AI, automation and analytics: 3 critical strategies for CMOs in 2019, and beyond
In the next 24 months CMOs should focus on the three 'A's of accountability to make impactful changes.
The digital revolution has triggered tidal waves of innovation and change that show no sign of slowing down. It’s tempting to look back as we head into the holidays, but CMOs are even now peering at the horizon, trying to stay ahead of what comes next.
Across a martech landscape that has grown exponentially in size in recent years, there are some technologies and trends that have certainly jumped the shark. Many hyped-up start-ups will be pressured to prove value in order attract the C and D series investment that would make them true players over time. More established brands and tools will swallow up some of this struggling tech and talent as the industry consolidates; others will perish as it matures.
Technology now accounts for a whopping 29 percent of the total marketing expense budget, making martech the single largest area of investment when it comes to marketing resources and programs. With this massive investment and a colossal amount of data come supersized expectations of for CMOs, who are not accountable for ROI but customer experience, as well.
While you have likely been planning your 2019 strategy for months now, it’s important to keep your eye on that next horizon — on the impactful changes you’ll be able to make in the coming 24 months. In this post, we’ll explore three areas that will prove critical into 2020 and beyond.
These are the 3 ‘A’s of Accountability for CMOs: AI, automation and analytics.
1. Artificial Intelligence: Forego the hype
Though we’ve really only begun to scratch the surface as far as AI’s potential goes, business leaders are sold on the possibilities. Investments in AR and VR, a small segment of AI, are expected to grow from $11.4 billion in 2017 to $215 billion in 2021, according to IDC.
The challenge now is in developing an artificial intelligence mindset, in order to make best use of AI-powered tools and tech. There’s a push to explore the myriad ways AI can improve data collection and analysis, create organizational efficiencies, enhance the customer experience and ultimately drive greater ROI.
Yet even as leadership are buying into AI and ready to invest, it’s proving challenging to find enough workers with AI skills to delve into it. Paysa recently found that there are more than 10,000 AI-related positions open across the country. Meanwhile, 56 percent of senior AI professionals peg a lack of qualified workers as the single greatest barrier to AI implementation (Ernst & Young).
As exciting as it is, it’s important that CMOs keep AI in perspective. It’s not a cure-all for every problem. Avoid the temptation to start with the tech; rather, define the problems you want your technology to solve and from there, you can begin to explore AI-powered solutions.
In the coming year, we’ll see a lot of first-adopted AI applications fade away as organizations track and analyze performance data and rethink their strategy. The volume of data created worldwide is growing at a staggering 40 percent per year, feeding a near-unimaginable level of intelligence into organizations attempting to make sense of and activate it. As consumers increasingly move away from the touch interface and instead choose to converse with search engines and personal assistants, we’ll gain and even deeper understanding of intent at every point in the customer journey.
Smart CMOs aren’t concerned about the shiniest new kid on the block or getting the sexiest toys. They’re figuring out a stack with AI-powered tools in all the right places — tools that enable them to come to consumers with a better informed, more compelling experience on any device or platform.
2. Making your automated processes more accountable
Automation is near-universally considered a threat by the workforce — at first. In reality, automation in marketing frees up valuable time to focus on areas of greater impact; on creativity, communication and decision-making.
We tend to think of automation as a task-based imperative. However, what we define as “tasks” is changing. Even a decade ago, it was inconceivable to all but the most forward-looking visionaries that communicating directly with customers could be an automated task that actually improves customer experience. In fact, 45 percent of end users actually prefer conversing with a chatbot as their primary method of communicating with customer service.
Today, you can automate search campaigns, email campaigns, lead flow processes, analytics, content promotion, interactions triggered by actions within your CRM, and more. There’s a tool out there to automate delivery of every message and the collection of every bit of data that generates.
It’s the next step in automation that gets exciting, and that is the combination of automation and AI. Intelligent automation takes the efficiency and productivity of automation and layers on data analysis, decision-making and even prioritizing next steps. Automation allows for the collection and analysis of super datasets; natural language processing and machine learning drive predictive analytics and evidence-based learning.
Applications that interact with your customers and even make decisions about how to interact with them must be extensively tested and monitored closely by team members skilled in AI. ROI cannot be measured in dollars alone. Customer satisfaction must be tracked at various points in the journey in order to fully realize the impact of the technology on your customer-facing operations.
3. Maximizing the value of your analytics
Marketing and customer analytics have been named by 40 percent of CMOs as the top capabilities needed to support the delivery of their marketing strategies over the next 18 months.
Yet this is another area where we’re barely even scratching the surface. At least 99 percent of big data is not even analyzed, according to the IDC.
It’s no longer enough to gather data from your website and other analytics, using it to inform the next year or even the next month’s strategy. We are no longer restricted to activating our data by feeding the machine only select pieces of data. Today, deep learning allows us to take massive and sometimes unstructured datasets and reap actionable, relevant insights.
What’s more, predictive analytics activates those insights by facilitating real-time optimization, ongoing measurement, and further optimization.
This year, you’ll be able to set up increasingly complex trigger-based campaigns tailored to individuals at a more granular level. Prioritize the experience you’re delivering through the content you’re serving up based on these better informed triggers. The capability is there, but are you matching it with equal creativity?
And not too far in the future, you can expect deep learning to power even more effective, compelling and timely content creation.
Like Ben & Jerry, caramel and sea salt, the three ‘A’s of accountability for CMOs just go better together. It’s AI that is driving the smartest automations, and analytics that both helps you prove the value of these efforts and optimize them for even greater performance in future.
This is the 2020 vision we need to home in on; one in which technology isn’t disruptive at all, but empowering and accountable for marketers and customers alike.
Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.