Putting First-Party Data to Work With AI, Machine Learning & Natural Language Processing
Big data means today’s publishers could potentially buy third-party data on anything and everything about their audiences, from their favorite breakfast cereal to the color of their socks.
Alongside these highly specific -- and questionably relevant -- pieces of information, publishers can also identify more profitable trends such as what interests their audiences and how they consume content. These valuable insights can be used to gain a commercial advantage by improving inventory and advertising quality. But despite a wealth of unique first-party data, the majority of publishers are struggling to extract actionable insights to improve web content or to maximize ad revenue from their inventory.
So how can publishers use first-party data to optimize online content and increase ad revenue?
Step 1: Create Detailed Audience Profiles
Better understanding its audiences should be a publisher’s first goal and sophisticated analysis of first-party data can be used to create detailed 360-degree consumer profiles. Leveraging techniques such as natural language processing -- a form of cognitive technology where content is interpreted as if read by a human, taking into consideration context, sentiment, and grammatical nuances -- publishers can use data to understand audience interests, requirements, and behaviors at a granular level. That enables publishers to tailor their content strategies accordingly.
By interpreting the content that audiences read and interact with, combined with insights into an individual’s attitude, interests, and values gathered through a their historic interactions with a brand, highly specific audience segments can be created. Publishers can leverage these segments to offer enhanced targeting capabilities to advertisers. Detailed audience interest profiles will enhance the appeal of online inventory to buyers, allowing them to target consumers accurately and efficiently, which will naturally boost publisher ad revenues. Through the construction of digital user portraits -- including demographic data, psychographic data, and behavioral analysis -- advertisers have more opportunities to develop increasingly engaging and customized campaigns for their target audience. Examples would include dynamic creativity solutions – where ad creative can change dependent on external factors such as the weather forecast -- or native advertisement solutions.
Step 2: Anticipate What Audiences Want
Creating detailed audience profiles from first-party data is highly beneficial in itself, but today’s advanced technologies can take content optimization one-step further. By analyzing data around how consumers react to content they receive, publishers can obtain “propensity profiles” that are able to predict what they are likely to do in the future or what offer is going to engage them more effectively. This allows content and ad placements to be tailored to needs their audiences don’t even know they have yet, increasing the effectiveness of online ads. These new “propensity models” use cognitive technologies -- the application of artificial intelligence and machine learning – and enhance old “standard cluster modelling” -- where a similar set of data is grouped together -- using unique first-party publisher data as a base.
By using advanced technology to get ahead of real time and anticipate what audiences want; publishers can use their own web content to create a surprising and relevant experience that resonates with interested individuals. They can also maximize yields by selling advertisers extremely valuable audiences that already have the propensity to take particular actions or buy specific products.
So how can publishers ensure they are taking the necessary steps towards optimizing online content? First and foremost, a DMP -- or data warehouse -- on its own is not enough: in order to glean actionable audience insights, publishers must make sure they have a robust strategy in place which utilizes emerging technologies to navigate the mounds of data available. Technologies are needed to interpret, integrate, and analyze data to make it useful, which are beyond the capabilities of a DMP without a strategy. In addition, publishers must consider whether their in-house teams have the expertise to bring together necessary skills from separate areas, such as content creation, paid media, and e-commerce. If these skills are not readily available, agencies must be on hand to ensure their teams are given the training needed to understand how to optimize data to its best advantage.
By considering these steps, adopting advanced profiling, and digging into data, publishers have the ability to uncover valuable insights hidden within their own sites, enhancing their content and better monetizing their online inventory. Whether that includes separating the Fruit Loop munching sports socks fans from the dress sock wearing Lucky Charms addicts is entirely up to them.