For many marketers, decisions are still being guided by assumptions and generalised marketing data. A modern approach needs to recognise the diverse responses of individuals and the influence of their unique characteristics, attitudes, and behaviours.
Today, with the advancement of generative attribution technology coupled with access to detailed data, marketers can optimise their efforts by understanding how individuals respond across various platforms and optimising and personalising messages accordingly.
Key points ahead:
- Media models assume everyone reacts uniformly to marketing efforts
- It’s possible to understand customer media journeys even with broken data and privacy challenges
- Automated data to overcome outdated marketing methods
- The advantage of granular media insights
- Unlocking the power of generative attribution in modern marketing
Media models assume everyone reacts uniformly to marketing efforts
Traditional marketing mix models assume that all individuals respond uniformly to marketing efforts.
In reality, each person possesses distinct preferences, needs, and behaviours that significantly shape their reaction to marketing messages.
If your modelling can’t account for the differences, media plans will assume all eyeballs are created equal when evaluating media mixes.
Customising strategies to specific audiences and customer segments will help to maximise marketing ROI and effectiveness.
It’s possible to understand customer media journeys even with broken data and privacy challenges
Getting to this level of individual customer-level media journey data has been challenging for digital marketers and impossible for omnichannel advertisers.
But with advancements in technology, like machine learning and generative AI, it is indeed now possible to intelligently reconstruct full customer journeys using a mix of aggregate and person-level data sources.
Applying smart attribution to each media exposure (and accounting for the existing propensity to convert), advertisers can now work with a dataset that forms the backbone of more personalised media strategies.
Automated data to overcome outdated marketing methods
MMM approaches to marketing attribution were devised in a time of slow data and minimal channels. Even with the power of machine learning and AI that is underpinning some new tools in the market, the underlying approach is still classic MMM, which continues to struggle with media granularity, quantifying the impact of new activity (i.e. when new channels, publishers or owned media are undertaken by an advertiser for the first time).
Marketers must base their decisions on accurate and current data quality in our fast-paced, and data-centric world that embraces a test-and-learn approach.
The leap to automated data-driven decision-making helps marketers avoid missed opportunities and misallocation of budget to poorly performing media tactics.
The advantage of granular media insights
Traditional generalised data methods are limited as they only offer a surface-level understanding of audience behaviour and preferences.
However, with the full customer media journey rebuilt thanks to generative attribution, marketers can delve into specific data points and comprehensively understand their target audience.
For example, media reporting can get down to the specific creative, format and placement associated with each media exposure leading to a conversion.
So, not only can marketers access channel and publisher-level media mix optimisation, they can get right down to understanding which creative message is better resonating with a specific audience. This facilitates optimisation opportunities within a specific publisher by moving budget to get the right allocation at the placement level.
Analysing and interpreting data quickly and cost-effectively allows marketers to pinpoint their actions and make real-time adjustments based on audience engagement and preferences.
Unlocking the power of generative attribution in modern marketing
Traditional marketing strategies can be improved by capturing the complexities of the full consumer journey.
But with so many channels for brands to connect with their audiences and countless creative options, coupled with data privacy regulations, an experienced media planner can’t do it alone. They need the assistance of advanced measurement tools for structured data to handle this complexity, and the nuances of media synergy and differences in media response across audiences.
MMM approaches provide a limited and biased view of communication influence, even if they’re powered by AI or supplied by Meta or Google.
Marketers need a system that focuses on the customer when measuring results, and generative AI offers an opportunity to transform how data is used for planning, measuring, and optimising campaigns.
Deployed in the right environment with the right data, generative AI’s predictive capabilities can help marketers understand consumer journeys in detail and create campaigns that unlock the full potential of their strategies.
Marketing Evolution is the world pioneer in using generative AI to rebuild customer journeys and enable omnichannel marketing attribution.
This approach gives marketers the holistic benefits of MMM but is built from a granular dataset that leverages individual-level media response data for more effective media plans and understanding of the full customer journey.
Get in touch for a demo or a deeper look at the methodology.
Adapted from Marketing Evolution’s post
