Big Data

Pandora

Big Data analytics is on top of business imperatives in many important industries oriented to retail consumers, including communications, banking and insurance, retail and FMCG. Market leading vendors are trying to collect feedback about user experience related to their brands and services and to compare it to user experience of competitive brands. Conclusions based on relevant information are used for planning their market activities.

Most valuable data are spread on various review web sites, different discussion forums and social networks such as Facebook and Twitter, unstructured and unformatted and in different languages. To gather relevant feedback from customers all of those data will be collected, filtered, stored, aggregated and analyzed.

Requirements for such analysis can be clearly defined:

  • A Big Data analytical system must deliver information in real-time or as near to real-time as possible.
  • Data sources must be as complete as possible. Defining a new data source and mapping the data from new data source must be easy and fast to implement and should not require technical skills.
  • Semantic analysis, filtering of non-relevant content, relevancy rating and rules for scoring of sentiment must be clearly defined and tested.
  • Information dissemination and analysis must be simple to use but powerful, with set of predefined intuitive visualizations of relations between relevant variables and possibility for end-user to create new reports and analysis.

 

There are several ways to perform such analysis and each of the standard ways has some advantages and disadvantages. Market research agencies can deliver on-demand research study, but the relevancy of such study is always questionable and in most cases results are outdated due to Volume-Velocity-Variety nature of Big Data. Another option is to implement internal Big Data analytics solution that will assure relevant data, but with high initial investment in technology and development and long time to deliver first results.

Our unique Big Data Analytics as a Service Solution is the ideal solution for businesses that need such information. You can have a powerful and meaningful set of analytics within a few days, without large investment in technology infrastructure and skilled technology resources, based on our robust, scalable and comprehensive service architecture.*

Our Solution has three main Tiers - Storage, Engine and Access Tier. Storage tier is based on standard open source Hadoop technologies, including NoSQL HBase and Impala Massively Parallel Processing (MPP) query engine. Storage tier ensures required scalability and performance. It is tightly integrated with Data Access Interface Framework.

The Engine Tier is the heart of our solution. Defined content from web sites and social networks is streamed and crawled, and then parsed, filtered, mapped to a set of predefined attributes, aggregated and stored for each Product Type. Modular Sentiment Analysis is the next step in the process. Dictionaries are organized hierarchically, with Foundation level applicable for all Products. The third level is made of Blade dictionaries that are Product Type specific and have the best score, like Smartphone or Tablet Blade in Technology Box. Blades and Boxes for additional Products Groups and Types and content languages can be added easily. On top of the scored content we have a set of powerful analyses and visualizations.

The Access Tier handles Administration & Configuration, Management & Monitoring and End User requests through HTML5 web and mobile access interfaces. Users can do all required tasks from one simple interface without knowing the complexity of the underlying technologies. The process starts by opening the account and defining users within the account with different privileges, choosing of the already mapped sources or mapping the content of the web-site that you want to include in your analysis, defining the keywords and brands for analysis, scheduling and monitoring data processing, using existing visualizations and analysis or defining new visualizations.

 

* The service is available for user testing at www.pandora-insight.com

 

Visualization

Visualization, presentation and correlation of extracted information is of vital importance in every decision making process. In order to be useful, visualizations need to be simple and easy to understand but also very clear and informative. Comparison of one’s own products or comparison with similar products of the competitors according to different categories based on users opinions is of great value in market positioning and in planning penetration into new market segments. Our solution offers a variety of pre-defined visualizations where user can choose products for comparison as well as categories on which comparison will be based. Two examples of product comparisons are described in detail.

Product comparison with a radar chart (Chart 1.) allows the user to gain insight into product analysis for all categories defined for respective product type and is very valuable for comparison of fewer products. All products are analysed by categories defined specifically for respective product type and graded according to 0 to 10 rating scale.

 

BASS grafikon 1

Chart 1. Radar chart showing comparison of analysis results for chosen types of smartphones.

 

Product comparison with a bubble chart (Chart 2.) enables insight and comparison of a larger number of products by three categories – two of which are presented on chart’s axes while the third one is presented by bubble size. Comparison includes three categories from the radar chart from Chart 1. Two categories (Quality and user experience) are shown as chart axes, while the third category (Value for Money) is shown as bubble size. On mouseover bubbles exact grades for three chosen categories for respective product are shown.

 

BASS grafikon 2

Chart 2. Bubble chart representing comparison of analysis results for a larger number of smartphones.

 

Parent Category: Solutions

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