Ilja Gorelik is a cofounder and COO of Mitto.
Within less than a year, generative AI (GenAI) has seen rapid adoption across practically every industry. According to Goldman Sachs, as many as two-thirds of workers in the U.S. and Europe could see a significant share—potentially as much as half—of their responsibilities taken on by GenAI-enabled technologies.
Marketers, especially, have been quick to embrace GenAI, and major MarTech platforms, including Salesforce and Hubspot, have incorporated GenAI into their flagship offerings. To date, we’ve seen the biggest impact of GenAI through its ability to develop and iterate content quickly, which is central to attracting site visits, capturing leads and generating sales.
GenAI can speed the development of content such as blog posts, email messages and other longer-form texts; however, it can also facilitate considerable improvements in short-format content, such as SMS text campaigns. Here are four ways artificial intelligence can help your SMS programs.
1. Tailoring Personalized Messages
Personalization remains a top priority for marketers. Generative AI can help marketers customize messages by analyzing customer behavior and data, predicting their needs and preferences, then tailoring messages to those individual interests. For example, a clothing brand that wants to promote its new collection can analyze each customer’s purchase history, search queries and browsing behavior. Based on this analysis, GenAI can tailor an SMS marketing campaign down to the preferred outfit color in the accompanying image.
GenAI also can be useful in providing product recommendations, such as new apparel designs in similar styles and colors as a consumer’s previous purchases. And it can be used to generate personalized offers based on a customer’s behavior. For example, when a customer abandons a purchase in their cart, GenAI can prompt an SMS message with a special discount code to encourage them to buy.
To be sure, humans still play a vital role in this process. Content needs to be reviewed for accuracy and to ensure proper tone and targeting.
2. Bots For Entry-Level Service
Employing chatbots to handle low-level consumer inquiries and customer service issues has become commonplace for many of today’s marketing teams. GenAI can further automate these responses. For simple queries—asking for product information or availability, for example—GenAI can be trained to identify specific keywords, such as product names and variations of “available,” and quickly respond based on current stock levels and other information.
Similarly, AI can be trained to proactively contact a customer based on preferences and past behavior. For example, as part of a holiday promotion, a department store could alert shoppers who have purchased a specific brand of designer fragrance that a new scent is being introduced with a limited-time offer. Or a pharmacy might proactively send an SMS message to its opt-in notification list in the fall to remind at-risk patients to schedule a flu vaccine.
3. Speed A/B Testing
GenAI also can speed A/B testing for SMS marketing campaigns by automating the process of creating different variations of messages, analyzing the success of each and gathering insights to improve future campaigns.
Say a marketing team is testing two messages to support a seasonal promotion. One message might highlight the percentage discount, while another promotes the limited length of the sale. In the past, humans would manually create and send each variation of the messages. With GenAI-enhanced marketing software, teams can automatically generate and send both versions to a subset of the company’s SMS subscribers, then track and analyze the results of the A/B test—number of clicks, conversions and overall engagement—with each variation of the message. It can also use machine-learning algorithms to uncover patterns and insights from the data, such as which variation performed better with different customer profiles or at different times of the day.
4. Real-Time Network Optimization
AI also can be used to monitor delivery networks in real time, checking availability, message volume and delivery rates across multiple carriers and routes. Based on this data, the AI system can then use its algorithms to predict the optimal routing path for each message, taking into account factors such as message type and destination.
In the event network conditions change—such as a carrier outage or other issue—the AI-enabled system can reroute the messages to alternate carriers or pathways—again, in real time—to ensure the highest delivery rates while minimizing delays or disruptions. Over time, the delivery system can analyze this data to detect patterns and insights such as which carriers or routes perform best for different destinations or message types. This can optimize message quality and delivery rates as well as improve performance and customer satisfaction.
The use of artificial intelligence by marketing teams continues to evolve. Advances like the advent of easy-to-use generative AI uncover new use cases on a nearly daily basis. As marketers become more comfortable with these new platforms—and these platforms continue to become more powerful—algorithms should increasingly be able to take on low-level tasks, freeing human marketers to do the creative and strategic work they are best suited for.
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