Application for the global sales force powered by the NLP Data Science-algorithm
Adanto markedly improves the productivity of sales and marketing teams at the global leader in the Professional Staffing field by delivering targeted real-time leads via a powerful, yet simple-to-use enterprise application to their mobile or PC device of choice.
Situation
- Sales teams dissatisfaction with the leads process, lead quality and lead management.
- Very poor sales productivity as measured by the sales effectiveness index.
- Non-standard individual lead generation activity leading to poor quality, wasted time
- Stagnant quarterly revenue and market share loss to niche players
- Hard to find information while on-the-go
- Complexity.
Solution
- The Profile Writer application installation package
- Quill Data Science App connector
- Sovren connector
- S3Bucket connector
- RDS connector
- Amazon connector
- SHIM connector
- SHIM services implementation
- Positive final security remediation report
Services performed
Data Science
Data Analytics & Business Intelligence
Data Warehousing
Machine Learning
Artificial Intelligence
Natural Language Processing
Web & Mobile Apps
UX/UI
Custom Application Development
DevOps
Security
Infrastructure Services
Administration Services
Azure Cloud
Key goals
- Improve lead conversion rate by 30%
- Establish more predictable and more sustainable profitable revenue growth
- Empower sales and marketing while on-the-go
- Significantly Improve Sales Effectiveness, Sales Productivity, and Sales Satisfaction.
Technologies used
- C#/.Net Application Framework
- Enterprise SOA Platform
- SOA-connected external services (i.e. AWS)
- Natural Language Processing algorithms for data correlation and analytics of many file formats (*.pdf, *.rft, *.doc, *.txt)
- Amazon AWS Cloud
Result
- Improved sales leads process – overnight
- Improved sales productivity, marketing effectiveness and job satisfaction
- With time, gradual improvement in top line revenue growth.
The main challenge in reaching the requested objective of getting accurate leads for sales personnel was building an accurate and repeatable analytics engine capable of teaching itself and improving its results over time, for data was not stable, of very different formats, with different sizes, and with a different meaning altogether. Therefore, before analysis and results were delivered, the engine had to be capable of data cleansing, data extrapolation, data refresh, and data updates with the most recent field changes. Data needed to be auto-updated to fill the missing fields correlated its meaning with existing external sources, and compare with the history of similar uses.
