these kind of messages, they make for the perfect message to use the testing and optimization machine learning technology above. Since these messages constantly go out, it’s paramount that these messages are optimized to perform as best as they can.

Not optimizing triggered automations is almost like leaving money on the table. Thanks to the application of machine learning to programmatically test and find the best performing variation of a message, you can easily test and optimize your triggered email automations to ensure they’re performing as best as they possibly can.

3. Using machine learning for product recommendations

Finally, product recommendations are one of the best ways to utilize machine learning. Product recommendations are a magical feature that can be a game-changer for your personalization efforts.

Cordial Recommendations uses machine-learning technology and predictive modeling to deliver product suggestions based on real-time customer interactions and behavior. Recommendations improve and adapt over time to increase opens, clicks, and purchases as more is learned about your customers.

Related: Machine learning is just one of several game-changing opportunities for email marketers. Read more on Four Steps To Becoming An Email All-Star

An Example: REVOLVE

REVOLVE migrated to Cordial after using a legacy ESP for years and uses the platform to send promotional and triggered messages to their customer base. Because of the gaps in their legacy tech they were unable to provide their customers with personalized or triggered messages, severely limiting their marketing efforts. It simply was impossible to scale personalization using the segmentation model they followed previously

Cordial enabled REVOLVE to completely rethink how they communicate with their customer base, relying heavily on triggered communications that engage customers in the moment they are primed for action. They now use programmatic templates to personalize millions of messages each day.

Since signing with Cordial, Revolve has pushed live 15 different personalized triggered campaigns which now account for over 20% of their overall revenue. These campaigns have a 2X engagement rate over traditional promotional campaigns and have resulted in massive gains in efficiency for the team. Instead of building promotional campaigns one by one, they can rely on Cordial to automate and deliver messages for them, freeing up time for the team to be more strategic and effective.

They also leverage Cordial Experiments to optimize all the triggered automations they implemented and can now be confident that they’re generating as much revenue as possible. It all wouldn’t have been possible without the use of machine learning, which has streamlined their efforts to optimize and personalize messages in a repeatable, scalable way.

In Summary

To become an email all-star, it’s time to move past the old way of doing things and adopt the new, namely, machine learning technology. Think differently about testing and optimization using machine learning to finally move beyond A/B testing. Leverage your data to learn what your customers want to buy next. Machine learning doesn’t have to be scary, confusing, or out-of-reach.

Let the machines do the work!

Allison serves as Cordial’s founding member of the partnerships channel and growth ecosystem. She entered into the world of email in 2013, after spending time being a creative designer for print media, hopping into technology sales, and being an entrepreneur of a local fitness club. Allison works closely with the leading technology in various industries, giving her an unparalleled perspective in the space. 

Cordial is a next-gen messaging platform that helps marketers leverage their data to create timely, personalized experiences for their customers across channels. Instead of relying on multiple technologies and messaging providers, Cordial enables brands to simplify their processes by consolidating promotional, triggered, transactional, and lifecycle messaging to create unified brand experiences that make the customer the center of every interaction.

You can find more content like this on the Cordial blog.