The perfect scalable marketing strategy requires a balance of human expertise and machine learning. You can’t overstrain your team with manual analysis and conclusions and expect consistent gains. But relying too heavily on machine-generated content will negate a necessary human element in your landing pages. This balance can be tricky, but machine learning is a vital element of creating hyper-relevant customer journeys at scale.
In an age where some industries fear the idea of machines replacing human roles, MarTech has found a unique balance. Machine learning and artificial intelligence have allowed marketers to focus on bigger picture goals without stressing the minute details it takes to build out an ad campaign.
Need a headline? Run it through a machine and get 20 variations instantly. Want to test page layouts? Automate new content placements with the click of a button. Wondering what triggers a purchase with a new audience? Set your hypothesis in place and let the AI get to testing.
With such proven benefits, machine learning is a marketer’s competitive must-have. But could it be so simple? Just put AI at the front of your marketing stack and watch as results flow in? Even with current advancements in technology, we’re not quite at Tesla levels of self-run ad campaigns. And just like a car that could drive itself, would you trust it to safely get you to your destination? Even with a self-driving vehicle, you still want a capable driver behind the wheel.
The misconception about relying on machine learning is that you can take the human element out of the equation. This sentiment can be dangerous to stand by in the world of advertising—especially personalized advertising. The truth is, to derive something genuinely meaningful from AI-driven analysis and optimization, there needs to be a specialist at the helm.
That’s what we’re exploring today. Join us as we define the perfect balance of success with machine-driven insights and human interference.
Starting with data
What is machine learning? Does it differ from artificial intelligence?
While machine learning belongs to the same technical category as AI, they are different forms of technology.
Machine learning is a system of analysis. Its purpose is to learn from data models, identify patterns, and make calculated predictions to evolve its intelligence. Many AI systems use machine learning to further develop the system and help it make informed decisions.
The most common practice for machine learning in digital advertising is to analyze customer behavior—allowing marketers to make informed decisions about what their customers care about and optimize their campaigns accordingly.
Where does the data come from?
Gathering information on how your customers engage with your brand or products takes time. You can rely on data from other advertising efforts, other consumer bases, or alternative products your target audience has engaged with—but how accurate will that data be to your unique audience?
The issue of overreliance
- You can’t simply trust any machine learning tool to rely on relevant data to your goals or customer base. There needs to be a clear understanding of where your AI solution collects its data from before knowing its insights are appropriate for your strategies.
- If you want accurate results and reliable insights, you need a human specialist who understands the back end of things. The specialist needs to be someone who can track the data and make sure you’re not pulling analysis from unfocused reporting.
Solution: network-wide learnings
- A machine learning network built on shared learnings provides a trustworthy look into how it’s developed its insights. You can easily take a look at other brands on the system, explore reviews or testimonials, and understand the experiences that went toward developing the platform’s intelligence.
- The additional bonus of shared learnings is you’ll have a clear understanding of industry benchmarks, rising trends, and best practices from contemporary marketers.
- Also, double-check that the system can aggregate data safely and responsibly, ensuring you can use it to protect your consumers’ private information.
This advice doesn’t bypass our earlier caveat. You’ll need a specialist who can understand how to deploy the platform’s insights—especially when you’re first starting out. Industry-wide learnings are valuable, but they’re not applicable to every landing page or ad campaign merely because you run a similar business. A human partner who specializes in the data will be able to take the already gathered insights and apply what works best to your unique strategies.
Oversaturation and authenticity
AI is excellent for using data to make informed decisions. Pinpointing details about why a CTA didn’t trigger an action with an audience is an important part of AI-driven testing. But you can’t always take data at a face value. You know the CTA didn’t perform, but that doesn’t mean the AI can build one that will.
Today’s e-commerce audiences are more intuitive than ever. They deal with machines every day, from customer service to social media to more aggressive forms of spam. They’ve witnessed the good, the bad, and the ugly of machine learning throughout their shopping experiences. They can spot poorly calibrated machine-driven content, which can have negative results on your brand-to-consumer relations.
The issue of overreliance
- AI can tell you what is or isn’t performing, in some cases even going as far to explain why something isn’t working, but it cannot build authentic narratives, unique designs, or end-to-end experiences. With every insight and conversion influence identified, there needs to be a CRO specialist to capitalize on the results. The insights are there to sharpen your decisions, not make them for you.
Solution: partner with proven expertise
- Just because machine learning can’t craft fully authentic experiences for you doesn’t mean you have to hire a whole team of designers and copywriters.
- A reliable expert can properly direct any collected data. Consider partnering with a CRO expert to help create a unique experience that you can build upon. Once you have an expert-crafted campaign experience, you can plug it back into your machine learning solution to continue making the smaller changes that have a significant impact on your conversion rates.
The results of a perfect balance
Postclick’s Advertising Conversion Cloud™ is an AI-driven platform that enables marketers to focus on more mission-critical work. It relies on a specific balance of human expertise and network-wide intelligence to develop its framework.
The Advertising Conversion Cloud’s platform grew from a foundation of data spanning a wide variety of industries, millions of landing pages, and billions of ad clicks. It provides an all-in-one focus on customer engagement and behavioral analysis. No matter your goals, audience, or strategies, the network has the insights to get you started and the processes to enhance your results.
Explore what the Advertising Conversion Cloud has already done for businesses like yours.
- 75% reduction in production time
- 20% increase in 3-day customer acquisitions
- 14% increase in conversion rates
Our CRO experts are always available to help start your machine learning journey. Whenever you’re ready, reach out for a complimentary conversion analysis. We’ll identify opportunities where the platform can enhance your audience targeting capabilities. Request a free conversion analysis here.