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How adaptive analytics is transforming customer experience

Tara DeZao,
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Marketers and customer engagement practitioners are on a perma-quest to understand and predict customer behavior. That’s because consumers have ever-evolving and increasingly sophisticated demands from brands when it comes to delivering customer experiences. They expect personalization that respects privacy, transparency, convenience, consistency, and responsiveness as well as socially responsible and sustainable values. That’s a tall order even for the most innovative brands!

Delivering against these needs now requires advanced technology. But more fundamental to executing flawless customer experiences is a mastery of data and analytics to obtain a 360-degree view of the customer as a foundational layer for activation. Marketers have been using predictive analytics to do this for years, and in fact, humans have used predictive analytics since before the invention of modern computing. Think weather prediction, navigation, and beyond.

Predictive analytics simply uses historical data to predict future behavior or outcomes. But in the new era of customer experience, both predictive and adaptive analytics are required to effectively power artificial intelligence – the inevitable backbone of successful marketing technology stacks.

Predictive analytics: Forecasting the future

As mentioned above, predictive analytics involves using historical data to forecast future outcomes. This technique employs statistical algorithms and machine learning to identify the likelihood of future events based on past trends. For instance, predictive analytics can help businesses identify which customers are likely to churn, which products they are likely to buy, and even predict the lifetime value of a customer. This insight allows companies to tailor their marketing strategies, improve customer retention rates, and optimize product offerings.

A classic example of a brand using predictive analytics successfully is Netflix's content recommendation system, which uses predictive analytics to suggest movies and shows based on a user's viewing history. Most of us who use this service can vouch that Netflix has been able to suggest content that has resonated with us. However, predictive analytics has its limitations. It relies heavily on historical data, and as we all know, past performance is not always indicative of future results. It wouldn’t be an accurate tool for a live interaction because the data used isn’t real-time data, it’s from interactions that have already taken place. This is where adaptive analytics comes in.

Adaptive analytics: Engaging in real time

Adaptive analytics represents a more dynamic approach to customer engagement. It not only analyzes historical data but also incorporates real-time data to continuously update its models and predictions. This approach allows businesses to react to changes in customer behavior as they happen, rather than relying solely on historical trends.

The real power of adaptive analytics lies in its ability to learn and evolve. By constantly updating its algorithms based on new data, it provides a more accurate and timely understanding of customer behavior. For example, adaptive analytics can adjust marketing messages in real time based on customer interactions, ensuring that the communication is always relevant and engaging.

This real-time adjustment is particularly useful in fast-paced or hyper-competitive industries. Some examples include financial services, communications service providers, travel and hospitality, and gaming. Trends in these industries can change rapidly and adaptive analytics helps businesses stay agile, enabling them to pivot their strategies and messaging quickly to meet evolving customer needs.

Combining predictive and adaptive approaches

While predictive and adaptive analytics can be used independently, their real strength lies in their combined power. Predictive analytics provides a solid foundation by identifying overarching trends and patterns. Adaptive analytics builds on this by continuously refining and updating these predictions with real-time data.

A simple example of how a brand might do this could be a telecoms customer that consistently exceeds their monthly data limit. Predictive analytics can identify this pattern and suggest that they are likely to benefit from a plan with a higher data allowance. Once the predictive model identifies potential needs or preferences, adaptive analytics takes over to tailor the customer experience in real time. As the customer interacts with the telecom company’s digital platforms (like the mobile app or website), the adaptive system monitors these interactions and can dynamically adjust the content displayed to them, highlighting plans that align with their predicted needs (such as those with higher data limits).

The combination of these technologies helps businesses to not only make informed decisions based on historical data but also to stay flexible and responsive to immediate changes in customer behavior. This approach leads to more effective and personalized customer engagement strategies, enhancing customer satisfaction and loyalty.

The Pega Customer Decision Hub™ uses both predictive and adaptive analytics for AI-powered decisioning, which gives brands the capability to always understand what their next best action should be. Next best action is a customer engagement strategy that uses artificial intelligence to read a customer’s contextual signals and choose the next best action out of an existing library of actions to engage them at any point in their customer journey. This includes service, nurture, acquisition, cross-sell/upsell, retention, or resilience messages. It can even include “no action” at all depending on the customer’s real-time data.

Both predictive and adaptive analytics have crucial roles to play for organizations who want to perfect the customer experience. Businesses that harness the strengths of both will be better positioned to understand their customers deeply and engage with them in more meaningful, effective ways. Though predictive analytics is now table stakes for marketing programs, adaptive analytics will soon be necessary for organizations who want to keep pace with escalating customer expectations and fierce competition.

Want to learn more about how AI can drive the real-time decisions needed to deliver exceptional experiences? Download this eBook.


Défi: Engagement client Groupe de produits: Customer Decision Hub Industry: Tous secteurs

À propos de l'auteur

Tara DeZao, Pega’s Product Marketing Director for AdTech and MarTech, helps some of the world’s largest brands make better decisions and get work done with real-time AI and intelligent automation.

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