Seamlessly build Customer 360 Application with Fire Insights
Organizations across multiple industries are in pursuit of Customer 360, which aims to integrate customer information across multiple channels, systems, devices and products in order to improve the interaction experience and maximize the value delivered.
Customer 360 powers a variety of use cases for Businesses. These can include Recommendations, Churn Prediction, Customer Support etc.
However, building successful Customer 360-degree Platforms has been very hard. Cleaning the incoming datasets, joining very different datasets, further enriching them, doing machine learning to find more results, loading them into serving stores like Apache HBase™, Apache Cassandra™, becomes a lot of things to be brought together. Also handling NRT data becomes essential for Business needs.
Fire Insights solves it very fluently by allowing each of the above steps to be done with pre-built Connectors, Processors and Workflows. Hence, the pipelines are built and maintained in order in hours instead of weeks. Sparkflows also provides streaming workflows for processing NRT streaming data, processing it and loading it into Apache HBase™ etc.
Fire Insights also provides machine learning for building different models, calculating results and loading them into Apache HBase etc. for serving.
Existing and Potential Customers interact with organizations through
various channels and expect their integrations to be relevant and highly
Holistic Customer View
Companies need a holistic view of their customers across all products, systems, devices and channels to deliver a relevant and contextualized experience that will drive customer loyalty, higher wallet share and ability to pitch the right product at the right time.
Irrespective of the space they are in - B2C or B2B, Fortune 500 companies are levering power of Customer 360 to grow their topline and decrease operational cost.
While B2B are focusing on personalized content to acquire new customers and to increase lifetime value, B2C companies are using it to predict customer buying needs and pitching the right product when customers are ready to buy.
In order to offer a relevant and contextualized view, organizations need to build a 360 view of existing and potential customers
Depending on the business you are in, customer view can vary. Typically, Customer 360 view can answer questions around who they are, where they are, what they have purchased, what content they prefer, what challenges they are facing, what products they are in-market for, what they can afford etc.
If you are B2B business, you will focus on business needs while if you are B2C you will focus on consumer needs.
Customer 360 Use Cases
Customer 360 powers a number of Use Cases.
Once you have the Customer 360 built out, it provides the base for building out various use cases.
While you might have the data, but the challenge is to bring it together - correctly
Data from too many Systems needs to be connected
Complex Streaming and batch job needs to be built , tested and deployed
Big Data Machine Learning
Complex Big data machine learning needed for various use cases
Building Profiles needs lot of data cleaning and enrichment
Engineers have to build every use case from grounds up as there are no reusable components
Operationalizing the distributed system end to end quickly becomes complex
Customer 360 use cases can be build on Sparkflows quickly, using the pre-built connected and processors
Fire Insights powers each step of Building Customer 360-degree. Building the Customer 360-degree is a highly iterative complex process with many people involved in building it.
Hence, it becomes immensely difficult to build them out.
Fire Insights makes it seamless to power each step of the process. It makes it easy for anyone to understand and update the system at any point of time.
Customer 360 powered by Sparkflows
Step 1: Choose your data source
Fire Insights supports a variety of data sources both batch and streaming.
Connectors for CSV, Apache Kafka, JDBC, Markato, MongoDB, Apache HBase etc. are available out of the box.You will need to configure them to point to the right data source.
Step 2: Clean, Enrich and Transform
Clean, Combine, Join, De-dupulicate, Transform and Enhance data with over 230+ pre-built nodes.
Step 3: Perform ML & Predict
Use ML, NLP or other Fire Insights processors to find predictions
Step 4: Load and Power Intelligent Applications
Load profiles into serving stores such as HBase, Cassandra and Elastic and power intelligent applications such as Personalization, Virtual Assistant, Proactive case, Demand prediction, Churn Prediction, Fraud detection etc. with ease.
Bringing it All Together
Fire Insights makes it seamless to build out Customer 360 Profiles and Platforms end to end.
Fire Insights handles both the Streaming and Batch workloads. Process streams from Kafka and load them into HBase/Solr etc.
Process batch jobs, perform ML/NLP and load results into the serving stores.