OVERVIEW
In this excerpt from the Customer Experience 2026: A Frost & Sullivan Executive MindXchange Chronicles, Gavin Johnston, who has over 25 years of experience in CX research and strategic planning, discussed how to go beyond standard personalization to deliver real-time, highly individualized interactions that balance data, creativity, and user preferences.
KEY QUESTION
The ultimate goal of every business is to tailor content, offerings, etc., so well that it’s like you’re reading the customer’s mind. And while we are not there yet, technology (AI in particular) is bringing us closer to this reality. But at what point does this become invasive?
IDEAS AND STRATEGIES COVERED
Hyper-Personalization
Hyper-personalization was defined as being:
- Proactive.
- Predictive.
- Individualized.
- Targeted.
- Contextualized.
Hyper-Personalization Challenges
- Data privacy concerns.
- Resource-intensive data collection.
- Over-personalization.
- Constant maintenance and adaption.
- Potential for algorithmic bias.
- Data accuracy.
Striking a Balance
- Align quantitative + qualitative data.
- AI + people.
- Tools to optimize the information streams.
- Heed pitfalls and recommendations.
- Avoid creepiness.
Quantitative Data: What and How
- A large sample data is the backbone, but quantitative data alone is problematic.
- Quantitative in incremental, so long-fall behaviors can be difficult to categorize.
- Data accuracy can be easily skewed.
- Data focuses on individuals versus cohorts (i.e. relationships).
Qualitative Data: Why
- Individual behavior is shaped by three overarching factors:
- Psychology: taste, temperament and experience.
- Language: semantics and semiotics.
- Culture: shared worldview.
The Promise of AI
Step 1: Remove clutter: Identifying “noise” and feigning causality.
Step 2: Align “outliers” and data through time.
Step 3: Increase opportunities for rapid test and learning.
Understanding AI and Context
- While the data may come from multiple sources, AI rarely understands context such as shared interactions, culture, positive outliers, socio-political landscape, demographics, seasonality, time of the day, emotion, religion, location, and more.
- Measure everything – sales productivity, customer outcomes, and operational impact.
- Let the data decide how you lead your hyper-personalization.
Avoid “Creepy Personalization”
- Filter Bubbles: Excessive personalization leads to consumers being trapped in “filter bubbles,” where they only see content that reinforces their existing beliefs and preferences. AI has a tendency to amplify this problem.
- Limited Discovery: Over-personalization can limit customers’ exposure to new ideas and options, hindering their growth and engagement. Satisfaction becomes hedonistic and diminishes experiential highs.
- Trust Erosion: Over-personalization damages customer trust if it feels like the brand is using personal information inappropriately. AI without human intervention can become overly intrusive.
- Over-Saturation: Too many offers can become annoying, leading to other messaging and offers being ignored. Frequency of messaging must align with the frequency of use of the product or service, as well as mental and cultural models of appropriate frequency.
- Mistaken Adjacencies: Just because the system has identified a correlation between data points, thus providing opportunities, be careful about how you act or react.
ACTION ITEMS
- Identify the pitfalls of overemphasizing one type of data over another.
- Learn how context-based service blueprints can be used to react to behavioral changes.
- Identify how AI can speed up targeting opportunities.
- Learn how to avoid going from helpful to creepy with your personalization strategy.
FINAL THOUGHTS
- Data is not enough.
- AI will streamline the research process but cannot replace it.
- Seek behavior change through time, not reactionary impulses.
- Aligning user context with your business needs is more important than ever.
