What Human Teams Can Learn From Machine Learning Marketing Algorithms
Source: Kazu Takiguchi
Artificial-intelligence-powered automation of human jobs has been a hot topic over the past decade. But while factory workers and other manual roles may have grimly accepted their position in the firing line, most skilled white-collar workers with creative roles have been assured that time is on their side.
However, ex Google president Kai-Fu Lee’s recent prediction that AI would soon obliterate one-half of all jobs, and that white-collar jobs would disappear first, may have marketers, content creators and other creatives feeling the heat.
The biggest disruption to marketing as we know it comes from machine learning AI applications, which can learn and improve over time independently after being taught with massive datasets of information. Ironically, the technology that is now making humans redundant was actually designed based on how the human brain works.
However, before marketers start running to the hills, it should be noted that smart marketing apps are not actually new. Facebook has long been using machine-learning algorithms for targeted advertising, and Gartner predicts that 20 percent of all business advertising content is already being authored by smart machines.
So, with AI getting smarter every day, what qualities can human teams learn from machine learning marketing algorithms in order to stay relevant?
Focus on making creative campaigns more relevant and real-time
In the fast-paced internet age, it can be difficult to stay up to date with consumer trends. To market effectively to consumers, you need to know what is hot and what is not for different demographics in real time.
The main lesson to be learned from machine learning AI is the importance of being relevant and assessing trends in real-time. Machine learning marketing apps are designed to provide the most relevant information instantly by constantly analyzing data from millions of sources in order to ensure that insights are always up-to-date and of value.
Traditionally, a common pitfall for human marketing teams has been building creative campaigns around their clients’ products and services, rather than actually working out what content is resonating most with their target audiences at that time and handcrafting campaigns around it.
Even today, in a world when consumers are constantly leaving digital clues about their behavior online using search engines or social media, marketers still use outdated techniques to gain insights such as focus groups and surveys. To top it off, once information has been gathered and processed, creative campaigns can take months to actually come to fruition, by which point they risk having become irrelevant, or old news.
If human teams are to stay competitive, they need to become more data-driven and find a way to accelerate creative projects that speak to their target audience on a personal level, based on hard data about what is catching eyeballs. This could mean harnessing free tools like Google Trends or using software-as-a-service providers like Sprout Social to monitor internet chatter on particular themes and current events.
Teams also need to find ways to streamline the creative content-production process by building a library of potential content based on a forecast of upcoming events and reducing the amount of approvals needed for sign-off.
If they can do so, human teams have a head-start over AI due to their creativity. However, as AI becomes smarter with time, this gap will shorten—and quickly.
Evolve naturally by staying on top of technological trends
The most amazing aspect of machine learning algorithms are their ability to evolve, adapt and make decisions independently, without needing to be programmed to do so.
In an age where trends and technologies are evolving at breakneck speed, creative teams need to adopt this level of proactivity and stay in tune with developments and technological advances in their industry. Advertising has moved well out of the comfort zone of print and TV. Whether it be on social media or sponsoring esports teams, marketers need to keep their finger on the pulse of where their target audience is spending its time and move at lightning speed.
Already, we can see a distinction between brands that seized the opportunity of video advertising on Facebook and others that saw their static image ads get phased out by algorithm updates. Marketers need to hit the ground running with new media trends, or they will find themselves left in the dust.
For example, more than 50 percent of businesses on Instagram published Stories last July, but that’s already old news. As the carousel ad format comes to Instagram Stories, creative teams will need to figure out how to make the most of it.
Teams need to be thinking five steps ahead and move into new spaces as they emerge, however weird or wonderful they are. For example, augmented reality is set to be the next hot space, and wearables are taking off, but looking further ahead, as driverless cars become common, in-car ads could soon be a reality, too.
The best ways to stay up on trends are regularly monitoring industry and tech publications and attending as many events and trade shows as possible. Evolution requires experimentation and risk taking, so don’t be afraid to try new thing out—the returns can be much higher when you are a leader, instead of a follower.
Start making use of historical data
One of the challenges for adoption of machine-learning applications for startups has traditionally been gaining access to large enough sets of proprietary data for algorithms to train themselves with. To learn and evolve, machine learning algorithms digest huge amounts of data before making improvements to the way they work in the future.
Forward thinking marketing teams need to take a page out of AI’s manual by taking advantage of historical data they have access to in order to learn from previous campaigns and improve future processes. Every campaign undertaken should offer a wide range of useful data resources, from content and media that resonated best with different demographics, to shares and likes on social media, to the amount of traffic driven back to clients’ sites.
Campaigns provide a wealth of data that can be analyzed to reveal what went well (and what went wrong). However, far too often, busy creative teams move straight onto the next project, missing out on important learnings to optimize future marketing campaigns.
If done correctly, instead of merely glossing over results, creative teams can identify actionable insights that they can possibly put to use for their next campaign. Aside from guiding teams in content strategies, the data can also be used to increase personalization in content and messaging for specific consumer demographics.
A recent report by Salesforce highlights that 52 percent of consumers are extremely or somewhat likely to switch brands if a company doesn’t make an effort to personalize their messaging for them. Looking at past data makes it easy to optimize content across different touchpoints, as well.
As omnichannel campaigns become the standard moving forward, teams can find the right message to be delivered at the right place and time for the best results. The importance of data in creative campaigns within the advertising industry has already been recognized in Cannes, which introduced the Creative Data Lions three years ago.
While machine learning can’t match human creativity in marketing (at least for now), the clock is ticking as machine learning algorithms get smarter with every new campaign. To stay relevant and maintain an advantage over machines, creative teams have much to learn from their AI counterparts. As they say, if you can’t beat them, copy them.
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