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Customer 360

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, and loading them into serving stores like Apache HBase, and Apache Cassandra becomes a lot of things to be brought together. Also handling NRT data becomes essential for Business needs.​ Sparkflows solves it very fluently by allowing each of the above steps to be done with pre-built Connectors, Processors, and Workflow. Hence, the pipelines are built and maintained in the order of hours instead of weeks. Sparkflows also provides streaming workflows for processing NRT streaming data, processing, and loading it into Apache HBase, etc.

​Sparkflows 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 contextualized

Holistic Customer View

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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 the 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 the power of Customer 360 to grow their topline and decrease operational costs.


While B2B is focusing on personalized content to acquire new customers and to increase lifetime value, B2C companies are using it to predict customer buying needs and pitch 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 


Key Questions

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.

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Customer 360 Use Cases

Customer 360 powers a number of Use Cases.

  • Recommendations

  • Virtual Assistant etc.

Once you have the Customer 360 built out, it provides the base for building out various use cases.


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Distributed Systems

Data from too many Systems needs to be connected

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Complex Jobs

Complex Streaming and batch job needs to be built , tested and deployed

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Big Data Machine Learning

Complex Big data machine learning needed for various use cases

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Building Profiles

Building Profiles needs lot of data cleaning and enrichment 

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Engineers have to build every use case from grounds up as there are no reusable components

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Operationalizing the  distributed system end to end quickly becomes complex 

Customer 360 use cases can be build on Sparkflows quickly, using the pre-built connectors and processors


Sparkflows 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.

Sparkflows 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.


Step 1: Choose your data source

Sparkflows 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 Sparkflows processors to find predictions.

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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

Sparkflows makes it seamless to build out Customer 360 Profiles and Platforms end to end.

Sparkflows 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.

Sparkflows Difference 

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10X Faster

Build out use cases in weeks instead of months with native connectors and processors

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Iterate Quickly

Iterate quickly with visual workflows and built-in version control

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Go Further

Go even further with built-in nodes for ML, MLP,  Sentiment analysis, etc.

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