By John Piccone
AdWeek: Pablo Picasso, when discussing calculating machines said, “But they are useless. They can only give you answers.” He was not wrong. Without humans asking the questions, algorithms would stand idly by.
The advertising industry has no shortage of algorithms—they’re running constantly—but without the input of marketers who know the kinds of data they need to drive growth for their businesses, in many cases the algorithms might as well be idle.
Since the advent of digital advertising, machines have been collecting an unfathomable amount of data points about customers and their digital journey. Unfortunately for advertisers, many of these have become media metrics that are masquerading as media measurements. Clicks, likes and swipes (oh my!) as well as other technical metrics have become the de facto supply of inventory for sale. While these metrics are tempting to track due to their familiarity and availability, they should not be used exclusively as proxies for a customer’s interest or intent.Still, these are the metrics our machines have been programmed to collect, and for too long marketers have been held hostage by them. Now, with the more widespread availability of data that can help marketers sufficiently capture their business problems, they’re beginning to punch back. Adding data to customer data platforms is no longer enough—it has to be the right data, such as identity, product interactions, chat records and service requests. This helps inform marketing decisions, but can also help determine which features get prioritised in the product roadmap.
By collaborating on what data should be captured, marketers will gain a new level of customer insight that will enable them to use their data proactively instead of reactively.
By collaborating on what data should be captured, marketers will gain a new level of customer insight that will enable them to use their data proactively instead of reactively. Rather than asking machines to measure clicks, swipes and likes, they should be asking the tough questions, such as “Why did customers engage with my products and services in the past?” and “Where will I find my customers and prospects in the future?”
When they do, the algorithms will crunch millions of data points to identify patterns that artificial intelligence will eventually forecast to more accurate and predictable outcomes. It may sound complicated, but it will be child’s play for the machines that love to solve complex questions.
This will have a tremendous impact on the media ecosystem and the environments where customers are bought and sold. Firstly, transparency will be table stakes to buy and sell advertising. That’s because the machines will require a clear, unvarnished view of all marketing inputs and outputs. With a better understanding of how the money is spent and its impact on their cash registers, marketers will drive media sellers and their agencies to quantify their value beyond just media and technical metrics. Marketing outcomes will matter more than ever.
Secondly, marketers and their tech stacks will require more and higher quality data services and integrations from their media partners to mutually justify their advertising dollars. Lastly, marketers will need to build and grow their data analytics and science teams to measure and synthesize the return on questions (ROQ) about the cost to acquire and retain customers.
The data scales are tipping in favour of the marketer, not because of their sheer weight but because of their business value. By asking the right questions, machine-powered, predictable customer journeys will allow marketers to look past technical metrics and focus on business objectives. More than ever, marketing is a blend of art and science, and by helping to write the algorithms, marketers can leverage the data to prove Picasso right once again from when he said, “Art is the elimination of the unnecessary.”