Case Study
Maximizing Sales Potential
with Informed Data Strategies
Company |
Industry |
 |
Sales |
Client
Selli AG, headquartered in Switzerland, is at the forefront of shaping the next generation of user-centric sales intelligence. Leveraging data-driven insights and AI technology, their innovative service offers a seamless user experience that drives growth and enhances efficiency.
Impact
We supported a project's software development that resulted in an innovative, data-driven sales and lead detection tool, revolutionizing salespeople's strategies. This dynamic tool delivers new insights, proposes leads, and reveals growth opportunities. It aggregates diverse data sources, both structured and unstructured, such as news posts and financial data, offering real-time updates to ensure no crucial opportunities are overlooked.
Problem
One of the most challenging things in sales is to find and identify potential customers. The more relevant data you have, the more potential leads you can find. Salespeople usually manually aggregate the data from different sources and try to find insights that uncover potential leads. Our work included the ingestion of data from various sources daily, saving time and creating new potential opportunities.
Challenges
Data source variability
Every data source is different, so it had to be parsed with a different parser
Pipeline automation
We participated in building and automating the whole pipeline. The pipeline process involves fetching, cleaning, processing, and storing the data.
Data quality concerns
Quality issues were also present, requiring extensive data cleansing efforts
Solution
In close collaboration with the client's in-house experts, we've contributed to the project by supporting critical aspects:
- Data pipeline and storage: the data pipeline starts with data extraction from different vendors and sources, after which the collected data is stored in a Postgres database.
- Processing data: raw data goes through multiple stages of processing where it gets cleaned, normalized, merged, and enriched. The Airflow scheduler handles the scheduling of different stages and imports.
- Merging data: data from different sources are also linked and stored to enable the backend app to efficiently show and fetch all required information.
- Efficient Search: processed data is additionally stored in ElasticSearch. The ElasticSearch cluster is deployed as a distributed system that enables fast searches through tens of millions of companies, insights, and related data. Search parameters and queries are finely tuned for the specific data to facilitate high-speed searches across extensive datasets.
- Cloud Deployment: The entire system was deployed on the cloud, but as all services are in docker containers, it can be easily deployed on-premise.
Tools and Technologies
Python
PostgreSQL
Elasticsearch
Airflow
Pandas
Azure
Results
Using the collected data, the system generates a variety of insights that are tailored to the specific organization and sales role. These insights are presented to facilitate the discovery of new opportunities, identify expanding companies or sectors, provide guidance toward optimal actions, and offer suggestions for new contacts and corporate clients.
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