Unleashing CX Potential with Predictive Analytics


The Gist

  • Anticipating the future. Predictive analytics enables brands to anticipate future outcomes and proactively prepare responses to customer issues, leading to better customer experiences.
  • Five stages. The five-stage process of organizational analytical maturity involves exploring, visualizing, testing, predicting and scaling up predictive models.
  • Addressing challenges. Effective use of predictive analytics requires addressing challenges such as data quality and integration, as well as selecting the appropriate models to achieve specific CX goals.

Predictive analytics involves using data, statistical algorithms and artificial intelligence to anticipate future outcomes, trends, behaviors and events based on historical customer data. This approach includes several common models that can enhance the customer experience. In this article, we will explore the predictive analytics process, examine some of the most common models and discuss how they can improve the customer experience.

Why Are Predictive Analytics Important for CX?

The COVID-19 pandemic brought many business leaders to the realization that it’s much better to be proactive than reactive when it comes to customer experience. Being able to predict future outcomes allows brands to be prepared ahead of the actions of customers, market changes and even economic downturns. Predictive analytics facilitates the knowledge of what might occur and allows a brand to prepare a response ahead of time.

In addition to predicting issues that directly impact customers, predictive analytics can also anticipate and prevent indirect issues that affect the customer experience. By identifying potential problems such as machine malfunctions, manufacturing delays and product shortages, brands can take proactive steps to prevent mission-critical issues and enhance the customer experience.

Abhishek Gupta, chief customer officer at CleverTap, an omnichannel customer engagement and user retention platform provider, told CMSWire that another critical example of how brands can harness data to provide better customer experiences is when it comes to app engagement. “Increasingly, customers are leaving their digital footprints all across your properties such as when they come to utilize your app,” said Gupta. “There is so much data available that brands can harness to provide better experiences to their users.” Gupta gave the example of a customer who is uninstalling a brand’s app. “You can go back in time to see what things they did that led to the uninstall of the app. Or if someone is engaging with your brand, you can go back and see what they did right. And you can stitch it all together very neatly to ensure that all future customers are well served,” Gupta explained.

Predictive Analytics: Predictive Analytics: Overcoming Data Swamps in Tech’s Dynamic Landscape

Predictive Analytics Is a Five Stage Process

Predictive analytics is a complex process that involves multiple areas of expertise and occurs in various stages. Tamara Gruzbarg, vice president of strategic services and data and analytics leader at ActionIQ, a data-driven personalization platform provider, told CMSWire that predictive modeling is the process of seeking to predict future outcomes based on the statistical analysis of historical data. “To maximize the value of big data and drive results, brands must start by recognizing the key components of business data analysis and decision-making.” Gruzbarg said that there are five primary stages of organizational analytical maturity:

  1. Exploration
  2. Visualization
  3. Testing
  4. Prediction
  5. Predictive analytics at scale

Gruzbarg explained that each of these stages corresponds to different roles, responsibilities and processes. “Exploration can be conducted in even the most basic analytic environment, managed by a data analyst using spreadsheets and SQL,” said Gruzbarg. “Visualization — when reports are being designed and shared across your organization — typically requires analysts to team up with a business intelligence specialist who can help them conceptualize trends using data visualization software.”

“Next comes testing, when hypotheses are being evaluated against business as usual,” said Gruzbarg. “This requires analysts and business intelligence specialists to collaborate with a statistician who can run rigorous tests and recommend actions based on the results, usually by using statistical analysis software that will help determine how confident you should be in the results of your testing and if you gathered enough evidence to roll out new strategies.”

“Predictive customer scores — which are built on the results of the tests and/or your historical data — are then leveraged in the prediction stage, when a data scientist takes the work of data analysts, business intelligence specialists and statisticians to develop and test models,” said Gruzbarg, adding that during the predictive analytics at scale stage, machine learning engineers work with their colleagues to develop and operationalize scalable models with machine learning software.

Related Article: What Predictive Analytics Are and How They Can Help Your Business

Types of Predictive Analytics Models for CX

Although there are many different types of predictive analytics models, there are several that are often used for customer experience:


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