Jul 12, 2023
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Completed
Predict Price Sensitivity & Minimize Churn with Advanced Analytics
$50,000+
4-6 months
United States
6-9
Service categories
Service Lines
Big Data
Domain focus
Business Services
Programming language
R
Challenge
The client had significant investments in Google Big Data and their previous analytics efforts were on R. Data sources and ingestion of data needed to be reviewed for quality and consistency. The previous models were not being used because of the lack of an architecture integration approach that could connect the output of the models to the operational systems.
In addition the client had a large and complex pricing table with outdated rows. The data lake had inputs from multiple sources. So data cleanup and reconciliation was also a challenge.
The client had significant investments in Google Big Data and their previous analytics efforts were on R. Data sources and ingestion of data needed to be reviewed for quality and consistency. The previous models were not being used because of the lack of an architecture integration approach that could connect the output of the models to the operational systems.
In addition the client had a large and complex pricing table with outdated rows. The data lake had inputs from multiple sources. So data cleanup and reconciliation was also a challenge.
Solution
Ignitho collaborated closely with the client to execute the project in clearly defined phases.
First we analyzed and proposed a high-level roadmap of data integration architecture. To develop the roadmap, we performed an assessment of the data sources to identify the gaps needing reconciliation and consolidation of data sources. We also identified the kinds of data pipelines that needed to be built to automate the flow of information.
Then we managed the delivery of the AI models in 2 workstreams:
Workstream 1: Advanced Analytics
Workstream 2: Integration of Insights
Ignitho collaborated closely with the client to execute the project in clearly defined phases.
First we analyzed and proposed a high-level roadmap of data integration architecture. To develop the roadmap, we performed an assessment of the data sources to identify the gaps needing reconciliation and consolidation of data sources. We also identified the kinds of data pipelines that needed to be built to automate the flow of information.
Then we managed the delivery of the AI models in 2 workstreams:
Workstream 1: Advanced Analytics
Workstream 2: Integration of Insights
Results
1.Significant improvement in promotional effectiveness
2.Reduced cost overhead of manual analysis
3.Higher user experience
4.Reduced churn
Ignitho collaborated with a leading global business based in the US to develop a predictive analytics model that optimized their promotional periods and rates while considering the likelihood of churn.
1.Significant improvement in promotional effectiveness
2.Reduced cost overhead of manual analysis
3.Higher user experience
4.Reduced churn
Ignitho collaborated with a leading global business based in the US to develop a predictive analytics model that optimized their promotional periods and rates while considering the likelihood of churn.