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AI-Powered Personalization Drives Improved Customer Engagement

According to McKinsey research, shoppers have a strong point of view on personalization. 72% said they expect the businesses they buy from to recognize them as individuals and know their interests. Furthermore, over 76% of consumers said that receiving personalized communications was a key factor in prompting their consideration of a brand, and 78% said such content made them more likely to repurchase. Personalization is especially effective at driving repeat engagement and loyalty over time. ¹

Introduction: AI-Powered Personalization is the Key to Improved Customer Engagement

AI-powered personalization enables businesses to provide their customers with personalized content, recommendations and services at the right time via the right channel. The key to AI-powered Customer Engagement is to create a rich profile of customers powered by declared and observed data, as well as personalized insights and inferences. Personalized insights are the output of AI models and algorithms, decisioning systems, rules engines, and combinations of all of these - insights like AI-powered personalized predictions, risk scores, and likelihood scores.

With personalized insights, organizations can then develop AI-powered applications that impact customer experience and key performance metrics (KPIs). Where insights and AI model output help to identify customers, value comes as a result of the impact of insights on customer experience, sales, service, and more. In order for AI-powered applications to achieve their full promise, AI-powered Customer Engagement needs to drive results via goal-oriented Campaigns, Missions and personalized interventions.

According to 360iResearch, Global Personalization Software Market size was estimated at $764.30 million in 2021 and expected to reach $943.25 million in 2022, at a CAGR 23.58% to reach $2,723.68 million by 2027. ²

AI-Powered Customer Engagement Use Cases

CogntiveScale is differentiating Customer Engagement from Customer Experience: AI-Powered Personalization use cases in Customer Engagement means delivering professional services with personalized insights, whereas Customer Service includes contact center inquiries. Of course, there is overlap, but we are trying to look beyond how Customer Service engages with clients to how the entire enterprise engages with customers. For example:

• Healthcare Customer Engagement means Care Optimization
  • Higher quality health outcomes at lower costs via personalized, proactive and prescriptive provider and patient interventions
  • Care Management insights for high risk patients, e.g. identify high risk patients and provide personalized insights (prescriptive information) that change the care trajectory (“guided path”)
  • Wellness insights for patients and providers, e.g. flu shot, Covid booster, and check-up reminders
  • Goal-driven Patient Optimization Campaigns, e.g. not only deliver personalized reminders and interventions, but monitor specific missions to drive actions
  • Signal detection to identify new risks or reasons to intervene in a specific patient’s care
• Banking Customer Engagement means use cases that help Customers and Bankers optimize banking products and services
  • Cross-sell and up-sell insights for individual clients based on a range of personal information (location, income, credit scores, etc.)
  • Service alerts and predictions for bankers that highlight at-risk customers (e.g. loan defaults or late payments)
  • Socio-economic data, insights and trends that can impact customer’s portfolios or products (e.g. mortgage refinance)
• Wealth Management Customer Engagement means use cases like Investor & Portfolio Insights:
  • Personalized investment insights for investors based on investment and market movement, including signal detection based on investor preferences or portfolio risk profiles
  • Proactive notifications or alerts to wealth management advisors with personalized investor, portfolio or investment insights
• Insurance Customer Engagement means Personalized Risk Management and Policy or Coverage Optimization
  • Personalized claims predictions and proactive outreach to Agents and Members to optimize coverage
  • Cross-sell and up-sell opportunities based on individual customer profiles

KPMG’s The Orchestrated Experience report (Global Customer Experience Excellence Research 2021) states that Personalization remains the clear driving pillar of loyalty, leading in 21 of the 26 markets. Furthermore, the report concludes that Success is no longer predicated on a great product or service, or even a passably good experience; it is based on how the customer is engaged over their life cycle, how firms remain present in their customers’ lives and how technology makes all of this happen. ³

Personalized Customer Engagement is Challenging

Organizations looking to leverage AI-powered personalization solutions in Customer Acquisition are challenged in a number of ways:

1. A Lack of Technology Integration. Companies need to integrate a broad set of technologies in order to deliver personalization across channels. 
   
2. Customer Trust Is Low. Consumers are wary of how data is collected and used, and regulations like the CCPA have made this even more difficult. 79% of consumers say they’d more likely trust a company with their information if its use were clearly explained. ⁴
3. Execution is too Purchase Obsessed. Most personalization today puts the business, not the customer, first. Current efforts are most often narrowly applied to product recommendations and next-best offers that will drive an incremental purchase to meet short-term revenue targets set by the business.
4. Decisioning Logic Resides in Individual, Channel-based, Black-box Systems—or it does not exist at all, resulting in a disjointed experience for customers. ⁵
5. Challenges with AI-powered Applications that Leverage Personalization
1. Data collection in support of personalized insights
2. Turning customer data into personalized insights
3. Personalized insights that deliver hyper-personalized customer experiences
4. Productionalizing and operationalizing personalized customer insights as AI-powered applications

According to Gartner, 63% of marketers face a moderate or significant challenge in delivering personalized experiences to customers. ⁶

Benefits of AI-powered Personalization for Customer Engagement

• Increased Revenue. Greater awareness of customer needs and wants can drive higher revenue. (Deloitte⁷)
  
• Better Customer Experience. Deeper understanding of problem patterns and issues can help companies improve the customer experience. (Deloitte⁸)
• Lower Costs. AI and machine learning can be used to handle routine tasks, enabling customer service centers to operate more efficiently at reduced cost. (Deloitte⁹)
• Personalization is especially effective at driving repeat engagement and loyalty over time. Recurring interactions create more data from which brands can design ever-more relevant experiences—creating a flywheel effect that generates strong, long-term customer lifetime value and loyalty. (McKinsey¹⁰)
• Improves Overall Customer Satisfaction. When brands can meet customer expectations, they are more likely to increase customer satisfaction. (McKinsey¹¹)
• Increases sales funnel velocity (Gartner¹²)
• Boost sales success with customer engagement. Build trust between your sales team and customers through consistent, personalized touch points on a variety of relevant channels. Remember that each touch should meet the customer’s progress along their purchase journey, and provide useful information or content that drives their movement through the sales funnel. Supply your sales teams with customer interaction insights, which they can use to develop thoughtful messaging and select content that supports specific outreach goals. (Gartner¹³)

Solution: CognitiveScale’s Cortex Enterprise AI Platform & Profile-of-One Technology

 

The key to valuable AI-powered Personalization in Customer Engagement is to create a rich profile of Customers powered by AI that includes personalized insights, leveraging data from across the enterprise. These insights then drive actions across all systems of engagement. 

CognitiveScale’s Cortex Enterprise AI platform accelerates the AI application development life cycle for personalization use cases in Customer Engagement:

1. Connect and curate data and models that leverage existing data and data science investments (e.g. conversion and churn models)
   
2. Orchestrate the components and compose AI applications from models, algorithms and rules engines (or a combination of multiple of these)
3. Drive personalized insights via proprietary skills and capabilities like our Profile-of-One capability 
4. Learn via feedback loops that improve model and application performance
5. Govern AI applications to ensure trusted, responsible use of AI

Cortex’s Profile-of-One technology is a core capability that:

• Provides a 360° view of a customer
  
• Includes declared and observed data as well as inferences about customers
• Provides personalized insights through learning and interactions
• Stores important attributes about customers to effectively deliver the right insights at the right time (context) and at the right place (channels)
• Drives key business outcomes & KPIs

In the KPMG 2020 Customer Experience Excellence Report, among the six pillars of customer experience excellence surveyed, personalization is again the top driver for building enduring customer relationships. Personalization is the individualization of the experience. Beyond the curation of content related to past interactions, it shows you understand the customer’s specific circumstances and will adapt the experience accordingly. ¹⁴

Learn More

 

Our AI-Powered Personalization White Paper includes a deeper dive into CognitiveScale’s Cortex Enterprise AI Platform and Profile-of-One Capabilities.

Contact us to explore how we can help your organization to transform Customer Engagement with AI-Powered Personalization.

Email: sales@cognitivescale.com

Phone: 1-855-505-5001

www.cognitivescale.com

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¹ McKinsey

² 360iResearch

³ KPMG

⁴ SalesForce

⁵ McKinsey

⁶ Gartner

⁷ Deloitte

⁸ Deloitte

⁹ Deloitte

¹⁰ McKinsey

¹¹ McKinsey

¹² Gartner

¹³ Gartner

¹⁴ KPMG

 

 

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