Tuesday, August 13, 2013

Top 30 Uses of Predictive Analytics

Predictive Analytics will be the force to reckon in the future because of its power to predict and foretell industry events based on the previously existing conditions. In other words, predictive analytics helps organisations across industries to anticipate negative events and misgivings thereby helping them to prevent failures and frauds.

While most Analytics gurus opine that Predictive Analytics is best suited for financial institutions, we believe that it is equally beneficial to any organisation that wants to be prepared for the challenges and opportunities of the future.

Therefore, we listed 30 uses of Predictive Analytics for various organisations:

  • Improve customer service
  • Build and nurture great customer relations
  • Detect and prevent fraud
  • Sharpen your businessintelligence
  • Plan future strategies and chart out revenue boosting methodologies

Financial Services:
  • Anticipate risk, especially interest rate risks and price risks
  • Calculate credit scores with greater integrity
  • Increase accuracy while making pricing decisions
  • Prevent future risks and reduce defaults
  • Curb fraud – be it in transactions, customer authenticity or credit applications
  • Credit card companies – take charge of credit lines
  • Zero-in on the right customer to sell your new offers

  • Enhance underwriting by leaps and bounds
  • Be well informed of the risk factors while fixing premiums
  • Alleviate fraud –  false claims and wrong claimant profiling

  • Forecast sales, improve service and buying experience
  • Understand customer purchase behavior
  • Target the right product for the right customer
  • Accentuate product marketing and promotions
  • Improve store(s) management
  • Streamline and take control of overall operations 

  • Better decision making – back important policies and bills with concrete data
  • Predict electoral results
  • Detect fraud
  • Study citizens’ reaction to government policies and measures
  • Predictive Analytics was used extensively in the US during the 2012 presidential election to find newer ways to persuade voters.

Sales and Marketing
  • Know whom to target and with what products
  • Increase sales; save time
  • Propel cross selling
  • Boost customer service, customer relation management and customer retention

So, if you belong to any of the above industries, you have reasons enough to invest in Predictive Analytics. Do you see/ look forward to any other uses of it? Share your thoughts in the comments below or write to us at info@shoutanalytics.com.

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