Editorial
Guide
Post-Acquisition Guide: How to Bring Two Digital Experiences Together
The Gist
- What's the core argument? AI has removed the constraints that forced marketers to rely on mass campaigns instead of real one-to-one conversations, but most brands are just using AI to automate the old campaign workflow.
- What's the better alternative? Direct, real-time conversational channels — embedded shoppers, AI chat search, social commerce, interactive ads — that respond to what a customer actually wants in the moment.
- What has to change to get there? Brands need real-time data access, orchestration systems, touchpoint infrastructure, customer acquisition methods and trust — plus a willingness to rethink strategy, martech and org structure.
The legendary corner grocer, that ur-marketer so often invoked by personalization advocates, didn’t have marketing campaigns. She simply had a conversation with each customer when they entered the store, making suggestions based on what the customer wanted today, what the customer had done previously and current conditions (what was in stock, the weather, upcoming holidays, etc. — what today we’d call context.)
Campaigns came later, when personal conversations with customers were no longer possible. Constrained largely by the cost of human experts, companies could produce only a limited number of campaign designs, content pieces, audience selections, media plans and response analyses. They had limited knowledge of who would receive their messages and few ways to interact with customers in real time. So they built a limited number of campaigns, starting with messages that addressed the most profitable situations for the largest customer segments.
AI Is Automating Campaigns, Not Replacing Them
Today’s data sources, interaction systems and AI technologies remove most of the constraints that made campaigns necessary. Yet the most common marketing application of AI is to automate tasks in the campaign workflow: agents help or replace humans with ideation, content creation, audience selection, media plans, execution, and response analysis. The cutting edge is to build orchestration agents that combine all these tasks to develop an entire campaign from start to finish.
In other words, companies are largely using AI to support a process that AI makes unnecessary. While that may sound silly, it’s entirely normal: when new technologies are developed, the first application is almost always to use the technology as a substitute for specific functions within otherwise-unchanged existing systems. The classic example is early automobiles, which were horse-drawn carriages where an engine replaced the horse, but there are many others.
What’s important to realize is that substitution is just the first stage in a well-documented process of technology application. It is followed by a period of innovation as experimenters try different systems to find the best way to use the new technology. Ultimately, a dominant approach emerges and becomes the new industry standard.
What Matters Here: Why Are Marketers Using AI to Automate Campaigns Instead of Replacing Them?
Most AI marketing use today substitutes automation into the existing campaign workflow, similar to how early automobiles simply replaced the horse in a horse-drawn carriage.
Related Article: 3 Ways Marketers Move Beyond AI Tools to AI Thinking
The Rise of Conversational Marketing Channels
Those second-stage innovations are happening in marketing technology today. Examples include personal shoppers embedded in websites, apps and online marketplaces; AI search results delivered by chatbots; social commerce systems that integrate discovery with purchasing; and interactive ads on the web and TV.
Any, all, or none of these could turn out to be the best approach, or something entirely different may emerge. But what’s clear is they all rely on direct interactions with customers: that is, conversations where companies listen and respond to customer intentions in real time, helping them to achieve their goals in a way that feels more like service than marketing.
What Matters Here: What New Channels Are Enabling Direct AI-Driven Customer Conversations?
Embedded personal shoppers, AI search chatbots, social commerce and interactive ads all rely on real-time, two-way interaction rather than pre-built campaign messaging.
Campaigns vs. Conversations: The Butcher Example
To clarify the difference between personalized campaigns and individual conversations, let’s return briefly to the ‘hood and visit the local butcher. His equivalent of a personalized campaign would be:
- “Hi Mrs. Jones. I know you like chicken. Do you want some today?”
Compare that to his equivalent of an individual conversation:
- “Hi Mrs. Jones. What can I get you today?” If Mrs. Jones has no particular preference, the butcher might offer the chicken. But if she says, “I want something special. Billy gets home from prison today,” he might offer a steak or pork chops, since he knows they’re Billy’s favorite.
Both approaches draw on personal data. But only one truly treats the customer as an individual.
Conventional marketing campaigns may not vanish entirely. After all, there is still a need to attract new customers to whatever touchpoint will host the new conversation. But, as I’ve written elsewhere, conventional campaigns themselves are changing as publishers take more control over how paid advertising messages are created and delivered.
So it’s entirely possible that many of those agents now taking over campaign-building tasks will themselves soon be unemployed: a delicious irony to anyone who lost their job to them.
What Matters Here: How Does the Butcher Analogy Illustrate the Difference Between Personalized Campaigns and Real Conversations?
A campaign-style greeting assumes a fixed preference, like offering chicken by default, while a real conversation asks an open question and responds to what the customer actually says.
Why Marketers Must Master Conversational Methods
The implication for marketers is clear. While there’s value in automating your current campaign processes, you can’t stop there. The new conversational methods will be increasingly important, especially as customers come to expect the value they provide. Companies that master the new methods will thrive; those who only optimize existing campaigns will struggle.
What Matters Here: Why Can't Marketers Rely Solely on Automating Existing Campaign Processes?
Customers will increasingly expect real-time, conversational value, so brands that only optimize legacy campaigns risk falling behind competitors that build new interaction models.
Building the Infrastructure for AI-Driven Conversations
Part of mastering the new methods is to experiment with them as they come along. But don’t stop there. You should also address the constraints that will hamper AI-based methods.
No matter exactly how conversations are delivered, they will require:
- Strong data foundations (especially real-time access to contextual data)
- Effective orchestration algorithms to support multi-channel operations
- Touchpoint systems that can host the conversations while connecting with central data and orchestration tools
- Ways to attract customers to the touchpoints
- Trust so that customers are receptive to the messages.
These have always been challenges but they become more important in a world where AI has removed the constraints that shaped the campaign-based era.
What Matters Here: What Infrastructure Do Brands Need to Support Real-Time AI Conversations?
Real-time conversations require strong data foundations, orchestration algorithms, touchpoint systems, customer acquisition methods and trust to be effective.
Rethinking Enterprise Strategy, Martech and Organization
The new world may also require rethinking business fundamentals including enterprise strategy, martech architecture and organization. Companies that based their value proposition on low cost or product quality may find they cannot compete unless they also deliver unprecedented levels of intimate customer service.
Companies that based their martech architecture on centralized data storage and orchestration logic may find that touchpoint systems work best when they have direct access to volatile contextual data (e.g., inventory levels, local weather, call center workload) and run local versions of shared orchestration rules. Departments organized around the constraint of scarce human specialists — the copy department, media department, system development department, etc. — may vanish as each marketer is given her own team of specialist AI agents.
New departments may be based on the new constraints (data, orchestration, touchpoint systems, customer acquisition and trust). For example, a “trust” department might combine security, privacy, compliance, customer loyalty and governance functions now scattered across IT, legal and data teams.
From Automation to Intelligence: How Leading Teams Are Rethinking Operations
Register
Content Leaders Collective: When Your Documentation Tools Can't Keep Up
Register
The Next Era of Enterprise Experience: AI, Automation and What It Means for CX Leaders
Register
How to Win the War for Agentic Citations: The AEO Playbook You Need Now
Register
What Matters Here: How Could a Dedicated Trust Department Replace Scattered Compliance and Loyalty Functions?
A trust department could combine security, privacy, compliance, loyalty and governance work that is currently split across IT, legal and data teams.
The Bottom Line: Don't Just Automate the Old Model
Some of these changes may seem overwhelming. The good news is you don’t have to achieve them all at once. The less good news is you do have to achieve them eventually.
There is simply no question that AI is reshaping marketing in fundamental ways. Using it only to improve campaigns is like using a motorcycle to pull an oxcart: you’ll get some value but soon be left behind by systems that take full advantage of the new technology.
Learn how you can join our contributor community.