Today, 60% of organizations¹ harness the power of AI and machine learning not just to enhance operational efficiency but also to foresee customer behaviors and trends.

Yet, prediction is just one part of the formula. The real magic of business growth begins when these predictions are transformed into precise, actionable strategies in real time to enhance every customer interaction.

So while many have turned to AI-led predictive analytics to anticipate customer behavior, the big question to ask today is:

Can AI extend beyond mere predictions to also recommend specific marketing actions tailored for each customer?

The answer lies in prescriptive analytics.

This blog explores the field of data analytics, with particular attention to prescriptive analytics—a crucial component that transforms marketing strategies through hyper-personalization and by enabling next-best action capabilities. Let's see how this sophisticated approach can transform your marketing initiatives to increase the effectiveness of every interaction.

Understanding the Four Types of Data Analytics

The global big data analytics market was valued at $348.21 billion² and is estimated to grow to a staggering $924.39 billion by 2032!

It's safe to say that data is the new currency of business success, but collecting it isn’t enough. The true power lies in how businesses analyze and act on it.

It's therefore important for businesses to understand the four types of data analytics and why they're essential to moving beyond intuition and in making informed, strategic decisions. Let's consider a company evaluating its customer retention and churn rates as an example.

1. Descriptive Analytics 

This foundational analytics type emphasizes summarizing past data to highlight what happened within a business.

Example: Examining past campaign data, for instance, helps a business identify which tactics resulted in the highest increase in customer retention rates last year.

2. Diagnostic Analytics 

Building on the capabilities of descriptive analytics, this analysis type delves into why something happened. It employs data correlations and trend analysis to unearth the root causes of past events.

Example: After noticing a peak in churn rates during a specific quarter, the company uses diagnostic analytics to find that the customers leaving had the lowest engagement with loyalty program communications.

3. Predictive Analytics 

It helps forecast future behaviors and business outcomes by leveraging historical data. It helps answer the question: What will happen?

Example: Leveraging data from past customer behaviors and campaign outcomes, the firm's analytics tool predicts which customers are most likely to churn in the next quarter based on their current engagement levels and service satisfaction scores.

4. Prescriptive Analytics 

The most sophisticated of the four, prescriptive analytics answers the question: What should I do? It suggests actionable recommendations and next-best actions that enable businesses to act on predictions in real time and optimize strategies dynamically.

Example: The company employs a prescriptive analytics model, which advises a targeted retention campaign and provides personalized plan upgrades or benefits to customers identified as high risk for churn. This increases loyalty and reduces turnover.

While descriptive and diagnostic analytics offer a retrospective view, predictive and prescriptive analytics open the path for proactive strategy refinement and real-time decision-making.

This forward-looking approach is challenging yet crucial for those aiming to lead in data-driven decision-making.

Unlocking Hyper-personalization with Prescriptive Analytics: Driving Next-best Actions at Scale

A Gartner survey revealed that around 54% of corporate strategists are utilizing prescriptive analytics³ in the deployment/pilot stage, while another 19% are actively exploring its potential. So, why is it becoming essential for personalization and marketing success?

This is because prescriptive analytics can forecast customer behavior and recommend the next best action, which enables businesses to choose the most effective customer retention strategy to use while minimizing delays and errors often associated with manual processes.

For instance, a bank might use predictive analytics to identify customers who may not renew their credit cards, and prescriptive analytics to suggest the right offer, the right contact times and the right personalization tactics to reduce the risk of customer loss. 

While prescriptive analytics can elevate personalization, it is equally important to consider the trustworthiness of the recommendations it generates. After all, the value of AI depends on the reliability, fairness, and transparency of those recommendations.

Ensuring Trust in Prescriptive AI

How can we trust the accuracy and impartiality of AI-driven recommendations?

Forward-looking analytics, by nature, have the potential for errors. Businesses must ensure their AI solution is reliable, fair and fully compliant with data privacy and AI laws before relying on the recommendations.

Here are a few things to consider while choosing the right prescriptive AI solution:

• "Black Box" nature of AI: Most AI models are a black box and provide little to no explanation as to how decisions are made. To circumvent this, businesses need to implement explainable AI (XAI), which provides the logic behind every decision. This allows marketers to detect and correct biases or errors in real time.
• Training data bias: AI models will unknowingly retain biases from the training data, leading to unfair recommendations or leaving out particular groups. To avoid this, it’s essential to ensure that your data is prepared for AI. To discover the right data preparation strategies read our article - Is Your Data AI-Ready? A 5-Step Strategy to Prepare Your Data.
• Ability to override AI decisions: It should be noted that AI is not a substitute for human intelligence, but a powerful tool meant to enhance it. Businesses should maintain the ability to intervene when needed to ensure that human judgment is at the core of key decision-making especially in service industries like banking and healthcare sectors.
• Compliance with AI regulations: The EU AI Act and other global AI regulations like the Digital India Act or the AIDA in Canada, are transforming how businesses implement AI solutions. These laws not only require AI systems to meet specific standards for transparency, accountability, and ethical design but also mandate adherence to local legal constraints where AI usage may be restricted or certain data cannot be processed by AI. For prescriptive analytics to be effective and compliant, businesses must:
  • Understand the regulatory landscape in all regions where they operate, including areas with prohibitions on AI usage or data sharing.
  • Implement AI systems that can adapt to different regulatory requirements
• Prioritize data privacy: With AI processing huge volumes of customer data, businesses must ensure adherence to global data privacy regulations, like GDPR and CCPA. This involves ensuring transparent data usage, providing clear consent mechanisms and conducting regular audits to avoid legal issues and maintaining trust. They must prioritize investing in secure data management platforms that prioritize compliance.
• Flexible control over AI-driven decisions: Rigid AI systems can become outdated, non-compliant, or misaligned with business goals. To address this, businesses can leverage marketing software solutions that support a Bring Your Own Model (BYOM) framework, which allows them to integrate and customize their AI models. This approach lets them align with compliance requirements or achieve strategic goals and yet maintain full control over their own AI models.

These key considerations help businesses and stakeholders build trust in AI-driven decision-making and could allow for more accurate, ethical and effective marketing strategies.

GenAI: A Practical Extension of Prescriptive Analytics 

Data analytics can tell you what will happen and what to do next, but what if your AI could also create new content, generating the perfect message, offer, or experience instantly?

This is where generative AI extends the capabilities of prescriptive analytics to deliver hyper-personalization to your customers. 

While prescriptive analytics offers recommendations on optimal marketing strategies, GenAI goes a step further by generating dynamic, real-time content for these strategies, ensuring every customer interaction is timely, relevant and impactful. 

Here's how -

Insights Agent of HCL Unica+ determines the next best offer or experience along with the best timing and channel for engaging a customer across various touchpoints and channels in real time, whether via email, chat, mobile, POS, ATMs, or web.

MaxAI Assistant, the always-on marketing assistant of HCL Unica+ then enhances this decision by dynamically generating personalized email subject lines, messaging and chatbot responses tailored for maximum engagement. 

For example, If a user browses a product webpage but doesn’t convert, Insights Agent could suggest the product offer, the ideal channel and the best time to re-engage that user.

MaxAI Assistant can then support the marketer in crafting the right message for that specific offer—helping refine tone, format, and delivery to ensure it resonates across channels such as:

• Email: “Still interested in [Product Name]? Enjoy an exclusive 10% off—claim now!”
• Chatbot: “Need help deciding? Chat with us or grab your 10% discount!”
• SMS: “Your deal on [Product Name] is waiting! Tap to claim 10% off.”

Together, they ensure that your marketing messages are delivered instantly to the right customer at the right time!

Now that we've explored how prescriptive AI-driven marketing is transforming business, the next question that arises is: How can we execute it correctly so that it’s done at scale, with precision and in compliance with evolving regulations?

HCL Unica+ comes into play as an AI-first, data-driven platform built to drive hyper-personalized, memorable experiences

• Real-time Personalization: Enables hyper-relevant engagement.
• MaxAI Assistant: Your always-on marketing assistant for real-time decisions and optimization.
• Transparent, flexible decision-making: Enabled by XAI models and BYOM frameworks.
• Segmentation Agent: Automates zero-touch, segment-of-one targeting with contextual precision.

When paired with HCL Customer Data Platform (CDP), HCL Unica+ delivers even greater impact—combining AI intelligence with unified, high-quality data to drive more accurate, compliant, and personalized experiences.

So, are you ready to start your AI-powered marketing journey with HCL Unica+?

Request a demo for HCL Unica+ today!

References:

1. https://edgedelta.com/company/blog/data-analytics-statistics
2. https://www.fortunebusinessinsights.com/big-data-analytics-market-106179
3. https://www.gartner.com/en/newsroom/press-releases/2023-07-05-gartner-survey-finds-79-percent-of-corporate-strategists-see-ai-and-analytics-as-critical-to-their-success-over-the-next-two-years