New technologies have made it easier to collect data from multiple sources e.g. click streams, social media insights and other connecting platforms. This allows organizations to know the customers at an individual level. Innovyt has complete solution approach to build single view of customer and drive the related innovation. Single view of customer provides opportunity to understand customers in many different ways. This becomes an opportunity to create value for organization and customer in many different ways. This allows organizations to plan, market, price and offer right products and solutions.
Relevance is key when it comes to influence the buying behavior of the customers. It’s not only about segmenting the customers into small target-able groups but also understanding where the true potential is.
Organizations understand the value of building solution but struggle with effort in making it happen. Innovyt has agile approach to build customer analytics solution. Our solution approach involves around building quick pilot using our solution accelerator to prove the value and ROI. Our recommended approach is to roll out incremental value in agile way after initial pilot.
It is difficult to have one architecture for customer 360 solution as we have many technology and architectural choices. Our blueprint architecture is based upon Hadoop and related technologies. However our architecture blueprint is flexible and integrates with existing data warehouse investments . Our blueprint architecture and accelerator works on premise and cloud.
Source data layer represents the source structured and unstructured datasets of prospects, identified or unidentified customer. Each of the datasets comes from various customer touchpoint – transaction, enquiry, survey, click stream, social media interaction, etc.
Data lake represents storage layer for the processed and collected data from various sources. Technology used to collect data from source depends upon type of data and nature of data e.g. real time vs batch . Our blueprint architecture makes these choices based upon best practices from multiple projects. We recommend Avro serialization for initial collection for optimal storage and schema flexibility. One of our approach is to leverage Hbase to store the processed data and store the single view of customer. Parquet tables are used to create dimension data and improve the query performance.
Analytics Sandpit is play area for the analytics team to build analytical models based on the consolidated data from the data lake and the data warehouse. Hence, leveraging the knowledge from both the data repositories.
Presentation Layer represents all the systems that would need data from the single customer view and the analytical outputs.
Our blue print architecture at high level is divided into four layers – data sourcing, data lake, analytics sandpit and visualization.
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