Web and Social Media Analytics

Aricent’s Web-Social framework can be used to develop solutions that can help understand and predict customer behavior. Our framework generates Web metrics and is capable of supporting various formats of click stream data such as Adobe Omniture, Yahoo Web Analytics and Google Analytics. It is also capable of generating metrics based on data from various social media platforms, including Facebook, Google+, Twitter and LinkedIn.

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Web and Social Media Analytics

Aricent’s Web-Social framework can be used to develop solutions that can help understand and predict customer behavior. Our framework generates Web metrics and is capable of supporting various formats of click stream data such as Adobe Omniture, Yahoo Web Analytics and Google Analytics. It is also capable of generating metrics based on data from various social media platforms, including Facebook, Google+, Twitter and LinkedIn.

The framework enables a user to run descriptive, predictive and prescriptive analytics over the collected data. It then prepares a unified view of customer from both Web and social media channels. The framework also helps in generating reports, charts and dashboards that provide insights into customer behavior. The framework has been developed over the Hadoop platform and utilizes array of technologies such as Flume, Scala, Spark and Hive — for collecting, validating and storing the data. Automated Oozie work flow is used to orchestrate the end-to-end data processing and Tableau platform is used for visualization.

Ingestion

  • Clickstream data is periodically processed using Hadoop component (Flume Agent). The data is ingested periodically to Hadoop HDFS.
  • Social aggregator from Aricent generates a unified data format from various social networks, including Facebook, Twitter, Google+ and Linkedin — and is processed using Hadoop component (Flume Agent).The data is ingested periodically from social networking sites. Whenever new data is received, this stage gets auto-triggered.

Cleansing and Enrichment

This is the main stage for transformation and operates on tokenizing and normalizing approaches. Scala and Spark are used for cleansing and enriching the data. The enriched data is then stored in Hive data warehouse.

Storage

The enriched data is converted to customized event data model. FACT tables are created using Spark scripts from the data model and stored in the partitioned Hive data warehouse. Cloudera Impala is then used as a query engine to query the data from the FACT table to generate reports.

Features

The key features of our Web and Social Media Analytics that allow you to get insights into your customer behavior are:

  • Web Analytics: Web metrics dashboards are used to monitor traffic, understand the market reach and plan any capacity changes. It also provides behavior metrics to understand customer loyalty.
  • Social Analytics: Social metrics dashboards enable users to understand the social penetration and the impact of social media on organizations.
  • Unified Web-Social Analytics: The accelerator creates dashboards that enable users to understand the customer behavior based on location and traffic trends for deeper analysis.
  • Sentiment Analytics: The accelerator performs sentiment analysis on customer comments from social media platforms and then prepares sentiment dashboards.
  • Visualization: The accelerator generates visually rich interactive dashboards, reportsand charts using the Tableau analytical and visualization platform. These reports provide high level view on customer trends including traffic,behavior and sentiment from both Web and social media platforms.Reports can be drilled down further to get specific details about thecustomer behavior.

Benefits

  • Gain actionable insights
  • Gain competitive advantage
  • Mitigate risks effectively
  • Extensive product engineering experience
  • Domain-specific solutions
  • Quicker time-to-market
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