E-commerce marketers have long dreamed of delivering the perfect marketing message to a specific shopper at the ideal time to close a sale.
Even 20 years ago, marketers could set “business rules” (if this, then this) and segment shoppers to deliver custom messages and recommendations.
What changed was the advent of generative artificial intelligence, which dramatically expanded personalization through automation and scaling. labor intensive Or an unrealistic task.
hyper personalization
Combining generative AI with large amounts of real-time data and delivery mechanisms (email, text, chat) enables hyper-personalization.
Marketing teams can now create briefs that detail the company’s key selling points, brand differentiators, tone of voice, and more. AI will be able to generate its own optimized messages per shopper.
Additionally, the software to hyper-personalize these messages is becoming affordable for even small and medium-sized merchants.
Combining shopper data with generative AI could potentially allow marketers to create highly personalized messages.
3 Issue resolved
Consider the example that inspired this article.
backstrokea generative AI email platform, has announced a tool to create complete eCommerce marketing emails, including layout, images, copy, subject line, and preheader.
Although it is not so achieve real time The new tool addresses three issues: The biggest problem associated with hyper-personalization.
data capture
Hyper-personalization requires large amounts of data.
Ecommerce marketing teams typically have access to first-party or third-party shopper demographic and behavioral information, but lack the technical skills, time, or funding to use it in a meaningful way. Masu.
For example, Backthrough is tightly integrated with Klaviyo, one of the best email service providers. For e-commerce data collection. blueshift offers similar services and integrates with Shopify and Magento as well.
Both Backstorm and Blueshift recognize that e-commerce marketers need help gathering the shopper data needed for hyper-personalization. Before generating personalized emails, AI needs to know what’s important to shoppers.
Understanding and using data
Another common problem with hyper-personalization is understanding and leveraging collected shopper data. Backstroke, Blueshift, and other generative AI companies organize shoppers into groups or create profiles for individual shoppers.
Think of it this way. Marketers can manually create many files email segments — Segmented by gender, repeat customers, lapsed buyers, and more. A diligent lifecycle marketer might manually manage 20 such segments. But AI can produce 10 times that amount.
So things like Backstroke and Blueshift can continually refine change segments as the AI learns more about shoppers and their purchase intent.
Content production
Finally, the words and images needed for hyper-personalization are a hurdle.
Imagine an e-commerce lifecycle marketer creating, testing, and optimizing an email sequence of three messages for each of 10 shopper segments. You need to create 30 messages. To test your subject line, you might need three variations for each message. That’s a total of 90 emails.
Over time, message maintenance and optimization becomes unmanageable. And this is one of the hyper-personalization problems that generative AI platforms are addressing. Instead of maintaining 90 or 900 message versions, marketers may provide a framework for AI to create and optimize the entire campaign.
AI for small businesses
As of October 2024, you can’t send personalized messages to each customer or prospect at scale. But the rapid growth of generative AI means that such hyper-personalization is just around the corner. Innovations in machine learning and data processing are steadily increasing AI’s ability to tailor messages to individuals, promising more accurate and effective marketing.
Additionally, the cost of using AI is decreasing. Probably a small to medium sized e-commerce seller Instant access Personalization tools that were once only accessible to corporate sellers.
