Harnessing AI For Success In Sales Training Ppt

Slide 1

This slide discusses the implementation of Artificial Intelligence in Sales. AI can be used to interpret patterns and make decisions based on data collected through consumer behavioral tracking. These include message receipts & opens, clicks, search histories, keywords, navigation pathways traveled from one website to another, social media interactions, etc.

Slide 2

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 3

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 4

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 5

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 6

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.

Slide 7

This slide showcases the potential future of AI in Sales by illustrating the top AI use cases for companies all across the world in 2020 and over the next two years. In 2018, AI was used by 21% of sales teams. However, by 2020, the figure had risen to 54%.

Slide 8

This slide lists types of Artificial Intelligence tools for Sales. These include Conversica, Appier, Node, Exceed, and Troops.

Instructor’s Notes:

• Conversica: Conversica is a critical sales tool. This tool's assistants interact with the visitors on a website. Conversica employs Artificial Intelligence (AI) to automatically contact and speak with leads, tailoring relevant responses to their questions
• Appier: Appier analyses buyer databases with AI to classify these and forecast how they might respond to sales campaigns. The need to pay data scientists to analyze the market will be eliminated with the use of Appier's expertise. Hence, saving money as well as time spent on research
• Node: Node uses unique AI to sift through large amounts of web data to identify potential clients. Node examines a company's target buyers and provides more leads, it can also improve forecasts due to this approach.
• Exceed: Exceed's advantage is that it engages with the leads entering a company's pipeline by communicating with them in a human-like manner. The interactions are two-way, which means the lead may ask questions and receive timely responses via email or chat
• Troops: This tool offers a fantastic salesforce slack integration that allows the automation of essential tasks. Businesses can benefit from the tool's insights

Slide 9

This slide lists the challenges of Artificial Intelligence in Sales. These challenges are lack of IT infrastructure, the huge investment of resources needed, and lack of talent.

Instructor’s Notes:

• Insufficiency of IT Infrastructure: A robust IT framework is required for an efficient AI-driven marketing approach. Artificial Intelligence generates enormous amounts of data, and this requires high-performance hardware. These computer systems can be costly to set up and maintain. These also most likely necessitate regular upgrades and maintenance to ensure that system functions properly. This can be a significant roadblock, especially for smaller businesses with limited IT budgets
• Investment of Resources : The execution efforts and costs for AI applications are typically a source of concern for decision-makers. As a result, it's critical to assess the AI capabilities of the company's present marketing tool
• Lack of Talent: There is an AI skills gap at present, which can significantly impact businesses that seek to develop in-house AI marketing solutions. As the number of AI technology companies and employment prospects grow, this problem is expected to worsen. The reality is that the present AI talent pool isn't developing fast enough to fill these new positions