How can you use AI to improve lead qualification?

Most companies are already capturing a lot of data about their leads, but they are not using it. On top of that, there is a lot of data that can be enriched, e.g. from LinkedIn. The first challenge is to define which data even would be relevant to you. Easy ones could be "how many employees does this company have?" or "what is the headcount growth in the sales department over the last 6 months?". More sophisticated approaches could include questions like: "Based on the number of employees that work in finance, how many invoices do we assume this company is processing per month?". As long as you'll find the data, anything that allows you to better qualify your leads goes.

"Lead-Deal-CustomerProspectivemer" at every stage AI has a pivotal role to play. One can leverage AI at the following stages not limited to 1. Availability of Prospective customer data 2. Analysis of and processing of the data 3. Segmenting the leads with Wining probability 4. MQL to SQL (Lead nurturing personalization) 5. SQL to Deal (Creating Customer) 6. Nurturing customers for product advocacy Thousands of AI tools are available in the market however as digital marketers we should know which one is a good fit for the expected outcome at the various stages as mentioned. Fine-tuned data is the backbone and the foundation of the next step. AI's role is pivotal at the data collection and processing stage.

One of the critical areas where AI shines is sorting and prioritizing leads, a fundamental task in sales and marketing. By harnessing the power of AI, organizations can tackle this Herculean task with ease and unmatched efficiency. Moreover, AI can play a pivotal role in shaping buyer personas, continuously evolving them based on historical buyer behavior data. This iterative process of persona refinement is vital, and machine learning (ML) can help organizations stay attuned to the ever-evolving trends in the market. Additionally, AI contributes to measuring the dynamics of lead scoring and accurately assessing the probability of a successful conversion.

Many of us at this stage missed the importance of lead scrutiny and breaking down into lead categories. The leads are treated as just leads and further pushed to sales is the main reason for nonconversion. Lead nurturing is of prime importance. Systematic personalized communication with the prospective customer is possible with ease. Al NLP chatbots understand the pain points and sentiments of the customer. Conversation across email, inquiry calls or chatbots can be vital directives to resolve the query in the first place to build trust for your product in customers' minds. AI comes in handy and does the job with ease helping the customer to reach to next level which is the final deal talks.

In all these stages—MQL, SQL, Deal, Customer, and Loyal Customer—AI plays a valuable role in assisting marketers as they guide buyers through their decision-making journey. AI is particularly useful in automating timely responses based on conversation history, reducing the need for human intervention. AI lacks the competency to fully replace human decision-making. Instead, it acts as a facilitator throughout the buyer's journey, aiding in engagement, decision-making, and conversion by leveraging predictive data trends. Nevertheless, there are exceptional cases where the final decision may require human intervention, particularly in B2B scenarios with high deal values and numerous back-and-forth interactions.