It´s happening. AI has proven to be a real game-changer in the realm of conversion rate optimisation (CRO). Today, AI has transcended its status as a futuristic concept and become an essential tool in the marketer’s arsenal, enabling businesses to achieve remarkable customer-oriented outcomes. It’s quickly becoming a critical part of how we understand and interact with our customers, how we create and share content, how we make decisions, and how we optimise our marketing campaigns.
With AI-powered algorithms and predictive models, marketing agencies can uncover patterns, identify trends, and predict customer behavior with remarkable accuracy.
Moreover, through natural language processing and machine learning, AI can generate personalised and engaging content at scale. Marketers can leverage AI-powered tools to automate content creation, ensuring consistency and relevancy across various channels. This not only saves time and resources but also enhances the overall customer experience by delivering highly tailored and relevant content to each individual.
However, while AI offers unparalleled analytical capabilities, it remains an amplifier of human potential rather than a replacement. Strategic and holistic thinking is a quintessential skill that only a human brain possesses. AI tools provide marketers with valuable outputs, but it is the human mind that connects the dots between different research and past test insights. By integrating AI with human ingenuity, businesses can formulate clear user problems and devise innovative solutions to enhance the customer experience. The synergy between AI and human intelligence paves the way for remarkable breakthroughs in CRO.
Despite the significant benefits that AI-powered predictive analytics brings to conversion rate optimisation (CRO), it is essential to acknowledge some potential downsides and challenges. One of the main concerns with predictive analytics is the risk of bias and over-reliance on historical data.
In order to generate meaningful predictive metrics such as purchase and churn probabilities, it is crucial to ensure a substantial amount of data is included in your reports. According to Google’s recommendations, you should aim to have a minimum of 1000 returning users who have triggered a predictive condition, such as a purchase or a churn event, within a seven-day period over the past 28 days. Additionally, you need to have another set of at least 1000 returning users who have not triggered any predictive conditions. Essentially, this means you should have a consistent record of 1000 purchases or churn events per week for a duration of 28 days, along with 1000 users who have not performed any conversions.
Regarding predictive analysis in Google Analytics 4 for lead generation, certain requirements need to be met. The first essential aspect is to ensure a sufficient amount of data related to lead generation activities is available in your analytics reports. This includes capturing relevant information such as lead sources, interactions, and conversions. By gathering a substantial volume of data on lead generation efforts, Google Analytics 4 can effectively analyse patterns and provide valuable insights into the predictive behavior of leads. This information enables businesses to make informed decisions and optimise their lead generation strategies for better outcomes.
While predictive analysis is a power tool, it can only work when sufficient levels of returning visitors and conversions are achieved