email and analytics

Reading Minds: How to Boost Your Email Conversion Rate

Alan Melton

Email and analytics are cornerstones of marketing, but, as is often the case with strategies considered “obsolete,” laypeople tend to overlook the strength of email marketing. As one of the first significant marketing trends in the digital age, email marketing gave rise to just about every other form of campaigning before the social media takeover.

Today, the numbers for email marketing continue to validate its value as a marketing method that can reach countless qualified leads.

  • 293.6 billion emails are received every day, with 52% of responders saying that email is their primary communication method.
  • Most professionals (86%) will give communications priority to email, and analytics show that the average open rate for emails was 22.5%, with a click-through rate of 3.43%.
  • 89% of marketers prefer this method of lead generation, and 47% of marketers find that this method is the most effective means of marketing to potential and existing customers.
  • Email marketing is so potent that it can deliver up to 4,200% ROI. It’s also 40% superior when it comes to bringing in conversions.
  • And most of all, customers are more loyal through email newsletters, with 37% of respondents saying that it’s effective in building brand loyalty, retention, and purchase influences.

How to Boost Email Conversion Rate Using Predictive Analytics

There are numerous ways to get utility out of email, and analytics data is one of them. From getting an IP lookup through email to gathering your audience’s demographics—there’s a lot to be gleaned from information in your email campaign.

But why is it crucial to be able to gather data from your campaign? It’s not just about analytics: it’s about getting the factors and data for predictive analytics. This makes all the difference when trying to determine interest in products and services. This includes the potential direction of your marketing campaign in its entirety.

Understanding the Power of Predictive Analytics

Marketers know that if they need customer data, they can get that information from the email. And analytics uses that data to create models for customer personas, see buying trends, and discover interests. This is referred to as predictive marketing, a more efficient way of selling to customers and improving conversions. Predictive analytics does all of this and more, utilizing the power of machine learning to make computations beyond human capability.

By strict definition, predictive analytics is the use of statistical algorithms on user data. With machine learning technology, the algorithms can predict future outcomes based on existing historical data. AI and machine learning can show marketers what trends are forthcoming and what customers are likely to be purchasing next. Essentially, it’s user information from email and analytics for predictive marketing but performed on an exponential scale.

The use of AI and machine learning for analytics has steadily been on the rise, but in recent times, more so in marketing. This is because marketers have a lot easier access to customer data in large quantities. Furthermore, what was once inaccessible technology is now more widely available in the form of automation tools. Algorithms have also become far more sophisticated. And finally, cloud computing has made it so that even marketers can access the greater computing power needed to make predictive analytics work.

Currently, there are five major predictive analytics applications used for marketing purposes:

Predictive Modeling for Customer Behavior

This one goes beyond just email and analytics. Predictive modeling is used by large “shopping mall” type of companies such as Amazon and eBay. Often, it’s a way to create predictive models about their customers and determine which customers are more likely to purchase specific items. This can even predict the likelihood that they’ll make a particular purchase.

Qualifying and Prioritizing Leads

Analytics can also enable marketers to “filter out” leads who are most likely to be interested and make a purchase (and therefore improve conversion). Through predictive scoring, identification models, and automated segmentation, marketers can prioritize the most qualified leads. They can approach them, for example, with a personalized email, and analytics proves that these leads will be the most responsive.

Introducing Products and Services

One of the great things about predictive analytics is that it can help companies reduce production costs and determine what customers are looking for. This allows companies to figure out which products and services to introduce to the market. It reduces the risk of losses, wasted resources and improves production chain management.

Timely Content

It’s not just products and services that become timely; the customers’ needs can be addressed at the right time. Customers gravitate towards a positive user experience, such as being alerted through email of a new product that they need. And analytics show that customers are more likely to make a purchase when alerted about it through personalized communication like this.

Successful Marketing Strategies

Through the use of predictive analytics, marketers gain valuable insight that helps them plan their upcoming strategies. Analytics use internal structured data, social media data, and behavior scoring in customer data. All these data are used to figure out if a marketing campaign would be successful in one platform or another or if it’s suitable to a specific demographic of the target audience (such as the predictive models mentioned previously).

There’s a reason that predictive analytics has recently gained steam among marketers: it lets them create more personalized and relevant campaigns and messages for their target audience. It gives them access to information that allows them to reach an even more comprehensive range of demographics. It produces better business decisions and metrics. And most of all, it ensures a better overall customer experience that leads to brand retention and customer loyalty.

And when you combine the power of email and analytics, things only get even better.

email and analytics

Give Your Email Campaigns a Big Boost Through Predictive Analytics

Email isn’t just a tool that marketers can use to gather data that powers predictive analytics. It’s also an instrument to put what predictive analytics has learned into action. Email is 40% more effective when it comes to getting new customers—that’s more than Facebook and Twitter combined. And when used with knowledge gleaned from predictive analytics, marketers can create better, more distinctly personalized, targeted campaigns to make emails even more effective.

Determine your potential customers’ purchase intent.

A great thing about analytics is that it uses AI and machine learning to determine how a customer will make a specific purchase. For example, predictive analytics can identify that one particular customer group is likely to be interested in a new product. Then, marketers can disburse a targeted campaign about that product through email. And analytics will show if these customers will make the purchase and convert with just a gentle nudge.

Narrow down a user’s preferred category.

Another use for predictive analytics is to study your customers’ purchase history. The algorithms take this information into account and look for purchasing trends and habits that indicate if they have a preferred category to shop in.

This sort of analysis is helpful for eCommerce sites that want to send tailored campaigns to their users. A specific user might often shop in the sports department and has a high purchase rate from the category—this opens the opportunity to send an email about a discount or free shipping for items in that section.

Upselling and cross-selling to customers.

For upselling and cross-selling purposes, email and analytics are a godsend. Using the analytics, marketers can now use email to cross-sell to them by recommending certain items that the AI suggests they are most likely to purchase.

For example, a customer makes a swimwear purchase on the website. The analytics may provide insight that they are likely to go on a trip and require other swimming items such as a snorkel, beach shoes, or goggles. It keeps marketers one step ahead of the apparent trends and actively anticipates customers’ needs.

Offering personalized content to users.

Customizing email and analytics aren’t just for selling to customers: it’s also great for improving their experience on your website. This is a method that sites like Netflix and Spotify use to cater to their customers’ interests. Based on what analytics have determined as their interests, a user might see different content on the site than others. Analytics can help you make sure that when your customers come to your website, they see content that they are interested in. It keeps them on the site longer and improves their user experience.

Build customer retention and keep them active.

Using email and analytics for customer retention involves tracking customer behavior as they interact with your website. Predictive analytics tell you how a customer thinks as they browse through the website. And you can use this knowledge for retention by sending an email to remind them if they’ve left anything in their cart. It also keeps them active by making them frequent buyers; if analytics say they have made a specific amount of purchases, you could send a marketing email to them that lets them know they get a loyalty reward such as a promo code.

Create follow-up emails.

To expand on the previous point: email and analytics go hand-in-hand when adapting to customers’ shifting moods. By collecting customer data on their past purchases, site movement, and interests, machine learning can also determine if they are losing interest or are likely to make a comeback with the right incentive.

The correct follow-up email urging them, for example, to check out through a discount can help you close the sale and improve conversion. Furthermore, custom emails with more items that might interest them, as shown by the analysis, may make them want to add more things to the cart, thus increasing sales.

Prompt them to re-engage through something new.

Another effective tactic in email and analytics is to bring back leads that may have been “lost.” If the data shows that the customer has lost interest or hasn’t made a purchase in a long time, predictive analytics can figure out what could bring them back.

Looking into previous browsing and purchase history, the team can introduce a product or a service that will appeal to their interest. It may solve the problem that the other items were not able to address. Not only will this allow you to manage the customer’s needs better, but it also anticipates them and reminds customers that you have something to offer for them. It makes them re-engage and renews their interest.

Final words

Predictive analytics, inherently, is not a new concept. In terms of marketing, people have been trying to use customer information to open up new opportunities since commerce began. Understanding and anticipating those needs gives a company the edge.

Therefore, by using the power of email in compelling users, coupled with the strength of data backed by predictive analytics, businesses can make more intelligent and better decisions. Customers get more of what they want when they want it and when they need it most. Timing is everything, and email and analytics keep you one step ahead of the competition every time.

Has your company been using predictive analytics for marketing campaigns? What have you learned about your customers? Let us know in the comments below.