How did Kapiva automate its processes and analyze its customer personas better?
Kapiva is India's first modern holistic ayurvedic brand that excels at delivering innovative solutions to millennials.
This brand manufactures everyday products that are infused with pure Ayurvedic herbs. Thus, it is widely known for providing solutions for everyday lifestyle issues such as digestion, hair and skin, weight management, diabetes, hypertension, and more.
So far, this brand has successfully delivered one million+ products to over 300,000 customers in the last 1.5 years.
What was the problem?
Kavipa sells its products on its website and on multiple platforms, such as Amazon, Flipkart, and other major platforms.
Since they were actively selling products on multiple platforms, they had problems with two things. One is that they collected the data on the number of products sold via different marketplaces, in-stock details, and other such data MANUALLY. So this consumed immense time and effort.
Besides this, they wanted to analyze their customers' buyer personas to understand them better and provide services and products accordingly.
How did Fornax provide solutions?
At Fornax, we helped automate all these processes by building a data warehouse for their company. We built multiple pipelines to fetch data from these marketplaces and shipping providers to understand the customer persona.
The basic step of our solution includes planning and designing tables. So, based on the data requirement, we designed extensive tables with around 100 to 150 columns.
Solution offered by Fornax.
First, we started building a data warehouse. We choose AWS Redshift for the data warehouse. This tool helps you get fast, easy, and secure analytics and run complex analytical queries.
ETL pipelines were used to draw information from the raw data collected by the ETL tool. Next, AWS glue, a serverless data integration service that helps to extract, transform, and load (ETL) jobs as new data arrives, was used. The data collected via the pipelines were added to the data warehouse.
We transformed the data using the DBT tool. And then, we used Metabase for data visualization, where we set up clear tables containing the information we collected and simplified via the pipelines.
For instance, let’s take the Amazon platform as an example, where their products are regularly sold. We collected the sales data using pipelines, processed it, simplified it, and added it to the tables. Thus, the company got a clear picture on the status of their sold products on different platforms.
The second challenge was customer analytics; they wanted to understand their customers better.
For instance, if a particular customer has been purchasing Kapiva products regularly, they could track what the customer ordered, the order value, and the products in the order.
For instance, if a particular customer has been purchasing Kapiva products on one platform regularly, they could track what the customer ordered, the order value, and the products in the order.