Web Analytics

Identify Business Objectives → Identify Key actions That led to Business Objectives → Identifying Metrics that would help analytical people to measure key actions

The term Web Analytics means the study of impact of a website on its users. Web Analytics involves understanding the behavior of visitors of website and acting based on its understanding. Web Analytics is becoming increasingly important to all businesses. Online spending is an increase and more and more people are using web for pre-purchase research. The key concepts of web analytics are interpretation of web log data and reports statistical estimates. It involves the study of the user’s online experiences so as to find ways to improve. This involves studying the behavior of the user regarding:

  • How they entered the site?
  • What are the keywords used in search engine to find the page?
  • How long they were in each of the section?
  • Which were the links within the site and they clicked on?
  • How much time they spent on their website and more?

How does web analytics help?

By finding out the users online experiences, a business executive can utilize web analytical solutions to suggest activities that affect customer behaviour.The activities include advertising, marketing campaigns, site design, element placement and merchandising, cross-sell and up-sell opportunities, retention and loyality.Some of the advantages that companies can realize from web analytical solutions include.

  • Optimization of site design to generate revenue and gain customer loyalty
  • Visitor segmentation depending on the technology used , the visitor behaviour, purchase patterns and registration data.
  • Increasing the conversion rate from browsers to buyers by developing the content and structure of the website
  • Analyzing the success of online  marketing initiatives by comparing own results with the competitors results
  • Finding out effectiveness of each Internet marketing tool and spotting the effort that gives the best results as well as the one that pays the least. This helps to develop the sections which are not doing well.
  • Probing on the reasons why visitors leave a site before a transaction is completed.
  • Analyzing the trends to help compare the periodic changes in trends{daily, weekly, monthly and yearly}.

Cloud Computing-The Emerging area in Analytics

Cloud computing offers a powerful and revolutionizing way for putting Data Mining models to work. It is a road map for science and industry to leverage the power of predictive analytics. Cloud computing will allow us to lower the cost for Data Mining and provide a roadmap for predictive analytics to take their place in new applications and industries. The cloud will become a channel for software and service offerings, making deployment and execution faster and easier, through minimizing the common IT overhead on one side or by providing unprecedented scalability in other cases.

Conclusion

Business Analytics gives you the insight you need to stay in control, sharpen your competitive edge, and focus on your business. Providing the tools to help you find the meaning behind customer, transactional, finance, sales performance, and other data, Business Analytics empowers you make confident decisions, gain business insight, and make faster, better-informed decisions. With Business Analytics many organizations are making increased profits in their business. It provides an incredibly insight into their business activities. SMEs must adopt Business Analytics in order to keep their business on   a systematic track. Moreover the concept of cloud computing is becoming quite popular and is bound to have a positive impact on the business world.

Predictive Models that are commonly practiced

Two different Predictive Models are in Vogue:

  1. Descriptive Models-meant to classify customers and their prospects. It identifies relationships within the data.
  2. Decision Models-They predict the results of decisions made by using a number of variables pertaining to customer data.

Predictive Analytics are widely used in SMEs-Small and Medium Enterprises like Clinical Decision Support Systems, Collection analytics, Cross-sell, Customer retention, Direct marketing, Fraud detection, Portfolio, product or economy level prediction, Underwriting. The following graph indicates the profit attained by the use of Predictive Analytics in an organization

A Typical Profit Curve with predictive analytics

Predictive analytics

Predictive analytics helps connect data to effective action by drawing reliable conclusions about current conditions and future events. In Predictive Analytics we analyze the data using statistical, mathematical, and other algorithmic techniques. Data should be cleansed and transformed before using it. Cleansing and Transformation are the steps in KDD.

The techniques adopted in Predictive Analytics generate models for

  • Classification– Classification is  technique used to predict group membership for data instances,
  • Segmentation– People with similar attributes tend to display similar patterns in various ways,
  • Forecasting– process of making statements about events whose actual outcomes (typically) have not yet been observed,
  • pattern recognition– encompasses a variety of techniques from statistics, Data Mining and game theory that analyze current and historical facts to make predictions about future events.,
  • sequence and association detection-,
  • anomaly identification-,
  • profiling-,
  • propensity scoring-,
  • rule induction-,
  • text mining-, and
  • Advanced visualization-.

Tactically, predictive analytics identifies precisely whom to target, how to reach them, when to make contact, and what messages should be communicated. The benefits achieved from predictive modeling are as follows:

  • Targeting the optimal allocation of operational resources
  • Ways to acquire more customers who will perform like your most profitable customers
  • Optimizing agronomic indicators for treatments that will maximize crop yield
  • Accuracy improvements to inventory forecasting
  • Anticipating who is about to leave as a customer and the most effective treatment to retain
  • Focus auditing efforts for more effective loss prevention, etc.

Business Analytics vs Business Intelligence

In Business Intelligence we collect organization data and analyze the data for several financial / non-financial metrics / key performance indicators. Common functions of business intelligence applications are

  • reporting,
  • OLAP,
  • analytics,
  • Data Mining, – process of Knowledge Discovery
  • business performance management,
  • benchmarks,
  • text mining, and
  • Predictive analytics.

Business Analytics is how organizations gather and interpret data in order to make better business decisions and to optimize business processes. Analytics are defined as the extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based decision-making. Business Analytics is a subset of Business Intelligence. BI and BA have emerged as core tools guiding decisions and strategies for areas as diverse as marketing, credit, research and development, customer care and inventory management.

Business Analytics- Applications

In Business Analytics we basically use a number of statistical techniques out of which surveys and product tracking systems are quite common. From these we can optimize and augment a business model useful to an organization. The techniques used in Business Analytics can be used for the following purposes:

  • Critical product analysis– how well a product is received by the target audience and also product customization
  • Improved customer service– keep track of their recurring customer queries and support issues and enhancing customer satisfaction
  • Up-selling opportunities-Keep Track of customer behavior trends
  • Simplified inventory management– minimizing losses due to outdated inventory items
  • Competitive pricing insights– competitive pricing without having to cut down too much on profits

Kick off with Business Analytics

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Any company can trigger off Business Analytics with the following “Asta Kriyas” i.e., eight actions:

  1. get better  flow and litheness of data
  2. Get the right technology in place
  3. Develop the talent you need-Talent Acquisition
  4. Demand fact-based decisions
  5. maintain the process crystal clear
  6. build  an analytical center of excellence
  7. renovate the culture
  8. amend your strategies – often

Before setting off with Business Analytics one can use Davenport’s five stages rating system in order to judge the organization where it is and where to move on. Those stages are as follows:

Business Analytics five stages

Initially any organization suffers from poor quality and missing data. Then the company collects the data efficiently may be by using fact finding techniques but lacks appropriate data for competent decision making.Once the company has right data it uses data marts to store the data and has the support of executives to use this data for analytics. The deficiency in this stage is it lacks standard, accessible and integrated data. Going a step further the organization begins to develop an enterprise wide analytics capability with high-quality data, an enterprise wide analytical plan and governance principles. Finally the organization is routinely reaping big benefits from its full-fledged, enterprise wide analytics architecture, which is fully automated and integrated into processes.

Regimented Business with Business Analytics

Business Analytics is widely used in the various industries, organizations and corporate in a number of ways. For example Business analytics is used in banks to predict and prevent credit fraud. In retail industry it is used predict the best location for stores and how to stock them. In Pharmaceutical industry business analytics is used to invent new drugs. Even in sports like cricket business analytics is used to determine the strategy of the game and analyze the performance of a player. It is also used for players’ segmentation. In steel sector business analytics can be used to invent new variety of steels and also for better Quality Control.

Benefits attained from Business Analytics

By this Analytical approach companies attain the following:

  • identify their most profitable customers
  • accelerate product innovation
  • optimize supply chains and pricing
  • identify the true drivers of financial performance
  • Better Quality Control.

Business Analytics is the essence for the success of a company for the reasons-(based on the Computerworld survey of 215 IT and business professionals)

  • It improves  and speeds up the decision making process
  • It is useful for Better alignment of resources with strategies.
  • It is useful in realizing cost efficiencies
  • It can be used in responding to user needs for availability of data on a timely basis.

Business Analytics Sample Report

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