Why Data-driven Marketing Needs Artificial Intelligence And Machine Learning
Today’s marketers face major challenges as the regulatory and technical environment in which they operate continues to shift, while the days of the third-party cookie are severely numbered. Bobby Gray, head of analytics and data marketing at Artefact, looks at how organizations can tackle the resulting data loss with a first-party data mindset – and the adoption of artificial intelligence and machine learning.
No one needs reminding that the life of the third-party cookie is increasingly finite, with only just over a year left before they are obsolete. (90% of desktop and mobile browsers will block third-party tracking by late 2023.) At the same time, privacy regulation is tightening and consumers are getting more and more data-savvy, with 90% wanting more data privacy built into their devices .
The net result? The loss of a lot of data that marketers have, to date, taken for granted – meaning that more decisions will have to be made with less data.
Despite alternatives to the third-party cookie progressing hugely over the past two years, the way ahead is still daunting for many marketers.
Targeting and measurement are the areas where the lack of third-party data will be felt most strongly. Smaller audience pools and reduced precision will impact how well consumers can be targeted, while measuring who has seen an ad will be complicated by the loss of both customer journey data and the ability to track user interactions on other websites and apps.
And as marketers struggle to attribute conversions accurately and are unable to scale their targeting, advertising spend is likely to reduce – ultimately leading to less revenue.
All in all, this is a potentially glowy picture.
Futureproof strategies are data-driven
However, marketers can prepare for this altered landscape by adopting a change in mindset. Moving the focus from third-party data to first-party data (that, by its nature, has consumer consent built in), while also moving away from the reliance on last-click attribution, will enable them to achieve the same advertising goals.
First-party dates will be paramount; brands will need to mine as much insight as possible from it to understand customer activities – such as purchase history, web browsing behavior, responses to direct marketing and overall engagement. In addition, second-party data – obtained through relevant partnerships – can add volume to the data available and also has a key role.
Artificial intelligence and machine learning drive customer insight
But the data itself is just the first step required to address today’s challenges and create ‘future ready’ data-driven marketing strategies. Artificial intelligence (AI) and machine learning (ML) technologies are also vital tools because they can unlock significant value that might otherwise go untapped. AI and ML provide marketers with the insight to make more effective decisions, as well as ensure marketing budgets are used as efficiently as possible and achieve outcomes that contribute to business objectives.
Adding AI and ML on top of customer data can help marketers to get a deeper understanding of their customers; this can inform product recommendations, predict customer lifetime value, provide insight on churn, indicate preferred communication channels and start to determine how likely it is that different marketing activities will result in customer action.
AI and ML can seem frightening (in part due to too many dubious headlines and fictitious storylines), but getting comfortable with these disciplines is critical; they tackle some of the major challenges currently faced by marketers and help them to create the robust data-driven strategies that are essential for a cookie-less world.
AI and ML can be implemented incrementally, gradually closing the gap between human decision-making and the tracking pixels that used to be relied on but are no longer available. Data maturity is not the end point; rather it is an organization’s journey toward using its data better to achieve greater efficiency and effectiveness.
An audience engine enables segmentation
One critical part of this new paradigm is an audience engine, which leverages a wide variety of customer data points to create hyper-targeted audience segments for activation on all advertising platforms.
Applying ML and AI models on top of an audience engine is relatively straightforward; it can be used to understand simple scenarios, such as determining who visits a website and what action they are likely to take (if any) in the following month.
Data can then be activated with first-party identifiers (such as Google first-party Client ID or Facebook FBP cookie), contextual profiles, email addresses or phone numbers. First-party customer data can also be integrated into one of the platforms that usually receives data through a third-party pixel.
This can be initiated by identifying the particular use case or customer segment that marketing relies on to achieve key activation strategies. Once that individual capability is built and driving incremental marketing effectiveness, the process can be replicated with other customer data points or modeling approaches; these act as individual building blocks in the audience engine, and ultimately mean that a customer data platform won’t be needed.
Consumer insight and competitive advantage
The loss of third-party cookies poses undeniable risks to organizations and their brands in terms of their ability to target relevant customers. However, creating future strategies using a data-driven approach and applying AI and ML in increments offers a safe route to gaining all-important consumer insight – which may not be on the radar of competitors.