Four Ps Of AI In Sales Training Ppt
This set of slides discusses the four Ps of AI in sales. These are productivity, prediction, personalization, and performance. It lists how AI can help boost productivity, such as in its capability to gather and analyze data on its own, provide insights and recommendations, and produce actionable insights. Conversation intelligence, sales training, and asset management are rapidly growing areas of AI-powered enablement that improve sales performance.
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Slide 1
This slide depicts the 4 Ps of Artificial Intelligence in Sales. These are productivity, prediction, personalization, and performance.
Instructor’s Notes:
- Productivity: Aggregating and normalizing incoming leads from numerous channels, validating and nurturing leads with relevant material, and other automated interactions are essential sales tasks and processes that could benefit from AI-powered productivity
- Prediction: By studying and learning from aggregated data of similar historical encounters with clients, a robust CRM system with inbuilt Artificial Intelligence can provide predictions regarding consumer behavior. To boost the possibility of closing sales, AI can recommend which items to pitch, when to pitch, and through which channels
- Personalization: Better prediction allows for more personalization, which can substantially improve consumer experience. AI can determine the best time and way (email, phone, text message) to get in touch with the clients and ensure proper personalized engagement
- Performance: The ultimate goal of AI in sales is to close high-profit deals quickly and reliably. AI in sales can forecast which prospects to target and can constantly realign the sales pipeline, allowing a sales team to focus their efforts on deals with the best return on investment
Slide 2
This slide talks about productivity as a use case of Artificial Intelligence in Sales. It lists the ways in which AI can help boost productivity such as in its capability to gather & analyze its own data, provide insights & recommendations, and produce actionable insights.
Slide 3
This slide talks about prediction as a use case of Artificial Intelligence in Sales. To anticipate impending demand surges, AI looks for patterns in marketing engagement (opens, clicks, social media buzz) and related sales actions (new inquiries, repeat calls, repeat website visits, and so on)
Slide 4
This slide talks about personalization as a use case of Artificial Intelligence in Sales. Where AI comes into customization is predicting how someone will act in new scenarios based on their profile or previous behaviors. After doing this, the equation is complete if AI can also advise businesses on the most likely actions that will persuade them to buy in each case.
Slide 5
This slide talks about performance as a use case of Artificial Intelligence in Sales. Conversation intelligence, sales training, and asset management are rapidly growing areas of AI-powered enablement that improve sales performance.
Instructor’s Notes: Conversation intelligence, or the real-time monitoring and transcription of sales conversations, is another rapidly growing area of AI-powered sales enablement that promises to improve sales performance. Such programs track conversations in real-time, assessing word choices, emotions, tones, and other parameters that reflect a prospect's sentiments or purchase intents as they move and grow naturally in conversation, using a combination of NLP and Machine Learning.
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