Our fifth chapter explains how predictive advertising management bridges skills gaps - along with many of the most crucial gaps in digital advertising and marketing as a whole.
What are the biggest skills gaps and knowledge gaps in advertising today?
Think of how many devices and advertising channels are on the market today, compared to 10 years ago. The evolution is almost unfathomable. The technology landscape is changing. There are more touch points between companies and their customers than ever before. As a result, marketers need to make more in-the-moment decisions to optimize campaigns. With too many campaign elements for the human eye to track, key details can easily slip through the cracks.
In the last chapter, we talked about the knowledge gaps that exist with the bid management software of today. You have limited insight into social channels, for instance, which are often early indicators to search intent. It’s also impossible to maintain a high-level perspective across your marketing campaigns. CMOs, especially, need to make aggregate-level decisions quickly. But they also face a skills gap on their teams, which can often be mired in day-to-day technical marketing operations when they should be able to look, think and act more strategically. Here’s how predictive advertising management closes the biggest knowledge gaps that marketers face today:
1. Removing uncertainty in the advertising ecosystem
Advertising isn’t static. Consumer preferences, and the channels toward which they’re gravitating, are changing. Lizzie Widhelm, SVP of Ad Product Sales and Strategy at Pandora, suggests in an interview on digital advertising that consumers want info that’s highly targeted and relevant to them as part of "today’s ‘show me you know me’ culture."
Advertisers already have this uncertain, constantly evolving future in mind. The skills gap here is the lack of ability to keep up with this evolution by keeping close tabs on your target market, understanding their needs and ultimately their purchase intent. Predictive advertising management solutions will help marketers quickly zero in on their prospects by way of a host of modifiers encompassing their location, device and demographic details to ensure you can always find and message to the right people at the right time.
2. Focus in an era of data and information overload
Advertisers are sitting on volumes of information. But the key to winning here isn’t simply having more data - it’s knowing what to do with it. To that end, digital is increasingly about using that growing repository of user info to deliver the right message to the right prospect - at the right time, in the right location and on the right device.
According to EY and Forbes Insights, 81% of CMOs recognize the importance of using data and analytics to build trust with customers, but only 37% feel capable of using analytics to actually execute on this idea and customize their messaging to prospects. The skills gap here is, of course, the lack of ability to make sense of thousands of pages of spreadsheet data. The solution is the smart automation provided by predictive advertising management solutions - crunching huge amounts of user data for you to identify the most relevant prospects and guide marketers to make more-informed decisions.
Ryan Ausanka-Crues, VP of Engineering and Product at QuanticMind, provides additional explanation in the context of search-based performance marketing: “A good example: Just because you have a search term, that doesn’t mean the landing page you’re going to take someone to will be effective at communicating and converting.”
80% of CMOs see the importance of data. Only 37% feel they can use it in their daily marketing.
“How do you optimize the period between a click and a conversion? It’s not necessarily a specific, hard-and-fast plan...it’s more a class of ideas on how we make more-informed decisions, so that the marketing dollars you spend result in more conversions, faster.”
With so much data available for decision making, how can marketing leaders make sure they’re focusing on the right strategic areas? This question is the springboard for where predictive advertising management begins.
3. Strategic oversight of granular, split-second decisions
One of the biggest challenges that marketing leaders face is that they need to make judgment calls from 50,000 feet. So much of digital is in the details, buried in massive campaigns of minutiae. The question here might be more than just a simple skills gap - Can strategic leaders make sure make sure their teams are focusing on the right details for the campaigns they choose?
Complicating matters further, in a survey of 100 enterprise marketing leaders from research and advisory firm Real Story Group, only half of respondents reported having the right tools in place. Three in five felt that their existing toolkits underutilized. Another survey from the CMO Council confirmed this general trend: just five percent of marketers indicated that they’ve mastered the ability to adapt and predict the customer journey.
“It’s something that many marketing leaders struggle with,” explains Ausanka-Crues. “It’s not being able to have the time to think strategically because you’re so caught up with the ongoing maintenance of your marketing campaigns. As an executive, you get all sorts of data points, massaged in different ways, that tell only part of a picture. And a lot of time, you’re spending the bulk of your time figuring out what, exactly, to communicate.”
The key value that predictive advertising management brings - and the way it bridges this increasingly challenging skills gap - is that it effectively automates decision-making at the day-to-day level. Managing and bidding on campaigns cease to be a series of agonizing and ceaseless judgment calls that must often be made with limited information and context. Instead, predictive advertising management helps marketing teams get past this morass of keywords, individual campaign budgets and highly specific key performance indicators (KPIs) and reclaim the mantle of being marketing strategists.
“Let’s look at search engine marketing as an example,” says Ausanka-Crues. “The assumption is that we need to spend money to make money. And the question that marketing leaders are facing, as a result, is how much money to allocate to different business areas. The goal of predictive advertising management is to answer this question off-the-bat by honing in on the primary objective of the marketer — to optimize ROI.”
If your software tools aren’t empowering you to make better decisions, maybe they’re part of the problem.
One of the biggest areas where marketing leaders often feel constrained is that decision-making process. Specifically, they are constrained by the “analysis paralysis” that results when tactical teams come forward with concerns over bidding optimization for PPC campaigns, keywords, return on ad spend (ROAS) (and many others) - struggling to bridge the gap between the day-to-day tactical operations and the strategic choices they know they need to be making.
Unfortunately, too many marketing teams get stuck in this decision-making process, unsure of where to turn. Predictive advertising management brings clarity and direction to a previously cluttered space - closing the skills gap produced by a constantly changing ad ecosystem with smart and granular targeting; closing the skills gap of managing data overload with intelligent automation; and closing the skills gap of never-ending decision points by up-leveling the decision process above the day-to-day to the strategic level. (But for those interested in the tactical, we’ll walk you through some examples of ways that marketers are reimagining their tactics for digital by way of search engine marketing in the coming chapters.)
(See more recent search marketing insights by checking session recaps from the SEM PDX Engage 2017 event.)
About the Author
Andrew Park is a content marketing manager at QuanticMind. A UC Berkeley graduate and lifelong Bay Area resident, Andrew has done tours of duty in editorial, PR and marketing, and now works with the QuanticMind team to communicate the importance of data science and machine learning in digital advertising.More Content by Andrew Park