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The world of marketing has grown more complex in the last decade, with multiple and varied approaches to customer acquisition, numerous new channels to learn, and a staggering number of content assets to develop for each program.
With this complexity comes new tools that throw off massive amounts of data, though most aren’t equipped to offer useful customer insights that help you know what to do next. The tools that were supposed to provide us with automation have added to the list of jobs marketing teams now have to perform.
Meanwhile, customers are harder to reach and less loyal to brands. And with the growth of ad blockers, users browsing incognito, and cleared cookies, it’s clear that customers are fed up with online experiences that are irrelevant to their interests.
This widespread customer resistance has led to a general decline in marketing performance across the board in every tool, channel, and medium.
How can marketers keep up with this dual threat of increasing job skill requirements while customers are pulling back from brands? While you might have once been able to throw money at the problem, budgets aren’t going up like they used to. To reach customers in ways that are meaningful and timely, you need to have highly targeted conversations delivered via contextual experiences. This means even more specialized customer segments and each will need its own dedicated content—maybe even its own set of channels, which means more tools and more reporting.
So, how can we all keep up? Is your marketing team big enough to take on all this work? Can (and should) you automate? And where does true automation even start?
One of the newer arrivals in the marketing technology world is artificial intelligence (AI) and, specifically, machine learning.
At the heart of these technologies is a simple concept: Finding relevant patterns in a sea of data that drive next actions.
AI can surface opportunities that are beyond what humans can typically see. Through machine learning, a system can 1) find those golden nuggets of data that are relevant to your marketing goals and 2) describe how to recreate desirable outcomes at scale. These two abilities are what give modern AI-powered solutions the ability to help marketers overcome the shortage of skills, resources, and budget.
The bottom line? You don’t need more people; you need better intelligence.
Over the last 10 years, the job of marketing has become less about finding new customers through creative storytelling and more about setting up automation tools and chasing numbers. It’s become almost impossible to keep up with the continually growing list of skills you are supposed to add to your resume.
Data scientist? Big data expert? Customer experience expert?
There aren’t enough hours in the day to stay on top of it all. Add to that, each tool operates and reports in isolation, leading to a lack of holistic insight into what’s happening with your customers across their overall journey. Instead of focusing on critical strategic elements of the work, marketers are struggling to make sense of what all this data means.
With machine-learning technology in the mix, marketers gain deep insights that help them focus on how best to reach specific customer groups, which channels to use, when to reach out, and which offers to focus on. And marketers return to what they’re best at—orchestrating the customer experience—instead of the dreaded uncertainty of “spray and pray.”
Every day, there seem to be new channels to incorporate into marketing programs, each with its own content asset requirements, plus specialized workflows, and tools.
Often, these tools depend on a complex decision tree of manually created if-then rules and just getting tools into a state where they’re remotely useful can take weeks or months. Multiply this effort across the ecosystem of tools you’re using—and then again if you’re asked to integrate the tools—and whole marketing teams quickly get buried in the constant upkeep effort. It’s easy to see how spread-thin marketing teams can lose sight of the strategic picture.
Machine learning can offload this effort from a marketer’s plate. Instead of programming a bunch of rules to govern customer experience, machine learning can use your customer data to identify the next journey step to present to individual customers based on what’s led to successful outcomes with similar customers before.
Beyond the obvious benefits of time savings, there is another benefit: You move from guessing the right next step to knowing the right next step. This level of certainty frees teams up to imagine new and innovative ways to reach customers.
Let’s be honest: Marketing dollars just don’t go as far as they used to go. Performance in every channel seems to decline while the costs trend upward.
To combat this, marketers need to find efficiencies anywhere they can.
Machine-learning algorithms can spot patterns in customer behavior with precision at scale in ways that humans aren’t capable of doing.
This lets you invest in programs with the biggest payoff and reduce budget waste.
From preventing ad spend going to your employees and existing customers to prioritizing your marketing dollars only to highly-engaged prospects, these tactics can increase return on advertising spend 40 – 60% on average.
Artificial intelligence is not magic, but it is a powerful way to deliver targeted marketing programs. While some tools practically require you to have an advanced degree to use them, modern applications like Lytics put the power of machine learning in your hands. And with these new insights and holistic knowledge, your teams are freed up to get back to serving customers and driving growth with confidence. After all, isn’t that why we became marketers in the first place?
If freeing up your teams sounds like a relief, we’d love to show you what Lytics can do. Schedule a demo today.