{"id":141595,"date":"2025-09-08T12:02:07","date_gmt":"2025-09-08T12:02:07","guid":{"rendered":"https:\/\/www.newsbeep.com\/us\/141595\/"},"modified":"2025-09-08T12:02:07","modified_gmt":"2025-09-08T12:02:07","slug":"can-ai-send-the-perfect-ecommerce-promo","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/us\/141595\/","title":{"rendered":"Can AI Send the Perfect Ecommerce Promo?"},"content":{"rendered":"

The combination of first-party behavioral data and artificial intelligence may transform ecommerce outbound marketing.<\/p>\n

Called \u201cAI individualization,\u201d the goal is to create a personalized shopping experience tailored to an individual\u2019s preferences, behaviors, and buying history.<\/p>\n

The Perfect Send<\/p>\n

\u201cInternally, we strive for the \u2018perfect send,\u2019 when 100 percent of the people who get the message click or engage, and no one opts out,\u201d said Alex Campbell, the chief innovation officer and co-founder at Vibes, a mobile marketing platform.<\/p>\n

Campbell was discussing the potential for AI individualization (AI-I), Rich Communication Services<\/a>, and mobile marketing in the retail sector when he described this 100% engagement, 0% opt-out scenario.<\/p>\n

Ecommerce marketers might modify that definition, but the perfect send is when messaging meets a shopper\u2019s need at the moment.<\/p>\n

Shopper Expectations
\n\"Image<\/p>\n

Shoppers who opt-in to email, text, or push messaging want relevant offers.<\/p>\n

\u201cWe do a customer survey every year\u2026and we always ask a question like, \u2018What would make you opt out?\u2019 Two years ago was the first time we heard, \u2018You are not sending me enough messages,’\u201d said Campbell.<\/p>\n

The folks surveyed had signed up to receive mobile marketing. They wanted to receive relevant and timely product notifications and discount offers.<\/p>\n

AI-I can help.<\/p>\n

First Party Data<\/p>\n

Ecommerce AI-I is possible because online stores can collect first-party data \u2014 purchase history, browsing behavior, engagement data \u2014 without relying on third-party cookies<\/a> or providers.<\/p>\n

Humans cannot sort through all the data. Even rules and automations would struggle to reveal individual preferences in real-time.<\/p>\n

An AI layer, however, can apply even during the deployment of the messages.<\/p>\n

Not Merely Segments<\/p>\n

Ecommerce marketers typically segment shoppers<\/a> around common behaviors. A wine merchant, for example, might have a segment for \u201cvalue wine shoppers\u201d or \u201cpremium wine collectors.\u201d<\/p>\n

AI-I creates segments of one, such as a customer who buys red wine under $20, prefers Rh\u00f4ne varietals, responds to Friday sends, and often redeems mobile offers.<\/p>\n

Composing the perfect send is much easier with a single segment.<\/p>\n

Say the wine merchant implements an AI-I tool. This tool can send shoppers Rich Communication Services (RCS) messages and can access both the product catalog and shopper behavioral data.<\/p>\n

Testing can lead to the perfect send.<\/p>\n

The AI broadcasts an RCS message containing a product carousel. (RCS has app-like features.) The message has two offers: (i) an Argentine Malbec for $18, as recommended by AI based on the data, and (ii) a Portuguese red blend for $17, meant to introduce new wines to this shopper.<\/p>\n

The shopper swipes, taps, visits the site, clicks a \u201cMalbecs Under $20\u201d filter, and ultimately makes a purchase. The AI adds the data from these touchpoints to the customer profile, recording the purchase under $20 or adding a note to test copy around value.<\/p>\n

Each new message is an experiment, bringing the AI-I closer to discerning what a shopper wants and when.<\/p>\n

That process is nothing new. Data scientists might describe it as \u201cindividualized multivariate tests\u201d or a \u201ccontextual bandit.\u201d It is an established way to identify individual preferences.<\/p>\n

What is different is AI\u2019s speed and scale.<\/p>\n

Process Details<\/p>\n

For the hypothetical wine shop<\/a>, harnessing AI-I would require initial setup for more granular data collection, data normalization, and integration.<\/p>\n

Once it\u2019s up and running, however, the AI-I tool would likely follow a simple workflow for each new customer.<\/p>\n

Base segmentation. Start with broad wine categories based on the initial purchase, such as red or white, sparkling or still, and high-end or value.<\/p>\n

Early engagement. Begin sending messages and track, for example, whether the shopper clicks a Bordeaux at $40, ignores ros\u00e9, but buys a Malbec at $15.<\/p>\n

Individual testing. Generate shopper-specific messages. Each one is an experiment. Offer a Bordeaux at $35 or a Syrah at $18. Continue tracking engagement and behavior. Repeat.<\/p>\n

Refine the profile. Over time, the AI-I system identifies probabilities, such as \u201cthe customer is 70% likely to purchase when the price is under $20 and the varietal is bold red.\u201d<\/p>\n

Balance with discovery. Introduce a \u201cwild card\u201d wine every few sends \u2014 perhaps a Spanish white or sparkling wine \u2014 to extend the system\u2019s knowledge of the customer and prevent marketing fatigue.<\/p>\n

Feedback. All clicks, purchases, and opt-outs feed the AI model, both for the individual and to perfect the overall system.<\/p>\n

With each iteration, the AI-I gets closer to the perfect send.<\/p>\n","protected":false},"excerpt":{"rendered":"The combination of first-party behavioral data and artificial intelligence may transform ecommerce outbound marketing. Called \u201cAI individualization,\u201d the…\n","protected":false},"author":2,"featured_media":141596,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[45],"tags":[182,181,507,74],"class_list":{"0":"post-141595","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-artificial-intelligence","8":"tag-ai","9":"tag-artificial-intelligence","10":"tag-artificialintelligence","11":"tag-technology"},"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/141595","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/comments?post=141595"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/141595\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media\/141596"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media?parent=141595"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/categories?post=141595"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/tags?post=141595"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}