Case Study: eSputnik AI Helps Ecommerce Businesses Increase Email Marketing ROI

Challenge

Many online retailers tend to send out too many marketing emails that are irrelevant to their customers and therefore never opened. This leads to money losses, affects the sender reputation badly, and forces customers to unsubscribe. Who would want that?

Solution

eSputnik has developed the AI-based Frequency Recommendation Engine (FRE). The engine uses the available data on each subscriberâ€™s clicks and opens to predict whether they will open a certain email or not.

Results

FRE helps eÐ¡ommence businesses optimize the frequency of their emails and make sure that only relevant content will reach their customers. This results in ROI growth and increased customer engagement.
Better customer engagement helps improve sender domain reputation, therefore helping more emails to reach the subscriberâ€™s Inbox.

eSputnik Frequency Recommendation Engine in Action

Background

eSputnik received a request from one of their clients to help them target their audience easily and precisely using a variety of parameters. The client, a large online shopping club selling fashion and lifestyle products, was sending up to 65 million emails to their customers each month. As a result, each subscriber received 1-3 emails per day; however, this approach wasnâ€™t effective.

First, the company decided to filter out those subscribers who didnâ€™t open any of their emails in the last 6 months. Unfortunately, that didnâ€™t help much: the recipients were still receiving too many emails and only a few of the emails got opened or clicked in the end. Thatâ€™s when the company decided to turn to eSputnik for help.

How This Works

The eSputnik Data Science team has developed FRE â€” a unique AI-based engine designed to help eSputnikâ€™s clients boost the efficiency of their email campaigns. FRE utilizes machine learning algorithms and runs a number of calculations to suggest the optimal email frequency both for triggered and bulk email campaigns.

In order to achieve that, FRE calculates the probability of emails being opened by analyzing the user data that was previously collected.

The engine takes into account the following parameters:

The time since the last relevant email open
The total number of emails a user had received within a campaign
The total customer lifetime
The types of emails a user opens most often
and many more.

This helps FRE identify those subscribers who are most likely to be interested in a particular message from the company. Furthermore, the engine also filters out those recipients who would ignore a particular email or are likely to unsubscribe after receiving it.

Does it work? Indeed. Actual tests have proven that with the help of FRE eCommerce businesses can boost their email CTR by more than 63%. This is made possible thanks to sending emails only to customers that are likely to open the email. As a result, the company is now able to send about 40% fewer emails without losing email opens or clicks.

Takeaways

You can improve your sender reputation and email return on investment (ROI) while sending fewer emails to your customers. With the help of the eSputnik FRE, your emails will reach only those subscribers that are interested in them.
FRE uses machine learning algorithms to make sure that you Ñan improve your email marketing performance while spending up to 40% less money on email campaigns.

FRE does most of the work. All you need to do is to pick a specific segment for your email campaign and let the engine filter the segment to help you achieve the results you want.