As automation takes hold in digital marketing, where does that leave the analyst?
A post from International Partner Manager
We’ve all heard about the analytics talent gap that’s shaking the business world. This gap will likely persist, as the need to act on Big Data continues to rise while schools and universities struggle to groom and churn out people who can act.
The McKinsey Global Institute has projected that by 2018, the United States alone could face a shortage of as many as 190,000 people with deep analytical skills.
And while this analytics gap affects all functional areas, it is uniquely painful in companies’ marketing departments.
Admittedly, marketing analytics is a new skill set. That’s not to say that marketing did not attract analytical minds in the past. But most diehard analytics devotees entered more obvious analytical fields, like underwriting and finance.
Yet to be competitive in this new technological landscape, companies need to move fast. And in order to move fast, companies need more of the right-minded people.
Why the analytics talent gap?
As is true with any shortage, scarcity is the source. And analytics talent – especially marketing-specific analytics talent – is scarce for multiple reasons.
For one, the particular skill set demanded is very hard to find in a single profile. Hiring managers are looking for masterful analytical minds that also speak the language of marketing.
While stereotypes are inherently flawed, there is a reason data whizzes aren’t often thought to be the graceful communicators.
What’s more, this specific skill set is very difficult to cultivate. It wasn’t until very recently that industry leaders partnered with universities to develop the curriculum needed for these roles. Meanwhile, this kind of industry-academic partnership has occurred in other disciplines for years.
Lastly, scarcity exists because the rate of growth in the need for this kind of competency has far surpassed that of supply.
Competitive advantage is dictated by the agility and effectiveness of an organisation’s decision-making. And agility and effectiveness in decision-making hinge on having complete and accurate information at the right time, analysed in the right way.
Talent is needed for this, and talent is needed for this yesterday.
How can marketing decisions be simplified?
Amidst the commotion of the talent gap, technology companies are beginning to develop solutions that automate much of that agility and effectiveness in decision-making.
So the question is this: How long will this gap continue if technology begins automating the very things for which these human resources are required?
For the purposes of this article, we are referring to online-based marketing automation – using any kind of data (online, offline, third party, CRM, call centre, retail, etc.) to guide online marketing decisions such as generating personal product recommendations to individual users.
Marketing automation enables companies to put their analytics insights to use and to personalise their customers’ experiences. Marketing automation transforms how companies market and sell, be it onsite, such as product recommendations, or offsite, such as Real-Time Advertising (RTA), or Real-Time Bidding (RTB).
We’ve begun to see not just what computers can accomplish on their own, but also how they can learn from themselves.
Just take a look at IBM’s Watson, a cognitive system designed to learn over time. Much like Watson, marketing automation technology uses available data to make optimal decisions on its own. Decision-making is then based on algorithms defined on the backend, and not hunches defined on the human end.
One could argue, then, that “winning” marketing automation competence will be solely a question of algorithm quality. The technology that uses the most complete data – in the right way, to the right users, at the right value, at the right time, via the right channel – will prevail.
So where do people fit in?
How does marketing automation affect the role of the analyst?
As with any industry, marketing evolves as technology does. Take a look at the graphic below.
Decision-making becomes less manual and more automated. Classic advertising (think “Mad Men”) has given way to today’s human-technology combo.
But tomorrow, technology will assume more of our tasks. While this is not meant to imply that all decisions will be automated, many will be.
Today, many analysts are repeating the same tasks over and over. That’s the equivalent of placing a brilliant mind on an assembly line.
Some will even admit that much of their work has become redundant and, put kindly, less than stimulating.
Take a look at this chart. From Data Collection to Reporting and Analysis, many of these steps recur across clients or across projects, leaving analysts with repetitive tasks and less time to actually act on the data. It also happens that reporting and analysis are the most time-consuming, even though action is the goal of those steps.
Reporting, Analysis, Insights and Action become more automated undertakings. And “Business Optimization,” the next step, could, in theory, be largely automated as well.
So, let’s just assume for a second that it can be – where does that leave the role of the analyst?
The human advantage remains
Before discussing where automated business optimisation could leave the role of the analyst, let’s take a step back. How do we define competitive advantage between similar companies in the same industry?
In the long run, even with computer-run business optimisation, people will still be essential idea generators and strategic thinkers. As iPropsect puts it: "It can’t be one world versus the other, creativity versus data. To truly evolve, it is vital for both practices to come together."
But again, where does that leave the role of the analyst?
For now, at least, it leaves the analyst in a good place. In the medium run, the analyst still needs to make the decisions the computers cannot make on their own. Their projects will involve more critical thinking and more business optimisation. Analysts themselves will remain in high demand.
In the long run, they will be strategy enforcers and may need to detect patterns that computers themselves have not learnt to detect. Quite possibly the marketing analysts of today will morph into the right-hand-men and -women of the Chief Digital Officer (CDO).
But what is true in the short-, medium- and long-term is that, yes, companies need to invest in the technology, but more importantly, they need to invest in the people who think beyond the limits of the algorithms. And they must advocate for analytics adoption across all functional areas.
The first adopters of marketing automation can win big with technology, but maintaining that edge when all competitors have access to the same technology is impossible without creativity and ideas.