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Predictive Analytics: Facts That Will Instantly Put You in a Good Mood

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Customers offer a business with abundant of data, which if leveraged intelligently can help a business predict the future behaviour of its customers.

Predictive analytics (PA) is an intelligent means of extracting data from historical or existing sets of data to identify patterns/trends and predict future trends or outcomes.

Today, predictive analytics is not only available for industry giants, since smll enterprises too can leverage the power of predictive analytics by mining the data and generating insightful intelligence. Be it mapping purchase trends of a customer to optimizing marketing campaigns, predictive analytics is the new normal for businesses across the globe. In addition, predictive analytics can be put to use in smart CRM software systems.

Here’s a cool infographic that throws light on predictive analytics and its impact on business:

predictive analytics

Here’s all you should know about predictive analytics:

  • Key techniques used
  • Target areas
  • Impact on Business
  • Key Data Sources

 Key techniques used

  1. Predictive modeling: A process that uses data along with probability to predict outcomes. Here, each model consists of several predictors or variables that would influence the future outcomes.
  2. Predictive search: Predictive search utilizes a predictive search mechanism or algorithm based on common or popular searches for predicting a user’s search query as he/she types by offering a dropdown list of recommendations or suggestions. Google search is the perfect example of predictive search algorithm.

Target areas

  1. Business intelligence: Predictive analytics can be used to harnessing the surging data and offer actionable insights that could help transform critical business operations. Business intelligence includes financial intelligence, fraud/security intelligence data.
  2. Sales: In sales, predictive analytics is about analyzing past behavior or interests of leads and customers to identify patterns that might predict whether they can be deemed as prospects or not in the future.
  3. Marketing: Predictive analytics in marketing is a type of data mining process, which uses statistical modeling and machine learning algorithms to predict future outcomes and create effective marketing campaigns basis of past data.
  4. Risk management: Since predictive analytics involves the practice of data extraction from past data to determine trends and patterns for predicting future outcomes, it brings to light what-if scenarios as well as to help with risk assessment.

Impact on Business

  1. 40% of businesses use PA to come up with product recommendations and offers
  2. 45% businesses rely on PA for offering optimum customer services
  3. 30% of businesses are convinced that PA is helping them achieve what they couldn’t before
  4. More than 50% of organizations that use PA have gained competitive edge
  5. 50% of businesses are successfully creating new business opportunities with PA
  6. More than 70% of tech experts believe that PA can have a positive impact on a business

Key Data Sources

  1. Transactional data like customer purchases (50% businesses sales data)
  2. Customer profiles (60% businesses using customer data)
  3. Campaign histories and responses (60% businesses using marketing/campaigns data)
  4. Customer interactions via surveys, emails, calls, etc. (50% of businesses)
  5. Social media (Facebook, Twitter, LinkedIn, etc.) 40% of businesses already using social media data

To learn how a CRM solution can help you win the predictive analytics game, contact us here. You can also SMS SAGE to 56767 or drop a mail at sales@sagesoftware.co.in for a quick demo or consultation.

Disclaimer: All the information, views and opinions expressed in this blog are those of the authors and their respective web sources and in no way reflect the principles, views or objectives of Sage Software Solutions (P) Ltd.

Sources: dummies, Entrepreneur India, TechEmergence, Innovation Enterprise, Information Age, Forbes, Economic Times,   

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