Can artificial intelligence (AI) tools do a better job at marketing than humans? Some might say yes. But the reality is that we’re nowhere near AI replacing humans in marketing. Instead, marketers are working alongside AI tools to be more effective in their roles.
A little over 61% of marketers have incorporated AI into their activities, according to Influencer Marketing Hub’s 2023 AI Marketing Benchmark Report. 54% of respondents in the same report said they were optimistic about AI greatly enhancing their efforts.
Keep reading to understand how AI positively impacts data analysis and other decisions marketers must make in their roles.
Collecting Data Across Multiple Touchpoints
For marketers to be truly data-driven in their approach, they must collect data from multiple touchpoints. Doing so gives you a well-rounded view of who your customers are and how they engage with your brand and marketing content.
The process of gathering data is rooted in human intervention. However, expert systems within AI data collection tools are an integral part of the process as well. These systems are programmed by humans to emulate the problem-solving and decision-making abilities of humans.
So, marketers or data specialists tell these systems what to collect and how to behave. You can set up data collection tools with expert systems on each digital touchpoint to collect relevant data. And as that data is collected, it’s typically filtered into a central system that helps organize the information and make it accessible for analysis.
Processing Vast Amounts of Data
Collecting a lot of data from multiple sources is the easy part. The difficulty for most marketers lies in the processing and analysis of this data. The whole point of collecting data is to be able to uncover trends, patterns, and actionable insights that you can use to create more effective marketing strategies.
But trying to do this by sifting through massive datasets manually is nearly impossible because of how time-consuming it is and how complex data can be to understand. With the expert systems mentioned above combined with machine learning capabilities in AI, processing and analyzing vast amounts of data is much more doable.
Machine learning tools, specifically, can learn and adapt without instructions from human programmers. So, while you can tell an AI data analysis tool what you want to learn, you can also let it develop its own insights that inspire a data-driven marketing approach.
Powering Predictive Analytics
Predictive analytics uses historical data, patterns, and trends to make predictions about future events and outcomes. Marketers are especially fond of predictive analytics because they help identify the customer pool to focus on conversions and the marketing campaigns, content, and tactics with the most promising ROI.
You can use predictive analytics to ensure you’re allocating your resources and budget to what will aid future marketing growth. Like the analysis process, machine learning, specifically deep learning, powers predictive analytics.
Extensive training data is the key to igniting accurate deep learning in predictive analytics tools. Machine learning needs something to help it learn to educate itself on its own. So, the more information you put in front of these tools to help them make decisions on their own, the more patterns and trends they’ll be able to identify naturally to inform predictions.
Leveraging Customer Segmentation
Personalized content is critical for marketing success. People are no longer interested in content that’s for everybody. Instead, they want to feel like a brand has curated content specifically for them.
How do marketers achieve this? By leveraging customer segmentation. Organizing customers into specific groups based on shared similarities like demographic information or interactions with a business makes it easier to create content that seems uniquely tailored to each individual.
Machine learning plays a big part in finding the shared characteristics you use to segment your customers. A CRM system with automation features that automatically gather and organize customer information is also critical in customer segmentation.
Improving Content Creation and Management
One of the most important jobs a marketer has is content creation and management. Without an intentional approach to both, the quality and consistency of your marketing content will suffer. Not only that, you need to know what’s working and what isn’t with your content.
Fortunately, AI can help elevate the content you create and increase your team’s efficiency. AI content creation is emerging as a way to quickly produce quality content. These tools can create complete pieces. They can also assist with other aspects of content creation like topic ideation, developing visual content, and editing.
Natural language processing (NLP) is a core part of facilitating AI-generated content, as it focuses on how computers understand human languages and generate content mindful of that understanding. Computer vision is also critical because it applies to how computers see and understand digital images and videos.
More Effective Physical Products
As vital as digital content is in successful marketing, traditional content and physical products are too. Market research is integral to creating products and package designs that compel customers to purchase them.
The AI-powered data collection and analytics tools we touched on above are foundational in effective market research. Through machine learning and expert systems, AI picks up patterns, trends, and characteristics within your target audience that are indicative of their wants and needs.
This market research can help marketers and designers develop physical products and packaging that draw in ideal customers.
AI-Powered Attribution Modeling
Attribution modeling is the process of giving credit to specific touchpoints for inciting particular customer outcomes or conversations.
Another way to look at this is, “a customer came across these different marketing channels in their journey before they made a purchase, and something on each inspired them to keep going in their journey until they converted.”
Marketers should know how a customer’s journey unfolds and which touchpoints they encountered before converting. That way, they can focus their efforts on optimizing the most common touchpoints.
It’s nearly impossible to manually track each customer’s journey and accurately attribute a percentage to each touchpoint that tells how influential it was in inspiring a conversion. But AI can ease the process.
Not only can AI-powered data collection and analytics tools pinpoint precisely which touchpoints a customer encounters, but the machine learning capabilities in these tools can tell you with accuracy how much credit to give each for a conversion.
Conclusion
Marketers shouldn’t discount their gut and personal experiences when creating campaigns and content. Not coupling these with data-driven decision-making and actionable insights is where the mistake lies.
Embrace AI to empower your use of data in your marketing strategy.
