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.
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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:
- Exploration
- Visualization
- Testing
- Prediction
- 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.
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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:
- Clustering models: This model uses algorithms to group customers based on multiple variables, resulting in distinct customer segments. Popular clustering algorithms include brand-based clustering, behavioral clustering and product-based clustering.
- Propensity models: In terms of customer experience, propensity models inform a brand about the propensity of a customer’s future behavior, i.e., the actions they are most likely to perform.
- Collaborative filtering: Collaborative filtering models can be thought of as recommendation models. For instance, if a customer bought an aquarium pump, they are likely to be interested in different types of aquarium pump tubing.
Jonathan Moran, head of martech solutions marketing at SAS, an analytics platform provider, told CMSWire that there are several other analytics models that are useful for customer experience, including:
- Forecasting Models: Moran said that forecasting models can be used for front-end CX, not just back-end inventory planning. Additionally, brands can forecast customer needs based on their customers’ previous purchases, products or services that have been viewed, as well as the purchase history of similar customers. “Being able to forecast demand, traffic, staffing, etc., can lead to better CX, ensuring appropriate resources are allocated.”
- Optimization Models: “These models can take many forms — using contact policies and business constraints to understand tradeoffs,” said Moran. Questions that revolve around the optimization of various elements of the customer experience can be answered by optimization models.
- Churn Models: Predictive analytics can help to identify high churn-risk customers so brands can focus their attention on them before they leave. “Predicting churn is obviously important for organizations that must maintain a certain customer base or level of demand,” suggested Moran, adding that understanding whether a customer is close to churning or attriting from the business can result in differing communications and interactions.
Other Aspects of Predictive Analytics for CX
Conversational and generative AI are receiving a lot of focus in the media this year. Many brands are using conversational AI chatbots to provide customers with the ability to have a quick conversation with the bot, allowing them to obtain answers to their queries and control their own narrative. In order for these types of conversations to be useful for predictive analytics, text analytics and sentiment analysis must be incorporated into the process.
“This is a must-have for conversational AI. By analyzing chat text strings and the sentiment in those text strings, brands can understand customer attitudes and intent,” said Moran. Additionally, obtaining actionable insights from these chat strings requires the use of natural language processing (NLP) and generation. “The ability to process natural language data coming from documents such as chats and convert speech-based conversations into natural language text (NLG) are also foundational components of conversational AI.”
The Challenges of Predictive Analytics for CX
Predictive analytics is an effective way to improve the customer experience, but it doesn’t come without challenges. To begin with, predictive analytics depends on the quality of the data it analyzes. As with other data-driven tools, the effectiveness of predictive analytics is limited by the accuracy and completeness of the underlying data — poor quality data can lead to inaccurate predictions and unreliable insights.
To ensure that accurate insights are obtained, data governance and the capture of complete information become critical. Brands should also address the issue of unstructured data, particularly when using sources like chat logs, phone conversations and handwritten notes.
Integrating customer data from various systems and formats across a business can also be challenging, as data silos can impede analysis and integration. To effectively employ predictive analytics, the integration of customer data is crucial.
Additionally, predictive models can be complex, making it challenging to interpret the results and communicate actionable insights to managers, team members and stakeholders.
Final Thoughts on Common Predictive Analytics Models
Predictive analytics can significantly improve the customer experience by leveraging the most appropriate models to achieve specific goals. Through predictive analytics, brands can deliver an exceptional omnichannel experience, anticipate future trends, identify customers’ needs and proactively prevent customer churn.
