CASE STUDY

Sales Intelligence and Customer Acquisition Journey for a Leading Chemical Manufacturer

Industry

Manufacturing

Work Done

Business Intelligence, Data Analytics, Data Warehousing,

Challenge

The client is a leading chemical manufacturer that supplies chemicals to many of its customers belonging to different industries, such as agrochemicals, personal care, textiles, etc. The client wanted to see their product growth and customer acquisitions on dashboards.

They also wanted to see two important aspects of business.

Customer life cycle: This focused on a stage in the customer acquisition process. Analysis of this data gives insights on lead generation, customer acquisition, and expected revenue from customers.
Sales and Revenue: This involves the quantity of products sold and the income generated. Using this information, the client can identify valuable customers, the most profitable products, and non-profitable products and take appropriate action.

The solution

The project was initiated by understanding the model provided for data in SAP and how to create a model in a data warehouse to ingest, transform, and process data.

The analytics design team collaborated with the client to understand requirements and the business analyst team worked on consolidating and formulating requirements.

We created an end-to-end process to get business data from different sources, i.e., SAP Hana and Pipedrive, in different formats and transform them into the required schema of Snowflake, which acts as a data source for visualization and analytics.

Data pipelines were automated using Apache Airflow and deployed on the AWS cloud for security, availability, reliability, and scalability.

In Airflow DAGs, SQL and Python were used to fetch data from upstream.

Optimization was taken care of for streams by marking existing rows, updated rows, unchanged rows, and new rows. We optimized the code in such a way that it only fetches changed data.

To establish secure practices, AWS Secret Manager was used for all credentials.

To be cost-effective, we have written an AWS lambda function to start and stop EC2 at a specific time.

Tableau and Embedded Analytics were used for displaying analytics and intelligence solutions, with authentication in place for each user and department.

Technologies used

Results

We created different visuals in Tableau and Embedded Analytics for important business metrics that help businesses understand their current growth and make decisions effectively.

Some of the business metrics are:

New customer acquisition & lost customers
Top & bottom customers
Best-selling productss and Least-selling product
Quantity sold & Revenue by MoM, QoQ, YoY

The client established a better vision and allocated funds to products more effectively, resulting in a 19.65% increase in fund utilization and a 6.54% increase in profitability.