Learn how scientific email marketing can generate 70% of your company revenue

When it comes to choosing an effective digital marketing tool, email just doesn’t seem as sexy anymore. In a space inundated with viral video campaigns, hashtag wars, and the flash-bang tactics of mobile marketing, connecting with customers through email appears outdated, old-fashioned, and not nearly trendy enough to compete.

Yet, recent studies are showing that email marketing is more relevant than ever, simply because it’s so effective. Email marketing generated 70% of revenue across a range of European companies, according to a study by NimbleCommerce. McKinsey & Company released survey results stating that email is 40 times more effective in reaching and converting customers than Facebook and Twitter combined.

So why does email marketing get such a bad rap? In the current landscape of inundated inboxes and spam folders, communicating via email has become tricky business for the unequipped marketer. The “batch-and-blast” tactic of old, sending your entire customer list the same generic email campaign and essentially hoping for the best, won’t do much besides land you more unsubscribers. Effective email marketing requires a smarter, more personalized and, data-driven approach – what we call scientific email marketing.

Here’s what it entails:

1. Making sense of data: Data aggregation & analysis

Online companies have access to more data than ever before. Behavioral, transactional, demographic, and social data about your consumer base is there for the taking, however ordinary email marketing only skims the surface of this information, if they use it at all.

One common challenge is that the data can be difficult or time-consuming to aggregate, as various types of information are found on different platforms or sources. Then, knowing what to do with the data is what truly makes the difference. Without the correct technology and analysis, the collected data can become white noise. Ordinary email marketing might use the basic, most surface data like open and click-through rates to measure what campaigns might be successful, but overall, campaigns are rolled out with very little input from gathered data.

With scientific email marketing, on the other hand, analysis of the collected data is key. Through statistical modeling and regression analysis, targeted campaigns can be created based on scientific analysis and predictions, geared toward sending the right message to the right customer at the right time.

2. Telling them apart: Customer segmentation

Customer segmentation entails grouping your customers together based on their similarities and interests, so that you can send targeted messages that are more relevant and engaging. Ordinary email marketing oftentimes includes basic segmentation, profiling customers based only on demographic or geographic data to figure out what message will appeal most. Although a vast improvement from batch-and-blast emails, there are still limitations on this manual segmentation style, which boils down to some basic profiling and more than a bit of guesswork based on those few data points.

Scientific email marketing goes a step further by also incorporating and analyzing behavioral and transactional data in order to paint a more complete picture. For example, behavioral metrics like product views and frequency of visits to the website, combined with transactional data that tracks how the customer’s interests and needs evolve, can be analyzed to predict a customer’s probability to purchase.

3. Monitoring your margins: Dynamic incentives

Incentives are often treated like the secret to driving sales, and in the most basic sense, it is. Create an offer deep enough, and customers are pretty much guaranteed to purchase – but at what cost? Incentives eat away at your profit margin, and often unnecessarily: for every customer that won’t buy without a significant incentive, there is another who would have purchased at full price.

Data-driven customer segmentation, as mentioned above, helps identify what categories customers belong in. Analyzing data like browsing and transactional history provide clues as to what motivates the customer to purchase, whether it’s free shipping or percentage-off incentives. Unique customer profiles based on this information can be built to then offer incentives that appeal to that customer.


Personalized incentives keep customers happy and your profit margin higher.

The benefits of using the scientific approach are twofold: first, it makes more business sense. Free shipping is only offered to customers who will appreciate it, and discounts are tiered to only those that require the final push to buy, which translates into less wasted revenue. Second, the incentives are now personalized, and therefore more likely to resonate with the customer. This not only provides the right type of push for that customer to purchase, it also ensures that the messaging stays relevant, and out of the spam folder.

4. Relevant recommendations: Campaign personalization

In order for customers to stay engaged with your brand and continue to purchase, they expect to receive content relevant to them. Sending product recommendations in your campaigns demonstrates you care and want to build a personal relationship with them.

It may seem simple enough to match up and recommend a product to specific customers based on their past purchases, however when it comes to personalizing communications for each customer when you have a large list – time wise it’s an impossible and long manual task.

The scientific approach takes a more precise automated approach by analyzing both the customers past transactional history, noting what they purchased, as well as their online behavioral data, such as what products they viewed. Predictive algorithms and marketing automation dynamically update each message, placing in the relevant product recommendation for each customer.

5. Seizing the moment: Optimized timing

As it turns out, successful email marketing also relies on timing. The best content and personalized offers amount to nothing if the customer never sees it. One of email marketing’s biggest pitfalls is when customers hit the unsubscribe button, oftentimes because the email was poorly timed and therefore deemed unimportant.

Ordinary email marketing involves a lot of gambling on when and how often to send the campaigns to the whole list or certain segments. It’s not that such a method never works – but it is ineffective and can often lead to missed opportunities or loss of customer interest.

Scientific email marketing, on the other hand, analyzes customers’ online behavior, transaction history, and email open times to determine what time they usually open emails, browse your site, and make purchases. By then comparing similar profile groups, the analyzed data can provide insight on what times, what days, and what sending frequency will most encourage the customer to engage.

For more insights and tips on how you can implement a more scientific approach to your email marketing, download our Free Scientific Guide to Email Marketing.


Like what you read? Share the knowledge!

ReSci is a team of marketers and data scientists on a mission to democratize AI. We make powerful recommendations and predictions accessible to brands. Find out how we can help you connect with your customers.