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.
- Single view of customer enables creation of meaningful segments and microsegments based upon customer browsing behavior, social profile, behavior and demographics.
- Organizations can build strategy for targeted marketing based upon the segment potential and behavior. Organizations can build personalized campaigns based upon single view of customer.
- Organizations can measure the profitability and performance of each segment.
- Organizations can plan product bundling , pricing , offerings based upon meaningful segments
- Organizations can plan customer related strategy based upon lifetime value and improve their operations and profitability
- Single view of customer enables understanding of key drivers and trigger for customer churn from data. Organizations can build specific retention strategies based upon data.
PRODUCT PURCHASE ANALYSIS
- Product bundling strategy can vary based upon customer segments. Product purchase analysis combined with browsing behavior of customer segments can enable more dynamic product bundling.
- Product purchase analysis becomes basis for building recommendation engine.
- Purchase analysis combined with customer browsing behavior can be used for upselling , cross selling and planning of promotions, coupons etc.
- Social Media platforms has revolutionized the sentiments sharing process and reach which is equally beneficial for both the sides of any business.
- Single view of customer with unstructured data enables understanding of customer feedback from multiple channels . This is important input to manage customer experience.
- Organizations can build effective brand management strategies using Sentiment analytics.
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.
Our blue print architecture at high level is divided into four layers – data sourcing, data lake, analytics sandpit and visualization.
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.
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