Ecommerce 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 up “business rules” — if this, then that — or divide shoppers into segments to deliver custom messages or recommendations.
What’s changed is the emergence of generative artificial intelligence, which has dramatically expanded personalization by automating and scaling labor-intensive or impractical tasks.
Hyper-Personalization
Combine generative AI with heaps of real-time data and a delivery mechanism — email, text, chat — and you get hyper-personalization.
A marketing team can now develop an outline detailing the company’s key selling points, brand differentiators, and tone of voice. AI can then produce unique and optimized messages per shopper.
What’s more, the software to hyper-personalize those messages is becoming affordable for even small and mid-sized merchants.
Mix shopper data with generative AI, and marketers may be on the verge of hyper-personalized messages.
3 Problems Solved
Consider the example that sparked this article.
Backstroke, a generative AI email platform, has announced a tool that produces complete ecommerce marketing emails, including layout, images, copy, subject lines, and preheaders.
Although it doesn’t achieve real-time individualization, the new tool addresses three of the top problems associated with hyper-personalization.
Data capture
Hyper-personalization requires lots of data.
Ecommerce marketing teams typically have access to first- or even third-party shopper demographics and behavioral information but lack the technical skills, time, or money to use it in a meaningful way.
Backstroke, for example, has a deep integration with Klaviyo, one of the best email service providers for ecommerce data collection. Blueshift provides similar services and similarly integrates with Shopify and Magento.
Both Backstroke and Blueshift recognize that ecommerce marketers need help gathering the shopper data that hyper-personalization requires. Before it can generate personalized emails, AI must know what’s important to shoppers.
Data comprehension and use
Another common problem with hyper-personalization is understanding and employing the shopper data once collected. Backstroke, Blueshift, and other generative AI companies organize shoppers into groups or develop individual shopper profiles.
Think of it this way. Marketers can manually create many email segments — by gender, repeat customers, lapsed buyers, and more. An industrious lifecycle marketer might manually maintain 20 such segments. Yet AI can generate 10 times as many.
Thus Backstroke, Blueshift, and the like can constantly refine change segments as the AI learns more about shoppers and their buying intent.
Content creation
Finally, the words and images needed for hyper-personalization are a hurdle.
Imagine an ecommerce lifecycle marketer composing, testing, and optimizing email sequences of three messages each for 10 shopper segments. That’s 30 messages to compose. Testing subject lines could require three variations per message — 90 emails all told.
Before long, maintaining and optimizing the messages becomes unmanageable. And it’s one of the hyper-personalization problems generative AI platforms are addressing. Instead of maintaining 90 or 900 message versions, marketers might instead provide a framework for AI, which then produces and optimizes the entire campaign.
AI for SMBs
As of October 2024, sending unique messages at scale to each customer or prospect is not possible. But the rapid growth of generative AI means such hyper-personalization is on the verge. Innovations in machine learning and data processing are steadily enhancing AI’s ability to tailor messages to individuals, promising more precise and effective marketing.
What’s more, the cost of using AI is declining. Likely, SMB ecommerce merchants can soon access personalization tools once accessible only to enterprise sellers.
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