AI tools powered by high-quality data are transforming CRM into a revenue-driving engine. When marketing, sales, and service align, real ROI follows. The challenge is to provide timely and relevant messages with offers to customers in targeted campaigns.
AI-powered CRM systems help marketing organizations and brands improve their customer experience (CX), and much of this is plainly visible. Customer-facing AI-powered chatbots, for example, can quickly respond to requests for information, according to Jonathan Moran, head of MarTech solutions marketing at data and AI provider SAS.
Much of AI’s real power happens behind the scenes in the back office. The focus is on customer segmentation, fulfillment automation, workforce scheduling, and redaction of personally identifiable information (PII).
Effective targeting and segmentation still begin with quality data. Moran offered that combining and analyzing customer data from across the business, including data from sales, support, and website visits, gives insights into customer preferences and tendencies.
Beyond segmentation, AI-powered journey orchestration delivers ROI by activating unified customer data, supporting real-time decision-making, and executing precise next-best offers. He explained that it does this while seamlessly integrating with other key marketing functions such as planning, marketing analytics, testing and optimization, and attribution.
“With these AI-driven insights, marketers can refine target audiences for each campaign and hyper-personalize messages and offers,” Moran told CRM Buyer. “The result is improved response rates, happier and more loyal customers, and a boost to the bottom line.”
Predictive Analytics Enhances CRM Forecasting
As the name implies, predictive analytics helps organizations understand what customers will do and which offers and services will delight them. Many organizations rely on predictive analytics and the effective use of data and AI to enhance their marketing efforts and fortify CX.
Moran cited Ulta Beauty, one of the largest beauty retailers in the U.S., as an example. Over the past several years, the company has reimagined CX and personalized marketing campaigns, specifically focusing on its Ulta Beauty Rewards loyalty program.
Targeted recommendations powered by AI improved customer satisfaction. Predictive models helped optimize campaigns, decreasing marketing costs without reducing effectiveness.
Another example is the World Wildlife Fund, a conservation-focused charity. The organization continually seeks to ensure its donors stay active, aware, and engaged. To do this, WWF must understand what types of donors it has, their preferred channels, and how long they are likely to remain donors.
Analytical models, such as donor lifetime value models, forecast each donor’s future giving. This data helps WWF determine when and how to reach out to them and predict who will respond to different types of offers.
“With predictive analytics underpinning AI- and gen-AI-powered solutions, marketers can better plan campaigns, improve audience selection, generate more relevant content, and make better business decisions that elevate customer experiences,” he added.
Optimize Customer Engagement and Retention
Moran noted that marketing teams rely on various approaches to using AI-driven insights. Two key areas are customer targeting and acquisition methods, and customer retention scenarios.
AI-based optimization and customer-routing technologies, known as customer journey optimization, improve targeting and acquisition. They use advanced analytics to guide customers to end-conversion events rather than push them down a brand’s predefined path.
“This is done by combining historical customer data and behaviors with current customer data and potential state-action values. Then, through trial and experiment, marketers can determine the most likely outcomes. All of this is rooted in reinforcement learning,” he detailed.
Marketers use AI technologies like natural language processing (NLP), text and voice analytics, and sentiment analysis to power chatbots, voice recordings in call centers, interactive voice response (IVR), and other voice-based digital interaction tools. With AI-driven insights from these tools, marketers can infer consumer preferences and emotions to understand, direct, and respond to them more precisely.
“When these insights are combined with other information, such as customer lifetime value (CLV) scores, they can help brands further refine their marketing efforts and improve the customer experience,” he said.
Real-Time Data Powers Marketing Decisions
Moran added that marketing success hinges on effective data use. A solid strategy includes effective data management and processing, which helps brands track customer behavior across all devices and digital channels and with offline data in real time.
Consumers bounce between devices and channels and expect fast, relevant replies. To keep up, brands need to spot key moments as they happen and respond instantly — with smarter offers, personalized messages, and decisions powered by real-time data.
“Real-time data processing and effective data management can support hyper-personalization with AI. By leveraging data analysis and real-time insights, AI helps businesses deliver highly tailored content, recommendations, and services that resonate with individual consumer preferences and behaviors,” Moran explained.
This level of customization enhances engagement, boosts customer satisfaction, and ultimately drives loyalty and conversion rates. He added that it significantly shifts how brands interact with their customers in an increasingly competitive landscape.
AI CRM Strategies Deliver Measurable ROI
A large North American insurer used AI to reduce contact center call volume by shifting routine service requests, such as policy updates or contact changes, to lower-cost digital channels.
As a result, these simple tasks no longer require live assistance, freeing agents to focus on more complex customer needs.
This insurer developed a digital coach chatbot that provided tips, nudges, and personalized reminders. The technology was based on a combination of NLP, context-based analytics, and enterprise decision-making capabilities.
“This generated a 14% increase in client Net Promoter Score (NPS), a benchmark for measuring customer satisfaction and loyalty. It has lifted digital-channel client engagement by 50% over four years, driving on average yearly increases of around $650 million in insurance coverage via cross-sell and upsell,” Moran said.
Data Privacy, Compliance Are AI Tool Essentials
Moran insisted that marketers must act responsibly and ethically with AI-powered CRM tools. If customers feel their data isn’t protected or AI decisions are biased or unfair, they’ll take their business elsewhere.
Real-time decisioning, next-best offers, and triggers to send relevant and timely messages must also align with consumers’ and regulators’ increasing focus on data privacy. Customizable events that collect required data in real time must also identify critical PII that should not be captured.
“Any AI-powered CRM solution should provide strong digital guardianship with APIs for adding, merging, or removing offline customer information and the ability to encrypt sensitive data in the cloud,” he urged.
Be Aware (or Maybe Beware) of Emerging AI Trends
Moran sees three emerging AI-powered trends becoming more widespread in CRM functionality. If used properly, they can be key to staying ahead in the competitive marketplace.
The effectiveness of agentic AI in making decisions depends on the decision-making layer that underpins it. He advised that this layer should be enterprise in nature and go beyond simple rules-based methods to use AI to make automated business decisions.
Reinforcement learning leverages analytical models and algorithms that “learn” from past data, patterns, interactions, and trends. AI enables CRM systems to make adaptive decisions and offer actions and outcomes that improve over time, all without human intervention.
Additionally, AI can analyze large amounts of structured and unstructured data by looking for patterns and insights humans might miss. Marketers use these systems to predict future outcomes, such as propensity to purchase or attrite.
“Because AI is involved, a natural characteristic is that decisions can be made automatically without needing constant human oversight, using models that continuously improve,” Moran noted.
Loading ...
