How Big Data Reshapes the Future of Digital Advertising - 3 Examples

These days, big data is truly omnipresent. According to revenue forecasts, by 2026, big data volumes are expected to reach a whopping $92 billion. What August 2019 CMO Survey goes on to say is that the majority of ad tech and martech leaders agree - big data and innovative technologies are two pillars on which their marketing strategies are based. Businesses use big data in order to develop a detailed portrait of each segment of their customer base and apply these marketing strategies properly. 

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In the same way, big data helps companies to predict product and service sales, analyze web click streams and significantly reduce the cost per user acquisition. Big data has become an irreplaceable tool that’s used to generate new insights about consumer behavior. 

“Without big data, companies are blind and deaf, wandering out onto the web like deer on a freeway,” said Geoffrey Moore, an organizational theorist, known for his book, Marketing and Selling Disruptive Products to Mainstream Customers. As a march of big data in digital advertising continues forward, the marketer no longer has to be a deer on the freeway, but rather a fish in the water of opportunities that digital space has to offer. Today, we’re going to review three of them.

Customer experience personalization

These days, in case users grant you with personal data processing consent, you can  collect almost any type of data - transactional data, real-time data gathered from web sources or geolocation movements. Plus, it is possible to gather behavioral data from third-party providers and social networks. Thanks to this, the definition of personalization has reached a new level. 

User data is a powerful thing - but it changes faster than anything else, and user preferences fluctuate with time. Programmatic, which accounts for two-thirds of all digital display spending in 2019, is a tool that helps marketers stay on track of these changes, no matter what happens. A key feature of the programmatic is a smart, multi-factor algorithm which determines how advertising placements are bought and sold online. Most importantly, it processes a huge amount of user data, such as geo, IP, device type, demographic, social and income characteristics of potential ad viewers to deliver a relevant offer. 

The algorithm analyzes an array of user data in a fraction of a second and aptly determines what kind of ads should be shown to a particular user at a particular time. Thanks to data-driven technologies like this, brands can personalize service and product offers, because this is what 48% of users expect from companies according to eConsultancy. What’s also important, programmatic delivers messages across the channels that users prefer, be it a smartphone, laptop, or Smart TV, which enables brands to stay in constant contact with their target audiences. 

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Building proprietary ad infrastructures

For many digital businesses, including Spotify, programmatic has become an excellent additional source of income; thus, e.g., 20% of all ad revenues this music streaming service obtains are from their audio programmatic platform only. Amazon, which is a also a freshly-baked programmatic platform owner, in 2018 seized up to 60% of ad budgets that previously went to Google’s AdSense.

The great share of companies that open their own advertising businesses don’t actually start development from scratch, but rather cling to a white label model: a ready-made, unlabeled software for sale. This way, data-driven technologies are easily purchased and sold. YouTube, acquired by Google in 2006, was actually Flash technology created by Adobe. Mint.com, purchased by Intuit in the 200th, and the number of similar examples only add up to the general trend.  

While building advertising platform is a pretty resource-consuming task, white-label becomes a salvation for all of those who strive to become a part of advertising business as quickly as possible. How does it happen? A business owner typically buys a technologically ready-made solution, like a data management platform, demand-side platform, supply-side platform or ad exchange, and then promotes it under its own brand, applying its own sales model. 

It isn’t only advantageous in terms of time and money - using it, entrepreneurs and startups build completely new platforms without putting results at risk. They don’t reinvent the wheel, but simply acquire a time-proven piece of technology from tech providers and capitalize on them using new business models. 

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Applying behavioral microtargeting

When the volume of data is overwhelming it is impossible to process it manually. On the contrary, when you have too little data, the right interpretation of it, and the effectiveness of advertising as a result, may be at stake.  

Data management and customer data platforms are those technologies that help to cover both problems. Such cloud-based systems allow focussing on the entire customer lifecycle instead of paying attention only to their CRM (first-party data), which is what most companies do. They integrate the data already contained in internal CRMs and then enrich it with purchased, third-party data and other cross-channel data sources, which then create unique IDs for every customer.

But it’s not enough to just collect information. Data science solutions are able to process it effectively and build an analytical breakdown based on who your best customers are. Using personalized targeting, marketers then direct their ad budgets towards the right audiences, which amplifies advertising impact and saves ad budgets. Applying data modeling, a revolutionary, psychographic profiling approach to targeting, is what helped Cambridge Analytica to become a leader of behavioral microtargeting once. 

Another example is Macy’s, one of the biggest U.S. retail networks, which used big data technologies personalized communication with each customer in all 840 stores in 45 US states. As soon as their customers approach the store, they receive a push ad notification with a personal discount. 

The contents of the unit are based on targeting information as well as on an entire history of purchases that guarantees customers won’t be offered something they don’t need or already use. Each store is equipped with Bluetooth iВeacons, which determine the location of the customer in the store in real-time. Then, they send a signal to the data platforms that immediately process information and give advertising platforms a good hint on what better suits this or that customer.

To wrap it up

Big data is, first of all, raw material for a marketer, which, at certain circumstances, can turn into smart data: the actionable information bites that will help to identify trends in consumer behavior and develop the right approach to promotion. The purpose of analyzing the vast number of data arrays is to answer the most important questions about your clients: who they are, what they need and when they need it.

The exact choice of marketing tools can determine the path that leads to successful brand-customer interaction. If most of the described, data-driven solutions are actively used at your company, then you are living in the era of smart data, and your audience is lucky. If not, then it's probably about time to start.