AIMIA Transforms Customer Loyalty with AI
公司规模
Large Corporate
地区
- America
国家
- Canada
产品
- H2O Driverless AI
- AIMIA’s SmartJourney®
技术栈
- Machine Learning
- AI
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Revenue Growth
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 零售
- 消费品
适用功能
- 销售与市场营销
- 商业运营
用例
- 预测性维护
- 欺诈识别
服务
- 数据科学服务
- 软件设计与工程服务
关于客户
AIMIA is a company based in Quebec, Canada, that specializes in customer engagement and loyalty solutions. They work with leading retail, CPG, travel & hospitality, financial services, and entertainment brands. AIMIA's solutions help brands identify where their customers fall in key milestones along the customer journey, influencing touch points along that journey. They have developed a new methodology called SmartJourney® that helps predict gaps in this journey, identify at-risk revenue or new growth opportunities. Building such complex loyalty solutions can be a very daunting effort involving business, data science, engineering, and IT teams. It requires building sophisticated machine learning models, iterating them with the right datasets, deploying them in their customer environments, and eventually monitoring them in production.
挑战
AIMIA, a global leader in customer engagement and loyalty solutions, faced challenges in predicting customer churn and detecting fraud due to the lack of relevant datasets and the steady evolution of fraudulent behavior. The development of machine learning techniques in the face of these challenges was difficult. There was a need to increase the agility of model development, build newer use-cases quickly, iterate on them faster, improve overall trust in AI by making the results of machine learning algorithms transparent to business stakeholders, as well as benchmark the performance of the models already in production.
解决方案
AIMIA implemented H2O Driverless AI to reduce the model development time in half, delivering a 700% increase in cost savings for their customers’ campaigns. AIMIA’s SmartJourney® methodology analyzes customer behavior and engagement across a variety of stages. Using Driverless AI, they developed a classification model that predicts whether a certain customer will churn or not. Automating the complex feature engineering process to derive new features from existing variables significantly contributed to overall time-savings. In addition, visualizing the datasets before starting model building, understanding the results in different formats and the confusion matrix made it convenient to derive meaningful insights from the data in a few hours, as opposed to days or months.
运营影响
数量效益
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