Feature
The Gist
- Defining analytics. Predictive analytics use data and AI to forecast outcomes, growing rapidly in market value.
- Recommendation power. Predictive analytics drives hyper-personalized product recommendations, influencing buying behavior.
- Churn prevention. Analytics identifies high-churn risk customers, enabling targeted retention strategies.
Predictive analytics offers brands a powerful tool to boost customer retention and improve the customer experience. By leveraging data and predictive modeling, brands can gain granular insights into customer behavior and predict the churn risk for each customer. With the costs of acquiring new customers up to 25 times higher than retaining existing ones, the far-reaching benefits of predictive analytics for customer retention are undeniable. This article will examine best practices for brands to use predictive analytics and deliver personalized experiences that enhance customer loyalty and retention.
What Are Predictive Analytics?
Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes. The goal of predictive analytics is to go beyond describing what has happened to predicting what will happen. As AI technology has improved, the use of predictive analytics has become a growing trend. A 2023 Statista report indicated that the predictive analytics software market was $5.29 billion in 2020 and is predicted to grow to $41.52 billion by 2028.
At its core, predictive analytics applies models that analyze current and historical facts to make predictions about unknown events. A variety of techniques such as regression analysis, forecasting and machine learning are used to uncover patterns and insights that can inform predictive modeling. The models identify relationships among many factors that allow probabilities and trends to be calculated for individual future events.
Frank Sanni, a fractional CMO and marketing consultant, told CMSWire that the customer experience is enhanced by offering the right product to the right person at the right time. “This takes many forms. For example, a retailer may offer a mother a new educational item for her child, or a bank may offer a lower interest rate to a high-value customer,” said Sanni. “A company may offer enhanced service to a customer who may be at risk of leaving the brand. A bookseller may offer the next logical volume to a customer who has been reading a series of novels.” Sanni said that all of these examples are done via computer modeling that looks at transaction history and uses predictive modeling to recommend the right offer at the right time.
Sanni said that his company has used models to identify the most likely responders to a campaign (response modeling), identify customers at risk of lapsing (attrition modeling), identify those most likely to be interested in another brand in the organization’s portfolio (brand propensity modeling), and identify the best external prospects for acquisition marketing efforts (prospect modeling). “These models improve the customer experience by only presenting marketing communications and offers to consumers that are likely to be genuinely interested.”
Brands can leverage predictive analytics across many use cases, from predicting customer churn to forecasting demand. The models continuously learn from new data to refine predictions. Predictive analytics delivers deep insights into future probabilities — actionable insights that can be used to make strategic business decisions and take targeted actions. While descriptive analytics centers on reporting what did happen, and diagnostic analytics explores why it happened, predictive analytics focuses on what will happen. The data-driven glimpses into the future provided by predictive analytics deliver invaluable business value.
Jonathan Moran, head of martech solutions marketing at SAS, an analytics, artificial intelligence and data management company, told CMSWire that there are four types of predictive analytics models:
- Propensity Models: These model types indicate the propensity of a customer to perform an action — such as accepting an offer, defaulting on a product/service, and performing some other type of behavior.
- Forecasting Models: Forecasting can be used for front-end CX, not just back-end inventory planning. 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. For example, what is the optimal number of communications to send given a certain budget? How do I optimize customer contacts? When does customer saturation of a certain message occur — and at what time intervals?
- Churn Models: Predicting churn is obviously important for organizations that must maintain a certain customer base or level of demand. Understanding if a customer is close to churning or attriting from the business can result in differing communications and interactions — to retain or for low-value customers/segments — allow for churn to occur.
Related Article: 3 Ways AI-Powered Predictive Analytics Are Transforming Ecommerce
Predictive Analytics for Recommendations
Predictive analytics uses customer data such as purchase history, browsing history, demographics, and more to identify associations between frequently purchased products in order to build sophisticated recommendation models using techniques such as collaborative filtering, clustering and market basket analysis. Product recommendations have been used for years to increase sales and customer engagement. In 2013, a McKinsey report revealed that 35% of Amazon’s sales come from product recommendations, and in 2017, in an interview with MobileSyrup, Netflix’s then vice president of product innovation, Todd Yellin, said that approximately 80% of viewer activity comes from its personalized recommendations.
Predictive analytic models generate ranked lists of recommended products for each customer which are then filtered and optimized before delivering the top personalized product recommendations to each individual customer through a brand’s channels. The purpose of predictive analytics in this respect is to serve up highly relevant product recommendations that are tailored to each customer's unique interests, history and preferences in order to influence purchasing behavior.
The customer interaction data is further used to refine the underlying predictive models to make the system even smarter over time. This is how predictive analytics converts prospective customers into buyers through hyper-personalized recommendations.
Sharad Varshney, CEO of OvalEdge, a data governance consultancy and end-to-end data catalog solutions provider, told CMSWire that in the last year, a typical shopper, via search and rich product imagery, could come very close to finding products matching their tastes and preferences, but in today’s competitive, fast-paced world of shopping, attention spans are at a premium. “Retailers now can display exactly the kind of product the shopper would like to buy on the very top of the results,” said Varshney. “While the consumer has a product open to view its specifications and description, they can also see product recommendations they are much more likely to buy based on the insights gleaned from their past shopping history — including the typical amount of money spent on such items.”
Related Article: What Predictive Analytics Are and How They Can Help Your Business
Predictive Analytics to Prevent Customer Churn
Predictive analytics uses techniques such as logistic regression, decision trees and neural networks to analyze historical customer data and develop sophisticated models that predict each customer's likelihood to churn. These churn prediction models identify high-risk customers and illustrate the key churn drivers for different segments.
By using these actionable insights, brands can deploy targeted, proactive retention campaigns addressing the specific churn risks and motivations for valuable at-risk customers. Predictive analytics also enables the optimization of churn models and retention strategies by monitoring customer interactions with personalized initiatives. In essence, predictive analytics brings data science to bear on understanding and minimizing the churn risk for each individual customer.
Predictive analytics isn’t limited to predicting customer churn — it is also being used to determine the delivery risks associated with specific addresses. Ryan Fannon, director of product management at UPS Capital, a multi-carrier, multi-modal shipping insurance provider, told CMSWire that the number of uncertainties that a package can face when getting from the warehouse floor to a customer’s front door can be costly to merchants. "Between the 260M instances of porch piracy that happen each year, rising instances of delivery fraud, diversity of goods sold online, and extreme weather, shipping resolution practices have become increasingly complex,” said Fannon. “As a result, the logistics industry has turned to predictive analytics to uncover address-related data to determine high-risk, average-risk, and low-risk delivery areas.”
Fannon explained that this data is providing merchants with insight on the risk level of specific deliveries and if customized safeguards (i.e., merchant or customer elect insurance, alternative delivery options, or requesting a signature) are warranted. “Predictive analytics is currently being used by luxury and technology merchants, but as such data services become more accessible, we can expect to address risk analytics used by retail businesses, large and small," said Fannon.
Related Article: The 5 Stages of Predictive Analytics for CX Success
Predictive Analytics to Personalize the Customer Experience
Predictive analytics drives hyper-personalization by enabling brands to forecast individual customer preferences and behaviors. Techniques such as machine learning and AI analyze past interactions, purchases, web activity and other customer data to build profiles identifying interests and likely engagement pathways for each customer.
These predictive insights allow brands to tailor messaging, product recommendations, offers, and customer experiences to align with what each specific customer wants at the moment. For example, customers predicted to be low in brand loyalty could be served retention incentives. Predictive analytics also allows personalization at scale across large customer bases. This hyper-personalization enhances and improves customer retention as well as the customer experience. Segment's 2023 State of Personalization report revealed that 56% of customers said that they will become repeat buyers after a personalized experience with a brand. Additionally, the report indicated that 62% of business leaders cited improved customer retention as a benefit of their personalization efforts.
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Varshney said that "predictive personalization" has been on the rise with the advent of more sophisticated AI and the ability to quickly harvest and analyze the resulting data. "Machine learning models are analyzing customers’ past searches, buying patterns and demographic details, and real-time activity, enabling companies to target personalized content to individual shoppers," said Varshney. "In addition, when displaying a product description to the potential shopper, product content can be adapted to match demographic and psychographic understanding in terms of language and culture, the nuancing of which will greatly enhance the level of personalization and the ultimate experience of the shopper.”
As customer interactions occur, predictive models continuously update to refine the personalization. Varshney explained that many online retailers’ algorithms are also adjusting prices to create a more competitive experience for individual shoppers. This data-driven approach to individually tailoring everything from emails to prices to call center interactions maximizes relevance while building strong one-to-one customer connections. This enables brands to create deeply personalized experiences that feel like they were designed just for each customer.
Final Thoughts on Predictive Analytics
Predictive analytics is transforming customer experiences and brand strategies by enabling data-driven glimpses into the future behavior of each customer. As the capabilities and adoption of predictive modeling grow, brands have a unique opportunity to use these future insights to delight customers, circumvent churn, optimize spend and gain a competitive advantage.
