In our AI-driven world, words take on new meanings.
AI, our new team member, sees the world in its unique way. Understanding its perspective will contribute to a better human-non-human partnership.
Let’s explore what the middle of the funnel means in our new working environment from both sides: the conventional perspective representing human concepts and AI’s take on it, for fostering a collaborative partnership between humans and non-humans.
Middle of the Funnel Conventional Definition of
By conventional definition, the Middle of the Funnel’’ refers to a stage in the marketing process where potential customers have progressed beyond the initial awareness phase but have not yet reached the final decision or conversion stage.
During this stage, leads demonstrate a heightened level of interest and engagement, showing signs that they are considering the products or services offered. Marketing efforts at this point focus on providing more detailed information, addressing specific needs, and nurturing leads toward the decision-making phase.
In traditional marketing, leads follow a linear path from top to bottom, categorized as warm or cold based on activity.
If leads became inactive, they were moved back to the top of the funnel for a fresh start.
What is AI’s perspective on the Middle of the Funnel?
From an AI perspective, the middle of the funnel represents a crucial stage in the customer journey where prospects transition from initial awareness to deeper engagement with a company’s products or services.
Here’s how AI perceive the middle of the funnel:
Data Analysis: AI recognizes the middle of the funnel as a critical phase for collecting and analyzing data. It understands that this stage provides valuable insights into prospect behaviour, preferences, and intent, which can inform targeted marketing strategies and personalized content.
Lead Qualification: AI acknowledges the importance of lead qualification during the middle of the funnel. It understands that identifying high-quality leads requires sophisticated algorithms that can analyze various data points, such as demographic information, online behaviour, and engagement metrics, to predict conversion likelihood accurately.
Personalization: AI recognizes the opportunity for personalized interactions during the middle of the funnel. It understands that delivering relevant content and tailored messaging based on prospect interests and needs can significantly enhance engagement and move prospects closer to conversion.
Automation: AI sees the middle of the funnel as an ideal stage for implementing automation techniques. It understands that automating repetitive tasks, such as lead nurturing, email campaigns, and follow-ups, can streamline the process, improve efficiency, and ensure consistent engagement with prospects.
Optimization: AI perceives the middle of the funnel as a testing ground for optimization strategies. It understands that continuously monitoring and analyzing campaign performance metrics allows for iterative improvements to messaging, targeting, and conversion pathways, ultimately driving better results.
Overall, from an AI perspective, the middle of the funnel represents a pivotal stage in the customer journey where data-driven insights, personalized experiences, and automation play key roles in nurturing prospects and driving conversions.
My Thoughts:
At this stage of the customer journey, organizations leverage AI and automation for productivity and efficiency gains, particularly in nurturing leads. Humans typically become active participants only as leads progress further down the funnel.
However, I advocate for a balanced approach. I propose integrating human employees into the nurturing process, albeit selectively, through A/B testing.
In this approach, some leads are handed over to human employees for part of the journey, allowing them to actively nurture these leads.
This integration of human touchpoints allows for several benefits.
Firstly, it enables human employees to challenge AI recommendations and devise new strategies for nurturing. Additionally, it ensures that human employees remain connected to the business and the day-to-day operations, fostering a deeper understanding of customer needs and preferences.
When working alongside AI, human employees become subject matter experts, tasked with managing and guiding the growth of our non-human employees and maintaining the quality of data used for learning and training. This direct engagement ensures that human employees remain integral to the process, contributing their expertise alongside AI capabilities.
