What is predictive CX?

The increasing volumes of customer data being generated presents an opportunity for companies to predict the behaviors of their customers and provide them with a more personalized service.

In fact, it is well-known among CX practitioners that the use of predictive analytics to anticipate preferences can help organizations boost customer retention and establish themselves as industry leaders. But what exactly is predictive CX and how can you use it to personalize experiences?

In this guide we explore the tools and methods behind predictive CX, how to leverage customer data to improve customer experiences, and real-world examples of leading companies that have used predictive CX to boost customer loyalty and satisfaction.

Contents

• What is predictive analytics in CX?
• What are the benefits of predictive CX?
• The importance of data in predictive CX
• Types of predictive analytics models for CX
• Predictive analytics software for CX
• Case studies: Leading companies using predictive analytics
• The future for predictive CX

What is predictive analytics in CX?

Predictive analytics in customer experience (CX) refers to the use of data analysis and statistical algorithms to forecast future customer behaviors, preferences and trends. The primary goal is to anticipate what customers are likely to do or need, enabling businesses to proactively tailor their interactions and strategies to enhance the overall experience. This approach goes beyond traditional analytics, which typically focuses on historical data and current performance.

Predictive CX relies heavily on data, including data gathered from customer interactions, purchase behavior, feedback and demographics.

André Grandt, CX and digital transformation lead for Saudi Arabia and the Gulf at Roche, says that CX practitioners should invest in robust analytics tools to better understand their customers and monitor changing customer behavior in real-time. This can be done through Voice of the Customer programs, collecting data through surveys, interviews, feedback forms and focus groups among other methods.

Grandt says: “Whatever else you use, ideally build it so you can sense your customer signals in real time. You can then use that in real time and afterwards act on the customer signals and continuously learn from the process.” After this, Grandt says that the next step for any organization is to improve its understanding of customer data to inform predictive analytics.

What are the benefits of predictive CX?

By analyzing data from past behaviors and trends, businesses can predict what products or services a customer might be interested in, even before the customer explicitly expresses a need. Therefore, the key benefit is the ability to anticipate the customer’s requirements and provide the service they are likely to want.

It also enables companies to predict issues that may occur and proactively fix these ahead of time. Other benefits include:

• Predicting customer churn. Analyzing patterns that precede customer disengagement allows businesses to take preventative measures to retain customers.
• Providing a personalized service tailored to customers' wants and needs. According to a survey by Acxiom, 47 percent of its respondents engage more actively with individualized content and offers.
• Improving the customer journey. Predictive analytics can help organizations better understand the paths customers will take in their journeys, enabling them to optimize touchpoints and channels based on predicted customer behavior.

The importance of data in predictive CX

Collecting and analyzing customer data is vital for companies to provide positive customer experiences. However, if the data are of poor quality or irrelevant this can have negative consequences for analysis and decision-making.

Broadly speaking, there are two types of data: earned data, which is shared voluntarily by customers through social media interactions, reviews and comments; and first-party data, which customers provide with their purchases, loyalty program data and contact center interactions, for example.

Annette Franz CCXP, founder and CEO of CX Journey Inc, has written this comprehensive guide to the different types of customer data, along with how to collect and utilize them.

Types of predictive analytics models for CX

In predictive analytics, machine learning (ML) algorithms learn from historical data to identify patterns and make predictions based on new information.

“When an automated decision engine for CX is informed by data science, it gets smarter and more predictive over time,” explains Andrew Carothers, digital customer experience leader at Cisco in this blog on the five steps for scaling digital CX. “This translates into continual improvements across data science models (and outcomes) and the methods you use for implementing them into different channels and experiences. ML can also be applied to analyze historical customer data and generate predictive models that anticipate customer needs, preferences and behaviors.”

“With these insights, companies can proactively mitigate risk, anticipate demand and offer more personalized recommendations and offers,” Carothers adds.

Examples of these predictive models include:

• Regression analysis: Used to predict a continuous outcome, such as customer spending.
• Classification models: These categorize customers into pre-defined segments based on different variables.
• Forecasting models: These help brands to predict customer needs based on past purchases, for instance.
• Time series analysis: Models patterns and trends over time in customer behavior.
• Propensity models: These inform a brand about the actions a customer is most likely to perform in the future.
• Churn models: These can help companies to identify customers at risk of churn and subsequently take action to prevent them from leaving.

Predictive analytics software for CX

There are a number of predictive analytics software solutions for CX on the market. Some of the leading tools include:

• Twilio's CustomerAI Predictions is aimed at marketers, enabling them to trigger customer journeys and personalize multichannel experiences based on a customer’s lifetime value and their likelihood to purchase or churn.
• NICE has launched a tool called Enlighten AI that uses predictive models to help organizations understand the likelihood of churn, make a complaint, or commit fraud. Using AI, it is also able to understand intent and preferences.
• Salesforce provides predictive analytics capabilities through Einstein Analytics, allowing businesses to build custom predictive models with minimal programming.
• Adobe Analytics: Adobe's analytics platform includes predictive analytics features and has earned Leader status in the Gartner Magic Quadrant for Digital Marketing Analytics.

Case studies: Leading companies using predictive analytics

How Spotify uses predictive CX to improve the customer experience

Predictive CX is an important part of Spotify’s business model. A huge part of the streaming giant's appeal is providing users with personalized playlists by analyzing their listening habits.

The company also uses predictive models for customer churn and customer journey mapping.

Ruchika Singh, Spotify’s director of data science and insights, explains that this is an ongoing process. “You build a model, you build more features, you put them into production, and you constantly evaluate how they're doing. It is almost like managing a product of its own, where you have ML-based models that feed into your business decisioning, and you constantly improve the models based on how the market is evolving and how your business is evolving. That is really critical.”

Watch Singh discuss how Spotify approaches predictive modelling as part of All Access: Predictive CX 2023 

How McDonald’s 'moments of truth' help prevent negative experiences

Fast food chain McDonald's has 40,000 restaurants across 120 countries. To understand their customers’ preferences and create personalized experiences, McDonald's uses various touchpoints including in-restaurant, kiosks, drive-thru, mobile applications and delivery to capture data and customer feedback, with the goal of preventing negative experiences and resolving issues promptly.

Tarv Nijjar, global senior director for CX transformation, data analytics and AI at McDonald's, explains that the goal is to find what he terms ‘moments of truth’. “When you're [first] thinking about going to McDonald's and how to get there is the first moment of truth. The next one is when you're ordering – how can we deliver that seamless and memorable experience when you're actually ordering a McDonald's?” he says.

”The next one is when you're paying and waiting for your food. How can we make sure you have the right engagement at that stage? Then of course, there's when you're collecting and receiving your meal, and not forgetting when you're consuming your McDonald's.”

Nijjar adds: “When you start thinking about predictive CX, it's about how we think of ways to prevent an experience from happening that isn't seamless and memorable. Or if something does happen, how can we make it right.”

Watch Nijjar talk about McDonald's use of data to create frictionless experiences at All Access: Predictive CX 2023. 

The future for predictive CX

Colin Shaw, consultant and CEO of Beyond Philosophy LLC, believes that in the near future an organization's ability to predict customer behavior will define how successful its customer experience initiatives are. In a recent blog for CX Network he wrote, "Companies like Apple and Starbucks have leveraged customer behavior data to anticipate and cater to their customers' needs proactively. It is time to consider how your organization can adopt similar tactics to enhance your customer experience." 

Shaw adds that in the future AI, customer data and behavioral sciences will combine to form 'customer science', which will improve predictions to understand not just what customers are doing, but why, enabling the next stage of personalization: hyper-personalization. 

Olga Potaptseva, founder of CXpanda, concurs, saying that hyper-personalization harnesses “real-time data to generate insights by using behavioral science and data science to deliver services, products, and pricing that are context-specific and relevant to customers’ manifested and latent needs.”

“In essence, hyper-personalization goes beyond surface-level customization,” she adds, “it delves into the nuanced preferences and evolving needs of each individual customer.”

The future of predictive analytics in CX will also depend on technological advances and industry trends, while playing a vital role in strategies that are customer-centric.

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