Monitor specific trends in the industry and discover new ideas around key marketing segments;help marketing develop ideas to generate awareness for internal brand products/movies.
Develop a data collection pipeline (DCP), to analyze and monitor how competitor productscompare and are impacting audiences and company marketing plans.
This DCP process and method needs to scrape, collect and store web data on a day-to-day basis.
Make the findings available to the client via a web-based dashboard for immediate access.
Set up an AWS instance (server to run python script), to house data from select sources.
Write a python script to scrap and access API’s to collect movie data and sentiment around thosetitles and store data in AWS cloud database for easy access with SQL.
Develop special collection criteria (naming convention strategy), for client’s products, key words,and sentiment, as well as a Global data dictionary and definitions for the entire enterprise data collection process.
Scrape a list of Data Sources and URLS and specific details for analysis around title reviews, sentiment, and popularity.
Visualize; Create a web-based dashboard to study sentiment, popularity and other stakeholder KPIs.
An efficient DCP helped scrape all relevant websites and collect data to be stored in AWS on a daily basis.
A simple-to-read dashboard provided a variety of metrics and KPIs for monitoring title popularity and daily sentiment.
Stakeholders can now compare their data with industry content using buzz, sentiment and key metrics.
This data provided crucial insight to help develop future products, guide marketing decisions and plan campaigns.