'3': "Value proposition: Briefly explain how your product/service can benefit the prospect. Focus on the unique selling points and value proposition of your product/service that sets it apart from competitors.",
'4': "Needs analysis: Ask open-ended questions to uncover the prospect's needs and pain points. Listen carefully to their responses and take notes.",
'5': "Solution presentation: Based on the prospect's needs, present your product/service as the solution that can address their pain points.",
'6': "Objection handling: Address any objections that the prospect may have regarding your product/service. Be prepared to provide evidence or testimonials to support your claims.",
'7': "Close: Ask for the sale by proposing a next step. This could be a demo, a trial or a meeting with decision-makers. Ensure to summarize what has been discussed and reiterate the benefits."
}
# Agent characteristics - can be modified
config = dict(
salesperson_name = "Ted Lasso",
salesperson_role= "Business Development Representative",
company_name="Sleep Haven",
company_business="Sleep Haven is a premium mattress company that provides customers with the most comfortable and supportive sleeping experience possible. We offer a range of high-quality mattresses, pillows, and bedding accessories that are designed to meet the unique needs of our customers.",
company_values = "Our mission at Sleep Haven is to help people achieve a better night's sleep by providing them with the best possible sleep solutions. We believe that quality sleep is essential to overall health and well-being, and we are committed to helping our customers achieve optimal sleep by offering exceptional products and customer service.",
conversation_purpose = "find out whether they are looking to achieve better sleep via buying a premier mattress.",
conversation_history=['Hello, this is Ted Lasso from Sleep Haven. How are you doing today? <END_OF_TURN>','User: I am well, howe are you?<END_OF_TURN>'],
rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/sales_agent_with_context.html