Amanda Cole is the chief marketing officer of Bloomreach, an e-commerce personalization software company. She’s joining Marketing Brew next week at our event, The Marketer’s (Early) Guide to AI.
Ahead of the event, we had Cole tell us a little bit about how she and Bloomreach are using AI, and what her thoughts are on the possibilities of the tech.
Does your company have policies around using AI? If so, what are the main tenets? We do have an internal AI and machine learning policy at Bloomreach, which offers usage guidelines for our teams as they introduce AI into their daily operations. It’s very focused on privacy and safety—like when to use a private instance versus a public instance—and general integration procedures. I think it’s really important for organizations to have these kinds of guidelines in place. We want to encourage our teams to experiment and push the boundaries with AI, but we want them to do so in a way that protects the privacy and integrity of our work.
What AI tools are you currently using? In what capacity are you using them? Our marketing teams are using AI pretty much anywhere possible. We’ve really encouraged testing and experimenting. We’re using it to assist us in everything from content creation, testing, and SEO to account research and outbound message creation. I also love to use AI for data analysis. Anyone who works with me knows that I live in spreadsheets. AI has made it so much easier to gather insights and make data-driven decisions.
What is the best real-life application of AI that you have seen in the marketing world so far? I think some of the best AI applications for marketers are actually the ones that seem kind of mundane—automation, analysis, reporting. As a B2B marketer, one of the best applications I’ve seen came from a team member who created an automation that researched qualified accounts and contacts, then drafted outreach for each person. That’s had such a massive impact. On the B2C side, which I see with our marketing automation customers, some really impactful applications lie in reporting and data analysis. Using AI to analyze campaign performance, for example, you might discover that email and TikTok are performing better than SMS and Instagram for a certain segment. You can use that insight to quickly tweak your campaign or even adjust overall ad spend. You don’t need AI to figure that out, but the speed and agility it offers you are amazing.
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There are plenty of proposed use cases for AI, from customer service applications to improving brand voice to analyzing the customer lifecycle. Which applications are most promising to you? Which ones are least promising? I would say the least promising application is going from content creation to distribution without any human review. AI can be trained on brand voice, but that human element and the perspective of someone who knows the brand and the audience best just cannot be duplicated.
The most promising application to me is bringing conversational elements to online shopping—being able to find the products I actually care about in a conversational way. The reason we go into a store for certain purchases, or why we spend so much time adding to cart but never actually buying, often comes down to a lack of confidence and clarity. We need more guidance and expertise to feel good about making a purchase, and with AI, we’re going to be able to get that in a really authentic, conversational way.
What advice do you have for marketers and brands that are considering using AI but aren’t sure where to start? Start by finding areas where you can get excited about trying new things. Think of a part of your job that you’d love to automate, or an area where you’ve always felt you could do more if only you had more time or resources. That intersection of excitement and defined purpose is the sweet spot. After defining your use cases, start experimenting—as soon as possible and as much as possible. Self-education is super important when it comes to AI, so give your team time and space to investigate AI tools, learn how to prompt the AI well, and ask the right questions. And particularly in the early days of experimentation, I would say to look at the different ways you can test and try AI internally before looking at customer-facing use cases. Once you’ve become more acquainted with AI, for every problem moving forward, think about how AI could help you and your team solve it. This is a great way to naturally integrate AI into your planning and strategy process.
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