Marketers Can Apply AI and Machine Learning to Inform Audience Decision Making

As digital strategies take over the marketing ecosystem, marketers need to know who to target and if promotional methods are working. AI and Machine Learning are changing the game.

With all the effort put into addressing cross-platform marketing and trying to connect the dots between store visits, website sessions and transactions, match rates are thin, resulting in insight that is driven by a small sample of consumers that move transparently from onboarding to final purchase.

Marketers have reluctantly either settled for this reality or modeled out attribution forecasts based on the thin data they received by matching cookies. For anything less than a national brand and a campaign of six digit spend, the results are directional at best and statistically inaccurate at worst. Lack of scale is the enemy to transparency.

The common method of onboarding to cookies and using them to tie different devices, purchases, and website visits to an anonymous user profile is considered one way to go. There are others, and they start by building campaigns around a singular, persistent consumer ID.

Through the application of machine learning and AI decision making, a persistent ID can be associated with marketing campaigns no matter when they occur. That ID connects served impressions that are matched against the original audience, website visits, store visits and store purchases, all associated back to a particular marketing campaign. Each audience event (a web visit, request for information, store visit, purchase, or absence of action by the audience) can be turned into a score that is then rated against the offline and online history of the profile for the persistent ID. As the campaign progresses, the history evolves, scores change, and the audience is automatically upgraded.

Components of the history can include deterministic data elements such as age, income, purchase and age information for homes, cars and educational spend, as well as proximity to a store front. This offline data can also leverage predictive data types such as likelihood to refinance or purchase a vehicle. These offline signals can be combined with the historical performance of online IDs for contextual relevance (i.e. visit to an auto dealer site, home equity or remodeling contractor sites), click-through frequencies and time of day, and even the size and price of display ads viewed. Both online and offline signals are constantly being updated as hundreds of individual scores attached to a household and device.

Creating a learning system capable of scaling all of this information linked to households and devices requires the constant updating and refactoring of each ID in a big data environment. Decision algorithms can be deployed against updates every fifteen minutes to rank audiences on demand.  As updates are made, new audiences are informed and deployed.

Semcasting developed Smart Zones to address the need for identification of households and devices at scale. The patented technology contains the method to map offline locations, including households and businesses, to the associated IP addresses and devices. In this way, audiences can not only be based on deterministic offline data, they can also be mapped to their ISP delivery points. Turning digital transactions into a persistent ID informs us of that ID's consumption of online media, engagement and transactions. 

Website visitors, identified by their persistent ID, can be extended to their offline profiles to compare against offline campaigns or demographic attributes. For Semcasting, this process is known as the UDX (Universal Data Exchange), where a website visit is captured as an impression and the metrics are linked back and scored to improve onboarded audiences, ad-server impressions, pages visited, locations, and devices.
 
Contact us to learn more about how Semcasting Smart Zones® and UDX can close the loop for your multi-channel marketing campaigns.