AI tools are becoming a common sight in email marketing activities, but their effectiveness depends on how well the technology is integrated into existing systems and changing processes inside the marketing function. The approach that gets meaningful results focuses on governance, data quality, and measurement. Regardless of the details of any implementation of AI in this context, it’s fair to say up front that AI works best when treated as part of the marketing infrastructure rather than a media creation tool.
As is the case in any software integration, the success of AI in email campaigns relies on access to structured, reliable information, often housed in a CRM platform. But regardless of data’s source, initial work comprises of consolidating records, defining deal stages, and ensuring engagement history are all able to be mined from a single, or many systems. Without this, AI models find it difficult to distinguish between, for example, early-stage leads and firm prospects, or understand which messages support progression through sales funnel stages. Data quality, as ever, is a prerequisite for effective content generation.
Recipient consent is central with email, and AI-powered systems work fast: Their very speed and efficiency will expose an organisation to accusations of unsolicited mail unless care is taken. Marketing teams should review opt-ins and examine their existing compliance policies before ramping up the use of AI-generated workflows.
Once data and consent are in place – arguably the majority of the marketing team’s activity in any campaign – AI tools can be embedded in email workflows. Native assistants shipping with marketing platforms can be presented as more effective than disconnected tools, although companies may wish to diversify their software suppliers to avoid vendor lock-in and give themselves more options and testing possibilities (see below).
However, CRM-‘native’ AIs will be able to reference contact data, deal information, and past campaigns without integration. The work of establishing connections between a third party AI (perhaps running locally) is a task usually best performed by an IT specialist, and smaller organisations may not have the necessary staff or resources.
Getting going
Once given access to customer data, an AI can help marketers generate subject lines, body copy, rich media, and calls to action inside the email editor. Modular content – building messages with specific content blocks – helps retain visibility and provides the balance between impersonal, fully-automated messaging and manual content creation. The overriding ethos should be one of assisted content curation with oversight by a human marketer.
Building libraries of introductions, body text, product descriptions, and calls to action means the AI tools are given as much help as possible to assemble emails that are relevant to recipients. It also has the secondary benefit of tracking the effectiveness of individual content elements.
To help retain the human element and prevent breach of data policies (and ensure the brand’s messaging remains on point), approval processes are essential. In practical terms, pure AI-generated content is not necessarily ready for immediate deployment. Companies need to review their workflows and sample outputs, particularly for campaigns involving mention of price. In regulated industries or compliance-sensitive areas, this oversight is business-critical.
The art of the prompt
The quality of AI output depends on how clearly marketers can define the audience, set out a campaign’s objectives, and work out what constraints are necessary. Prompting an AI effectively is an acquired skill, and in the context of email campaigns, prompts should specify recipients’ lifecycle stages, segment membership, and the desired call-to-action, all translated into CRM-specific context (dictating raw field names, for example).
Welcome and activation emails should focus on introducing value and encouraging first actions. Nurture emails build understanding through examples and case studies, and also set the brand’s tone-of-voice and ethos. Sales acceleration messages target contacts who have already showed intent – here, repeated engagement with pricing information can be effective. Renewal and expansion emails focus on reinforcing value delivered and introducing relevant additions. It’s good practice to guide the AI to produce content aligned with a specific goal separately. Broad engagement, generated/aided by AI, or hand-crafted, can be too generic to be effective for sales.
Checks and balances
One phrase that often crops up with AI implementations in many contexts is guardrails. For the purposes of email campaigns, a two-stage QA process is often cited, with the first stage assessing clarity and accuracy of the message, and the second checking compliance, including data usage in terms of local (that’s local-to-the-recipient) regulation. This level of care helps prevent common AI-related issues such as invented statistics, exaggerated claims, inconsistent tone, or anodyne messaging. Artificial intelligence is still a new technology, and marketers, like most people, are still finding their way amid the myriad opportunities AI offers.
It’s important to consider privacy and consent early on. When prompting an AI, input should limit the levels of personalisation to that of given consent. When this is not known or denied, there should be responses and behaviours to fall back on. Erring on the side of caution is advisable.
Relevance or personalisation, it should be noted, does not necessarily need the exhaustive use of every available data point. A brand proving how much it knows about its prospects are more likely to create distrust than delight!
Testing
Like all marketing activities, measurement is central to evaluating AI’s contribution. A test-and-learn approach can be split along lifecycle stage lines, by demographic or desired outcome. AI is not a magic bullet to remove tasks like A/B testing, or response-tracking. With AI in particular – given its speed and efficiency, it’s advisable to change one variable at a time to maintain clarity around cause and effect.
With CRM-native AI, engagement, conversions, and movement along pipelines can be linked to specific content variants or prompts. This allows teams to compare AI-generated content with human-written alternatives and assess whether AI improves outcomes or simply reduces production time. The same considerations are possible with external AIs, of course (albeit coming with a technical overhead), and A/B testing of the language model itself can be highly effective. In short, if you have the resources and the CRM’s own AI is lacking, deploying a different model (one that’s more focused on a sector, or more capable generally) is an option worth exploring.
Finally, text produced by the optimum AI+human process can and should be repurposed for other channels. This save repeating work, and can help ensure a brand’s voice is maintained throughout all external messaging.
Conclusion
AI can help marketers work faster and cover more ground with email campaigns, but unless handled carefully, can multiply errors if not governed with care. Success comes from a sum of the parts: AI systems, prompt engineering, review processes, and the measurement of effectiveness. The sophistication of a model or marketing software rarely determines the outcome. AI adoption for email should be treated as an operational change, and as any change management specialist will inform you, change needs planning, control, and evaluation.
(Image source: “Mailbox” by jparise is licensed under CC BY-SA 2.0. )
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