Faster, More Consistent Question Handling with Gen AI

Objection handling is necessary in sales and customer service, where swift and precise handling of issues can influence the outcome of interactions. Traditional methods often rely heavily on individual skills and experience, which can lead to inconsistencies and time-consuming processes. Generative AI can provide quick, consistent, and effective responses to the most common objections or questions posed by prospects.

Challenges in Traditional Objection Handling

Sales and customer success teams face several challenges:

• Time Consumption: Crafting responses to objections can be time-consuming, particularly when responses need customization to address specific customer concerns.

• Variability in Quality: Depending on the team member’s experience and current workload, the quality and effectiveness of responses can vary, potentially affecting customer relationships and sales outcomes.

The Role of Generative AI in Streamlining Objection Handling

Generative AI transforms the objection handling process by automating the creation of responses. This approach not only standardizes the quality of responses across different team members but also speeds up the response time, allowing teams to handle more queries efficiently.

Implementing Generative AI for Handling Objections

The implementation of Gen AI for objection handling follows a structured framework:

Inputs: Collect data such as frequently asked questions, past customer queries, and specific company information relevant to objections.

Prompts: Design prompts that guide the AI to understand the context of the objection and generate appropriate responses. These prompts are crafted to reflect the company’s tone and customer handling strategies.

Outputs: The AI generates response drafts that can be reviewed and personalized as needed, ensuring they meet the specific needs of each customer interaction.

Practical Application

Consider a common scenario where a customer raises concerns about how a product works. The AI system is set up with inputs including product details, typical customer objections, and pricing justification materials. 

Here’s an example prompt you can copy-paste. Remember to update the placeholders, by including your FAQs, sales collateral, and the prospect’s question: 

You are a customer success manager at Nubara. I will give you a question from a prospective buyer, and you will draft a response that handles their concern or objection. 

For context, here is information about the company and product, Nubara: {paste sales & marketing collateral, especially existing FAQ}

A user asked:

“It’s an interesting concept, warm intros are great.  I’m curious how it scores the relationship - what about the inbox ranks it?  Quantity of emails, language used within emails, etc."

Help me brainstorm by drafting two response variations. 

{Optional: insert speech-to-text stream of consciousness response, e.g., by pressing the dictation button on your computer}

The AI then processes this information and provides a draft response that the representative can tweak to add personal touches or additional details specific to the customer.

Example Output: 

Variation 1: Understanding Relationship Scoring

Nubara’s relationship scoring system is engineered to accurately gauge the strength of your connections through a comprehensive analysis of your email interactions. Primarily, our algorithm considers the quantity of email exchanges you have with each contact—frequent communication suggests a strong relationship. Additionally, the recency of these emails is factored in to ensure the connection is current. While we do not analyze the content of your emails, the metadata provides sufficient insight to effectively map your network and highlight your strongest connections. This data-driven approach helps us facilitate the most promising introductions.

Variation 2: Metrics of Connection Strength

At Nubara, we utilize a sophisticated algorithm to score relationships within your email network. This scoring is based on several key indicators of interaction depth. First, we look at the volume of emails exchanged, as a higher frequency of correspondence generally indicates a robust relationship. We also consider how recently you’ve communicated with each contact, which helps in identifying active and relevant connections. Our system focuses solely on metadata like frequency and recency, ensuring your content remains private while still identifying strong ties within your network for more effective introductions.

Advanced Strategies for Objection Handling

Creating a Playbook

One effective strategy is to create an objection handling playbook using CustomGPTs that preload your FAQs, sales collateral, and company website. Then your sales or support team and simply open the CustomGPT and paste the prospect's question and obtain replies quickly. By continuously feeding the AI system with new data and feedback, it learns and improves the responses, making them more personalized and effective over time.

Near Real-Time Responses

AI can also assist in refining ad-lib responses that sales representatives create during live interactions. For example, a rep might use a rough idea of a response (dictated into a voice memo or speech-to-text keyboard), which the AI system refines into a more polished and professional reply. This capability ensures that even on-the-fly responses maintain a high quality and are aligned with the company’s communication standards.

Conclusion

Generative AI is setting a new standard for handling sales objections, offering a blend of efficiency, consistency, and customization that traditional methods struggle to achieve. By adopting AI tools, sales teams can enhance their responsiveness and effectiveness, turning potential obstacles into opportunities for engagement and conversion.