Gone are the days when setting customers up with digital marketing solutions meant getting them set up with Salesforce and HubSpot and calling it a day. These days, resellers and MSPs that specialize in marketing are competing against young, agile, and business-savvy digital marketing agencies. In order to make the sale, you’ll have to prove you can help your customers achieve their marketing and business development goals using cutting-edge technology.
Marketing isn’t just a job function; it’s a science, and not an easy one. Digital marketers have to be up-to-speed on a host of automation, big data, and e-commerce trends, and know how to leverage technology to achieve both micro and macro goals. They have to know how to implement agile and iterative processes to meet a set of constantly changing key performance indicators (KPIs), as well as how to support sales and operational goals.
In short, marketers have to be experts in consumer technology, emerging platforms, and pop psychology—and if you want to sell to them, so do you. It isn’t an easy play, but armed with the right knowledge and tools, partners looking to specialize in marketing line of business (LOB) functions can learn to speak marketers’ language.
As big data and the Internet of Things (IoT) gain widespread traction in businesses, partners wanting to stay on the leading edge of marketing technology are looking toward the Next Big Thing: Artificial intelligence (AI).
Here’s what you need to know to sound like a subject matter expert on AI applications for digital marketers:
A (Very) Brief History of AI
In order to explain to your customers how far AI has come and why it’s now becoming relevant to business and marketing functions, a little bit of background is helpful.
Back in the 1950s when AI was born, the concept was dubbed “neural networks.” The goal of early scientists and researchers was to create a computer system that mimicked the human brain’s ability to identify patterns, pull out the important data, prioritize information, and decide how to categorize it all.
Meanwhile, marketers were learning how to deal with the biggest disruptive technology of the mid-20th century: television. The arrival of the TV essentially elevated marketing from a tactical to a strategic function and forced marketers to learn how to collect and analyze demographic data, target audience segments, and become fluent in pop psychology, another emerging science of the day.
Marketing science moved at a rapid clip from speculative hooey to a valid area of business expertise due to the influence of TV. Between 1950 and 1960, annual spend on advertising increased nearly six-fold. Things hummed along for about the next 50 years, during which time marketers refined their approaches and technology grew in sophistication, but nothing rivaled the shakeup that TV caused.
But the dawn of the Internet Age in the early naughts introduced a whole new level of complexity. The goals of marketing were the same—to collect data, develop a strategy, and deploy campaigns—but the tools were completely different and far more sophisticated. Marketers had more channels of communication with prospective customers than ever before, as well as a slew of new technologies to help them refine their craft. Now their special mix of psychology, data analysis, and predicting future trends could be applied to websites, e-commerce platforms, online communities, email, podcasts, and webinars.
The dramatic increase in customer touchpoints and data gave new urgency to developing advanced applications for emerging technology like AI for marketing, and researchers found ways to take AI beyond simply classifying data. Developers could now apply code to the neural networks of the early 21st century in order to generate data—essentially, they were capable of learning. (And probably taking over the world, but that’s another story.)
In the years since, as online channels multiply and become more defined, advances in cognitive computing and AI mean that digital marketing tools have evolved in order to execute intuitive and automated processes, making the lives of digital marketers a lot easier.
Now that we’ve caught up to present day, let’s see what marketing AI looks like in action.
Advancing Marketing Science with AI
You can fancy it up any number of ways, but at its core the marketing process boils down to three main stages: data collection, strategy development, and campaign deployment. Marketers today use tools like Google Analytics or Omniture to capture, aggregate, and analyze data, and platforms like HubSpot and Salesforce to automate deployment. But when it comes to strategy, humans still have to connect the dots.
Today’s self-teaching neural networks can take unstructured data and extrapolate patterns, then initiate automatic processes depending on the conclusions they reach. How might that look in practice, and how can you convince customers to invest in AI in order to solve all their marketing woes? We’re glad you asked.
Problem: Pre-qualifying leads
It turns out that sales teams aren’t too keen on calling hundreds or thousands of potential customers only to find out the vast majority of them aren’t relevant. Cognitive computing processes can cross-reference customer data points at lightning speeds in order to make sure the leads marketers collect have valid online presences. For example, AI can check users’ social media activity and online interactions to build full profiles, then check those profiles against the specific data points sales is looking for. Kinda creepy, but terribly useful for qualifying leads.
Problem: Creating compelling content
Writers take heed: cutting-edge cognitive processes can totally do your job. (Gulp.) Natural language processing isn’t a new technology by any means, and AI algorithms can now analyze customer interaction with content to learn what works and what doesn’t. AI can customize web content depending on the location or connected social accounts of users and determine which calls to action really work. Don’t believe us? Gartner says that machines will create 20 percent of commercial content by 2018.
Problem: A/B testing consuming too many resources
Marketing science follows the same rules as any other scientific field of study. It identifies a problem, proposes hypothetical solutions, and tests them to see which is effective. But A/B testing is monotonous and can take up a great deal of time. AI automates testing and makes it significantly more efficient, especially when you consider that cognitive algorithms learn as they go. No more wasting development hours on time-consuming manual testing processes.
Are Robots the Marketers of Tomorrow?
It’s very likely that cognitive applications will soon automate many of marketing’s job functions. While that might be bad news for in-house marketing executives, it’s good news for channel partners. If you can craft end-to-end marketing solutions that address your customers’ business needs, integrate them with existing business applications, and provide ongoing support, marketing AI just might become the most valuable tool in your arsenal.