We talk to CRM marketing teams every day, and one of the most common things we hear is that they have a hard time sending relevant messages to their customers that get them closer to their goals. Unfortunately, the vast majority of the messages they send never get seen, opened, or responded to. In response, most marketers are trying to move away from batch & blast campaigns and towards personalized journeys and triggered marketing. Our perspective is that batch & blast campaigns have their flaws but are still a critical part of any marketing strategy. The question isn’t how to move away from them but how to make them work 100x better with AI agents built for marketing.
First, why is batch and blast critical in 2025?
Triggered marketing is great for the 10% of the market currently shopping. If someone recently visited certain pages of your website or abandoned a cart in your e-commerce flow, great—you should absolutely use that to trigger hyper-relevant communications.
But the reality is that such a signal doesn’t exist 90% of the time. If you’re like most brands, you need to proactively stay engaged with customers for when a compelling event to buy or act does happen. For most brands, that means recurring batch emails or batch & blast marketing. The problem is that for all the benefits that traditional batch & blast offers in terms of staying top of mind, it tends to be one of the least personalized and relevant things a brand does. It ends up resembling the dreaded spray & pray marketing.
Why is batch & blast broken, and what do we do about it?
The core problem of batch & blast marketing is that it tends to be a pre-planned and generalized answer to a very personal question: what do I say to this specific, real-life person to advance my business goals?
Today, marketers tend to have a finite, pre-determined allowance (maybe two communications per person, per week) for messages to their customers. They then have to balance the different priorities of the business for how to use that allowance. To manage this, they build highly structured calendars around how often they’ll send communications and then allocate slots in the calendar based on business priority (maybe 25% for repurchases, 20% for loyalty programs, another 20% for new products, etc.).
Sophisticated marketers cluster customers into different audience segments and vary messages, frequency, or priority per segment. Particularly sophisticated teams run experiments to figure out the best day to send certain messages or types of messages. And the most advanced teams have a data science partner that builds models of predictions, which they try to weave into all their promotional activities.
Ultimately, though, no matter the level of skill, there are two major problems:
- Managing and coordinating this is a lot of work, and most teams are never as granular and sophisticated as they want to be.
- Even advanced teams still make a lot of generalizations that reduce the relevancy and effectiveness of their program.
For example, imagine you’re an airline talking to a frequent flyers segment. Business travelers and long-distance relationship couples are not the same. They both might fly a lot, but the lonely boyfriend might happily open 3 emails and 2 text messages per week, while the busy consultant might opt out after the first in-app notification. Rigid calendars and segments can’t handle this nuance and end up treating everyone the same.
But what if your marketing program could be relevant to everyone in it? What if it could still scale but be executed in a personal way, at the individual level, instead of from the top down?
We believe this is the biggest opportunity for AI agents in marketing; we call it AI Decisioning.
How do I put AI agents to work for my batch & blast program?
AI Decisioning enables you to build AI agents to communicate on a 1:1 basis with every customer. It works by automatically running experiments at massive scale across all the different options of timing, frequency, content, offer, channel, and creative. The agents learn how each person should be communicated with and then automatically give them the experience most likely to achieve your business goal.
The process to activate AI Decisioning agents is fairly straightforward. First, you establish goals for your AI agents. Goals are as simple as picking a group of people to target and a tangible metric to pursue. They can be things like “re-activate lapsed shoppers” or “turn online shoppers into in-store shoppers.”
As part of your goals, weight outcomes for AI agents to optimize for.
Then you authorize actions the AI agent is permitted to take (or not take). Actions includes content and product recommendations, offers the agent can use, as well as factors like the maximum message frequencies that are permitted or eligibility of different offers and campaigns.
Set guardrails, such as frequency limits, to guide the AI agent’s actions.
Finally, AI Decisioning uses reinforcement learning to determine the best experiences to deliver each of your customers on a 1:1 basis, continuously experimenting, learning, and getting smarter over time. This means there are far more variations and experiments across a customer base than standard A/B testing could ever achieve. The major benefit is that the system learns faster and can find insights you would otherwise never discover.
Correlations between message topics and user activities lead to cross-targeting opportunities.
Because every batch campaign you send is now much more relevant, AI Decisioning drives greater performance and incremental lift across the goals you define. It has a broader impact, though. Through its experimentation at scale, you get a new source of insights to help you learn and uncover hidden opportunities within your customer base.
WHOOP—a wearable health and fitness company—used AI Decisioning to drive 10% lift in cross-sell conversions with email campaigns. Just as importantly, though, they gained more insights while using AI Decisioning for two months than from a full year of manual experimentation. For example, they discovered new opportunities when they saw that martial artists regularly clicked through on messages related to swimming. Insights like this have enabled them to improve their lifecycle marketing and create new non-digital opportunities.
“With AI Decisioning, we saw a significant lift in our cross-sell campaigns within just 6 weeks. It’s transformed our team’s focus from manual tasks to high-impact strategic work.”
Aoife O'Driscoll
AVP Lifecycle Marketing at WHOOP
The shift to AI-powered batch emails is one of the most significant opportunities in email marketing today. By evolving your batch campaigns with AI-driven personalization, you can transform a standard, often forgettable marketing program into an engine for customer engagement and revenue growth.
The technology is here. The question isn’t whether to enhance your batch emails with AI Decisioning but how quickly you can begin.
