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Predictive Analytics

Companies use predictive analytics—the identification of patterns in current and historical data—to anticipate customer behaviors and estimate a customer’s potential value. Understanding the likely next steps of various buyers helps companies improve their customer experience, increase loyalty and build value for the organization.

How companies use predictive analytics

  • Demand prediction. Accurate demand prediction can decrease inventory costs and improve stock availability, enhancing short-term revenues and long-term customer experience.
  • Product recommendations. If companies—particularly online retailers—know the products and services their customers are likely to buy next, they can provide relevant recommendations to help speed decision making.
  • Offer and product customization. Companies can boost their sales and improve customer loyalty by offering the products and services that are most relevant to a specific customer.
  • Calculating customer lifetime value. The ability to forecast a customer’s total spending over time can help companies prioritize customer segments and evaluate potential investments and changes.
  • Predicting Net Promoter Score® (NPS®) outcomes. Data analytics can help companies identify customers who are likely to be promoters, passives and detractors without surveying them. Predictive NPS enhances service and recovery, reduces survey volumes and associated costs, and helps companies prioritize their customer experience initiatives.

Key considerations with predictive analytics

The most successful users of predictive analytics consider the following principles as they adopt and deploy the technology:

  • Put business science before data science. A company’s predictive analytics goals should reflect itsbroader ambitions. Leaders identify the decisions they want to make and the data they will need.
  • Design the analytics with "the last mile" of adoption in mind. Bring data scientists and business stakeholders together to create the best solution. Make it easy for the front line and decision makers to adopt the output.
  • Look beyond traditional analytics. Go beyondstructured enterprise data and consider sources such as social media, web scraping, customer interaction transcripts, log files, sensor data, images and other publicly available content.
  • Find the shortest path from insight to action. Companies should maximize the yield on their existing data and become nimble enough to act on insights quickly. After all, the value comes from the action and not the input.
  • Test, learn and iterate. Follow an agile approach to continuously improve the solution and outcomes. Pilot new solutions on real customers and processes, even if the tools are barely viable, rather than wait for the seemingly perfect solution.
  • Manage the analytics transition with purpose. Successful predictive analytics solutionsrequiresound operating models to bridge strategy and execution across departments and functions.
  • Machine learning
  • Artificial intelligence
  • Big Data

Net Promoter®, Net Promoter System®, Net Promoter Score® and NPS® are registered trademarks of Bain & Company, Inc., Fred Reichheld and Satmetrix Systems, Inc.


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