AI Case Studies
Sales
Profet AI assists sales departments in automating data collection and cleaning to enhance sales efficiency and conversion rates. By analyzing sales data, we identify key factors that drive successful deals, improving customer satisfaction and trust.
Application Scenarios
Analysis and Prediction, Parameter Recommendation
B2B Sales Conversion Rate Prediction
In B2B sales, the conversion rate is a crucial performance metric. By building predictive models using CRM historical data, companies can optimize resource allocation and improve deal conversion rates.
Pain Points Analysis:
- Uncertain Sales Forecasting: Difficulty in accurately predicting sales deal probability affects business goal setting and strategy formulation.
- Resource Allocation Challenges: Sales personnel often spend excessive time and resources on uncertain cases, neglecting more promising potential clients, leading to uneven resource distribution and reduced overall sales
Outcome Benefits:
- Increased Conversion Rate: Predicting deal probability aids in personnel and resource allocation, boosting deal conversion rates by 15%.
- Identifying Key Sales Behaviors: Understanding which business actions promote conversions enhances customer satisfaction.
Optimal Pricing Suggestion
Traditional pricing relies on experience and past similar product pricing records, which can delay pricing decisions or result in overpricing or underpricing, leading to missed orders or suboptimal profits.
Pain Points Analysis:
- Inefficiency: Traditional pricing methods require manual comparison of past pricing records, which is time-consuming and labor-intensive.
- Subjective Judgment: Relying on manual judgment is prone to subjective bias, resulting in inaccurate pricing.
Outcome Benefits:
- Accurate Pricing: Model-based pricing suggestions provide a basis for pricing decisions, increasing pricing success rates by 9.5%.
- Improved Efficiency: Pricing efficiency improves by 30%, allowing personnel to complete pricing tasks more quickly.
Order Acceptance Rate Prediction Model
For companies, predicting whether a client will ultimately make a purchase before accepting an order is a significant advantage. Accepting an order often requires substantial manpower and time for multiple stages of communication and follow-up, but the final deal outcome is often unpredictable.
Pain Points Analysis:
- High Uncertainty: Even after initial engagement and sample submission, predicting the final deal outcome is challenging, creating uncertainty in marketing and sales resources.
- High Cost: Resources invested in the order-taking process are costs; if the deal fails, the investment is wasted, increasing costs.
Outcome Benefits:
- Targeted Marketing: Using the model for targeted marketing and sales activities accurately identifies potential clients, improving conversion rates by 15%.
- Efficient Resource Allocation: Analysis results help in efficiently allocating marketing resources to high-potential clients, reducing marketing costs by 21.2%.