Artificial Intelligence is an ever-evolving “brain,” which helps humans in many fields of study. Digital marketing, and by extension, e-mail marketing, has seen incredible progress in the past years with AI.
E-mail marketing is a highly competitive industry. An average office employee receives over 100 e-mails a day. With modern informational bombardment, it’s getting harder and harder to get attention. That’s why optimization through Ai is becoming a priority for businesses around the world. Here are some of the top six ways e-mail marketing is changing with Artificial Intelligence:
Subject line and personalization
Advancements in machine-learned language promise better text personalization and cost savings. Recent developments in English for Specific/Special Purposes (ESP) technology gave way to several copywriting applications for e-mail marketing, such as Phrasee or Persado. Similar tools are great for organizing custom e-mail campaigns at substantially lower costs than a traditional human best dissertation service.
It’s important to understand that ESP technologies aren’t entirely foolproof. A small percentage of e-mail campaigns aren’t suited for an exclusive use of copywriting tools. For instance, areas where direct human interaction is needed, such as psychotherapy or counseling, aren’t usually fit for autonomous use of AI tech. A pre-implementation human assessment of the subject matter is almost always required for a successful campaign. Nonetheless, most markets aren’t limited in such a manner.
There are quite a few pieces of MBA essay writing service software on the market. Different applications use different AI coding and designs. Each program may have distinct features and costs. Even so, general ESP characteristics include:
Artificial Intelligence Features
- Chatbot
- eCommerce
- Healthcare
- Sales
- Image Recognition
- Machine Learning
- Multi-Language
Natural Language Processing
- Predictive Analytics
- Process/Workflow Automation
- Rules-Based Automation
- Virtual Personal Assistant (VPA)
- Natural Language Processing Features
- Co-Reference Resolution
- In-Database Text Analytics
- Named Entity Recognition
- Natural Language Generation (NLG)
- Open-Source Integrations
- Parsing
- Part-of-Speech Tagging
- Sentence Segmentation
- Stemming/Lemmatization
- Tokenization
Integration
- Sailthru
- Salesforce Marketing Cloud
Optimal timing and frequency
Experienced digitals marketers know that the timing and frequency of e-mails are fundamental. Proper timing and frequency result in a higher e-mail interaction. While bad timing and frequency can harm a campaign or even nullify it. For instance, miscalculating time zones might entail that the user doesn’t even see the e-mail. Similarly, too many e-mails might bother the user and result in a block, or even worse, a spam report.
In this issue, an AI system is virtually unmatched. Not only will an AI system effortlessly calculate time zones and frequency, but it will determine the most favorable moment for each target. Due to its computing power and data analysis, an AI system could figure out when users are more prone to open and interact with an e-mail.
Even more, AI mechanisms could identify the best and worst moments for a potential sale or Call-To-Action (CTA). Suppose the AI device has legal access to information regarding the target’s financial status. Let’s say he’s been recently promoted at work; this might mean that an e-mail proposing a substantial investment will have a higher success rate. Contrarily, presume the target has been demoted; in this case, an e-mail suggesting a new career training program might prove relevant.
The question of frequency also follows the same logic: an AI system is much more likely to understand the perfect incidence rate than humans. Intelligent pieces of machinery can gather and process big data and pinpoint the ideal ratio for each designated target. Some users might respond to more e-mails, while others may find it disturbing; some users might prefer spaced-out e-mails, while others may opt for shorter intervals.
Retargeting customers
Only a small portion of window-shoppers are converted into buyers the first time they access an e-mail. That’s why it’s essential to keep track of them and reattempt the sale later. Manually doing such a thing would be highly ineffective and even close to impossible.
An AI system not only develops an archive of potential customers but also keeps evidence of interests, shopping cart transfers, wish list movements, and so on. Some basic examples would include shopping patterns, time spent comparing items, purchase history, or relatives/friends with similar hobbies.
Forecast and act on Churn
Of course, advanced AI algorithms could analyze much more subtle details and come up with innovative ways to sell, upsell, cross-sell, and refer new clients. In combination with other data, an AI strategy would take into consideration the best-personalized churn tactic. As time goes by, an individual’s profile is built continuously on his behavior and info. In theory, the AI could predict the target’s intention with increasing accuracy as more data is stored on his behalf.
For instance, after sufficient information is gathered about a potential client, the AI could determine if the person is seriously determined for purchase or just surfing around. It may seem a trivial difference at first sight, but it could mean a sale in the future.
Say a person has stumbled on a set of items he isn’t genuinely interested in. It would be useless or even damaging to hassle him with unwanted e-mails or ads. Alternatively, a well-placed ad of an actual interest might score a valuable sale.
A/B testing of e-mail campaigns
Determining whether an e-mail campaign is better than the other through trial-and-error is a relatively basic digital marketing method. The manager would manually compare the data from two e-mail strategies and decide which one produced better results. For a valid comparison, the campaigns would have to function for some time and go through a substantial number of people. Such a method is becoming more and more obsolete in light of new AI technologies.
An AI-assisted A/B e-mail testing shortens the time and number of users needed for a correct comparison. Modern AI testing is multi-dimensional, comparing many e-mail campaigns instead of traditional A/B testing methods that are typically bi-dimensional. Furthermore, high-computing AI testing would include millions of combinations and permutations every second. Thus, giving the opportunity of a complete mathematical analysis of strategies and outcomes.
New-age AI systems are capable of revolutionizing customer behavior evaluation. Mostly because of the remarkable quantity of gathered statistics. Sure, some questions regarding human behavior aren’t fully understood by machines. On the other hand, high numbers of eventful stats are undoubtedly better appreciated by computers. In any case, AI certainly opens a new door in data analysis and appreciation due to its powerful processing capabilities. Paired with human intuition, AI is set to improve the A/B testing accuracy of most e-mail campaigns in current markets.
Intelligent Segmentation
The article already covered some basics of AI’s capabilities of intelligent segmentation. However, just to put into perspective the complexity of an AI’s dynamic segmentation analysis, here’s the breakdown:
Classic segmentation:
Contemporary segmentation:
- Psychographic
- Behavioristic
Geographic:
Consumers are classified by geographic location.
Demographic:
Consumers are classified by common demographic variables.
a) Age:
b) Sex:
c) Profession:
Agriculture
Industry
Commerce
- Corporation
- Small business
- Importers
- Exporters
Students
- Over 21 years old
- Under 21 years old
Service sector
- Professionals
- Non-professionals
Households
Institutions
4) Income Amount
- Low class: $32,000 or less
- Lower-middle class: $32,000 – $53,000
- Middle class: $53,000 – $106,000
- Upper-middle class: $106,000 – $373,000
e) Family Lifecycle:
- Single
- Married
- No children
- Youngest child under six years old
- Youngest child over six years old
- And so on.
Psychographic:
Consumers are categorized based on their psychological features, i.e., attitude, lifestyle, and personality. There are several methods, but the majority focus on how they relate to new products and services.
- First Buyers
- Early Buyers
- Tardy Buyers
- Late Buyers
- Last Buyers
Behavioristic:
Consumers are segmented on their consumeristic conduct.
- Purchase Reason
- Sought After Benefits
- Consumer Status
Volume:
Consumers are grouped based on how often they use a product or service.
- Heavy Consumers
- Medium Consumers
- Light Consumers
Product-space:
Consumers are classified on how they perceive existing brands in comparison to an ideal brand.
Benefit:
Consumers are classified based on their perceived expectations of a product or service.
a) Status Symbol:
Buyers concerned with the reputation of a label.
b) Hipster:
Buyers who want to be up to date and fashionable.
c) Traditional:
Buyers opting for large and popular brands.
d) Rational:
Buyers that focus on cost, durability, practicality, and other logical considerations.
e) Advocate:
Buyers interested in ethics and self-concepts, i.e., sense of humor, honesty, and so on.
f) Pleasure Seeker:
Buyers focusing on pleasure and sensory gains.
The use of AI within e-mail marketing will almost definitely become the new standard. Enterprises try to stay ahead of the competition by adopting AI integration as soon as possible. Not only does it save time, energy, and money, but AI creates new economic opportunities and novelties.
AUTHOR BIO
John Peterson is a columnist with four years of experience working in the London magazine “Shop&buy.” He also works as a freelance editor for an online best essay writers UK company. In his free time, Jordan is a professional mini-golfer and a semi-professional futsal player.
