This case study showcases an impactful project undertaken by our team at Priorise, working in collaboration with a leading organization in the technology industry. The client aimed to enhance their sales enablement efforts by leveraging their in-house recommendation engine and an internal propensity to buy (PTB) model. The objective was to identify high-potential accounts for specific products, generate personalized marketing pitches, develop a comprehensive selling strategy, and recommend suitable learning tools to drive successful sales outcomes. This case study explores how our team addressed the communication challenge of delivering customized messages to over 5,000 employees, considering individual performance levels, recent market uncertainties, and the need to uplift employee morale. Through the seamless integration of performance data, the PTB model, recommendation engine, and the ChatGPT API, we achieved remarkable results, significantly improving engagement metrics and driving tangible business outcomes.
The client faced a dual challenge in their sales enablement initiative. First, they needed to identify the accounts under each seller with a high likelihood of purchasing specific products, utilizing their recommendation engine and the PTB model. Second, they sought to create personalized marketing pitches and comprehensive selling strategies for the selected sellers. Additionally, the communication process had to consider the impact of recent layoffs and uncertain market conditions, and flexibly cater to the performance levels (high, medium, or low) of each employee. The scale of the pilot program, encompassing over 5,000 employees, further intensified the challenge of delivering customized messages effectively.
The objective of this case study is to showcase a successful project where advanced models and technologies were leveraged to optimize sales enablement efforts for a leading organization in the technology industry.
Our team devised an innovative solution that seamlessly integrated performance data, the PTB model, the recommendation engine, and the ChatGPT API. By combining these components, we generated dynamic and customized prompts for each employee, enabling personalized messaging tailored to their performance level, while addressing morale concerns due to recent layoffs and market uncertainties. Leveraging the capabilities of ChatGPT's API, we ensured the delivery of compelling and engaging messages that resonated with each employee's unique circumstances.
Data Integration
We seamlessly integrated performance data, the PTB model, and the recommendation engine to identify high-potential accounts for specific products under each seller.
Performance Segmentation
Leveraging available performance data, we categorized employees into three performance levels: high, medium, and low. This segmentation allowed us to tailor the communication messages to address the specific needs and motivations of each group.
Dynamic Prompt Generation
Leveraging available performance data, we categorized employees into three performance levels: high, medium, and low. This segmentation allowed us to tailor the communication messages to address the specific needs and motivations of each group.
Personalized Marketing Pitches
Utilizing the insights from the PTB model, we crafted sample marketing pitches that were tailored to each seller, addressing the specific needs and interests of the identified accounts.
Comprehensive Selling Strategy
We developed a 5-pointer strategy for each seller, highlighting the value that the specific product could bring to the respective client. This strategy encompassed key selling points, benefits, and use cases, empowering the sellers with a comprehensive approach to engage potential buyers.
Learning Tool Recommendations
Leveraging the recommendation engine, we identified appropriate learning tools that would equip the sellers with the necessary knowledge and skills to effectively close the sale.
This case study highlights the successful utilization of an in-house recommendation engine, the PTB model, and ChatGPT's API to optimize sales enablement efforts for a leading organization in the technology industry. By effectively addressing the communication challenge and delivering dynamic, personalized messages to over 5,000 employees, we witnessed significant improvements in engagement KPIs and tangible business metrics. Our solution exemplifies the power of leveraging advanced models and technologies to drive sales success and empower employees with the necessary tools for impactful customer interactions.
Project Information
To measure the impact of our solution, we divided the employee pool into two groups: a test group that received the dynamic and customized messages, and a control group that did not. We evaluated the engagement Key Performance Indicators (KPIs) after 2 weeks of sending the messages.
Engagement KPIs
- Emails Sent: The treated group sent 84% more emails compared to the control group.
- Meetings: The treated group conducted 110% more client meetings compared to the control group.
- Employee Portal Visits: The treated group had 44% more visits to the employee portal compared to the control group.
Business Metrics
After week 3, we started measuring the impact on business metrics, which demonstrated significant improvements among the recipients of the customized messages. These metrics included:
- Sales Activity: The treated group generated $350k more deals in pipeline as compared to the control group after the 3rd week.
- Conversion Rates: By the 4th week, the treated group demonstrated a 20% higher conversion rate compared to the control group, indicating the effectiveness of the personalized communication strategy.
