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Case Studies

Here are some examples of how Berrijam AI helped in revealing insights from data

Industry: Telecommunication | Customer Retention

Goal: Understand the factors leading to customer churn

Why: Customer satisfaction and churn are important when it comes to retaining customers and growing a business. Understanding why some some customers are leaving, and designing targeted interventions can help business grow.

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What we found: Customers on Fiber Optics service for a US Telco had a 85% higher risk of leaving

About the data:

  • ​Data sourced from Maven Analytics Churn Challenge 

  • Preparation: We added a column 'Churn', where churned customers were set to 'yes' and stayed customers were set to 'no'.

Industry: Healthcare | ICU | Heart Attacks | Blood-works

Goal: Understand Heart Attack mortality 

Why: Accurately triaging patients and reducing time to treatment can save lives.
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What we found: Berrijam AI's analysis of ICU data on heart failure patients identified Anion Gap and Rel Failure as the top mortality indicators, with Anion Gap linked to over 60% mortality risk.

About the data:

  • ​Data sourced from Datadryad

  • Preparation: We added column 'Mortality' based on outcome value.

Industry: Strategy | Government Policy | Innovation

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Goal: Understand the factors of highly innovative regions in Australia 

Why: Insights can help guide strategic initiatives and development of policies to foster innovation.
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What we found: Only 7 of the 40 factors explored were predictive of innovative regions, reflecting that most innovation in Australia taking place in major cities south of Sunshine Coast and in areas with fewer businesses in Agriculture, Forestry and Fishing industries.

About the data:

  • ​Data sourced from Department of Industries, Science and Resources (DISR)

  • Preparation: We defined a column 'Highly Innovative', where a region had a value of 'yes', if the number of patent applied from the region were within the top 20% of number of patent applications.

Industry: Airline | Customer Experience

Goal: Understand the factors that impact customer satisfaction 

Why: Insights can help businesses understand/address customer needs and preferences, enabling them to tailor their products and services for better satisfaction and loyalty .
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What we found: Online boarding experience impacts customer satisfaction.

About the data:

  • ​Data sourced from Kaggle

Industry: Investment | Trade | Finance

Goal: Predict if a particular trade would be profitable

Why: Insights can help analysts focus on things that matter and improve their trading outcomes .
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What we found: Out of the 115 factors, only the top 29 were significant predictors of highly profitable trades.

Industry: Healthcare | Medical | Research

Goal: Assess the efficiency of Berrijam AI in analyzing clinical trial data for liver disease patient readmission rates. 

Why: Fast and accurate AI analysis can expedite research and improve healthcare outcomes. 
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What we found: Berrijam AI analyzed clinical trial data 60X faster than traditional methods, confirming that simple electronic reminders can reduce patient readmission rates by 40%.

About the data:

Industry: Manufacturing | Predictive Maintenance

Goal: Understand which combination of factors result in machine failure. 

Why: Predicting machine failure with high accuracy can significantly reduce downtime and maintenance costs in industrial settings.
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What we found: You can reduce 50% of machine failures by proactively
monitoring sensor readings.

About the data:

Industry: Home Loan Approvals | Policy | Risk

Goal: Investigate the Home Loan Approvals data to identify any patterns or biases affecting the approval rates. 

Why: Uncovering and addressing potential biases in loan approvals is crucial for ensuring fairness, maintaining compliance with anti-discrimination laws, and protecting the institution's reputation.
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What we found: Analysis of home loan approvals data suggests a hint of bias in favor of male applicants when there is no Credit History and incomes are below certain thresholds. This could expose the institution to risks of discrimination and legal penalties.

About the data:

Industry: Sports Analytics | Australian Football League 

Goal: Analyze the criteria for AFL Brownlow votes between the 2012 and 2021 seasons to identify any shifts in the importance of disposals and goals. 

Why: Understanding changes in voting criteria can help players, coaches, and analysts adapt strategies to maximize player recognition and performance.
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What we found: Berrijam AI analysis revealed a shift in the criteria for Brownlow votes between 2012 and 2021, with disposals becoming more important than goals.

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