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Business Analytics

You’ve got data. You’ve got questions. But how to find the answers that will help solve the most difficult problems? The solution is simple. Business Analytics.

Where to start? It’s worth answering the following questions: What opportunities and risks do I see in my business? If I had the knowledge of customers’ behavior, their buying preferences, current online sales or the effectiveness of marketing campaigns, would I be able to take advantage of the opportunities and address the risks?

Michał Koziara

CEO, Big Data Evangelist, 3Soft

Big Idea

 

The use of advanced analytics can realize the postulate of “data-driven business”

 

Traditional analytics

  • Mainly descriptive analytics and reporting
  • Little structured data from within the organization
  • Internal department of analysts
  • Internal decision support

Big Data

  • Huge amount of unstructured, complex data
  • New analytical and computational possibilities
  • Birth of a “data master” (data scientist)
  • Internet companies create data-based products and services

Quick understanding

  • Analytics as a strategic resource
  • Analytical tools available at decision points
  • Cultural evolution turns analytics into decisions and operational processes
  • All companies can create products and services based on data

Business analytics development

Analytics in eCommerce: Creating behavioral customer segments

Identification of substitutes based on sales of individual products enables:

  • logistics management (products A and B need not be both available at the same time but one of them does)
  • maintaining the level of sales despite the lack of product A by offering product B
  • proper exposure of products
  • optimization of marketing and promotions
  • controlling, e.g. exposure errors or shortages in stock

A customer buys cigarettes X  (Product A) and if they’re out of stock, he buys cigarettes Y  (Product B).
He will rarely buy both cigarettes X and Y (Produsts A and B).

Analytics in retail: Business Intelligence

  • Sales forecast: estimating the approximate quantity of goods sold in the shop
  • Identification of related products: identifying the relationship between purchasing preferences regarding individual products on the basis of data collected from receipts and presenting the identified correlations in the form of simple rules
  • Promotion assessment/ trend analysis: visualizing the sales of selected products for a selected time period and identifying the trend; optimizing the prices
  • Online presentiation of sales data: presenting the sales data and indicators calculated on the basis of the real-time sales data
  • Segmentation of Customers and Product
  • Dynamic regression and advanced forecasting methods
  • Analysis of variance with experimental design
  • Multivariate analysis and clustering
  • Decision trees
  • Association rules
  • Linear programming
  • Queuing theory for operations research

Analytics in industry: Predictive Maintenance

A concept of using BigData technologies for analysis and predicting machine failures:

  • gathering machine-generated data on a Hadoop Distributed File System
  • profiling life cycle of the machine to determine the most vulnerable elements
  • establishing failure prevention rules for machine maintenance based on data acquired from all machines of the same type
  • providing reports and visualization of the probability of failure of the elements of the machine

Analytics in medicine: Strategmed System / Big Data – Hadoop

  • Personalizing the treatment of acute lymphoblastic leukemia in children in Poland will allow achieving better results
  • Collection and processing of data for the entire population of sick children in Poland owing to Big Data technology
  • Project carried out jointly with the Silesian Medical University and leading clinical centers in the country

The analysis of genotypes, health condition, the applied therapy and the effects of treatment in children with leukemia throughout Poland for the purposes of treatment personalization.

Analytics in the bank

  • Call center and contextual advertising after transfer
  • Contextual advertisement in ATMs
  • Evaluation of marketing campaigns
  • Click stream analysis
  • Contact Database
  • Antifraud

Other examples of applications in business

A well-designed analytics platform should meet several, often competing, objectives. The knowledge derived from the application of analytics is expected to be provided to operational employees in a simple way. The regional manager should receive information about a specific product, customer or sales channel that he should deal with. Without having to analyze complex reports and without any knowledge of statistics. At the same time, employees taking strategic decisions regarding sales, marketing or logistics should have access to data through BI tools, MS Excel or generated reports.

Michał Koziara

CEO, Big Data Evangelist, 3Soft