Our fourth chapter introduces an emerging category - predictive advertising management, and how this next-generation class of advertising is shaping the future with smarter algorithms and intelligent automation that will 10X your advertising performance.
Disruption is happening now
The rise of predictive advertising management platforms comes at a time of disruption in the world of performance marketing, particularly with regard to bid management platforms. New devices and marketing channels are creating an odd combination of growth opportunities and bottlenecks for advertising campaign managers and CMOs alike. Never before have companies had the opportunity to run experiments in so many marketing channels. While having many new opportunities to explore is great, which channels offer actual ROI today - and which will be relevant tomorrow?
As we’ve discussed in previous chapters, customers are in control of the buying journey more than ever - which is why intent-based marketing, particularly search marketing will continue to grow in importance, as will its specific offshoots and cousins that are forming an increasingly complex multi-platform and multi-publisher ecosystem.
New platforms usher in a new sales funnel
Social adoption is growing. As Statista notes, in 2016, Facebook continued its growth in adoption, reaching 1.7 billion monthly active users worldwide, while nearly 78% of the United States population has a Facebook account.
Mobile adoption is growing. Worldwide mobile usage continues to increase, with 4.7 billion unique subscribers recorded in 2015, projected to increase to 5.6 billion by 2020 according to GSMA.
What’s more, cross-device transactions and offline conversions are growing. More than 35%-40% of digital shoppers use multiple devices when shopping online, and while foot traffic at traditional brick-and-mortar retail may seem to be in decline, the fact is that in-store browsing remains the preferred method for purchasing research for a majority of industries (such as clothing, home furnishings, sporting equipment, jewelry and others).
And with the exception of books/movies/TV media, which have given way to eBooks, audiobooks and online streaming, in-store shopping remains the overwhelmingly preferred method of purchasing for every major consumer category - 52% for electronics, 53% for clothing and 62% for furnishings, according to PWC’s Total Retail Survey. This means that while fewer shoppers overall may be walking the world’s malls, more of them are there to make actual purchases they’ve researched through other means.
Taken together with mobile/local - Google observes that 76% of people who perform a “near me” smartphone search visit a storefront within 24 hours - and mobile search converting to local click-to-call queries - Search Engine Journal notes that 48% of call volume comes from mobile search - you can see how the customer journey is increasingly becoming a tangled web of search, social, mobile and offline. Google also notes that “near me” searches have massively increased in volume, while 82% of smartphone users consult their phone while physically present in a store.
How predictive advertising management ties everything together
What does all of this mean? It means that the consumer journey is continuing to grow, stretch and twist itself into increasingly bizarre configurations as prospective customers continue to hop back and forth between different stages of what we once knew as the traditional sales funnel.
But you knew that.
What this data also means is that while intent-based marketing will only grow in importance, a last-generation, piecemeal approach that focuses only on a single, limited aspect, such as bid management, won’t be able to keep up. What’s needed is a next-generation marketing technology solution that can holistically tie together the intent-based segment of search marketing with social and multi-touch across multiple devices - including the increasingly important channel of call as it continues to drive offline conversions...
...While also solving for the dual problems of data overload - having far too much data from too many channels - and data scarcity - not having enough empirical test data from new channels or campaigns to confidently enact efficient strategies that will bring in optimal ROI. A predictive advertising management solution ties together these disparate touchpoints while deftly mitigating data overload with fast, intelligent automation that optimizes any campaign at scale, and countering data scarcity with smart, data science-based modeling that accurately forecasts costs, budgets and profits.
76% of “near me” mobile searches mean storefront visits in 24 hours - 28% result in purchases.
Q&A - How Predictive Advertising Management Replaces Bid Management
To continue this discussion, we have a behind-the-scenes look at how QuanticMind is building a predictive advertising management platform to adapt to and take advantage of the changing marketing landscape. This Q&A with the VP, Product and Engineering, Ryan Ausanka-Crues - previously VP of Engineering for Mobile Products and Services for American Express - was conducted by a professional interviewer to combine his unique perspectives in entrepreneurship and corporate innovation:
Q: What makes predictive advertising management different from bid management?
A: It’s about trying to understand what the goals are of search engine marketing. As a company, what you’re trying to do is maximize the return you get on the dollars you’re spending. You need to be asking: “Where should I be putting my money? What keywords should I be bidding on? How much should I be bidding on them? Should I continue to bid on the same keywords?”
Historically, the answers to these questions were based on the whims of personal knowledge, of an individual. But there’s only so much information that one person can hold in their head at any one time. There’s only so much they can use to actually manifest that information into decisions about how they should spend their marketing dollars. Over time, you need to figure out how to manage this process.
Q: Why is there such an important need for this type of solution in the market?
A: Humans aren’t great at being able to interpret massive quantities of data to articulate a decision quickly. As data volumes increase, both from a click perspective and a keyword perspective...we’ve got to figure out which ones we’re bidding on and what bids to make. Where do you want to spend your money? Where do you want to keep this money? These questions are getting harder to answer from a judgment call perspective, which means that predictive technology is key.
Q: What is the role that technology is going to play?
A: Naturally, as it is across many industries, technology comes in and says, “Well, how do we make it easier to automate rote processes?” Our goal is to have computers and algorithms automating the process of deciding how to spend your marketing dollars.
Technology should automatically maximize the value that you’re deriving, looking at historical trends to predict what decisions to make for the future. From a quantitative and strategic perspective, you’ll know which variables, exactly, are impacting your campaign. You can make more-important decisions about your marketing as a result.
Q: What are some additional points of value that predictive advertising management introduces?
A: Historically, the way that companies typically approached performance marketing was manual - either in-house or through an agency. There are good agencies out there, and outstanding internal marketing teams doing digital as well!
However, we noticed that there are times when an external vendor, like an agency, may miss the internal nuances of a company’s operations, business model, budget and long-term goals. Yet we’ve also noticed that even among the most talented internal teams, they enjoy full transparency into their company’s exact business goals and needs, but may lack the time or resources to fully offer their insights.
Within this context, bid management systems were designed to make life easier for external vendors - for instance, for agencies to identify repeatable patterns across their many clients. Sadly, they were not designed to support the unique optimization needs for campaigns belonging to businesses in different verticals and different stages of growth in any kind of depth. That’s where the limitations of bid management originated.
Predictive advertising management technology brings additional transparency to the advertising ecosystem. For instance, at QuanticMind, one of the most fundamental, basic philosophies that went into designing the platform’s engine was a focus on optimizing ROI. At our core, we aim to help marketers make smarter decisions. Using technical vision such as this as a basis, we’ve gone on to build out campaign management capabilities to help companies optimize to a variety of different goals, including growing net profits/margins, increasing conversions, and increasing efficiency/eliminating waste. Predictive advertising management brings an entirely new system for optimization to the market.
82% of smartphone users consult their phones in-store. Are you positioned to take advantage of this?
Predictive advertising management is the next generation of digital marketing - arguably including bid management as a subset but surpassing it with a fundamental focus, from the ground up, on individual performance goals for a variety of businesses, and continually buttressing the next level of performance with new functionality to tackle the changing face of the sales funnel.
As new platforms continue to emerge and even greater volumes of data require untangling, the bid management solutions of today are going to become obsolete. It’s critical that brands start thinking about the channels and technologies of the future - social, mobile and how they connect to offline.
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