It’s no surprise that one of the biggest opportunities for CEOs in 2024 will be understanding how to get the most out of AI. According to McKinsey, AI has the potential to create more annual value for retail and consumer products than any other industry, and than all business functions, marketing perhaps has the most to gain.
The core activities of understanding the customer’s mindset, offering the right products, and driving purchase intent are all functions that AI can significantly improve. As businesses progress from automating standalone tasks (such as dynamic ad copies) to integrated machine learning applications, this is the curve where we will begin to see early adopters break away from the pack.
To prepare for your next growth lever, it is essential to develop a corporate culture that encourages learning and change. This will ultimately determine your long-term advantage: Google data shows that execution speed plays an important role in its success.
Leaders need to think about how to instill test, learn, and scale behaviors in a way that allows the organization to scale quickly, while still managing bureaucracy. This could include having a dedicated testing fund, enabling agile processes with simplified decision-making, or setting targets for new channels or formats tested.
Next, make sure you’re prepared to succeed despite changes in the privacy landscape. With third-party cookies already deprecated in Safari and Firefox, Chrome last month blocked third-party cookies for 1% of users worldwide, allowing for large-scale real-world testing. Chrome plans to remove all third-party cookies by the end of this year, subject to regulatory oversight and resolution of any remaining competition concerns with the CMA.
Marketers wishing to prepare must acquire sustainable infrastructures and tools that compensate for the loss of visibility of online buying behavior. Tools like “consent mode” allow you to adjust tags based on user-selected preferences and allow modeling to fill in gaps in conversion signals.
Improved Conversions is also a feature that can unlock more powerful bidding. It complements the loss of visibility by sending encrypted first-party conversion data in a privacy-centric manner, allowing the algorithm to make more informed decisions about how much to bid for ad placements.
Businesses will ultimately see the greatest value from integrating new data sources into machine learning applications. A good way to start is to leverage external signals that affect demand, such as social discussion trends, health movements, seasonal activity and weather report.
After that, the use of proprietary sources will be the main competitive advantage, as models using public information can be easily copied by competitors. Examples could be site movements, margin data, days of inventory, real-time conversions and profitable audiences. AI-powered products can then use this data to optimize advertising across channels using live consumer demand signals.
The quality of first-party data will be the key to victory on the battlefield. Leaders should look to build and advance their customer data platforms that make sense of customer interactions and transactions. For retailers with a physical presence, re-evaluate the value exchange to encourage the use of loyalty programs.
This will help close the loop, tracking online and offline impact and enabling hyper-relevant individual marketing experiences. Businesses can then launch promotional offers based on individual purchasing behaviors and test price elasticity.
By enriching the data set for AI to leverage, brands can encourage change and effectively compete for a higher share of their wallet or basket size.
