Our Approach to Data Science
We see data science as a means to business results. Enabling businesses to act is as important as the predictive models themselves. Our approach to data science starts from a business problem and aims to demonstrate business results as early as possible. Below are the steps involved:
- Customer 360 degrees: Our engagements start with understanding your business goals.
- Micro-Segmentation and Targeting: We look at your data, as well as public sources of data, and figure out if there is a smart way to achieve your business goals using data science.
- Explore data analytics: We experiment with data, building and testing predictive models.
- Implement actions: We implement systems that allow you to use the results of the predictive models in decision making or to improve your customer experiences.
- Study Click Analytics (Mobile/ Web): Analyze billions of clicks to profile the customer behavior.
- Continuous refinement: We iterate and refine our systems to improve their accuracy and performance.
Data science is a broad field, but we have expertise in some of these areas:
- Data architecture Analysis: Structure data flow to store, integrate and deploy data in an organization. Use scalable architecture to adapt to changing volumes of data.
- Business Recommendations: Uncover consumer behavior patterns to target their future needs. Increase customer engagement and satisfaction. Generate data-based real-world models and simulations for more complete and consistent information in decision making.
- Brand Personalization: Tailor offerings and experiences to each individual customer. Analyze data in real-time to quickly respond to changes in customers' preferences in concern of Brand loyalty.
- Big Data Engineering: We help our clients account for scale and platform readiness while developing Big Data Engineering capabilities to drive vision and value with useful data collection, connect data with the business need to improve the overall web/app branding experience.