Read time 8 minutes
Agents that let you act while others are still analyzing!
Why it matters:
AI becomes much more useful to marketers when it can move beyond generating ideas and actually work with customer data, campaigns, content, measurement, and the tools teams already use.
By reading this post, marketers will see five practical ways Optimove AI can help them find opportunities, create and optimize campaigns, automate recurring work, and analyze performance, giving them the ability to act more instantly and independently.
Key takeaways:
When discussing AI in marketing, the inevitable question always arises: what can maketers actually do with it today?
For Optimove users, the answer extends well beyond writing copies or summarizing reports.
Optimove AI is the only marketing AI suite that works in three surfaces: inside Optimove's platform with Native AI, outside the platform through the Optimove MCP connected to most used AI tools, and on top of the platform through Optimove Custom Apps.
It can work with customer data, discover opportunities, build campaigns, make personalization decisions, connect CRM activity to other tools, and analyze what is working.
That changes the role of AI from a standalone assistant into something much closer to an operating layer for marketing.
Here are five examples of what it’s like to use Optimove AI in practice.
Optimove AI can help marketers identify valuable customer segments and uncover gaps in existing CRM activity by asking questions in natural language.
Ask "how many customers are both high value and at high risk of churn?" and Optimove AI can use the customer attributes and predictive models available in the Optimove environment to identify that audience and explain how the criteria were interpreted.
The next question could be "Are the existing churn prevention journeys actually reaching these customers?" Optimove AI will examine the relevant campaigns, target groups, conditions, and performance to identify potential gaps.
The analysis may reveal flaws in reactivation campaigns, such as targeting customers who have already left the company instead of focusing on active customers with a high probability of churn.
The same approach can be applied to questions around customer value, lifecycle stages, product preferences, future value, campaign coverage, and other customer attributes available within Optimove.
Marketing teams can use Optimove AI to move from an objective or brief to the actual components needed to execute the campaign.
Once an opportunity has been identified, the marketer can simply ask Optimove AI to act on it.
In the example above, Optimove AI created a target group for high-value customers at elevated risk of churn, and it can prepare the full campaign from conversational prompts.
You can ask Optimove AI to:
From there, Optimove AI will create the target group, campaign structure, offer variants, and email templates.
The marketer still sets the objective, business constraints, KPI, and guardrails, while AI helps compress the execution of work that follows.
Optimove AI can help determine not just which campaign a customer should receive, but which treatment within that campaign is most appropriate for them.
Consider a campaign with three levels of promotional generosity: a 10%, 15%, and 20% deposit bonus.
A conventional A/B test might search for one overall winner and eventually send that treatment to most customers. Optimove's AI Offer Decisioning takes a different approach.
The agent identifies smaller groups of similar customers within the campaign audience based on the data available in Optimove. That can include characteristics such as product preferences, recency, frequency, customer behavior, geography, and other attributes.
It then learns which offer performs best for different customers rather than assuming that one offer should win for everybody.
The allocation can continue adapting over time as customer behavior changes.
That becomes especially important when marketers are trying to balance customer response with business outcomes. They can instruct the agent to optimize for net revenue rather than gross revenue because the goal is to cross-sell customers while protecting margin.
The KPI matters because it defines what the agent is trying to improve.
The same principle extends beyond offer selection. Optimove's decisioning layer is designed to support decisions around audiences, journeys, content, send time, and other parts of the customer experience.
Content Intelligence can add another layer by analyzing how customers respond to different types of messages, imagery, promotional mechanics, and tones of voice. Those signals can then inform future personalization.
The result is AI-orchestrated personalization at scale that goes beyond simply inserting a first name into a message.
The Optimove MCP can connect CRM marketing workflows with the other tools marketers already use, allowing repeatable work to happen across systems instead of staying confined to the CRM platform.
This is where AI can start removing some of the repetitive operational work surrounding campaign execution.
One example is campaign ideation. A recurring task could review upcoming sporting events each week, recommend relevant matches for a mini-game, suggest the appropriate game mechanic, and send those recommendations to the team in Slack.
The marketer can then choose an idea and ask AI to create the game using existing brand guidelines.
The same approach can be applied to monitoring.
A scheduled workflow can identify campaigns that are underperforming against their control groups and surface them to the team for review. Instead of relying on someone to remember to inspect every campaign, AI can help bring the exceptions that require attention to the marketer.
Reporting workflows can also be automated. A monthly task can pull campaign data from Optimove into an Excel tracker, including fields such as campaign information, audience size, uplift, and calculated ROI.
Quality assurance is another example. AI can review email templates, categorize problems as copy, operational, or design issues, and create tasks in a project management platform such as Asana for the appropriate team member.
Teams can also give the AI shared context such as:
That means AI is not simply executing isolated prompts. It can work from a common set of instructions that helps different members of the marketing team operate from the same context.
Optimove AI allows marketers and executives to ask questions about CRM performance directly and turn the answers into analyses or dashboards.
A marketer can ask OptiGenie inside Optimove to analyze the previous week's email campaign performance.
From that relatively simple request, AI can examine campaign results and return information such as overall performance, uplift against control groups, statistically significant results, top-performing campaigns, and underperforming campaigns that may require investigation.
The marketer can then continue the conversation and ask why a particular campaign produced negative uplift or what should be investigated next.
Through the Optimove MCP, marketers can also create custom views of their CRM data.
A marketing leader could build a dashboard showing how the team is using Optimove, including campaign volumes, control-group adoption, use of decisioning agents, target-group sizes, or other operational metrics.
Another dashboard could focus on CRM performance, combining KPI trends, lifecycle movement, churn-risk segments, acquisition cohorts, and the best- and worst-performing campaigns into a single view.
This also changes who can interact with CRM data.
Executives who may not regularly navigate a CRM platform can ask questions, create reports, and access continuously updated dashboards without waiting for someone on the marketing or analytics team to prepare another Monday-morning report.
Optimove AI gives marketing teams more than a new way to generate content. It connects AI with customer data, campaign creation, decisioning, workflows, and measurement so marketers can move from a question to an insight, and from an insight to action, much faster.
That could mean identifying an overlooked high-value audience, creating an entire campaign from a brief, personalizing offers at a more granular level, automating recurring operational work, or analyzing performance without waiting for another report.
The common thread is that marketers remain in the lead. They define the goals, strategy, KPIs, and guardrails, while AI helps execute, analyze, and optimize the work around them.
For more insights into what your marketing team can accomplish with Optimove AI, contact us to Request a Demo.
AI built for marketers.
Predict, create, and optimize — without waiting on data, dev, or design.
Rony Vexelman is Optimove’s SVP of Marketing. Rony leads Optimove’s marketing strategy across regions and industries.
Previously, Rony was Optimove's Director of Product Marketing leading product releases, customer marketing efforts and analyst relations. Rony holds a BA in Business Administration and Sociology from Tel Aviv University and an MBA from UCLA Anderson School of Management.
FAQ
Marketing teams can use Optimove AI to analyze customer data, discover opportunities, create audiences and campaigns, generate content, personalize customer experiences, automate recurring workflows, and analyze CRM performance.
Yes. Marketers can use AI to create campaign components including target groups, campaign drafts, offers, and templates based on a campaign objective and defined requirements. Campaigns created through the MCP remain drafts so marketers can review them before activation.
A traditional A/B test typically looks for the treatment that performs best across a broader audience. AI Offer Decisioning continuously learns which treatment works best for different groups of similar customers, allowing different customers to receive different offers.
Yes. Marketers can ask Optimove AI questions about campaign performance and receive analyses covering metrics such as uplift against control groups, top and underperforming campaigns, and statistically significant results. They can then continue asking follow-up questions to investigate specific results.
