Marketers are constantly seeking innovation to enhance their strategies and maximize returns, especially as traditional approaches have become outdated and fail to capture the increasingly complex consumer journey.
With numerous channels available for brands to engage with various audiences, and endless creative options to place in that inventory, navigating the intricacies of non-linear privacy regulations poses a significant challenge. Existing measurement tools for structured data are ill-equipped to handle this complexity, making it difficult to comprehend, let alone optimize, customer interactions.
Conventional marketing measurement tools such as Multi-Touch Attribution (MTA) and Media Mix Modeling (MMM) provide only a partial and often biased view of communication influence due to issues related to data quality, restrictive assumptions and prediction methods originally designed for a different purpose. These tools tend to offer insights into historical actions, and lack intelligence about future customer behavior.
What marketers today truly need is a system that places the customer at the core of measurement.
AI offers an opportunity to rethink the entire marketing intelligence framework and transform how we utilize data for planning, measuring and optimizing campaigns.
With purpose-built prediction, marketers can recover a detailed understanding of consumer journeys. This approach starts by asking ourselves what the most fundamental information we are after is:
For example, what elicits a consumer's decision? Next, it designs a mathematical framework purpose-designed for mimicking decision outcomes and the things that influence them. Then, it arranges the data for algorithm training that optimally extracts the information to learn the consumer journey generation process. This contrasts the standard approaches that analyze standard data structures with machine learning methods that have nothing to do with consumer journeys.
Much like generative pre-trained transformers (GPTs) are designed for language prediction, data-augmenting predictive intelligence can be designed to predict consumer touchpoints and behavioral patterns. It subsequently uses that intelligence to guide the creation of campaigns in a way that pursues the marketer’s goals.
Consequently, AI serves as a generative engine that augments and decodes information in existing data we never realized was there.
Altogether, it enables marketers with a wide range of goals to build campaigns with a more dynamic and powerful approach than traditional structured data analysis that lacks consumer-centric design.
Here are some recommendations for unlocking marketing success with AI …
- Rethink what you can predict: Organize the data you have to extract the information you want with purpose-built algorithms to generate the data you need
- Put the customer first: Rather than map the success of your business platforms, start with the audiences you aim to influence and purpose build prediction to influence the journey you want them to take
- Test and learn in real time: Implementing an all-in-one measurement system based on predictive analysis allows marketers to test and learn, to build trust in the AI and validate the incremental value of that intelligence as it grows
Introducing Plus AIOS, the intelligent all-in-one system for marketing
Powered by predictive intelligence, Plus AIOS links all touchpoints and continuously evaluates creative performance to grow your business. Unlike traditional approaches that focus on channels, AIOS begins by defining the desired outcome, such as a purchase, and reverse-engineers customer shopping journeys, from initial ad exposure to online reviews.
AIOS doesn't demand extensive historical data or privacy-protected data about individuals. Similar to an observant salesperson identifying common shopper interests, AIOS identifies patterns from your available data and fills in gaps. The more informative data you provide, the better AIOS becomes at predicting the desired outcomes.
AIOS provides a dynamic market perspective, accounting for seasonality, economic conditions and the impact of repeated campaign exposure on buying decisions. It continually adapts to changing factors, guiding marketing and sales strategies to effectively engage with consumers.
AIOS was built by Plus Company, the entrepreneurial network of forward-thinking creative agencies, including Cossette, Citizen Relations, Mekanism, We Are Social and more.
Michael Cohen is the Global Chief Data & Analytics Officer at Plus Company, and a machine learning and AI product and market expert in consumer data technologies. He is passionate about unleashing a systems revolution, equipping the C-suite with actionable intelligence and the financial fluency to drive transformative business impact.