Vertical integrations always face the challenge of innovation. The importance of the whole limits variation of the parts. This applies to product packaging, innovations, and profit margins. Experimentation and pivots that disrupt platforms and margins are not allowed.
Modular architecture opens the door to innovation, allowing upgrades and replacements without disrupting workflows. Modules can insert two steps into a workflow, where there was one, or conversely converge two steps into one, depending on the needs and advantages of the innovations. The architecture allows evolutionary processes to take place, where ecosystems turn over as their parts evolve.
Television is expanding into streaming, and evolving privacy laws are driving experimentation in media measurements, analytics, planning, and activation.
In US streaming, everyone is trying to quantify whether planning reach matters and if so, how to estimate it. On the yes side, Nielsen is linking census data from the streaming services to its projectable panel to estimate the combined linear, delayed viewing, and streaming ratings and reach. Given the volume of correction notifications, Nielsen is finding this to be a bumpy road. Meanwhile, Comscore is charging in. Others are graphing together cable, smart tv and like datasets to crack this nut too.
The preeminence of walled gardens from the social / search companies (Facebook, Google, etc.), broadcast / cable companies (Disney, NBCU/Comcast, etc.), publishers (individuals and groups), information companies (IHS Markit, Transunion, Experian, etc.), research companies (Nielsen, Ipsos, Kantar, etc.), device OEMs (Apple, Samsung, Roku, etc.), and retailers (Amazon, Walmart, etc.) lead to gated and siloed data. Oddly, the rise of privacy laws may make synthesizing and analyzing these data easier by encouraging native digital people to see the value of probabilities.
In Europe and coming back to the US, measurement and analytics are converging on postcodes as a straightforward approach to associate granular data without requiring special permissions and handling for privacy. You can still track journeys of their customers through your own campaigns with cooperating publishers and pixels, as long as culled device graphs survive the privacy laws. However the prospect of analyzing potentials and targeting beyond your own customer base will become more probabilistic, as look-a-like projections anchor on postcodes. Tracking behaviors below the postcode will soon require both first party permissions and specialized machine learning techniques, like Federated Learning, to identify and quantify behaviors and characteristics while keeping personal data unidentifiable.
In China, Procter & Gamble is testing an algorithm dubbed CAID that collects anonymous device data through apps that Apple does not currently block, such as start-up time, model, time zone, country, language, and IP address, to keep device graphs alive by creating virtual device IDs to track behavior and performance.
Census samples measure all activities of specific apps or devices. But census samples are silos when limited to deterministic matching in a privacy aware world. The only way around this is to apply probabilities.
Traditional technique is to leverage panel samples drawn from enumerated universes to calibrate the relationships between anonymized versions of the census samples to estimate narrower segments within samples and behaviors across census samples.
Newer techniques leverage machine learning techniques to aggregate, “learn” is the term of art, the relationships of granular behavior while keeping personally identifiable information impossible to discover.
Projecting these samples still needs to be done and is more complicated. The classic approach is to use a randomly drawn panel to project.
Modular adtech allows varying data, governance rules, algorithms, and workflows to be experimented with without disrupting the whole platform.
We see an opportunity for modular initiatives that plug-and-play with mature platforms. Consultants who imagine new methods can work with companies like MediaBrain to code their method into plug-and-play modules, allowing Consultants to scale their custom work.
MediaBrain is a modular player that enhances ERPs (Enterprise Resource Plans). Its OptiBrain module ingests effectiveness measures and reports out optimal plans that deliver the most effective potential for a given price or guide decisions by framing the best price solutions for different levels of effectiveness.
The modular solutions are natural partners for the ERPs, being innovation labs for the adtech components, enabling their mature ERP ecosystems to become nimble and evolve with the digital transformation of advertising and communications.