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Most businesses use AI to some degree, whether it’s just inventory and accounting or also customer care and marketing. The rollout is often so subtle (think customer segmentation) that some marketers may not even be aware that the tools they use every day run on the power of AI. Even worse, they don’t always know how to fully leverage it.
The solution here is for marketers to understand how AI can improve the quality, quantity, and efficiency of customer engagement.
AI (artificial intelligence) performs processes typically associated with human problem solving. AI sometimes also employs ML (machine learning, a subset of AI) to improve processes, predict trends, and make decisions based on large amounts of data gathered over time.
In the world of marketing, where it’s critical to stay on top of trends and consumer behaviors, AI can help us do our jobs better, faster. It can identify patterns and relationships humans miss, make recommendations for the ideal content and tactics, or automate rote tasks to free up more time for complex problem solving.
Of course, without human emotional intelligence, creativity, and intuition, AI has its limitations. For instance, a brand might try to automate hyper-targeted or personalized content creation, only for the content to come across as weird, inhuman, or even offensive. But while AI is no replacement for human-made strategies, execution, and relationships, we can use it to optimize existing marketing efforts. So let’s talk about marketing automation.
AI helps streamline and automate processes so that marketing teams of all sizes can improve campaign performance, operational efficiency, and analytics — overall enabling them to engage customers smarter and faster.
As mentioned above, AI tools/systems/platforms can’t manage and execute a marketing operation autonomously — at least, not very well. The most beneficial way to integrate AI in your marketing is to think of it not as a replacement for humans, but as a supplemental tool. AI simply makes it easier for marketers to do their job faster and with more knowledge.
For instance, a marketing team might develop a strategy for automated responses, and use AI to implement those responses. They might also use natural language understanding (NLU) to teach the bot to recognize certain words or phrases, and direct incoming messages to the proper agents accordingly. These simple changes can save social media marketers hours of monotonous work in, for example, monitoring incoming Facebook messages. In this way, AI supplements, but doesn’t replace, the human in charge of customer engagement. Once a human steps in, they can use information gleaned from the AI to provide a more personal customer experience.
Okay, so AI tools are useful for marketing automation. But specifically, how can your brand apply it to improve campaigns and boost results? Here are four important ways.
Social listening enables brands to have a clearer, holistic view of conversations and engagement trends occurring on social media channels like Facebook and Twitter. A platform with robust listening capabilities pulls data from social media platforms to see how consumers are interacting with your business. And beyond your brand, it can alert trends or surface themes within an industry, segment of consumers, competitors, aspirational brands, or however it makes sense for your brand to slice it.
The applications for this are numerous, ranging from simple tasks like tracking social mentions to more intensive research, such as pulling audience details to inform buyer persona mapping or discovering new key terms that can impact campaign planning and targeting parameters. These capabilities extend far beyond what a single person or team could catch by manually searching for information and scraping data across public channels on social media one platform at a time.
Social listening technology catalyzes a marketing team’s agility and relevance both on and off social media. By immediately alerting your team about an influx of complaints or bad reviews, social listening solutions can help marketers nip issues in the bud before they become full-blown crises. Social listening can also provide insights to inform campaign planning and optimization, surfacing insights to help your team craft the right messaging at the right time in the right places.
In today’s digital environment, a brand’s public-facing conversations with consumers are an important part of marketing, and omnichannel conversation analytics helps to highlight both cheerleaders and naysayers. With machine learning, brands can place custom labels on mentions based on certain words or phrases that point to particular sentiments, which channels these mentions are occurring on, and level of engagement (e.g., how often is this person discussing your brand).
This structure makes it easier to prioritize engagement and track trends within engagement. If you’re noticing an influx of complaints about your product on a given day, for instance, this can signify a need to pull in the customer care team. If you see a small but outspoken group of unofficial brand advocates, you can nurture those relationships and even create a community for them to engage with each other.
As your brand grows, it becomes more challenging to maintain consistent messaging and a clear brand voice across social media, customer care, community management, and other teams. Social media management platforms like Khoros help marketers manage multiple channels at once by providing AI-driven features. These include scheduled publishing, tagging content for improved organization, and theme detection in your planned content calendar. Theme detection is a particularly valuable feature, as it matches topics in your content to topics trending on social media, helping you optimize and adapt your campaigns to be more relevant to your audience.
Brands often create pay-per-click ads in search and on social media based on hyper-segmented audiences to test and optimize ad copy, objectives, and calls-to-action. This strategy can yield important insights. Rather than targeting segmented audiences based on characteristics like age, gender, or location (to name only a few), marketers can now serve ads to much larger audiences based on their actions. That’s because AI can track a multitude of tiny actions — content interaction and past user behavior, including clicks, impressions, and CTR — via anonymous pixel data, and use machine learning to help predict whether particular people are quality leads. Since this data collection is anonymous, it maintains users’ privacy while also giving brands more insight into PPC strategy.
Tracking actions, in addition to demographics, enables marketers to push ads to a wider audience on both social media and search. And with a wider audience, testing opportunities also drastically increase, providing marketers with the insights needed to optimize ad copy, creative, landing pages, and social placements (like desktop vs. mobile), rather than simply focusing on individual keyword and ad management (which requires adjusting bids on a daily basis).
The easiest way to begin leveraging AI across multiple marketing efforts is to use a platform with built-in automation. Given the wide variety of AI and ML use cases, executing marketing strategies via a platform like Khoros means your brand has firsthand access to the latest technology, including social listening, social marketing, and brand experiences across every digital touchpoint.
To learn more about how your brand can level up marketing with AI, schedule a demo today to explore the Khoros platform.
AI is no longer new — but businesses still struggle to leverage it effectively. Learn how to use business automation for customer support in a way that improves operational efficiency and customer satisfaction.